From 9b99173c9fe0a1370fcbb870ddafe7930f96731b Mon Sep 17 00:00:00 2001 From: Ali Hassani <68103095+alihassanijr@users.noreply.github.com> Date: Fri, 8 Mar 2024 00:35:03 -0500 Subject: [PATCH] Fused neighborhood attention (#111) * Fused neighborhood attention (FNA) kernels (forward pass only for now) * 1D, 2D and 3D Neighborhood Attention are supported, * Causal neighborhood attention is implemented, * Window (kernel) size, dilation, and causality can be defined *per-axis*, * All GPU architectures since Maxwell (SM50) are supported, * SM50 up to SM70 are SIMT-only, but support both FP16 and FP32, * SM70 and SM75 target Tensor Cores in FP16, and SIMT-style in FP32, * SM80 and above target Tensor Cores in FP16, BF16, and FP32. * Relative positional biases are implemented (not defined for causal masking yet), * Memory layout in FNA is different from existing kernels (`[B, *, heads, dim]` instead of `[B, heads, *, dim]`.) * Eventually this layout can skip over the permute/explicit reshape step in the attention module following the QKV projection. * Naive kernels now implement and allow causal masking, * Naive kernels (CPU and CUDA) now allow varying parameters (window size, dilation, causal) across axes, * Major bug fix in Volta GEMM kernels * The epilogue was different for Volta, and it slipped through unit tests, * Tests are now more aggressive, and the issue has been fixed. * Minor torch bug fixed * Streams were not being selected correctly if users set a tensor to a device other than cuda:0. Thanks to @AdityaKane2001 for discovering it. * Documentation (finally): * Better late than never, but finally added more documentation and reorganized docs under docs/ instead of shoving everything into the readme. * So much more that I forgot (in part due to lack of documentation). --- CHANGELOG.md | 19 + LICENSE | 34 + Makefile | 2 +- README.md | 382 +- assets/README_pypi.md | 356 +- assets/cudamemory_dark.png | Bin 172479 -> 0 bytes assets/cudamemory_light.png | Bin 162966 -> 0 bytes assets/cudatime_dark.png | Bin 137097 -> 0 bytes assets/cudatime_light.png | Bin 130284 -> 0 bytes assets/gemm_vs_naive.png | Bin 436915 -> 0 bytes csrc/CMakeLists.txt | 2 + .../natten_autogen/cpu/naive/kernels.h | 476 +- .../natten_autogen/cuda/fna/dispatch_cm.h | 775 + .../natten_autogen/cuda/fna/dispatch_device.h | 85 + .../natten_autogen/cuda/fna/dispatch_dtype.h | 220 + .../natten_autogen/cuda/fna/interface.h | 38 + .../include/natten_autogen/cuda/fna/kernels.h | 14094 +++++++++++++ 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tools/utils/mappings.py create mode 100644 tools/utils/na_profiler.py create mode 100644 tools/utils/ops.py create mode 100644 tools/utils/problem.py delete mode 100644 tools/utils/profiler_1d.py delete mode 100644 tools/utils/profiler_2d.py diff --git a/CHANGELOG.md b/CHANGELOG.md index 05b1b96..4128d7c 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -1,5 +1,24 @@ # Changelog +## [Main branch] +* Fused neighborhood attention (FNA) kernels (forward pass only for now) + * 1D, 2D and 3D Neighborhood Attention are supported, + * Causal neighborhood attention is implemented, + * Window (kernel) size, dilation, and causality can be defined *per-axis*, + * All GPU architectures since Maxwell (SM50) are supported, + * SM50 up to SM70 are SIMT-only, but support both FP16 and FP32, + * SM70 and SM75 target Tensor Cores in FP16, and SIMT-style in FP32, + * SM80 and above target Tensor Cores in FP16, BF16, and FP32. + * Relative positional biases are implemented (not defined for causal masking yet), + * Memory layout in FNA is different from existing kernels (`[B, *, heads, dim]` instead of `[B, heads, *, dim]`.) + * Eventually this layout can skip over the permute/explicit reshape step in the attention module following + the QKV projection. +* Naive kernels now implement and allow causal masking, +* Naive kernels (CPU and CUDA) now allow varying parameters (window size, dilation, causal) across axes, +* Major bug fix in Volta GEMM kernels + * The epilogue was different for Volta, and it slipped through unit tests, + * Tests are now more aggressive, and the issue has been fixed. + ## [0.15.1] - 2024-01-24 * Attention tensors can now be views, which allows combining neighborhood and any other attention pattern (i.e. registers, cross attention tokens, and the like) without extra copies. ([#85](https://github.com/SHI-Labs/NATTEN/pull/85) and [#87](https://github.com/SHI-Labs/NATTEN/pull/87)). diff --git a/LICENSE b/LICENSE index 7c099a3..22378b5 100644 --- a/LICENSE +++ b/LICENSE @@ -19,3 +19,37 @@ AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. + +Fused Neighborhood Attention kernels are heavily based on the memory-efficient +attention kernels from the xFormers project by Meta Platforms, Inc. + +Copyright (c) Facebook, Inc. and its affiliates + +BSD 3-Clause License + +Redistribution and use in source and binary forms, with or without +modification, are permitted provided that the following conditions are met: + +1. Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + +2. Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + +3. Neither the names of Facebook, Deepmind Technologies, NYU, NEC Laboratories America + and IDIAP Research Institute nor the names of its contributors may be + used to endorse or promote products derived from this software without + specific prior written permission. + +THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" +AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE +IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE +ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE +LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR +CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF +SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS +INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN +CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) +ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE +POSSIBILITY OF SUCH DAMAGE. diff --git a/Makefile b/Makefile index a441400..b3a3e50 100644 --- a/Makefile +++ b/Makefile @@ -56,7 +56,7 @@ install: NATTEN_CUDA_ARCH="${CUDA_ARCH}" NATTEN_N_WORKERS="${WORKERS}" NATTEN_VERBOSE="${VERBOSE}" pip install -v -e . 2>&1 | tee install.out test: - pytest -v -x ./tests + PYTORCH_NO_CUDA_MEMORY_CACHING=1 pytest -v -x ./tests style: ufmt format $(check_dirs) diff --git a/README.md b/README.md index b2ab7dd..ea08ee0 100644 --- a/README.md +++ b/README.md @@ -1,325 +1,117 @@ ![NATTENLogo](assets/natten_dark.png#gh-dark-mode-only) ![NATTENLogo](assets/natten_light.png#gh-light-mode-only) +| [Documentation](docs/) *Neighborhood Attention Extension* Bringing attention to a neighborhood near you! +
+ + + Visualization of neighborhood attention in 2D. + + + + Visualization of dilated neighborhood attention in 2D. + +
+ NATTEN is an open-source project dedicated to providing fast implementations for -[Neighborhood Attention](https://scholar.google.com/citations?view_op=view_citation&citation_for_view=Ndu0dUcAAAAJ:b0M2c_1WBrUC), +[Neighborhood Attention](https://openaccess.thecvf.com/content/CVPR2023/html/Hassani_Neighborhood_Attention_Transformer_CVPR_2023_paper.html), a sliding window self-attention mechanism. -If you're not familiar with neighborhood attention, we recommend referring to -[our papers](https://github.com/SHI-Labs/Neighborhood-Attention-Transformer), or watching our +If you're not familiar with neighborhood attention, please refer to +[our papers](https://github.com/SHI-Labs/Neighborhood-Attention-Transformer), or watch our [YouTube video](https://www.youtube.com/watch?v=Ya4BfioxIHA) from CVPR 2023. -NATTEN is primarily a C++/CUDA library, which has so far only supported binding with the torch API, and therefore is mostly -usable through PyTorch. We plan to eliminate the torch dependency in the future and possibly support other frameworks / -engines. - -NATTEN's python interface provides Neighborhood Attention (local attention) -and Dilated Neighborhood Attention -(sparse global attention, a.k.a. dilated local attention) as autograd-compatible PyTorch modules for both 1D, 2D, and 3D data. - -It also has experimental support for -[forward mode automatic differentiation](https://pytorch.org/tutorials/intermediate/forward_ad_usage.html), -and nested tensors. - -## CPU support -Our CPU implementations are very limited and barely performance-optimized. -While we aim to provide the best implementation for different devices, optimizing our CUDA kernels is higher up on the list of -priorities. Contributions are always welcomed. - - -## CUDA support -NATTEN generally supports all architectures supported by PyTorch. More specifically, architectures since Kepler (SM35) are -supported. However, our most-performant kernels only support architectures since Volta, targeting tensor core math. - -### Half-precision support -Devices with compute capability greater than or equal to 6.0 (Pascal and later) allow running in FP16. - -Devices with compute capability greater than or equal to 8.0 (Ampere and later) allow running in BF16. - -### Naive kernels -NATTEN provides more than one set of kernel. Our naive kernels, which were developed during the first phase of the project, -provide a very basic implementation of neighborhood attention, and are the last resort for every problem. This means that if -your device and software support alternatives to our naive kernels (i.e. GEMM kernels), and your problem size is supported, -NATTEN will automatically pick the better kernels for you. (NOTE: this is done based on the class of kernels and not by actual -performance via profiling.) - -Naive kernels are always usable across different architectures, though not the most performant. - -### Tiled kernels -Naive kernels for the 2-dimensional neighborhood attention also come with a tiled implementation for one of the three -underlying operations, which is considerably more performant than the original. However, the tiled kernels only support problem -sizes with head dim 32, and up to kernel size 13x13. -Tiled kernels are also not supported in devices with compute capability smaller than 6.0. - -### GEMM kernels. - -Our GEMM-based kernels depend on and are modeled after -[CUTLASS](https://github.com/NVIDIA/cutlass/)'s [Implicit GEMM](https://github.com/NVIDIA/cutlass/blob/main/media/docs/implicit_gemm_convolution.md) -kernels for convolution. - -Devices with compute capability greater than or equal to 7.0 (Volta, Turing, Ampere, Ada Lovelace, Hopper), can run our GEMM -kernels, which are somewhat performance-optimized, thanks to the underlying mainloop from CUTLASS, and target Tensor Core math. - -However, do note that their current float16/bfloat16 implementations do not typically result in improved latency, -due to a memory alignment issue, which we aim to resolve in future kernels. - -Devices with compute capability greater than or equal to 8.0 (Ampere and later) support GEMM kernels with double, full, and -half precision (FP64, FP32, FP16, BF16). - -Devices with compute capability 7.0 or 7.5 (Volta and Turing) only support GEMM kernels with half precision (FP16). This is -because their tensor cores only allow FP16 math. - -![GEMMvsNaive](assets/gemm_vs_naive.png) - -NOTE: the table presents the average improvement in latency over different problem sizes with full precision (tfloat32). - -### How do I check my compute capability / architecture? -Simple, just Google your GPU model, and check its compute capability. -If you've already set up NATTEN, you could also run: -```python -from natten.functional import get_device_cc - -cc = get_device_cc() -cc = get_device_cc(0) # Optionally select a specific GPU - -print(f"Your device is SM{cc}.") -``` - -### How do I know if I'm using the new kernels? -The new NATTEN library sets up constants that are binded to the python interface, which will allow you to -check whether you've compiled with: a. CUDA, b. Float16 (half) support, c. Bfloat16 support, d. New GEMM kernels. - -```python -import natten - -# Whether NATTEN was built with CUDA kernels, -# and supports running them on this system. -print(natten.has_cuda()) - -# Whether NATTEN supports running float16 on -# the selected device. -print(natten.has_half()) -print(natten.has_half(0)) # Optionally specify a GPU index. - -# Whether NATTEN supports running bfloat16 on -# the selected device. -print(natten.has_bfloat()) -print(natten.has_bfloat(0)) # Optionally specify a GPU index. - -# Whether NATTEN supports running GEMM kernels -# on the selected device. -print(natten.has_gemm()) -print(natten.has_gemm(0)) # Optionally specify a GPU index. - -# Whether NATTEN supports running GEMM kernels -# in full precision on the selected device. -print(natten.has_fp32_gemm()) -print(natten.has_fp32_gemm(0)) # Optionally specify a GPU index. -``` - -If `natten.has_gemm()` returns true, by default NATTEN will call the faster GEMM kernels instead of the original naive kernels -for both NA1D and NA2D. 3D Neighborhood attention is not supported at this time, but you can still use the naive kernels. - -In addition, we will be adding scripts that allow you to profile and observe latency from the kernels with those options -available. - -## About NATTEN -Sliding window self attention mechanisms have been relatively overlooked, in part due to implementation difficulties. -For example, in a paper proposing one of the earliest examples of such methods, -[SASA](https://proceedings.neurips.cc/paper/2019/file/3416a75f4cea9109507cacd8e2f2aefc-Paper.pdf), -it was noted that -although such methods are theoretically efficient, they're relatively slow in practice, compared to convolutions, -which have been implemented in most well-known deep learning libraries. - -That is why we started developing NATTEN, an extension to existing libraries with efficient implementations of sliding window -attention mechanisms, which will enable research in this direction including building powerful hierarchical vision -transformers. - -For more information, we highly recommend reading our preprints [NAT](https://arxiv.org/abs/2204.07143) and -[DiNAT](https://arxiv.org/abs/2209.15001), and check out their [repository](https://github.com/SHI-Labs/Neighborhood-Attention-Transformer). - -## Requirements - -* python >= 3.8 -* torch >= 2.0 +## Getting started -NATTEN supports PyTorch version 2.0 and later, and Python versions 3.7, 3.8, 3.9, 3.10(only torch >= 1.11), and 3.11 (only torch >= 1.13). +NATTEN supports PyTorch version 2.0 and later, and Python versions 3.8 and above. +Python 3.12 is only supported with torch >= 2.2.0. Older NATTEN releases supported python >= 3.7 and torch >= 1.8. -**NOTE:** NATTEN only comes with pre-built Linux wheels, and supports Kepler and above (`SM >= 35`). -Make sure your GPU is supported by referring to -[this webpage](https://arnon.dk/matching-sm-architectures-arch-and-gencode-for-various-nvidia-cards/). -Future versions will extend support to older GPUs. - -## Getting started - -### Linux -Just refer to our website, [shi-labs.com/natten](https://www.shi-labs.com/natten/), select your PyTorch version and the CUDA -version it was compiled with, copy-paste the command and install in seconds! - -For example, if you're on `torch==2.0.0+cu118`, you should install NATTEN using the following wheel: -```bash -pip3 install natten -f https://shi-labs.com/natten/wheels/cu118/torch2.0.0/index.html -``` - -More generally: -```bash -pip3 install natten -f https://shi-labs.com/natten/wheels/{cu_version}/torch{torch_version}/index.html -``` - -**NOTE:** If you do not specify a wheel URL, pip will collect NATTEN and try to compile on locally, which depending -on your system might take up to 30 minutes. -We strongly recommend using our website if you're a Linux user. - -### Mac -Unfortunately we are not yet able to build Mac wheels (and do not yet have a Metal backend). However, you can compile upon -installing and use the CPU kernels: - -```bash -pip3 install natten -``` - -### Windows -The current release has not been successfully built Windows devices with CUDA, and therefore does not yet have Windows wheels. -If you are a windows user willing to help us figure out building with MSVC, please contact us or open an issue. - -### Build from source -Once you've set up your Python environment and installed PyTorch with CUDA, simply clone and build: - -```bash -git clone https://github.com/SHI-Labs/NATTEN -cd NATTEN - -pip install -r requirements.txt - -make -``` - -NOTE: NATTEN will use the PyTorch API to detect your GPU architecture, and will by default attempt to use 1/4th of the number -of processes your system allows to build. You can override them by passing in the following arguments: -```bash -# Build with 2 workers/processes -make WORKERS=2 - -# Build targeting SM89 (Ada Lovelace) -make CUDA_ARCH="8.9" -``` - -Please also note that building with the latest GEMM kernels can be a bit time consuming, which means at least 10 - 20 minutes -given that you use enough workers. It is technically possible to improve build time by generating more source files and using -more workers (at the expense of generating a larger binary), but that option will be made available in the future. - -#### Optional: run unit tests -You can optionally run unit tests to verify building from source finished successfully: - -```bash -make test -``` - - -## Catalog -- [x] Neighborhood Attention 1D (CPU, naive) -- [x] Neighborhood Attention 2D (CPU, naive) -- [x] Neighborhood Attention 3D (CPU, naive) -- [x] Neighborhood Attention 1D (CUDA, naive) -- [x] Neighborhood Attention 2D (CUDA, naive) -- [x] Neighborhood Attention 3D (CUDA, naive) -- [x] Neighborhood Attention 1D (CUDA, gemm-based, SM70 and above) -- [x] Neighborhood Attention 2D (CUDA, gemm-based, SM70 and above) -- [x] Dilation support -- [x] Float16 support -- [x] BFloat16 support -- [x] Kepler and Maxwell (30<=SM<60) support -- [ ] Windows builds -- [ ] Neighborhood Attention 1D (CUDA, fused kernels) -- [ ] Neighborhood Attention 2D (CUDA, fused kernels) - -## Usage -Simply import `NeighborhoodAttention1D`, `NeighborhoodAttention2D`, or `NeighborhoodAttention3D` from `natten`: -```python -from natten import NeighborhoodAttention1D -from natten import NeighborhoodAttention2D -from natten import NeighborhoodAttention3D - -na1d = NeighborhoodAttention1D(dim=128, kernel_size=7, dilation=2, num_heads=4) -na2d = NeighborhoodAttention2D(dim=128, kernel_size=7, dilation=2, num_heads=4) -na3d = NeighborhoodAttention3D(dim=128, kernel_size=7, dilation=2, num_heads=4) -``` - -NA3D also supports different kernel size and dilation values for depth: -```python -na3d = NeighborhoodAttention3D( - dim=128, - kernel_size=7, - kernel_size_d=5, - dilation=2, - dilation_d=3, - num_heads=4) -``` - -Modules expect inputs of shape `[batch_size, *, dim]`: -* NA1D: `[batch_size, sequence_length, dim]` -* NA2D: `[batch_size, height, width, dim]` -* NA3D: `[batch_size, depth, height, width, dim]` - - -### FLOPs -We recommend counting flops through [fvcore](https://github.com/facebookresearch/fvcore). - -```shell -pip install fvcore -``` - -Once you have fvcore installed, you can directly use our dedicated FLOP counter: -```python -from natten.flops import get_flops - -flops = get_flops(model, input) -``` - -Alternatively, if you are using fvcore's `FlopCountAnalysis` directly, be sure to add our op handles: -```python -from fvcore.nn import FlopCountAnalysis -from natten.flops import add_natten_handle - -# ... - -flop_ctr = FlopCountAnalysis(model, input) -flop_ctr = add_natten_handle(flop_ctr) - -# ... -``` +Please refer to [install instructions](docs/install.md) to find out whether your operating system and hardware accelerator is +compatible with NATTEN. + +## Feature availability + +| Problem space | CPU backend | CUDA backend | +| ----------- | ----------- | ---------------- | +| 1D | naive | naive, gemm, fna | +| 2D | naive | naive, gemm, fna | +| 3D | naive | naive, fna | + +### CPU + +| Problem space | CPU Backend | Causal masking | Varying parameters | Relative positional bias | Autograd support | +| ----------- | ----------- | ------------------ | ------------------ | ------------------------ | ------------------------ | +| 1D | naive | :white_check_mark: | :white_check_mark: | :white_check_mark: | Forward and reverse mode | +| 2D | naive | :white_check_mark: | :white_check_mark: | :white_check_mark: | Forward and reverse mode | +| 3D | naive | :white_check_mark: | :white_check_mark: | :white_check_mark: | Forward and reverse mode | + +Notes: +* Forward mode autograd does not support relative positional biases and causal masking yet. +* Relative positional biases are not yet supported when any axis has causal masking enabled. + +### CUDA + +| Problem space | CUDA Backend | Causal masking | Varying parameters | Relative positional bias | Autograd support | Min. Arch | +| ----------- | ----------- | ------------------ | ------------------ | ------------------------ | ------------------------ | --------- | +| 1D | naive | :white_check_mark: | :white_check_mark: | :white_check_mark: | Forward and reverse mode | SM35 | +| 2D | naive | :white_check_mark: | :white_check_mark: | :white_check_mark: | Forward and reverse mode | SM35 | +| 3D | naive | :white_check_mark: | :white_check_mark: | :white_check_mark: | Forward and reverse mode | SM35 | +| 1D | gemm | | | :white_check_mark: | Forward and reverse mode | SM70 | +| 2D | gemm | | | :white_check_mark: | Forward and reverse mode | SM70 | +| 1D | fna | :white_check_mark: | :white_check_mark: | :white_check_mark: | Coming soon | SM50 | +| 2D | fna | :white_check_mark: | :white_check_mark: | :white_check_mark: | Coming soon | SM50 | +| 3D | fna | :white_check_mark: | :white_check_mark: | :white_check_mark: | Coming soon | SM50 | + +Notes: +* FP16 kernels are only available on SM50 and above, and BF16 requires SM80 and above. +* GEMM backend on SM70 and SM75 can only do FP16. +* Tiled only implements 1/3 of the ops, is only implemented for 2D problems, and requires head dim = 32. +* Forward mode autograd does not support relative positional biases and causal masking yet. +* Relative positional biases are not yet supported when any axis has causal masking enabled. +* Naive backend allows FP16 for SM50 and above only. FP32/FP64 are available for SM35 and above. ## License NATTEN is released under the [MIT License](LICENSE). ## Citation ```bibtex +@misc{hassani2024faster, + title = {Faster Neighborhood Attention: Reducing the O(n^2) Cost of Self Attention at the Threadblock Level}, + author = {Ali Hassani and Wen-Mei Hwu and Humphrey Shi}, + year = 2024, + url = {https://arxiv.org/abs/2403.04690}, + eprint = {2403.04690}, + archiveprefix = {arXiv}, + primaryclass = {cs.CV} +} @inproceedings{hassani2023neighborhood, - title = {Neighborhood Attention Transformer}, - author = {Ali Hassani and Steven Walton and Jiachen Li and Shen Li and Humphrey Shi}, - year = 2023, - booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)} + title = {Neighborhood Attention Transformer}, + author = {Ali Hassani and Steven Walton and Jiachen Li and Shen Li and Humphrey Shi}, + year = 2023, + booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)} } -@article{hassani2022dilated, - title = {Dilated Neighborhood Attention Transformer}, - author = {Ali Hassani and Humphrey Shi}, - year = 2022, - url = {https://arxiv.org/abs/2209.15001}, - eprint = {2209.15001}, - archiveprefix = {arXiv}, - primaryclass = {cs.CV} +@misc{hassani2022dilated, + title = {Dilated Neighborhood Attention Transformer}, + author = {Ali Hassani and Humphrey Shi}, + year = 2022, + url = {https://arxiv.org/abs/2209.15001}, + eprint = {2209.15001}, + archiveprefix = {arXiv}, + primaryclass = {cs.CV} } ``` ## Acknowledgements -We would like to thank NVIDIA, and the [CUTLASS project](https://github.com/NVIDIA/cutlass/) and team for their efforts in +We thank NVIDIA, and the [CUTLASS project](https://github.com/NVIDIA/cutlass/) and team for their efforts in creating and open-sourcing CUTLASS. We would also like to thank Haicheng Wu for his valuable feedback and comments which led to -the creation of Implicit GEMM NA. -We also thank Meta, and the [PyTorch](https://github.com/pytorch/pytorch/) project and team. +the creation of GEMM-based NA. +We also thank Meta and the [xFormers](https://github.com/facebookresearch/xformers/) team +for their FMHA kernel, which is what our Fused Neighborhood Attention kernel is based on. +We thank the [PyTorch](https://github.com/pytorch/pytorch/) project and team. diff --git a/assets/README_pypi.md b/assets/README_pypi.md index 1182a1a..5cf06f1 100644 --- a/assets/README_pypi.md +++ b/assets/README_pypi.md @@ -10,307 +10,87 @@ NATTEN is an open-source project dedicated to providing fast implementations for [Neighborhood Attention](https://scholar.google.com/citations?view_op=view_citation&citation_for_view=Ndu0dUcAAAAJ:b0M2c_1WBrUC), a sliding window self-attention mechanism. -If you're not familiar with neighborhood attention, we recommend referring to -[our papers](https://github.com/SHI-Labs/Neighborhood-Attention-Transformer), or watching our +If you're not familiar with neighborhood attention, please refer to +[our papers](https://github.com/SHI-Labs/Neighborhood-Attention-Transformer), or watch our [YouTube video](https://www.youtube.com/watch?v=Ya4BfioxIHA) from CVPR 2023. -NATTEN is primarily a C++/CUDA library, which has so far only supported binding with the torch API, and therefore is mostly -usable through PyTorch. We plan to eliminate the torch dependency in the future and possibly support other frameworks / -engines. - -NATTEN's python interface provides Neighborhood Attention (local attention) -and Dilated Neighborhood Attention -(sparse global attention, a.k.a. dilated local attention) as autograd-compatible PyTorch modules for both 1D, 2D, and 3D data. - -It also has experimental support for -[forward mode automatic differentiation](https://pytorch.org/tutorials/intermediate/forward_ad_usage.html), -and nested tensors. - -## CPU support -Our CPU implementations are very limited and barely performance-optimized. -While we aim to provide the best implementation for different devices, optimizing our CUDA kernels is higher up on the list of -priorities. Contributions are always welcomed. - - -## CUDA support -NATTEN generally supports all architectures supported by PyTorch. More specifically, architectures since Kepler (SM35) are -supported. However, our most-performant kernels only support architectures since Volta, targeting tensor core math. - -### Half-precision support -Devices with compute capability greater than or equal to 6.0 (Pascal and later) allow running in FP16. - -Devices with compute capability greater than or equal to 8.0 (Ampere and later) allow running in BF16. - -### Naive kernels -NATTEN provides more than one set of kernel. Our naive kernels, which were developed during the first phase of the project, -provide a very basic implementation of neighborhood attention, and are the last resort for every problem. This means that if -your device and software support alternatives to our naive kernels (i.e. GEMM kernels), and your problem size is supported, -NATTEN will automatically pick the better kernels for you. (NOTE: this is done based on the class of kernels and not by actual -performance via profiling.) - -Naive kernels are always usable across different architectures, though not the most performant. - -### Tiled kernels -Naive kernels for the 2-dimensional neighborhood attention also come with a tiled implementation for one of the three -underlying operations, which is considerably more performant than the original. However, the tiled kernels only support problem -sizes with head dim 32, and up to kernel size 13x13. -Tiled kernels are also not supported in devices with compute capability smaller than 6.0. - -### GEMM kernels. - -Our GEMM-based kernels depend on and are modeled after -[CUTLASS](https://github.com/NVIDIA/cutlass/)'s [Implicit GEMM](https://github.com/NVIDIA/cutlass/blob/main/media/docs/implicit_gemm_convolution.md) -kernels for convolution. - -Devices with compute capability greater than or equal to 7.0 (Volta, Turing, Ampere, Ada Lovelace, Hopper), can run our GEMM -kernels, which are somewhat performance-optimized, thanks to the underlying mainloop from CUTLASS, and target Tensor Core math. - -However, do note that their current float16/bfloat16 implementations do not typically result in improved latency, -due to a memory alignment issue, which we aim to resolve in future kernels. - -Devices with compute capability greater than or equal to 8.0 (Ampere and later) support GEMM kernels with double, full, and -half precision (FP64, FP32, FP16, BF16). - -Devices with compute capability 7.0 or 7.5 (Volta and Turing) only support GEMM kernels with half precision (FP16). This is -because their tensor cores only allow FP16 math. - - -### How do I check my compute capability / architecture? -Simple, just Google your GPU model, and check its compute capability. -If you've already set up NATTEN, you could also run: -```python -from natten.functional import get_device_cc - -cc = get_device_cc() -cc = get_device_cc(0) # Optionally select a specific GPU - -print(f"Your device is SM{cc}.") -``` - -### How do I know if I'm using the new kernels? -The new NATTEN library sets up constants that are binded to the python interface, which will allow you to -check whether you've compiled with: a. CUDA, b. Float16 (half) support, c. Bfloat16 support, d. New GEMM kernels. - -```python -import natten - -# Whether NATTEN was built with CUDA kernels, -# and supports running them on this system. -print(natten.has_cuda()) - -# Whether NATTEN supports running float16 on -# the selected device. -print(natten.has_half()) -print(natten.has_half(0)) # Optionally specify a GPU index. - -# Whether NATTEN supports running bfloat16 on -# the selected device. -print(natten.has_bfloat()) -print(natten.has_bfloat(0)) # Optionally specify a GPU index. - -# Whether NATTEN supports running GEMM kernels -# on the selected device. -print(natten.has_gemm()) -print(natten.has_gemm(0)) # Optionally specify a GPU index. - -# Whether NATTEN supports running GEMM kernels -# in full precision on the selected device. -print(natten.has_fp32_gemm()) -print(natten.has_fp32_gemm(0)) # Optionally specify a GPU index. -``` - -If `natten.has_gemm()` returns true, by default NATTEN will call the faster GEMM kernels instead of the original naive kernels -for both NA1D and NA2D. 3D Neighborhood attention is not supported at this time, but you can still use the naive kernels. - -In addition, we will be adding scripts that allow you to profile and observe latency from the kernels with those options -available. - -## About NATTEN -Sliding window self attention mechanisms have been relatively overlooked, in part due to implementation difficulties. -For example, in a paper proposing one of the earliest examples of such methods, -[SASA](https://proceedings.neurips.cc/paper/2019/file/3416a75f4cea9109507cacd8e2f2aefc-Paper.pdf), -it was noted that -although such methods are theoretically efficient, they're relatively slow in practice, compared to convolutions, -which have been implemented in most well-known deep learning libraries. - -That is why we started developing NATTEN, an extension to existing libraries with efficient implementations of sliding window -attention mechanisms, which will enable research in this direction including building powerful hierarchical vision -transformers. - -For more information, we highly recommend reading our preprints [NAT](https://arxiv.org/abs/2204.07143) and -[DiNAT](https://arxiv.org/abs/2209.15001), and check out their [repository](https://github.com/SHI-Labs/Neighborhood-Attention-Transformer). - -## Requirements - -* python >= 3.8 -* torch >= 2.0 +## Getting started -NATTEN supports PyTorch version 2.0 and later, and Python versions 3.7, 3.8, 3.9, 3.10(only torch >= 1.11), and 3.11 (only torch >= 1.13). +NATTEN supports PyTorch version 2.0 and later, and Python versions 3.8 and above. +Python 3.12 is only supported with torch >= 2.2.0. Older NATTEN releases supported python >= 3.7 and torch >= 1.8. -**NOTE:** NATTEN only comes with pre-built Linux wheels, and supports Kepler and above (`SM >= 35`). -Make sure your GPU is supported by referring to -[this webpage](https://arnon.dk/matching-sm-architectures-arch-and-gencode-for-various-nvidia-cards/). -Future versions will extend support to older GPUs. - -## Getting started - -### Linux -Just refer to our website, [shi-labs.com/natten](https://www.shi-labs.com/natten/), select your PyTorch version and the CUDA -version it was compiled with, copy-paste the command and install in seconds! - -For example, if you're on `torch==2.0.0+cu118`, you should install NATTEN using the following wheel: -```bash -pip3 install natten -f https://shi-labs.com/natten/wheels/cu118/torch2.0.0/index.html -``` - -More generally: -```bash -pip3 install natten -f https://shi-labs.com/natten/wheels/{cu_version}/torch{torch_version}/index.html -``` - -**NOTE:** If you do not specify a wheel URL, pip will collect NATTEN and try to compile on locally, which depending -on your system might take up to 30 minutes. -We strongly recommend using our website if you're a Linux user. - -### Mac -Unfortunately we are not yet able to build Mac wheels (and do not yet have a Metal backend). However, you can compile upon -installing and use the CPU kernels: - -```bash -pip3 install natten -``` - -### Windows -The current release has not been successfully built Windows devices with CUDA, and therefore does not yet have Windows wheels. -If you are a windows user willing to help us figure out building with MSVC, please contact us or open an issue. - -### Build from source -Once you've set up your Python environment and installed PyTorch with CUDA, simply clone and build: - -```bash -git clone https://github.com/SHI-Labs/NATTEN -cd NATTEN - -pip install -r requirements.txt - -make -``` - -NOTE: NATTEN will use the PyTorch API to detect your GPU architecture, and will by default attempt to use 1/4th of the number -of processes your system allows to build. You can override them by passing in the following arguments: -```bash -# Build with 2 workers/processes -make WORKERS=2 - -# Build targeting SM89 (Ada Lovelace) -make CUDA_ARCH="8.9" -``` - -Please also note that building with the latest GEMM kernels can be a bit time consuming, which means at least 10 - 20 minutes -given that you use enough workers. It is technically possible to improve build time by generating more source files and using -more workers (at the expense of generating a larger binary), but that option will be made available in the future. - -#### Optional: run unit tests -You can optionally run unit tests to verify building from source finished successfully: - -```bash -make test -``` - - -## Catalog -- [x] Neighborhood Attention 1D (CPU, naive) -- [x] Neighborhood Attention 2D (CPU, naive) -- [x] Neighborhood Attention 3D (CPU, naive) -- [x] Neighborhood Attention 1D (CUDA, naive) -- [x] Neighborhood Attention 2D (CUDA, naive) -- [x] Neighborhood Attention 3D (CUDA, naive) -- [x] Neighborhood Attention 1D (CUDA, gemm-based, SM70 and above) -- [x] Neighborhood Attention 2D (CUDA, gemm-based, SM70 and above) -- [x] Dilation support -- [x] Float16 support -- [x] BFloat16 support -- [x] Kepler and Maxwell (30<=SM<60) support -- [ ] Windows builds -- [ ] Neighborhood Attention 1D (CUDA, fused kernels) -- [ ] Neighborhood Attention 2D (CUDA, fused kernels) - -## Usage -Simply import `NeighborhoodAttention1D`, `NeighborhoodAttention2D`, or `NeighborhoodAttention3D` from `natten`: -```python -from natten import NeighborhoodAttention1D -from natten import NeighborhoodAttention2D -from natten import NeighborhoodAttention3D - -na1d = NeighborhoodAttention1D(dim=128, kernel_size=7, dilation=2, num_heads=4) -na2d = NeighborhoodAttention2D(dim=128, kernel_size=7, dilation=2, num_heads=4) -na3d = NeighborhoodAttention3D(dim=128, kernel_size=7, dilation=2, num_heads=4) -``` - -NA3D also supports different kernel size and dilation values for depth: -```python -na3d = NeighborhoodAttention3D( - dim=128, - kernel_size=7, - kernel_size_d=5, - dilation=2, - dilation_d=3, - num_heads=4) -``` - -Modules expect inputs of shape `[batch_size, *, dim]`: -* NA1D: `[batch_size, sequence_length, dim]` -* NA2D: `[batch_size, height, width, dim]` -* NA3D: `[batch_size, depth, height, width, dim]` - - -### FLOPs -We recommend counting flops through [fvcore](https://github.com/facebookresearch/fvcore). - -```shell -pip install fvcore -``` - -Once you have fvcore installed, you can directly use our dedicated FLOP counter: -```python -from natten.flops import get_flops - -flops = get_flops(model, input) -``` - -Alternatively, if you are using fvcore's `FlopCountAnalysis` directly, be sure to add our op handles: -```python -from fvcore.nn import FlopCountAnalysis -from natten.flops import add_natten_handle - -# ... - -flop_ctr = FlopCountAnalysis(model, input) -flop_ctr = add_natten_handle(flop_ctr) - -# ... -``` +Please refer to [install instructions](docs/install.md) to find out whether your operating system and hardware accelerator is +compatible with NATTEN. + +## Feature availability + +| Problem space | CPU backend | CUDA backend | +| ----------- | ----------- | ---------------- | +| 1D | naive | naive, gemm, fna | +| 2D | naive | naive, gemm, fna | +| 3D | naive | naive, fna | + +### CPU + +| Problem space | CPU Backend | Causal masking | Varying parameters | Relative positional bias | Autograd support | +| ----------- | ----------- | ------------------ | ------------------ | ------------------------ | ------------------------ | +| 1D | naive | :white_check_mark: | :white_check_mark: | :white_check_mark: | Forward and reverse mode | +| 2D | naive | :white_check_mark: | :white_check_mark: | :white_check_mark: | Forward and reverse mode | +| 3D | naive | :white_check_mark: | :white_check_mark: | :white_check_mark: | Forward and reverse mode | + +Notes: +* Forward mode autograd does not support relative positional biases and causal masking yet. +* Relative positional biases are not yet supported when any axis has causal masking enabled. + +### CUDA + +| Problem space | CUDA Backend | Causal masking | Varying parameters | Relative positional bias | Autograd support | Min. Arch | +| ----------- | ----------- | ------------------ | ------------------ | ------------------------ | ------------------------ | --------- | +| 1D | naive | :white_check_mark: | :white_check_mark: | :white_check_mark: | Forward and reverse mode | SM35 | +| 2D | naive | :white_check_mark: | :white_check_mark: | :white_check_mark: | Forward and reverse mode | SM35 | +| 3D | naive | :white_check_mark: | :white_check_mark: | :white_check_mark: | Forward and reverse mode | SM35 | +| 1D | gemm | | | :white_check_mark: | Forward and reverse mode | SM70 | +| 2D | gemm | | | :white_check_mark: | Forward and reverse mode | SM70 | +| 1D | fna | :white_check_mark: | :white_check_mark: | :white_check_mark: | Coming soon | SM50 | +| 2D | fna | :white_check_mark: | :white_check_mark: | :white_check_mark: | Coming soon | SM50 | +| 3D | fna | :white_check_mark: | :white_check_mark: | :white_check_mark: | Coming soon | SM50 | + +Notes: +* FP16 kernels are only available on SM50 and above, and BF16 requires SM80 and above. +* GEMM backend on SM70 and SM75 can only do FP16. +* Tiled only implements 1/3 of the ops, is only implemented for 2D problems, and requires head dim = 32. +* Forward mode autograd does not support relative positional biases and causal masking yet. +* Relative positional biases are not yet supported when any axis has causal masking enabled. +* Naive backend allows FP16 for SM50 and above only. FP32/FP64 are available for SM35 and above. ## License -NATTEN is released under the [MIT License](https://github.com/SHI-Labs/NATTEN/blob/main/LICENSE). +NATTEN is released under the [MIT License](LICENSE). ## Citation ```bibtex @inproceedings{hassani2023neighborhood, - title = {Neighborhood Attention Transformer}, - author = {Ali Hassani and Steven Walton and Jiachen Li and Shen Li and Humphrey Shi}, - year = 2023, - booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)} + title = {Neighborhood Attention Transformer}, + author = {Ali Hassani and Steven Walton and Jiachen Li and Shen Li and Humphrey Shi}, + year = 2023, + booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)} } @article{hassani2022dilated, - title = {Dilated Neighborhood Attention Transformer}, - author = {Ali Hassani and Humphrey Shi}, - year = 2022, - url = {https://arxiv.org/abs/2209.15001}, - eprint = {2209.15001}, - archiveprefix = {arXiv}, - primaryclass = {cs.CV} + title = {Dilated Neighborhood Attention Transformer}, + author = {Ali Hassani and Humphrey Shi}, + year = 2022, + url = {https://arxiv.org/abs/2209.15001}, + eprint = {2209.15001}, + archiveprefix = {arXiv}, + primaryclass = {cs.CV} } ``` + +## Acknowledgements +We thank NVIDIA, and the [CUTLASS project](https://github.com/NVIDIA/cutlass/) and team for their efforts in +creating and open-sourcing CUTLASS. 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zvoCrd;97Towgsbtpmd5^u|Vq?F}n!H*yK`E3v5q*58*s1?jR>Bygis50|O)28tVA>k{QaF9P{iU+wx^*0m=GOU6Q`OG==LE2YeIT`hgaVn(znk<9*^r@{>IXoWjeke-6&S3v)Euu@ zoM4~%_lpr5^*R$A50!uyH~(%M1u~6Xl0#qgE-6e|~pRWLfDJLiqvy<^TIWQ3c z2Rn(lW{AmVuk(e$EqWDe>WICJOCwv2YvQy!w{3-6Fyl|Aix5E^r zTg4`ZfYJKz{rWFw`@jD)9JuAgAVN8j|D36R*ZDVG_& kernel_size, + const std::tuple& dilation, + const std::tuple& is_causal); void na1d_pn_cpu_naive_float( + bool is_grad, void * query_ptr, void * key_ptr, void * attn_ptr, - int batch_size, - int heads, - int length, - int dim, + int32_t batch_size, + int32_t heads, + int32_t length, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, - int kernel_size, - int dilation); + const std::tuple& kernel_size, + const std::tuple& dilation, + const std::tuple& is_causal); void na2d_pn_cpu_naive_double( + bool is_grad, void * query_ptr, void * key_ptr, void * attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, + int32_t batch_size, + int32_t heads, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, - int kernel_size, - int dilation); + const std::tuple& kernel_size, + const std::tuple& dilation, + const std::tuple& is_causal); void na2d_pn_cpu_naive_float( + bool is_grad, void * query_ptr, void * key_ptr, void * attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, + int32_t batch_size, + int32_t heads, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, - int kernel_size, - int dilation); + const std::tuple& kernel_size, + const std::tuple& dilation, + const std::tuple& is_causal); void na3d_pn_cpu_naive_double( + bool is_grad, void * query_ptr, void * key_ptr, void * attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, + int32_t batch_size, + int32_t heads, + int32_t depth, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); + const std::tuple& kernel_size, + const std::tuple& dilation, + const std::tuple& is_causal); void na3d_pn_cpu_naive_float( + bool is_grad, void * query_ptr, void * key_ptr, void * attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, + int32_t batch_size, + int32_t heads, + int32_t depth, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); + const std::tuple& kernel_size, + const std::tuple& dilation, + const std::tuple& is_causal); void na1d_pn_bias_cpu_naive_double( void * query_ptr, void * key_ptr, void * bias_ptr, void * attn_ptr, - int batch_size, - int heads, - int length, - int dim, + int32_t batch_size, + int32_t heads, + int32_t length, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, - int kernel_size, - int dilation); + const std::tuple& kernel_size, + const std::tuple& dilation, + const std::tuple& is_causal); void na1d_pn_bias_cpu_naive_float( void * query_ptr, void * key_ptr, void * bias_ptr, void * attn_ptr, - int batch_size, - int heads, - int length, - int dim, + int32_t batch_size, + int32_t heads, + int32_t length, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, - int kernel_size, - int dilation); + const std::tuple& kernel_size, + const std::tuple& dilation, + const std::tuple& is_causal); void na2d_pn_bias_cpu_naive_double( void * query_ptr, void * key_ptr, void * bias_ptr, void * attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, + int32_t batch_size, + int32_t heads, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, - int kernel_size, - int dilation); + const std::tuple& kernel_size, + const std::tuple& dilation, + const std::tuple& is_causal); void na2d_pn_bias_cpu_naive_float( void * query_ptr, void * key_ptr, void * bias_ptr, void * attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, + int32_t batch_size, + int32_t heads, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, - int kernel_size, - int dilation); + const std::tuple& kernel_size, + const std::tuple& dilation, + const std::tuple& is_causal); void na3d_pn_bias_cpu_naive_double( void * query_ptr, void * key_ptr, void * bias_ptr, void * attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, + int32_t batch_size, + int32_t heads, + int32_t depth, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); + const std::tuple& kernel_size, + const std::tuple& dilation, + const std::tuple& is_causal); void na3d_pn_bias_cpu_naive_float( void * query_ptr, void * key_ptr, void * bias_ptr, void * attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, + int32_t batch_size, + int32_t heads, + int32_t depth, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); + const std::tuple& kernel_size, + const std::tuple& dilation, + const std::tuple& is_causal); void na1d_nn_cpu_naive_double( void * attn_ptr, void * value_ptr, void * output_ptr, - int batch_size, - int heads, - int length, - int dim, + int32_t batch_size, + int32_t heads, + int32_t length, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, - int kernel_size, - int dilation); + const std::tuple& kernel_size, + const std::tuple& dilation, + const std::tuple& is_causal); void na1d_nn_cpu_naive_float( void * attn_ptr, void * value_ptr, void * output_ptr, - int batch_size, - int heads, - int length, - int dim, + int32_t batch_size, + int32_t heads, + int32_t length, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, - int kernel_size, - int dilation); + const std::tuple& kernel_size, + const std::tuple& dilation, + const std::tuple& is_causal); void na2d_nn_cpu_naive_double( void * attn_ptr, void * value_ptr, void * output_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, + int32_t batch_size, + int32_t heads, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, - int kernel_size, - int dilation); + const std::tuple& kernel_size, + const std::tuple& dilation, + const std::tuple& is_causal); void na2d_nn_cpu_naive_float( void * attn_ptr, void * value_ptr, void * output_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, + int32_t batch_size, + int32_t heads, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, - int kernel_size, - int dilation); + const std::tuple& kernel_size, + const std::tuple& dilation, + const std::tuple& is_causal); void na3d_nn_cpu_naive_double( void * attn_ptr, void * value_ptr, void * output_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, + int32_t batch_size, + int32_t heads, + int32_t depth, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); + const std::tuple& kernel_size, + const std::tuple& dilation, + const std::tuple& is_causal); void na3d_nn_cpu_naive_float( void * attn_ptr, void * value_ptr, void * output_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, + int32_t batch_size, + int32_t heads, + int32_t depth, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); + const std::tuple& kernel_size, + const std::tuple& dilation, + const std::tuple& is_causal); void na1d_in_cpu_naive_double( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, - int batch_size, - int heads, - int length, - int dim, + int32_t batch_size, + int32_t heads, + int32_t length, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, - int kernel_size, - int dilation); + const std::tuple& kernel_size, + const std::tuple& dilation, + const std::tuple& is_causal); void na1d_in_cpu_naive_float( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, - int batch_size, - int heads, - int length, - int dim, + int32_t batch_size, + int32_t heads, + int32_t length, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, - int kernel_size, - int dilation); + const std::tuple& kernel_size, + const std::tuple& dilation, + const std::tuple& is_causal); void na2d_in_cpu_naive_double( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, + int32_t batch_size, + int32_t heads, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, - int kernel_size, - int dilation); + const std::tuple& kernel_size, + const std::tuple& dilation, + const std::tuple& is_causal); void na2d_in_cpu_naive_float( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, + int32_t batch_size, + int32_t heads, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, - int kernel_size, - int dilation); + const std::tuple& kernel_size, + const std::tuple& dilation, + const std::tuple& is_causal); void na3d_in_cpu_naive_double( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, + int32_t batch_size, + int32_t heads, + int32_t depth, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); + const std::tuple& kernel_size, + const std::tuple& dilation, + const std::tuple& is_causal); void na3d_in_cpu_naive_float( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, + int32_t batch_size, + int32_t heads, + int32_t depth, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); + const std::tuple& kernel_size, + const std::tuple& dilation, + const std::tuple& is_causal); void na1d_rpbgrad_cpu_naive_double( void * d_bias_ptr, void * d_attn_ptr, - int batch_size, - int heads, - int length, - int dim, + int32_t batch_size, + int32_t heads, + int32_t length, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, - int kernel_size, - int dilation); + const std::tuple& kernel_size, + const std::tuple& dilation, + const std::tuple& is_causal); void na1d_rpbgrad_cpu_naive_float( void * d_bias_ptr, void * d_attn_ptr, - int batch_size, - int heads, - int length, - int dim, + int32_t batch_size, + int32_t heads, + int32_t length, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, - int kernel_size, - int dilation); + const std::tuple& kernel_size, + const std::tuple& dilation, + const std::tuple& is_causal); void na2d_rpbgrad_cpu_naive_double( void * d_bias_ptr, void * d_attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, + int32_t batch_size, + int32_t heads, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, - int kernel_size, - int dilation); + const std::tuple& kernel_size, + const std::tuple& dilation, + const std::tuple& is_causal); void na2d_rpbgrad_cpu_naive_float( void * d_bias_ptr, void * d_attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, + int32_t batch_size, + int32_t heads, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, - int kernel_size, - int dilation); + const std::tuple& kernel_size, + const std::tuple& dilation, + const std::tuple& is_causal); void na3d_rpbgrad_cpu_naive_double( void * d_bias_ptr, void * d_attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, + int32_t batch_size, + int32_t heads, + int32_t depth, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); + const std::tuple& kernel_size, + const std::tuple& dilation, + const std::tuple& is_causal); void na3d_rpbgrad_cpu_naive_float( void * d_bias_ptr, void * d_attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, + int32_t batch_size, + int32_t heads, + int32_t depth, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); + const std::tuple& kernel_size, + const std::tuple& dilation, + const std::tuple& is_causal); diff --git a/csrc/autogen/include/natten_autogen/cuda/fna/dispatch_cm.h b/csrc/autogen/include/natten_autogen/cuda/fna/dispatch_cm.h new file mode 100644 index 0000000..baab276 --- /dev/null +++ b/csrc/autogen/include/natten_autogen/cuda/fna/dispatch_cm.h @@ -0,0 +1,775 @@ +#pragma once + + +#include +#include +#include +#include +#include +#include +#include +namespace natten { +namespace cuda { +namespace fna { +#define DISPATCH_FNA_FORWARD_1D_SM50_float32(is_causal, has_rpb, cb) \ + [&] { \ + if (!std::get<0>(is_causal)) { \ + if (has_rpb) {\ + fna1_sm50_float32_cm_0_rpb(cb); \ + } else {\ + fna1_sm50_float32_cm_0(cb); \ + } \ + } \ + else if (std::get<0>(is_causal)) { \ + fna1_sm50_float32_cm_1(cb); \ + } \ + else { \ + std::cerr << "NATTEN FNA kernel launch failed!" \ + << "Causal mask dispatcher (FNA-1D, SM50, float32) got invalid causal mask!" \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_FNA_FORWARD_1D_SM50_float16(is_causal, has_rpb, cb) \ + [&] { \ + if (!std::get<0>(is_causal)) { \ + if (has_rpb) {\ + fna1_sm50_float16_cm_0_rpb(cb); \ + } else {\ + fna1_sm50_float16_cm_0(cb); \ + } \ + } \ + else if (std::get<0>(is_causal)) { \ + fna1_sm50_float16_cm_1(cb); \ + } \ + else { \ + std::cerr << "NATTEN FNA kernel launch failed!" \ + << "Causal mask dispatcher (FNA-1D, SM50, float16) got invalid causal mask!" \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_FNA_FORWARD_1D_SM70_float32(is_causal, has_rpb, cb) \ + [&] { \ + if (!std::get<0>(is_causal)) { \ + if (has_rpb) {\ + fna1_sm70_float32_cm_0_rpb(cb); \ + } else {\ + fna1_sm70_float32_cm_0(cb); \ + } \ + } \ + else if (std::get<0>(is_causal)) { \ + fna1_sm70_float32_cm_1(cb); \ + } \ + else { \ + std::cerr << "NATTEN FNA kernel launch failed!" \ + << "Causal mask dispatcher (FNA-1D, SM70, float32) got invalid causal mask!" \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_FNA_FORWARD_1D_SM70_float16(is_causal, has_rpb, cb) \ + [&] { \ + if (!std::get<0>(is_causal)) { \ + if (has_rpb) {\ + fna1_sm70_float16_cm_0_rpb(cb); \ + } else {\ + fna1_sm70_float16_cm_0(cb); \ + } \ + } \ + else if (std::get<0>(is_causal)) { \ + fna1_sm70_float16_cm_1(cb); \ + } \ + else { \ + std::cerr << "NATTEN FNA kernel launch failed!" \ + << "Causal mask dispatcher (FNA-1D, SM70, float16) got invalid causal mask!" \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_FNA_FORWARD_1D_SM75_float32(is_causal, has_rpb, cb) \ + [&] { \ + if (!std::get<0>(is_causal)) { \ + if (has_rpb) {\ + fna1_sm75_float32_cm_0_rpb(cb); \ + } else {\ + fna1_sm75_float32_cm_0(cb); \ + } \ + } \ + else if (std::get<0>(is_causal)) { \ + fna1_sm75_float32_cm_1(cb); \ + } \ + else { \ + std::cerr << "NATTEN FNA kernel launch failed!" \ + << "Causal mask dispatcher (FNA-1D, SM75, float32) got invalid causal mask!" \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_FNA_FORWARD_1D_SM75_float16(is_causal, has_rpb, cb) \ + [&] { \ + if (!std::get<0>(is_causal)) { \ + if (has_rpb) {\ + fna1_sm75_float16_cm_0_rpb(cb); \ + } else {\ + fna1_sm75_float16_cm_0(cb); \ + } \ + } \ + else if (std::get<0>(is_causal)) { \ + fna1_sm75_float16_cm_1(cb); \ + } \ + else { \ + std::cerr << "NATTEN FNA kernel launch failed!" \ + << "Causal mask dispatcher (FNA-1D, SM75, float16) got invalid causal mask!" \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_FNA_FORWARD_1D_SM80_float32(is_causal, has_rpb, cb) \ + [&] { \ + if (!std::get<0>(is_causal)) { \ + if (has_rpb) {\ + fna1_sm80_float32_cm_0_rpb(cb); \ + } else {\ + fna1_sm80_float32_cm_0(cb); \ + } \ + } \ + else if (std::get<0>(is_causal)) { \ + fna1_sm80_float32_cm_1(cb); \ + } \ + else { \ + std::cerr << "NATTEN FNA kernel launch failed!" \ + << "Causal mask dispatcher (FNA-1D, SM80, float32) got invalid causal mask!" \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_FNA_FORWARD_1D_SM80_float16(is_causal, has_rpb, cb) \ + [&] { \ + if (!std::get<0>(is_causal)) { \ + if (has_rpb) {\ + fna1_sm80_float16_cm_0_rpb(cb); \ + } else {\ + fna1_sm80_float16_cm_0(cb); \ + } \ + } \ + else if (std::get<0>(is_causal)) { \ + fna1_sm80_float16_cm_1(cb); \ + } \ + else { \ + std::cerr << "NATTEN FNA kernel launch failed!" \ + << "Causal mask dispatcher (FNA-1D, SM80, float16) got invalid causal mask!" \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_FNA_FORWARD_1D_SM80_bfloat16(is_causal, has_rpb, cb) \ + [&] { \ + if (!std::get<0>(is_causal)) { \ + if (has_rpb) {\ + fna1_sm80_bfloat16_cm_0_rpb(cb); \ + } else {\ + fna1_sm80_bfloat16_cm_0(cb); \ + } \ + } \ + else if (std::get<0>(is_causal)) { \ + fna1_sm80_bfloat16_cm_1(cb); \ + } \ + else { \ + std::cerr << "NATTEN FNA kernel launch failed!" \ + << "Causal mask dispatcher (FNA-1D, SM80, bfloat16) got invalid causal mask!" \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_FNA_FORWARD_2D_SM50_float32(is_causal, has_rpb, cb) \ + [&] { \ + if (!std::get<0>(is_causal) && !std::get<1>(is_causal)) { \ + if (has_rpb) {\ + fna2_sm50_float32_cm_0_0_rpb(cb); \ + } else {\ + fna2_sm50_float32_cm_0_0(cb); \ + } \ + } \ + else if (!std::get<0>(is_causal) && std::get<1>(is_causal)) { \ + fna2_sm50_float32_cm_0_1(cb); \ + } \ + else if (std::get<0>(is_causal) && !std::get<1>(is_causal)) { \ + fna2_sm50_float32_cm_1_0(cb); \ + } \ + else if (std::get<0>(is_causal) && std::get<1>(is_causal)) { \ + fna2_sm50_float32_cm_1_1(cb); \ + } \ + else { \ + std::cerr << "NATTEN FNA kernel launch failed!" \ + << "Causal mask dispatcher (FNA-2D, SM50, float32) got invalid causal mask!" \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_FNA_FORWARD_2D_SM50_float16(is_causal, has_rpb, cb) \ + [&] { \ + if (!std::get<0>(is_causal) && !std::get<1>(is_causal)) { \ + if (has_rpb) {\ + fna2_sm50_float16_cm_0_0_rpb(cb); \ + } else {\ + fna2_sm50_float16_cm_0_0(cb); \ + } \ + } \ + else if (!std::get<0>(is_causal) && std::get<1>(is_causal)) { \ + fna2_sm50_float16_cm_0_1(cb); \ + } \ + else if (std::get<0>(is_causal) && !std::get<1>(is_causal)) { \ + fna2_sm50_float16_cm_1_0(cb); \ + } \ + else if (std::get<0>(is_causal) && std::get<1>(is_causal)) { \ + fna2_sm50_float16_cm_1_1(cb); \ + } \ + else { \ + std::cerr << "NATTEN FNA kernel launch failed!" \ + << "Causal mask dispatcher (FNA-2D, SM50, float16) got invalid causal mask!" \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_FNA_FORWARD_2D_SM70_float32(is_causal, has_rpb, cb) \ + [&] { \ + if (!std::get<0>(is_causal) && !std::get<1>(is_causal)) { \ + if (has_rpb) {\ + fna2_sm70_float32_cm_0_0_rpb(cb); \ + } else {\ + fna2_sm70_float32_cm_0_0(cb); \ + } \ + } \ + else if (!std::get<0>(is_causal) && std::get<1>(is_causal)) { \ + fna2_sm70_float32_cm_0_1(cb); \ + } \ + else if (std::get<0>(is_causal) && !std::get<1>(is_causal)) { \ + fna2_sm70_float32_cm_1_0(cb); \ + } \ + else if (std::get<0>(is_causal) && std::get<1>(is_causal)) { \ + fna2_sm70_float32_cm_1_1(cb); \ + } \ + else { \ + std::cerr << "NATTEN FNA kernel launch failed!" \ + << "Causal mask dispatcher (FNA-2D, SM70, float32) got invalid causal mask!" \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_FNA_FORWARD_2D_SM70_float16(is_causal, has_rpb, cb) \ + [&] { \ + if (!std::get<0>(is_causal) && !std::get<1>(is_causal)) { \ + if (has_rpb) {\ + fna2_sm70_float16_cm_0_0_rpb(cb); \ + } else {\ + fna2_sm70_float16_cm_0_0(cb); \ + } \ + } \ + else if (!std::get<0>(is_causal) && std::get<1>(is_causal)) { \ + fna2_sm70_float16_cm_0_1(cb); \ + } \ + else if (std::get<0>(is_causal) && !std::get<1>(is_causal)) { \ + fna2_sm70_float16_cm_1_0(cb); \ + } \ + else if (std::get<0>(is_causal) && std::get<1>(is_causal)) { \ + fna2_sm70_float16_cm_1_1(cb); \ + } \ + else { \ + std::cerr << "NATTEN FNA kernel launch failed!" \ + << "Causal mask dispatcher (FNA-2D, SM70, float16) got invalid causal mask!" \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_FNA_FORWARD_2D_SM75_float32(is_causal, has_rpb, cb) \ + [&] { \ + if (!std::get<0>(is_causal) && !std::get<1>(is_causal)) { \ + if (has_rpb) {\ + fna2_sm75_float32_cm_0_0_rpb(cb); \ + } else {\ + fna2_sm75_float32_cm_0_0(cb); \ + } \ + } \ + else if (!std::get<0>(is_causal) && std::get<1>(is_causal)) { \ + fna2_sm75_float32_cm_0_1(cb); \ + } \ + else if (std::get<0>(is_causal) && !std::get<1>(is_causal)) { \ + fna2_sm75_float32_cm_1_0(cb); \ + } \ + else if (std::get<0>(is_causal) && std::get<1>(is_causal)) { \ + fna2_sm75_float32_cm_1_1(cb); \ + } \ + else { \ + std::cerr << "NATTEN FNA kernel launch failed!" \ + << "Causal mask dispatcher (FNA-2D, SM75, float32) got invalid causal mask!" \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_FNA_FORWARD_2D_SM75_float16(is_causal, has_rpb, cb) \ + [&] { \ + if (!std::get<0>(is_causal) && !std::get<1>(is_causal)) { \ + if (has_rpb) {\ + fna2_sm75_float16_cm_0_0_rpb(cb); \ + } else {\ + fna2_sm75_float16_cm_0_0(cb); \ + } \ + } \ + else if (!std::get<0>(is_causal) && std::get<1>(is_causal)) { \ + fna2_sm75_float16_cm_0_1(cb); \ + } \ + else if (std::get<0>(is_causal) && !std::get<1>(is_causal)) { \ + fna2_sm75_float16_cm_1_0(cb); \ + } \ + else if (std::get<0>(is_causal) && std::get<1>(is_causal)) { \ + fna2_sm75_float16_cm_1_1(cb); \ + } \ + else { \ + std::cerr << "NATTEN FNA kernel launch failed!" \ + << "Causal mask dispatcher (FNA-2D, SM75, float16) got invalid causal mask!" \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_FNA_FORWARD_2D_SM80_float32(is_causal, has_rpb, cb) \ + [&] { \ + if (!std::get<0>(is_causal) && !std::get<1>(is_causal)) { \ + if (has_rpb) {\ + fna2_sm80_float32_cm_0_0_rpb(cb); \ + } else {\ + fna2_sm80_float32_cm_0_0(cb); \ + } \ + } \ + else if (!std::get<0>(is_causal) && std::get<1>(is_causal)) { \ + fna2_sm80_float32_cm_0_1(cb); \ + } \ + else if (std::get<0>(is_causal) && !std::get<1>(is_causal)) { \ + fna2_sm80_float32_cm_1_0(cb); \ + } \ + else if (std::get<0>(is_causal) && std::get<1>(is_causal)) { \ + fna2_sm80_float32_cm_1_1(cb); \ + } \ + else { \ + std::cerr << "NATTEN FNA kernel launch failed!" \ + << "Causal mask dispatcher (FNA-2D, SM80, float32) got invalid causal mask!" \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_FNA_FORWARD_2D_SM80_float16(is_causal, has_rpb, cb) \ + [&] { \ + if (!std::get<0>(is_causal) && !std::get<1>(is_causal)) { \ + if (has_rpb) {\ + fna2_sm80_float16_cm_0_0_rpb(cb); \ + } else {\ + fna2_sm80_float16_cm_0_0(cb); \ + } \ + } \ + else if (!std::get<0>(is_causal) && std::get<1>(is_causal)) { \ + fna2_sm80_float16_cm_0_1(cb); \ + } \ + else if (std::get<0>(is_causal) && !std::get<1>(is_causal)) { \ + fna2_sm80_float16_cm_1_0(cb); \ + } \ + else if (std::get<0>(is_causal) && std::get<1>(is_causal)) { \ + fna2_sm80_float16_cm_1_1(cb); \ + } \ + else { \ + std::cerr << "NATTEN FNA kernel launch failed!" \ + << "Causal mask dispatcher (FNA-2D, SM80, float16) got invalid causal mask!" \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_FNA_FORWARD_2D_SM80_bfloat16(is_causal, has_rpb, cb) \ + [&] { \ + if (!std::get<0>(is_causal) && !std::get<1>(is_causal)) { \ + if (has_rpb) {\ + fna2_sm80_bfloat16_cm_0_0_rpb(cb); \ + } else {\ + fna2_sm80_bfloat16_cm_0_0(cb); \ + } \ + } \ + else if (!std::get<0>(is_causal) && std::get<1>(is_causal)) { \ + fna2_sm80_bfloat16_cm_0_1(cb); \ + } \ + else if (std::get<0>(is_causal) && !std::get<1>(is_causal)) { \ + fna2_sm80_bfloat16_cm_1_0(cb); \ + } \ + else if (std::get<0>(is_causal) && std::get<1>(is_causal)) { \ + fna2_sm80_bfloat16_cm_1_1(cb); \ + } \ + else { \ + std::cerr << "NATTEN FNA kernel launch failed!" \ + << "Causal mask dispatcher (FNA-2D, SM80, bfloat16) got invalid causal mask!" \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_FNA_FORWARD_3D_SM50_float32(is_causal, has_rpb, cb) \ + [&] { \ + if (!std::get<0>(is_causal) && !std::get<1>(is_causal) && !std::get<2>(is_causal)) { \ + if (has_rpb) {\ + fna3_sm50_float32_cm_0_0_0_rpb(cb); \ + } else {\ + fna3_sm50_float32_cm_0_0_0(cb); \ + } \ + } \ + else if (!std::get<0>(is_causal) && !std::get<1>(is_causal) && std::get<2>(is_causal)) { \ + fna3_sm50_float32_cm_0_0_1(cb); \ + } \ + else if (!std::get<0>(is_causal) && std::get<1>(is_causal) && !std::get<2>(is_causal)) { \ + fna3_sm50_float32_cm_0_1_0(cb); \ + } \ + else if (!std::get<0>(is_causal) && std::get<1>(is_causal) && std::get<2>(is_causal)) { \ + fna3_sm50_float32_cm_0_1_1(cb); \ + } \ + else if (std::get<0>(is_causal) && !std::get<1>(is_causal) && !std::get<2>(is_causal)) { \ + fna3_sm50_float32_cm_1_0_0(cb); \ + } \ + else if (std::get<0>(is_causal) && !std::get<1>(is_causal) && std::get<2>(is_causal)) { \ + fna3_sm50_float32_cm_1_0_1(cb); \ + } \ + else if (std::get<0>(is_causal) && std::get<1>(is_causal) && !std::get<2>(is_causal)) { \ + fna3_sm50_float32_cm_1_1_0(cb); \ + } \ + else if (std::get<0>(is_causal) && std::get<1>(is_causal) && std::get<2>(is_causal)) { \ + fna3_sm50_float32_cm_1_1_1(cb); \ + } \ + else { \ + std::cerr << "NATTEN FNA kernel launch failed!" \ + << "Causal mask dispatcher (FNA-3D, SM50, float32) got invalid causal mask!" \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_FNA_FORWARD_3D_SM50_float16(is_causal, has_rpb, cb) \ + [&] { \ + if (!std::get<0>(is_causal) && !std::get<1>(is_causal) && !std::get<2>(is_causal)) { \ + if (has_rpb) {\ + fna3_sm50_float16_cm_0_0_0_rpb(cb); \ + } else {\ + fna3_sm50_float16_cm_0_0_0(cb); \ + } \ + } \ + else if (!std::get<0>(is_causal) && !std::get<1>(is_causal) && std::get<2>(is_causal)) { \ + fna3_sm50_float16_cm_0_0_1(cb); \ + } \ + else if (!std::get<0>(is_causal) && std::get<1>(is_causal) && !std::get<2>(is_causal)) { \ + fna3_sm50_float16_cm_0_1_0(cb); \ + } \ + else if (!std::get<0>(is_causal) && std::get<1>(is_causal) && std::get<2>(is_causal)) { \ + fna3_sm50_float16_cm_0_1_1(cb); \ + } \ + else if (std::get<0>(is_causal) && !std::get<1>(is_causal) && !std::get<2>(is_causal)) { \ + fna3_sm50_float16_cm_1_0_0(cb); \ + } \ + else if (std::get<0>(is_causal) && !std::get<1>(is_causal) && std::get<2>(is_causal)) { \ + fna3_sm50_float16_cm_1_0_1(cb); \ + } \ + else if (std::get<0>(is_causal) && std::get<1>(is_causal) && !std::get<2>(is_causal)) { \ + fna3_sm50_float16_cm_1_1_0(cb); \ + } \ + else if (std::get<0>(is_causal) && std::get<1>(is_causal) && std::get<2>(is_causal)) { \ + fna3_sm50_float16_cm_1_1_1(cb); \ + } \ + else { \ + std::cerr << "NATTEN FNA kernel launch failed!" \ + << "Causal mask dispatcher (FNA-3D, SM50, float16) got invalid causal mask!" \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_FNA_FORWARD_3D_SM70_float32(is_causal, has_rpb, cb) \ + [&] { \ + if (!std::get<0>(is_causal) && !std::get<1>(is_causal) && !std::get<2>(is_causal)) { \ + if (has_rpb) {\ + fna3_sm70_float32_cm_0_0_0_rpb(cb); \ + } else {\ + fna3_sm70_float32_cm_0_0_0(cb); \ + } \ + } \ + else if (!std::get<0>(is_causal) && !std::get<1>(is_causal) && std::get<2>(is_causal)) { \ + fna3_sm70_float32_cm_0_0_1(cb); \ + } \ + else if (!std::get<0>(is_causal) && std::get<1>(is_causal) && !std::get<2>(is_causal)) { \ + fna3_sm70_float32_cm_0_1_0(cb); \ + } \ + else if (!std::get<0>(is_causal) && std::get<1>(is_causal) && std::get<2>(is_causal)) { \ + fna3_sm70_float32_cm_0_1_1(cb); \ + } \ + else if (std::get<0>(is_causal) && !std::get<1>(is_causal) && !std::get<2>(is_causal)) { \ + fna3_sm70_float32_cm_1_0_0(cb); \ + } \ + else if (std::get<0>(is_causal) && !std::get<1>(is_causal) && std::get<2>(is_causal)) { \ + fna3_sm70_float32_cm_1_0_1(cb); \ + } \ + else if (std::get<0>(is_causal) && std::get<1>(is_causal) && !std::get<2>(is_causal)) { \ + fna3_sm70_float32_cm_1_1_0(cb); \ + } \ + else if (std::get<0>(is_causal) && std::get<1>(is_causal) && std::get<2>(is_causal)) { \ + fna3_sm70_float32_cm_1_1_1(cb); \ + } \ + else { \ + std::cerr << "NATTEN FNA kernel launch failed!" \ + << "Causal mask dispatcher (FNA-3D, SM70, float32) got invalid causal mask!" \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_FNA_FORWARD_3D_SM70_float16(is_causal, has_rpb, cb) \ + [&] { \ + if (!std::get<0>(is_causal) && !std::get<1>(is_causal) && !std::get<2>(is_causal)) { \ + if (has_rpb) {\ + fna3_sm70_float16_cm_0_0_0_rpb(cb); \ + } else {\ + fna3_sm70_float16_cm_0_0_0(cb); \ + } \ + } \ + else if (!std::get<0>(is_causal) && !std::get<1>(is_causal) && std::get<2>(is_causal)) { \ + fna3_sm70_float16_cm_0_0_1(cb); \ + } \ + else if (!std::get<0>(is_causal) && std::get<1>(is_causal) && !std::get<2>(is_causal)) { \ + fna3_sm70_float16_cm_0_1_0(cb); \ + } \ + else if (!std::get<0>(is_causal) && std::get<1>(is_causal) && std::get<2>(is_causal)) { \ + fna3_sm70_float16_cm_0_1_1(cb); \ + } \ + else if (std::get<0>(is_causal) && !std::get<1>(is_causal) && !std::get<2>(is_causal)) { \ + fna3_sm70_float16_cm_1_0_0(cb); \ + } \ + else if (std::get<0>(is_causal) && !std::get<1>(is_causal) && std::get<2>(is_causal)) { \ + fna3_sm70_float16_cm_1_0_1(cb); \ + } \ + else if (std::get<0>(is_causal) && std::get<1>(is_causal) && !std::get<2>(is_causal)) { \ + fna3_sm70_float16_cm_1_1_0(cb); \ + } \ + else if (std::get<0>(is_causal) && std::get<1>(is_causal) && std::get<2>(is_causal)) { \ + fna3_sm70_float16_cm_1_1_1(cb); \ + } \ + else { \ + std::cerr << "NATTEN FNA kernel launch failed!" \ + << "Causal mask dispatcher (FNA-3D, SM70, float16) got invalid causal mask!" \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_FNA_FORWARD_3D_SM75_float32(is_causal, has_rpb, cb) \ + [&] { \ + if (!std::get<0>(is_causal) && !std::get<1>(is_causal) && !std::get<2>(is_causal)) { \ + if (has_rpb) {\ + fna3_sm75_float32_cm_0_0_0_rpb(cb); \ + } else {\ + fna3_sm75_float32_cm_0_0_0(cb); \ + } \ + } \ + else if (!std::get<0>(is_causal) && !std::get<1>(is_causal) && std::get<2>(is_causal)) { \ + fna3_sm75_float32_cm_0_0_1(cb); \ + } \ + else if (!std::get<0>(is_causal) && std::get<1>(is_causal) && !std::get<2>(is_causal)) { \ + fna3_sm75_float32_cm_0_1_0(cb); \ + } \ + else if (!std::get<0>(is_causal) && std::get<1>(is_causal) && std::get<2>(is_causal)) { \ + fna3_sm75_float32_cm_0_1_1(cb); \ + } \ + else if (std::get<0>(is_causal) && !std::get<1>(is_causal) && !std::get<2>(is_causal)) { \ + fna3_sm75_float32_cm_1_0_0(cb); \ + } \ + else if (std::get<0>(is_causal) && !std::get<1>(is_causal) && std::get<2>(is_causal)) { \ + fna3_sm75_float32_cm_1_0_1(cb); \ + } \ + else if (std::get<0>(is_causal) && std::get<1>(is_causal) && !std::get<2>(is_causal)) { \ + fna3_sm75_float32_cm_1_1_0(cb); \ + } \ + else if (std::get<0>(is_causal) && std::get<1>(is_causal) && std::get<2>(is_causal)) { \ + fna3_sm75_float32_cm_1_1_1(cb); \ + } \ + else { \ + std::cerr << "NATTEN FNA kernel launch failed!" \ + << "Causal mask dispatcher (FNA-3D, SM75, float32) got invalid causal mask!" \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_FNA_FORWARD_3D_SM75_float16(is_causal, has_rpb, cb) \ + [&] { \ + if (!std::get<0>(is_causal) && !std::get<1>(is_causal) && !std::get<2>(is_causal)) { \ + if (has_rpb) {\ + fna3_sm75_float16_cm_0_0_0_rpb(cb); \ + } else {\ + fna3_sm75_float16_cm_0_0_0(cb); \ + } \ + } \ + else if (!std::get<0>(is_causal) && !std::get<1>(is_causal) && std::get<2>(is_causal)) { \ + fna3_sm75_float16_cm_0_0_1(cb); \ + } \ + else if (!std::get<0>(is_causal) && std::get<1>(is_causal) && !std::get<2>(is_causal)) { \ + fna3_sm75_float16_cm_0_1_0(cb); \ + } \ + else if (!std::get<0>(is_causal) && std::get<1>(is_causal) && std::get<2>(is_causal)) { \ + fna3_sm75_float16_cm_0_1_1(cb); \ + } \ + else if (std::get<0>(is_causal) && !std::get<1>(is_causal) && !std::get<2>(is_causal)) { \ + fna3_sm75_float16_cm_1_0_0(cb); \ + } \ + else if (std::get<0>(is_causal) && !std::get<1>(is_causal) && std::get<2>(is_causal)) { \ + fna3_sm75_float16_cm_1_0_1(cb); \ + } \ + else if (std::get<0>(is_causal) && std::get<1>(is_causal) && !std::get<2>(is_causal)) { \ + fna3_sm75_float16_cm_1_1_0(cb); \ + } \ + else if (std::get<0>(is_causal) && std::get<1>(is_causal) && std::get<2>(is_causal)) { \ + fna3_sm75_float16_cm_1_1_1(cb); \ + } \ + else { \ + std::cerr << "NATTEN FNA kernel launch failed!" \ + << "Causal mask dispatcher (FNA-3D, SM75, float16) got invalid causal mask!" \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_FNA_FORWARD_3D_SM80_float32(is_causal, has_rpb, cb) \ + [&] { \ + if (!std::get<0>(is_causal) && !std::get<1>(is_causal) && !std::get<2>(is_causal)) { \ + if (has_rpb) {\ + fna3_sm80_float32_cm_0_0_0_rpb(cb); \ + } else {\ + fna3_sm80_float32_cm_0_0_0(cb); \ + } \ + } \ + else if (!std::get<0>(is_causal) && !std::get<1>(is_causal) && std::get<2>(is_causal)) { \ + fna3_sm80_float32_cm_0_0_1(cb); \ + } \ + else if (!std::get<0>(is_causal) && std::get<1>(is_causal) && !std::get<2>(is_causal)) { \ + fna3_sm80_float32_cm_0_1_0(cb); \ + } \ + else if (!std::get<0>(is_causal) && std::get<1>(is_causal) && std::get<2>(is_causal)) { \ + fna3_sm80_float32_cm_0_1_1(cb); \ + } \ + else if (std::get<0>(is_causal) && !std::get<1>(is_causal) && !std::get<2>(is_causal)) { \ + fna3_sm80_float32_cm_1_0_0(cb); \ + } \ + else if (std::get<0>(is_causal) && !std::get<1>(is_causal) && std::get<2>(is_causal)) { \ + fna3_sm80_float32_cm_1_0_1(cb); \ + } \ + else if (std::get<0>(is_causal) && std::get<1>(is_causal) && !std::get<2>(is_causal)) { \ + fna3_sm80_float32_cm_1_1_0(cb); \ + } \ + else if (std::get<0>(is_causal) && std::get<1>(is_causal) && std::get<2>(is_causal)) { \ + fna3_sm80_float32_cm_1_1_1(cb); \ + } \ + else { \ + std::cerr << "NATTEN FNA kernel launch failed!" \ + << "Causal mask dispatcher (FNA-3D, SM80, float32) got invalid causal mask!" \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_FNA_FORWARD_3D_SM80_float16(is_causal, has_rpb, cb) \ + [&] { \ + if (!std::get<0>(is_causal) && !std::get<1>(is_causal) && !std::get<2>(is_causal)) { \ + if (has_rpb) {\ + fna3_sm80_float16_cm_0_0_0_rpb(cb); \ + } else {\ + fna3_sm80_float16_cm_0_0_0(cb); \ + } \ + } \ + else if (!std::get<0>(is_causal) && !std::get<1>(is_causal) && std::get<2>(is_causal)) { \ + fna3_sm80_float16_cm_0_0_1(cb); \ + } \ + else if (!std::get<0>(is_causal) && std::get<1>(is_causal) && !std::get<2>(is_causal)) { \ + fna3_sm80_float16_cm_0_1_0(cb); \ + } \ + else if (!std::get<0>(is_causal) && std::get<1>(is_causal) && std::get<2>(is_causal)) { \ + fna3_sm80_float16_cm_0_1_1(cb); \ + } \ + else if (std::get<0>(is_causal) && !std::get<1>(is_causal) && !std::get<2>(is_causal)) { \ + fna3_sm80_float16_cm_1_0_0(cb); \ + } \ + else if (std::get<0>(is_causal) && !std::get<1>(is_causal) && std::get<2>(is_causal)) { \ + fna3_sm80_float16_cm_1_0_1(cb); \ + } \ + else if (std::get<0>(is_causal) && std::get<1>(is_causal) && !std::get<2>(is_causal)) { \ + fna3_sm80_float16_cm_1_1_0(cb); \ + } \ + else if (std::get<0>(is_causal) && std::get<1>(is_causal) && std::get<2>(is_causal)) { \ + fna3_sm80_float16_cm_1_1_1(cb); \ + } \ + else { \ + std::cerr << "NATTEN FNA kernel launch failed!" \ + << "Causal mask dispatcher (FNA-3D, SM80, float16) got invalid causal mask!" \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_FNA_FORWARD_3D_SM80_bfloat16(is_causal, has_rpb, cb) \ + [&] { \ + if (!std::get<0>(is_causal) && !std::get<1>(is_causal) && !std::get<2>(is_causal)) { \ + if (has_rpb) {\ + fna3_sm80_bfloat16_cm_0_0_0_rpb(cb); \ + } else {\ + fna3_sm80_bfloat16_cm_0_0_0(cb); \ + } \ + } \ + else if (!std::get<0>(is_causal) && !std::get<1>(is_causal) && std::get<2>(is_causal)) { \ + fna3_sm80_bfloat16_cm_0_0_1(cb); \ + } \ + else if (!std::get<0>(is_causal) && std::get<1>(is_causal) && !std::get<2>(is_causal)) { \ + fna3_sm80_bfloat16_cm_0_1_0(cb); \ + } \ + else if (!std::get<0>(is_causal) && std::get<1>(is_causal) && std::get<2>(is_causal)) { \ + fna3_sm80_bfloat16_cm_0_1_1(cb); \ + } \ + else if (std::get<0>(is_causal) && !std::get<1>(is_causal) && !std::get<2>(is_causal)) { \ + fna3_sm80_bfloat16_cm_1_0_0(cb); \ + } \ + else if (std::get<0>(is_causal) && !std::get<1>(is_causal) && std::get<2>(is_causal)) { \ + fna3_sm80_bfloat16_cm_1_0_1(cb); \ + } \ + else if (std::get<0>(is_causal) && std::get<1>(is_causal) && !std::get<2>(is_causal)) { \ + fna3_sm80_bfloat16_cm_1_1_0(cb); \ + } \ + else if (std::get<0>(is_causal) && std::get<1>(is_causal) && std::get<2>(is_causal)) { \ + fna3_sm80_bfloat16_cm_1_1_1(cb); \ + } \ + else { \ + std::cerr << "NATTEN FNA kernel launch failed!" \ + << "Causal mask dispatcher (FNA-3D, SM80, bfloat16) got invalid causal mask!" \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + + + +} // namespace natten +} // namespace cuda +} // namespace fna + diff --git a/csrc/autogen/include/natten_autogen/cuda/fna/dispatch_device.h b/csrc/autogen/include/natten_autogen/cuda/fna/dispatch_device.h new file mode 100644 index 0000000..9ebcb9e --- /dev/null +++ b/csrc/autogen/include/natten_autogen/cuda/fna/dispatch_device.h @@ -0,0 +1,85 @@ +#pragma once + + +#include +#include +#include +#include +#include +#include +#include +namespace natten { +namespace cuda { +namespace fna { +#define DISPATCH_FNA_FORWARD_1D(cc, dtype, is_causal, has_rpb, cb) \ + [&] { \ + if (cc < 70 && cc >= 50) { \ + DISPATCH_FNA_FORWARD_1D_SM50(dtype, is_causal, has_rpb, cb); \ + } \ + else if (cc < 75 && cc >= 70) { \ + DISPATCH_FNA_FORWARD_1D_SM70(dtype, is_causal, has_rpb, cb); \ + } \ + else if (cc < 80 && cc >= 75) { \ + DISPATCH_FNA_FORWARD_1D_SM75(dtype, is_causal, has_rpb, cb); \ + } \ + else if (cc < 100 && cc >= 80) { \ + DISPATCH_FNA_FORWARD_1D_SM80(dtype, is_causal, has_rpb, cb); \ + } \ + else { \ + std::cerr << "NATTEN FNA kernel launch failed!" \ + << "Fused neighborhood attention is not implemented for this device." \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_FNA_FORWARD_2D(cc, dtype, is_causal, has_rpb, cb) \ + [&] { \ + if (cc < 70 && cc >= 50) { \ + DISPATCH_FNA_FORWARD_2D_SM50(dtype, is_causal, has_rpb, cb); \ + } \ + else if (cc < 75 && cc >= 70) { \ + DISPATCH_FNA_FORWARD_2D_SM70(dtype, is_causal, has_rpb, cb); \ + } \ + else if (cc < 80 && cc >= 75) { \ + DISPATCH_FNA_FORWARD_2D_SM75(dtype, is_causal, has_rpb, cb); \ + } \ + else if (cc < 100 && cc >= 80) { \ + DISPATCH_FNA_FORWARD_2D_SM80(dtype, is_causal, has_rpb, cb); \ + } \ + else { \ + std::cerr << "NATTEN FNA kernel launch failed!" \ + << "Fused neighborhood attention is not implemented for this device." \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_FNA_FORWARD_3D(cc, dtype, is_causal, has_rpb, cb) \ + [&] { \ + if (cc < 70 && cc >= 50) { \ + DISPATCH_FNA_FORWARD_3D_SM50(dtype, is_causal, has_rpb, cb); \ + } \ + else if (cc < 75 && cc >= 70) { \ + DISPATCH_FNA_FORWARD_3D_SM70(dtype, is_causal, has_rpb, cb); \ + } \ + else if (cc < 80 && cc >= 75) { \ + DISPATCH_FNA_FORWARD_3D_SM75(dtype, is_causal, has_rpb, cb); \ + } \ + else if (cc < 100 && cc >= 80) { \ + DISPATCH_FNA_FORWARD_3D_SM80(dtype, is_causal, has_rpb, cb); \ + } \ + else { \ + std::cerr << "NATTEN FNA kernel launch failed!" \ + << "Fused neighborhood attention is not implemented for this device." \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + + + +} // namespace natten +} // namespace cuda +} // namespace fna + diff --git a/csrc/autogen/include/natten_autogen/cuda/fna/dispatch_dtype.h b/csrc/autogen/include/natten_autogen/cuda/fna/dispatch_dtype.h new file mode 100644 index 0000000..4f8556a --- /dev/null +++ b/csrc/autogen/include/natten_autogen/cuda/fna/dispatch_dtype.h @@ -0,0 +1,220 @@ +#pragma once + + +#include +#include +#include +#include +#include +#include +#include +namespace natten { +namespace cuda { +namespace fna { +#define DISPATCH_FNA_FORWARD_1D_SM50(dtype, is_causal, has_rpb, cb) \ + [&] { \ + if constexpr (std::is_same::value) { \ + DISPATCH_FNA_FORWARD_1D_SM50_float32(is_causal, has_rpb, cb); \ + } \ + else if constexpr (std::is_same::value) { \ + DISPATCH_FNA_FORWARD_1D_SM50_float16(is_causal, has_rpb, cb); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "FNA-1D does not support this data type on SM50." \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_FNA_FORWARD_1D_SM70(dtype, is_causal, has_rpb, cb) \ + [&] { \ + if constexpr (std::is_same::value) { \ + DISPATCH_FNA_FORWARD_1D_SM70_float32(is_causal, has_rpb, cb); \ + } \ + else if constexpr (std::is_same::value) { \ + DISPATCH_FNA_FORWARD_1D_SM70_float16(is_causal, has_rpb, cb); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "FNA-1D does not support this data type on SM70." \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_FNA_FORWARD_1D_SM75(dtype, is_causal, has_rpb, cb) \ + [&] { \ + if constexpr (std::is_same::value) { \ + DISPATCH_FNA_FORWARD_1D_SM75_float32(is_causal, has_rpb, cb); \ + } \ + else if constexpr (std::is_same::value) { \ + DISPATCH_FNA_FORWARD_1D_SM75_float16(is_causal, has_rpb, cb); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "FNA-1D does not support this data type on SM75." \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_FNA_FORWARD_1D_SM80(dtype, is_causal, has_rpb, cb) \ + [&] { \ + if constexpr (std::is_same::value) { \ + DISPATCH_FNA_FORWARD_1D_SM80_float32(is_causal, has_rpb, cb); \ + } \ + else if constexpr (std::is_same::value) { \ + DISPATCH_FNA_FORWARD_1D_SM80_float16(is_causal, has_rpb, cb); \ + } \ + else if constexpr (std::is_same::value) { \ + DISPATCH_FNA_FORWARD_1D_SM80_bfloat16(is_causal, has_rpb, cb); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "FNA-1D does not support this data type on SM80." \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_FNA_FORWARD_2D_SM50(dtype, is_causal, has_rpb, cb) \ + [&] { \ + if constexpr (std::is_same::value) { \ + DISPATCH_FNA_FORWARD_2D_SM50_float32(is_causal, has_rpb, cb); \ + } \ + else if constexpr (std::is_same::value) { \ + DISPATCH_FNA_FORWARD_2D_SM50_float16(is_causal, has_rpb, cb); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "FNA-2D does not support this data type on SM50." \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_FNA_FORWARD_2D_SM70(dtype, is_causal, has_rpb, cb) \ + [&] { \ + if constexpr (std::is_same::value) { \ + DISPATCH_FNA_FORWARD_2D_SM70_float32(is_causal, has_rpb, cb); \ + } \ + else if constexpr (std::is_same::value) { \ + DISPATCH_FNA_FORWARD_2D_SM70_float16(is_causal, has_rpb, cb); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "FNA-2D does not support this data type on SM70." \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_FNA_FORWARD_2D_SM75(dtype, is_causal, has_rpb, cb) \ + [&] { \ + if constexpr (std::is_same::value) { \ + DISPATCH_FNA_FORWARD_2D_SM75_float32(is_causal, has_rpb, cb); \ + } \ + else if constexpr (std::is_same::value) { \ + DISPATCH_FNA_FORWARD_2D_SM75_float16(is_causal, has_rpb, cb); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "FNA-2D does not support this data type on SM75." \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_FNA_FORWARD_2D_SM80(dtype, is_causal, has_rpb, cb) \ + [&] { \ + if constexpr (std::is_same::value) { \ + DISPATCH_FNA_FORWARD_2D_SM80_float32(is_causal, has_rpb, cb); \ + } \ + else if constexpr (std::is_same::value) { \ + DISPATCH_FNA_FORWARD_2D_SM80_float16(is_causal, has_rpb, cb); \ + } \ + else if constexpr (std::is_same::value) { \ + DISPATCH_FNA_FORWARD_2D_SM80_bfloat16(is_causal, has_rpb, cb); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "FNA-2D does not support this data type on SM80." \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_FNA_FORWARD_3D_SM50(dtype, is_causal, has_rpb, cb) \ + [&] { \ + if constexpr (std::is_same::value) { \ + DISPATCH_FNA_FORWARD_3D_SM50_float32(is_causal, has_rpb, cb); \ + } \ + else if constexpr (std::is_same::value) { \ + DISPATCH_FNA_FORWARD_3D_SM50_float16(is_causal, has_rpb, cb); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "FNA-3D does not support this data type on SM50." \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_FNA_FORWARD_3D_SM70(dtype, is_causal, has_rpb, cb) \ + [&] { \ + if constexpr (std::is_same::value) { \ + DISPATCH_FNA_FORWARD_3D_SM70_float32(is_causal, has_rpb, cb); \ + } \ + else if constexpr (std::is_same::value) { \ + DISPATCH_FNA_FORWARD_3D_SM70_float16(is_causal, has_rpb, cb); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "FNA-3D does not support this data type on SM70." \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_FNA_FORWARD_3D_SM75(dtype, is_causal, has_rpb, cb) \ + [&] { \ + if constexpr (std::is_same::value) { \ + DISPATCH_FNA_FORWARD_3D_SM75_float32(is_causal, has_rpb, cb); \ + } \ + else if constexpr (std::is_same::value) { \ + DISPATCH_FNA_FORWARD_3D_SM75_float16(is_causal, has_rpb, cb); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "FNA-3D does not support this data type on SM75." \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_FNA_FORWARD_3D_SM80(dtype, is_causal, has_rpb, cb) \ + [&] { \ + if constexpr (std::is_same::value) { \ + DISPATCH_FNA_FORWARD_3D_SM80_float32(is_causal, has_rpb, cb); \ + } \ + else if constexpr (std::is_same::value) { \ + DISPATCH_FNA_FORWARD_3D_SM80_float16(is_causal, has_rpb, cb); \ + } \ + else if constexpr (std::is_same::value) { \ + DISPATCH_FNA_FORWARD_3D_SM80_bfloat16(is_causal, has_rpb, cb); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "FNA-3D does not support this data type on SM80." \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + + + +} // namespace natten +} // namespace cuda +} // namespace fna + diff --git a/csrc/autogen/include/natten_autogen/cuda/fna/interface.h b/csrc/autogen/include/natten_autogen/cuda/fna/interface.h new file mode 100644 index 0000000..6bfeedf --- /dev/null +++ b/csrc/autogen/include/natten_autogen/cuda/fna/interface.h @@ -0,0 +1,38 @@ +#pragma once + + +#include +#include +#include +#include +#include +#include +#include +namespace natten { +namespace cuda { +namespace fna { +#define DISPATCH_FNA_FORWARD_KERNEL(rank, cc, dtype, is_causal, has_rpb, cb) \ + [&] { \ + if constexpr (rank == 1) { \ + DISPATCH_FNA_FORWARD_1D(cc, dtype, is_causal, has_rpb, cb); \ + } \ + else if constexpr (rank == 2) { \ + DISPATCH_FNA_FORWARD_2D(cc, dtype, is_causal, has_rpb, cb); \ + } \ + else if constexpr (rank == 3) { \ + DISPATCH_FNA_FORWARD_3D(cc, dtype, is_causal, has_rpb, cb); \ + } \ + else { \ + std::cerr << "NATTEN FNA kernel launch failed!" \ + << "Invalid spatial extent rank! Only 1, 2, and 3D are supported!" \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + + + +} // namespace natten +} // namespace cuda +} // namespace fna + diff --git a/csrc/autogen/include/natten_autogen/cuda/fna/kernels.h b/csrc/autogen/include/natten_autogen/cuda/fna/kernels.h new file mode 100644 index 0000000..79ed862 --- /dev/null +++ b/csrc/autogen/include/natten_autogen/cuda/fna/kernels.h @@ -0,0 +1,14094 @@ +#pragma once + + +#include +#include +#include +#include +#include +#include +namespace natten { +namespace cuda { +namespace fna { + + +/////////////////////////////////////////////////////////////////// +// FNA-1D / float32 / SM50 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 32, false>::kMinBlocksPerSm) +fna1_32x128x32_sm50_float32_cm_0(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 32, false>::kMinBlocksPerSm) +fna1_64x64x32_sm50_float32_cm_0(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 32, false>::kMinBlocksPerSm) +fna1_64x128x32_sm50_float32_cm_0(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 64, false>::kMinBlocksPerSm) +fna1_32x128x64_sm50_float32_cm_0(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 64, false>::kMinBlocksPerSm) +fna1_64x64x64_sm50_float32_cm_0(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 64, false>::kMinBlocksPerSm) +fna1_64x128x64_sm50_float32_cm_0(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 128, false>::kMinBlocksPerSm) +fna1_32x128x128_sm50_float32_cm_0(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 128, false>::kMinBlocksPerSm) +fna1_64x64x128_sm50_float32_cm_0(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 128, false>::kMinBlocksPerSm) +fna1_64x128x128_sm50_float32_cm_0(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 65536, false>::kMinBlocksPerSm) +fna1_32x128x65536_sm50_float32_cm_0(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 65536, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 65536, false>::kMinBlocksPerSm) +fna1_64x128x65536_sm50_float32_cm_0(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 65536, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 65536, false>::kMinBlocksPerSm) +fna1_64x64x65536_sm50_float32_cm_0(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 65536, false>::Params p); + + +template +void fna1_sm50_float32_cm_0(T cb) { + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 32, false>(), fna1_32x128x32_sm50_float32_cm_0); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 32, false>(), fna1_64x64x32_sm50_float32_cm_0); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 32, false>(), fna1_64x128x32_sm50_float32_cm_0); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 64, false>(), fna1_32x128x64_sm50_float32_cm_0); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 64, false>(), fna1_64x64x64_sm50_float32_cm_0); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 64, false>(), fna1_64x128x64_sm50_float32_cm_0); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 128, false>(), fna1_32x128x128_sm50_float32_cm_0); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 128, false>(), fna1_64x64x128_sm50_float32_cm_0); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 128, false>(), fna1_64x128x128_sm50_float32_cm_0); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 65536, false>(), fna1_32x128x65536_sm50_float32_cm_0); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 65536, false>(), fna1_64x128x65536_sm50_float32_cm_0); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 65536, false>(), fna1_64x64x65536_sm50_float32_cm_0); +} + +/////////////////////////////////////////////////////////////////// +// FNA-1D / float32 / SM50 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 32, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 32, true>::kMinBlocksPerSm) +fna1_32x128x32_sm50_float32_cm_0_rpb(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 32, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 32, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 32, true>::kMinBlocksPerSm) +fna1_64x64x32_sm50_float32_cm_0_rpb(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 32, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 32, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 32, true>::kMinBlocksPerSm) +fna1_64x128x32_sm50_float32_cm_0_rpb(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 32, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 64, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 64, true>::kMinBlocksPerSm) +fna1_32x128x64_sm50_float32_cm_0_rpb(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 64, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 64, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 64, true>::kMinBlocksPerSm) +fna1_64x64x64_sm50_float32_cm_0_rpb(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 64, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 64, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 64, true>::kMinBlocksPerSm) +fna1_64x128x64_sm50_float32_cm_0_rpb(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 64, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 128, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 128, true>::kMinBlocksPerSm) +fna1_32x128x128_sm50_float32_cm_0_rpb(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 128, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 128, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 128, true>::kMinBlocksPerSm) +fna1_64x64x128_sm50_float32_cm_0_rpb(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 128, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 128, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 128, true>::kMinBlocksPerSm) +fna1_64x128x128_sm50_float32_cm_0_rpb(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 128, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 65536, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 65536, true>::kMinBlocksPerSm) +fna1_32x128x65536_sm50_float32_cm_0_rpb(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 65536, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 65536, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 65536, true>::kMinBlocksPerSm) +fna1_64x128x65536_sm50_float32_cm_0_rpb(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 65536, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 65536, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 65536, true>::kMinBlocksPerSm) +fna1_64x64x65536_sm50_float32_cm_0_rpb(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 65536, true>::Params p); + + +template +void fna1_sm50_float32_cm_0_rpb(T cb) { + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 32, true>(), fna1_32x128x32_sm50_float32_cm_0_rpb); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 32, true>(), fna1_64x64x32_sm50_float32_cm_0_rpb); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 32, true>(), fna1_64x128x32_sm50_float32_cm_0_rpb); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 64, true>(), fna1_32x128x64_sm50_float32_cm_0_rpb); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 64, true>(), fna1_64x64x64_sm50_float32_cm_0_rpb); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 64, true>(), fna1_64x128x64_sm50_float32_cm_0_rpb); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 128, true>(), fna1_32x128x128_sm50_float32_cm_0_rpb); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 128, true>(), fna1_64x64x128_sm50_float32_cm_0_rpb); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 128, true>(), fna1_64x128x128_sm50_float32_cm_0_rpb); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 65536, true>(), fna1_32x128x65536_sm50_float32_cm_0_rpb); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 65536, true>(), fna1_64x128x65536_sm50_float32_cm_0_rpb); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 65536, true>(), fna1_64x64x65536_sm50_float32_cm_0_rpb); +} + +/////////////////////////////////////////////////////////////////// +// FNA-1D / float32 / SM50 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 32, false>::kMinBlocksPerSm) +fna1_32x128x32_sm50_float32_cm_1(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 32, false>::kMinBlocksPerSm) +fna1_64x64x32_sm50_float32_cm_1(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 32, false>::kMinBlocksPerSm) +fna1_64x128x32_sm50_float32_cm_1(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 64, false>::kMinBlocksPerSm) +fna1_32x128x64_sm50_float32_cm_1(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 64, false>::kMinBlocksPerSm) +fna1_64x64x64_sm50_float32_cm_1(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 64, false>::kMinBlocksPerSm) +fna1_64x128x64_sm50_float32_cm_1(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 128, false>::kMinBlocksPerSm) +fna1_32x128x128_sm50_float32_cm_1(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 128, false>::kMinBlocksPerSm) +fna1_64x64x128_sm50_float32_cm_1(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 128, false>::kMinBlocksPerSm) +fna1_64x128x128_sm50_float32_cm_1(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 65536, false>::kMinBlocksPerSm) +fna1_32x128x65536_sm50_float32_cm_1(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 65536, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 65536, false>::kMinBlocksPerSm) +fna1_64x128x65536_sm50_float32_cm_1(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 65536, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 65536, false>::kMinBlocksPerSm) +fna1_64x64x65536_sm50_float32_cm_1(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 65536, false>::Params p); + + +template +void fna1_sm50_float32_cm_1(T cb) { + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 32, false>(), fna1_32x128x32_sm50_float32_cm_1); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 32, false>(), fna1_64x64x32_sm50_float32_cm_1); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 32, false>(), fna1_64x128x32_sm50_float32_cm_1); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 64, false>(), fna1_32x128x64_sm50_float32_cm_1); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 64, false>(), fna1_64x64x64_sm50_float32_cm_1); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 64, false>(), fna1_64x128x64_sm50_float32_cm_1); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 128, false>(), fna1_32x128x128_sm50_float32_cm_1); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 128, false>(), fna1_64x64x128_sm50_float32_cm_1); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 128, false>(), fna1_64x128x128_sm50_float32_cm_1); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 65536, false>(), fna1_32x128x65536_sm50_float32_cm_1); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 65536, false>(), fna1_64x128x65536_sm50_float32_cm_1); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 65536, false>(), fna1_64x64x65536_sm50_float32_cm_1); +} + +/////////////////////////////////////////////////////////////////// +// FNA-1D / float16 / SM50 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 32, false>::kMinBlocksPerSm) +fna1_32x128x32_sm50_float16_cm_0(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 32, false>::kMinBlocksPerSm) +fna1_64x64x32_sm50_float16_cm_0(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 32, false>::kMinBlocksPerSm) +fna1_64x128x32_sm50_float16_cm_0(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 64, false>::kMinBlocksPerSm) +fna1_32x128x64_sm50_float16_cm_0(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 64, false>::kMinBlocksPerSm) +fna1_64x64x64_sm50_float16_cm_0(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 64, false>::kMinBlocksPerSm) +fna1_64x128x64_sm50_float16_cm_0(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 128, false>::kMinBlocksPerSm) +fna1_32x128x128_sm50_float16_cm_0(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 128, false>::kMinBlocksPerSm) +fna1_64x64x128_sm50_float16_cm_0(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 128, false>::kMinBlocksPerSm) +fna1_64x128x128_sm50_float16_cm_0(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 65536, false>::kMinBlocksPerSm) +fna1_32x128x65536_sm50_float16_cm_0(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 65536, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 65536, false>::kMinBlocksPerSm) +fna1_64x128x65536_sm50_float16_cm_0(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 65536, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 65536, false>::kMinBlocksPerSm) +fna1_64x64x65536_sm50_float16_cm_0(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 65536, false>::Params p); + + +template +void fna1_sm50_float16_cm_0(T cb) { + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 32, false>(), fna1_32x128x32_sm50_float16_cm_0); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 32, false>(), fna1_64x64x32_sm50_float16_cm_0); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 32, false>(), fna1_64x128x32_sm50_float16_cm_0); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 64, false>(), fna1_32x128x64_sm50_float16_cm_0); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 64, false>(), fna1_64x64x64_sm50_float16_cm_0); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 64, false>(), fna1_64x128x64_sm50_float16_cm_0); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 128, false>(), fna1_32x128x128_sm50_float16_cm_0); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 128, false>(), fna1_64x64x128_sm50_float16_cm_0); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 128, false>(), fna1_64x128x128_sm50_float16_cm_0); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 65536, false>(), fna1_32x128x65536_sm50_float16_cm_0); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 65536, false>(), fna1_64x128x65536_sm50_float16_cm_0); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 65536, false>(), fna1_64x64x65536_sm50_float16_cm_0); +} + +/////////////////////////////////////////////////////////////////// +// FNA-1D / float16 / SM50 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 32, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 32, true>::kMinBlocksPerSm) +fna1_32x128x32_sm50_float16_cm_0_rpb(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 32, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 32, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 32, true>::kMinBlocksPerSm) +fna1_64x64x32_sm50_float16_cm_0_rpb(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 32, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 32, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 32, true>::kMinBlocksPerSm) +fna1_64x128x32_sm50_float16_cm_0_rpb(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 32, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 64, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 64, true>::kMinBlocksPerSm) +fna1_32x128x64_sm50_float16_cm_0_rpb(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 64, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 64, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 64, true>::kMinBlocksPerSm) +fna1_64x64x64_sm50_float16_cm_0_rpb(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 64, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 64, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 64, true>::kMinBlocksPerSm) +fna1_64x128x64_sm50_float16_cm_0_rpb(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 64, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 128, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 128, true>::kMinBlocksPerSm) +fna1_32x128x128_sm50_float16_cm_0_rpb(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 128, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 128, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 128, true>::kMinBlocksPerSm) +fna1_64x64x128_sm50_float16_cm_0_rpb(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 128, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 128, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 128, true>::kMinBlocksPerSm) +fna1_64x128x128_sm50_float16_cm_0_rpb(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 128, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 65536, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 65536, true>::kMinBlocksPerSm) +fna1_32x128x65536_sm50_float16_cm_0_rpb(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 65536, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 65536, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 65536, true>::kMinBlocksPerSm) +fna1_64x128x65536_sm50_float16_cm_0_rpb(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 65536, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 65536, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 65536, true>::kMinBlocksPerSm) +fna1_64x64x65536_sm50_float16_cm_0_rpb(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 65536, true>::Params p); + + +template +void fna1_sm50_float16_cm_0_rpb(T cb) { + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 32, true>(), fna1_32x128x32_sm50_float16_cm_0_rpb); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 32, true>(), fna1_64x64x32_sm50_float16_cm_0_rpb); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 32, true>(), fna1_64x128x32_sm50_float16_cm_0_rpb); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 64, true>(), fna1_32x128x64_sm50_float16_cm_0_rpb); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 64, true>(), fna1_64x64x64_sm50_float16_cm_0_rpb); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 64, true>(), fna1_64x128x64_sm50_float16_cm_0_rpb); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 128, true>(), fna1_32x128x128_sm50_float16_cm_0_rpb); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 128, true>(), fna1_64x64x128_sm50_float16_cm_0_rpb); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 128, true>(), fna1_64x128x128_sm50_float16_cm_0_rpb); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 65536, true>(), fna1_32x128x65536_sm50_float16_cm_0_rpb); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 65536, true>(), fna1_64x128x65536_sm50_float16_cm_0_rpb); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 65536, true>(), fna1_64x64x65536_sm50_float16_cm_0_rpb); +} + +/////////////////////////////////////////////////////////////////// +// FNA-1D / float16 / SM50 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 32, false>::kMinBlocksPerSm) +fna1_32x128x32_sm50_float16_cm_1(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 32, false>::kMinBlocksPerSm) +fna1_64x64x32_sm50_float16_cm_1(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 32, false>::kMinBlocksPerSm) +fna1_64x128x32_sm50_float16_cm_1(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 64, false>::kMinBlocksPerSm) +fna1_32x128x64_sm50_float16_cm_1(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 64, false>::kMinBlocksPerSm) +fna1_64x64x64_sm50_float16_cm_1(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 64, false>::kMinBlocksPerSm) +fna1_64x128x64_sm50_float16_cm_1(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 128, false>::kMinBlocksPerSm) +fna1_32x128x128_sm50_float16_cm_1(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 128, false>::kMinBlocksPerSm) +fna1_64x64x128_sm50_float16_cm_1(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 128, false>::kMinBlocksPerSm) +fna1_64x128x128_sm50_float16_cm_1(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 65536, false>::kMinBlocksPerSm) +fna1_32x128x65536_sm50_float16_cm_1(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 65536, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 65536, false>::kMinBlocksPerSm) +fna1_64x128x65536_sm50_float16_cm_1(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 65536, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 65536, false>::kMinBlocksPerSm) +fna1_64x64x65536_sm50_float16_cm_1(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 65536, false>::Params p); + + +template +void fna1_sm50_float16_cm_1(T cb) { + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 32, false>(), fna1_32x128x32_sm50_float16_cm_1); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 32, false>(), fna1_64x64x32_sm50_float16_cm_1); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 32, false>(), fna1_64x128x32_sm50_float16_cm_1); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 64, false>(), fna1_32x128x64_sm50_float16_cm_1); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 64, false>(), fna1_64x64x64_sm50_float16_cm_1); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 64, false>(), fna1_64x128x64_sm50_float16_cm_1); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 128, false>(), fna1_32x128x128_sm50_float16_cm_1); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 128, false>(), fna1_64x64x128_sm50_float16_cm_1); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 128, false>(), fna1_64x128x128_sm50_float16_cm_1); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 65536, false>(), fna1_32x128x65536_sm50_float16_cm_1); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 65536, false>(), fna1_64x128x65536_sm50_float16_cm_1); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 65536, false>(), fna1_64x64x65536_sm50_float16_cm_1); +} + +/////////////////////////////////////////////////////////////////// +// FNA-1D / float32 / SM70 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 32, false>::kMinBlocksPerSm) +fna1_32x128x32_sm70_float32_cm_0(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 32, false>::kMinBlocksPerSm) +fna1_64x64x32_sm70_float32_cm_0(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 32, false>::kMinBlocksPerSm) +fna1_64x128x32_sm70_float32_cm_0(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 64, false>::kMinBlocksPerSm) +fna1_32x128x64_sm70_float32_cm_0(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 64, false>::kMinBlocksPerSm) +fna1_64x64x64_sm70_float32_cm_0(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 64, false>::kMinBlocksPerSm) +fna1_64x128x64_sm70_float32_cm_0(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 128, false>::kMinBlocksPerSm) +fna1_32x128x128_sm70_float32_cm_0(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 128, false>::kMinBlocksPerSm) +fna1_64x64x128_sm70_float32_cm_0(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 128, false>::kMinBlocksPerSm) +fna1_64x128x128_sm70_float32_cm_0(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 65536, false>::kMinBlocksPerSm) +fna1_32x128x65536_sm70_float32_cm_0(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 65536, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 65536, false>::kMinBlocksPerSm) +fna1_64x128x65536_sm70_float32_cm_0(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 65536, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 65536, false>::kMinBlocksPerSm) +fna1_64x64x65536_sm70_float32_cm_0(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 65536, false>::Params p); + + +template +void fna1_sm70_float32_cm_0(T cb) { + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 32, false>(), fna1_32x128x32_sm70_float32_cm_0); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 32, false>(), fna1_64x64x32_sm70_float32_cm_0); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 32, false>(), fna1_64x128x32_sm70_float32_cm_0); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 64, false>(), fna1_32x128x64_sm70_float32_cm_0); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 64, false>(), fna1_64x64x64_sm70_float32_cm_0); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 64, false>(), fna1_64x128x64_sm70_float32_cm_0); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 128, false>(), fna1_32x128x128_sm70_float32_cm_0); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 128, false>(), fna1_64x64x128_sm70_float32_cm_0); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 128, false>(), fna1_64x128x128_sm70_float32_cm_0); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 65536, false>(), fna1_32x128x65536_sm70_float32_cm_0); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 65536, false>(), fna1_64x128x65536_sm70_float32_cm_0); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 65536, false>(), fna1_64x64x65536_sm70_float32_cm_0); +} + +/////////////////////////////////////////////////////////////////// +// FNA-1D / float32 / SM70 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 32, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 32, true>::kMinBlocksPerSm) +fna1_32x128x32_sm70_float32_cm_0_rpb(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 32, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 32, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 32, true>::kMinBlocksPerSm) +fna1_64x64x32_sm70_float32_cm_0_rpb(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 32, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 32, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 32, true>::kMinBlocksPerSm) +fna1_64x128x32_sm70_float32_cm_0_rpb(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 32, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 64, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 64, true>::kMinBlocksPerSm) +fna1_32x128x64_sm70_float32_cm_0_rpb(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 64, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 64, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 64, true>::kMinBlocksPerSm) +fna1_64x64x64_sm70_float32_cm_0_rpb(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 64, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 64, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 64, true>::kMinBlocksPerSm) +fna1_64x128x64_sm70_float32_cm_0_rpb(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 64, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 128, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 128, true>::kMinBlocksPerSm) +fna1_32x128x128_sm70_float32_cm_0_rpb(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 128, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 128, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 128, true>::kMinBlocksPerSm) +fna1_64x64x128_sm70_float32_cm_0_rpb(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 128, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 128, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 128, true>::kMinBlocksPerSm) +fna1_64x128x128_sm70_float32_cm_0_rpb(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 128, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 65536, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 65536, true>::kMinBlocksPerSm) +fna1_32x128x65536_sm70_float32_cm_0_rpb(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 65536, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 65536, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 65536, true>::kMinBlocksPerSm) +fna1_64x128x65536_sm70_float32_cm_0_rpb(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 65536, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 65536, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 65536, true>::kMinBlocksPerSm) +fna1_64x64x65536_sm70_float32_cm_0_rpb(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 65536, true>::Params p); + + +template +void fna1_sm70_float32_cm_0_rpb(T cb) { + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 32, true>(), fna1_32x128x32_sm70_float32_cm_0_rpb); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 32, true>(), fna1_64x64x32_sm70_float32_cm_0_rpb); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 32, true>(), fna1_64x128x32_sm70_float32_cm_0_rpb); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 64, true>(), fna1_32x128x64_sm70_float32_cm_0_rpb); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 64, true>(), fna1_64x64x64_sm70_float32_cm_0_rpb); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 64, true>(), fna1_64x128x64_sm70_float32_cm_0_rpb); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 128, true>(), fna1_32x128x128_sm70_float32_cm_0_rpb); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 128, true>(), fna1_64x64x128_sm70_float32_cm_0_rpb); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 128, true>(), fna1_64x128x128_sm70_float32_cm_0_rpb); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 65536, true>(), fna1_32x128x65536_sm70_float32_cm_0_rpb); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 65536, true>(), fna1_64x128x65536_sm70_float32_cm_0_rpb); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 65536, true>(), fna1_64x64x65536_sm70_float32_cm_0_rpb); +} + +/////////////////////////////////////////////////////////////////// +// FNA-1D / float32 / SM70 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 32, false>::kMinBlocksPerSm) +fna1_32x128x32_sm70_float32_cm_1(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 32, false>::kMinBlocksPerSm) +fna1_64x64x32_sm70_float32_cm_1(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 32, false>::kMinBlocksPerSm) +fna1_64x128x32_sm70_float32_cm_1(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 64, false>::kMinBlocksPerSm) +fna1_32x128x64_sm70_float32_cm_1(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 64, false>::kMinBlocksPerSm) +fna1_64x64x64_sm70_float32_cm_1(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 64, false>::kMinBlocksPerSm) +fna1_64x128x64_sm70_float32_cm_1(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 128, false>::kMinBlocksPerSm) +fna1_32x128x128_sm70_float32_cm_1(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 128, false>::kMinBlocksPerSm) +fna1_64x64x128_sm70_float32_cm_1(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 128, false>::kMinBlocksPerSm) +fna1_64x128x128_sm70_float32_cm_1(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 65536, false>::kMinBlocksPerSm) +fna1_32x128x65536_sm70_float32_cm_1(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 65536, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 65536, false>::kMinBlocksPerSm) +fna1_64x128x65536_sm70_float32_cm_1(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 65536, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 65536, false>::kMinBlocksPerSm) +fna1_64x64x65536_sm70_float32_cm_1(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 65536, false>::Params p); + + +template +void fna1_sm70_float32_cm_1(T cb) { + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 32, false>(), fna1_32x128x32_sm70_float32_cm_1); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 32, false>(), fna1_64x64x32_sm70_float32_cm_1); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 32, false>(), fna1_64x128x32_sm70_float32_cm_1); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 64, false>(), fna1_32x128x64_sm70_float32_cm_1); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 64, false>(), fna1_64x64x64_sm70_float32_cm_1); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 64, false>(), fna1_64x128x64_sm70_float32_cm_1); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 128, false>(), fna1_32x128x128_sm70_float32_cm_1); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 128, false>(), fna1_64x64x128_sm70_float32_cm_1); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 128, false>(), fna1_64x128x128_sm70_float32_cm_1); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 65536, false>(), fna1_32x128x65536_sm70_float32_cm_1); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 65536, false>(), fna1_64x128x65536_sm70_float32_cm_1); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 65536, false>(), fna1_64x64x65536_sm70_float32_cm_1); +} + +/////////////////////////////////////////////////////////////////// +// FNA-1D / float16 / SM70 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 32, false>::kMinBlocksPerSm) +fna1_32x128x32_sm70_float16_cm_0(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 32, false>::kMinBlocksPerSm) +fna1_64x64x32_sm70_float16_cm_0(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 32, false>::kMinBlocksPerSm) +fna1_64x128x32_sm70_float16_cm_0(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 64, false>::kMinBlocksPerSm) +fna1_32x128x64_sm70_float16_cm_0(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 64, false>::kMinBlocksPerSm) +fna1_64x64x64_sm70_float16_cm_0(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 64, false>::kMinBlocksPerSm) +fna1_64x128x64_sm70_float16_cm_0(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 128, false>::kMinBlocksPerSm) +fna1_32x128x128_sm70_float16_cm_0(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 128, false>::kMinBlocksPerSm) +fna1_64x64x128_sm70_float16_cm_0(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 128, false>::kMinBlocksPerSm) +fna1_64x128x128_sm70_float16_cm_0(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 65536, false>::kMinBlocksPerSm) +fna1_32x128x65536_sm70_float16_cm_0(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 65536, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 65536, false>::kMinBlocksPerSm) +fna1_64x128x65536_sm70_float16_cm_0(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 65536, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 65536, false>::kMinBlocksPerSm) +fna1_64x64x65536_sm70_float16_cm_0(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 65536, false>::Params p); + + +template +void fna1_sm70_float16_cm_0(T cb) { + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 32, false>(), fna1_32x128x32_sm70_float16_cm_0); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 32, false>(), fna1_64x64x32_sm70_float16_cm_0); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 32, false>(), fna1_64x128x32_sm70_float16_cm_0); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 64, false>(), fna1_32x128x64_sm70_float16_cm_0); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 64, false>(), fna1_64x64x64_sm70_float16_cm_0); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 64, false>(), fna1_64x128x64_sm70_float16_cm_0); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 128, false>(), fna1_32x128x128_sm70_float16_cm_0); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 128, false>(), fna1_64x64x128_sm70_float16_cm_0); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 128, false>(), fna1_64x128x128_sm70_float16_cm_0); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 65536, false>(), fna1_32x128x65536_sm70_float16_cm_0); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 65536, false>(), fna1_64x128x65536_sm70_float16_cm_0); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 65536, false>(), fna1_64x64x65536_sm70_float16_cm_0); +} + +/////////////////////////////////////////////////////////////////// +// FNA-1D / float16 / SM70 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 32, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 32, true>::kMinBlocksPerSm) +fna1_32x128x32_sm70_float16_cm_0_rpb(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 32, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 32, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 32, true>::kMinBlocksPerSm) +fna1_64x64x32_sm70_float16_cm_0_rpb(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 32, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 32, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 32, true>::kMinBlocksPerSm) +fna1_64x128x32_sm70_float16_cm_0_rpb(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 32, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 64, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 64, true>::kMinBlocksPerSm) +fna1_32x128x64_sm70_float16_cm_0_rpb(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 64, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 64, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 64, true>::kMinBlocksPerSm) +fna1_64x64x64_sm70_float16_cm_0_rpb(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 64, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 64, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 64, true>::kMinBlocksPerSm) +fna1_64x128x64_sm70_float16_cm_0_rpb(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 64, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 128, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 128, true>::kMinBlocksPerSm) +fna1_32x128x128_sm70_float16_cm_0_rpb(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 128, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 128, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 128, true>::kMinBlocksPerSm) +fna1_64x64x128_sm70_float16_cm_0_rpb(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 128, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 128, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 128, true>::kMinBlocksPerSm) +fna1_64x128x128_sm70_float16_cm_0_rpb(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 128, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 65536, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 65536, true>::kMinBlocksPerSm) +fna1_32x128x65536_sm70_float16_cm_0_rpb(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 65536, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 65536, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 65536, true>::kMinBlocksPerSm) +fna1_64x128x65536_sm70_float16_cm_0_rpb(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 65536, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 65536, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 65536, true>::kMinBlocksPerSm) +fna1_64x64x65536_sm70_float16_cm_0_rpb(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 65536, true>::Params p); + + +template +void fna1_sm70_float16_cm_0_rpb(T cb) { + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 32, true>(), fna1_32x128x32_sm70_float16_cm_0_rpb); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 32, true>(), fna1_64x64x32_sm70_float16_cm_0_rpb); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 32, true>(), fna1_64x128x32_sm70_float16_cm_0_rpb); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 64, true>(), fna1_32x128x64_sm70_float16_cm_0_rpb); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 64, true>(), fna1_64x64x64_sm70_float16_cm_0_rpb); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 64, true>(), fna1_64x128x64_sm70_float16_cm_0_rpb); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 128, true>(), fna1_32x128x128_sm70_float16_cm_0_rpb); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 128, true>(), fna1_64x64x128_sm70_float16_cm_0_rpb); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 128, true>(), fna1_64x128x128_sm70_float16_cm_0_rpb); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 65536, true>(), fna1_32x128x65536_sm70_float16_cm_0_rpb); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 65536, true>(), fna1_64x128x65536_sm70_float16_cm_0_rpb); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 65536, true>(), fna1_64x64x65536_sm70_float16_cm_0_rpb); +} + +/////////////////////////////////////////////////////////////////// +// FNA-1D / float16 / SM70 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 32, false>::kMinBlocksPerSm) +fna1_32x128x32_sm70_float16_cm_1(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 32, false>::kMinBlocksPerSm) +fna1_64x64x32_sm70_float16_cm_1(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 32, false>::kMinBlocksPerSm) +fna1_64x128x32_sm70_float16_cm_1(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 64, false>::kMinBlocksPerSm) +fna1_32x128x64_sm70_float16_cm_1(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 64, false>::kMinBlocksPerSm) +fna1_64x64x64_sm70_float16_cm_1(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 64, false>::kMinBlocksPerSm) +fna1_64x128x64_sm70_float16_cm_1(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 128, false>::kMinBlocksPerSm) +fna1_32x128x128_sm70_float16_cm_1(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 128, false>::kMinBlocksPerSm) +fna1_64x64x128_sm70_float16_cm_1(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 128, false>::kMinBlocksPerSm) +fna1_64x128x128_sm70_float16_cm_1(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 65536, false>::kMinBlocksPerSm) +fna1_32x128x65536_sm70_float16_cm_1(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 65536, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 65536, false>::kMinBlocksPerSm) +fna1_64x128x65536_sm70_float16_cm_1(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 65536, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 65536, false>::kMinBlocksPerSm) +fna1_64x64x65536_sm70_float16_cm_1(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 65536, false>::Params p); + + +template +void fna1_sm70_float16_cm_1(T cb) { + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 32, false>(), fna1_32x128x32_sm70_float16_cm_1); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 32, false>(), fna1_64x64x32_sm70_float16_cm_1); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 32, false>(), fna1_64x128x32_sm70_float16_cm_1); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 64, false>(), fna1_32x128x64_sm70_float16_cm_1); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 64, false>(), fna1_64x64x64_sm70_float16_cm_1); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 64, false>(), fna1_64x128x64_sm70_float16_cm_1); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 128, false>(), fna1_32x128x128_sm70_float16_cm_1); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 128, false>(), fna1_64x64x128_sm70_float16_cm_1); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 128, false>(), fna1_64x128x128_sm70_float16_cm_1); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 65536, false>(), fna1_32x128x65536_sm70_float16_cm_1); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 65536, false>(), fna1_64x128x65536_sm70_float16_cm_1); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 65536, false>(), fna1_64x64x65536_sm70_float16_cm_1); +} + +/////////////////////////////////////////////////////////////////// +// FNA-1D / float32 / SM75 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 32, false>::kMinBlocksPerSm) +fna1_32x128x32_sm75_float32_cm_0(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 32, false>::kMinBlocksPerSm) +fna1_64x64x32_sm75_float32_cm_0(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 32, false>::kMinBlocksPerSm) +fna1_64x128x32_sm75_float32_cm_0(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 64, false>::kMinBlocksPerSm) +fna1_32x128x64_sm75_float32_cm_0(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 64, false>::kMinBlocksPerSm) +fna1_64x64x64_sm75_float32_cm_0(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 64, false>::kMinBlocksPerSm) +fna1_64x128x64_sm75_float32_cm_0(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 128, false>::kMinBlocksPerSm) +fna1_32x128x128_sm75_float32_cm_0(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 128, false>::kMinBlocksPerSm) +fna1_64x64x128_sm75_float32_cm_0(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 128, false>::kMinBlocksPerSm) +fna1_64x128x128_sm75_float32_cm_0(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 65536, false>::kMinBlocksPerSm) +fna1_32x128x65536_sm75_float32_cm_0(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 65536, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 65536, false>::kMinBlocksPerSm) +fna1_64x128x65536_sm75_float32_cm_0(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 65536, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 65536, false>::kMinBlocksPerSm) +fna1_64x64x65536_sm75_float32_cm_0(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 65536, false>::Params p); + + +template +void fna1_sm75_float32_cm_0(T cb) { + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 32, false>(), fna1_32x128x32_sm75_float32_cm_0); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 32, false>(), fna1_64x64x32_sm75_float32_cm_0); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 32, false>(), fna1_64x128x32_sm75_float32_cm_0); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 64, false>(), fna1_32x128x64_sm75_float32_cm_0); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 64, false>(), fna1_64x64x64_sm75_float32_cm_0); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 64, false>(), fna1_64x128x64_sm75_float32_cm_0); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 128, false>(), fna1_32x128x128_sm75_float32_cm_0); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 128, false>(), fna1_64x64x128_sm75_float32_cm_0); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 128, false>(), fna1_64x128x128_sm75_float32_cm_0); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 65536, false>(), fna1_32x128x65536_sm75_float32_cm_0); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 65536, false>(), fna1_64x128x65536_sm75_float32_cm_0); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 65536, false>(), fna1_64x64x65536_sm75_float32_cm_0); +} + +/////////////////////////////////////////////////////////////////// +// FNA-1D / float32 / SM75 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 32, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 32, true>::kMinBlocksPerSm) +fna1_32x128x32_sm75_float32_cm_0_rpb(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 32, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 32, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 32, true>::kMinBlocksPerSm) +fna1_64x64x32_sm75_float32_cm_0_rpb(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 32, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 32, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 32, true>::kMinBlocksPerSm) +fna1_64x128x32_sm75_float32_cm_0_rpb(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 32, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 64, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 64, true>::kMinBlocksPerSm) +fna1_32x128x64_sm75_float32_cm_0_rpb(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 64, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 64, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 64, true>::kMinBlocksPerSm) +fna1_64x64x64_sm75_float32_cm_0_rpb(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 64, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 64, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 64, true>::kMinBlocksPerSm) +fna1_64x128x64_sm75_float32_cm_0_rpb(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 64, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 128, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 128, true>::kMinBlocksPerSm) +fna1_32x128x128_sm75_float32_cm_0_rpb(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 128, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 128, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 128, true>::kMinBlocksPerSm) +fna1_64x64x128_sm75_float32_cm_0_rpb(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 128, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 128, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 128, true>::kMinBlocksPerSm) +fna1_64x128x128_sm75_float32_cm_0_rpb(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 128, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 65536, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 65536, true>::kMinBlocksPerSm) +fna1_32x128x65536_sm75_float32_cm_0_rpb(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 65536, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 65536, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 65536, true>::kMinBlocksPerSm) +fna1_64x128x65536_sm75_float32_cm_0_rpb(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 65536, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 65536, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 65536, true>::kMinBlocksPerSm) +fna1_64x64x65536_sm75_float32_cm_0_rpb(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 65536, true>::Params p); + + +template +void fna1_sm75_float32_cm_0_rpb(T cb) { + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 32, true>(), fna1_32x128x32_sm75_float32_cm_0_rpb); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 32, true>(), fna1_64x64x32_sm75_float32_cm_0_rpb); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 32, true>(), fna1_64x128x32_sm75_float32_cm_0_rpb); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 64, true>(), fna1_32x128x64_sm75_float32_cm_0_rpb); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 64, true>(), fna1_64x64x64_sm75_float32_cm_0_rpb); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 64, true>(), fna1_64x128x64_sm75_float32_cm_0_rpb); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 128, true>(), fna1_32x128x128_sm75_float32_cm_0_rpb); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 128, true>(), fna1_64x64x128_sm75_float32_cm_0_rpb); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 128, true>(), fna1_64x128x128_sm75_float32_cm_0_rpb); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 65536, true>(), fna1_32x128x65536_sm75_float32_cm_0_rpb); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 65536, true>(), fna1_64x128x65536_sm75_float32_cm_0_rpb); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 65536, true>(), fna1_64x64x65536_sm75_float32_cm_0_rpb); +} + +/////////////////////////////////////////////////////////////////// +// FNA-1D / float32 / SM75 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 32, false>::kMinBlocksPerSm) +fna1_32x128x32_sm75_float32_cm_1(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 32, false>::kMinBlocksPerSm) +fna1_64x64x32_sm75_float32_cm_1(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 32, false>::kMinBlocksPerSm) +fna1_64x128x32_sm75_float32_cm_1(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 64, false>::kMinBlocksPerSm) +fna1_32x128x64_sm75_float32_cm_1(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 64, false>::kMinBlocksPerSm) +fna1_64x64x64_sm75_float32_cm_1(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 64, false>::kMinBlocksPerSm) +fna1_64x128x64_sm75_float32_cm_1(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 128, false>::kMinBlocksPerSm) +fna1_32x128x128_sm75_float32_cm_1(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 128, false>::kMinBlocksPerSm) +fna1_64x64x128_sm75_float32_cm_1(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 128, false>::kMinBlocksPerSm) +fna1_64x128x128_sm75_float32_cm_1(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 65536, false>::kMinBlocksPerSm) +fna1_32x128x65536_sm75_float32_cm_1(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 65536, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 65536, false>::kMinBlocksPerSm) +fna1_64x128x65536_sm75_float32_cm_1(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 65536, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 65536, false>::kMinBlocksPerSm) +fna1_64x64x65536_sm75_float32_cm_1(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 65536, false>::Params p); + + +template +void fna1_sm75_float32_cm_1(T cb) { + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 32, false>(), fna1_32x128x32_sm75_float32_cm_1); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 32, false>(), fna1_64x64x32_sm75_float32_cm_1); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 32, false>(), fna1_64x128x32_sm75_float32_cm_1); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 64, false>(), fna1_32x128x64_sm75_float32_cm_1); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 64, false>(), fna1_64x64x64_sm75_float32_cm_1); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 64, false>(), fna1_64x128x64_sm75_float32_cm_1); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 128, false>(), fna1_32x128x128_sm75_float32_cm_1); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 128, false>(), fna1_64x64x128_sm75_float32_cm_1); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 128, false>(), fna1_64x128x128_sm75_float32_cm_1); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 65536, false>(), fna1_32x128x65536_sm75_float32_cm_1); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 65536, false>(), fna1_64x128x65536_sm75_float32_cm_1); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 65536, false>(), fna1_64x64x65536_sm75_float32_cm_1); +} + +/////////////////////////////////////////////////////////////////// +// FNA-1D / float16 / SM75 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 32, false>::kMinBlocksPerSm) +fna1_32x128x32_sm75_float16_cm_0(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 32, false>::kMinBlocksPerSm) +fna1_64x64x32_sm75_float16_cm_0(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 32, false>::kMinBlocksPerSm) +fna1_64x128x32_sm75_float16_cm_0(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 64, false>::kMinBlocksPerSm) +fna1_32x128x64_sm75_float16_cm_0(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 64, false>::kMinBlocksPerSm) +fna1_64x64x64_sm75_float16_cm_0(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 64, false>::kMinBlocksPerSm) +fna1_64x128x64_sm75_float16_cm_0(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 128, false>::kMinBlocksPerSm) +fna1_32x128x128_sm75_float16_cm_0(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 128, false>::kMinBlocksPerSm) +fna1_64x64x128_sm75_float16_cm_0(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 128, false>::kMinBlocksPerSm) +fna1_64x128x128_sm75_float16_cm_0(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 65536, false>::kMinBlocksPerSm) +fna1_32x128x65536_sm75_float16_cm_0(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 65536, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 65536, false>::kMinBlocksPerSm) +fna1_64x128x65536_sm75_float16_cm_0(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 65536, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 65536, false>::kMinBlocksPerSm) +fna1_64x64x65536_sm75_float16_cm_0(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 65536, false>::Params p); + + +template +void fna1_sm75_float16_cm_0(T cb) { + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 32, false>(), fna1_32x128x32_sm75_float16_cm_0); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 32, false>(), fna1_64x64x32_sm75_float16_cm_0); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 32, false>(), fna1_64x128x32_sm75_float16_cm_0); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 64, false>(), fna1_32x128x64_sm75_float16_cm_0); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 64, false>(), fna1_64x64x64_sm75_float16_cm_0); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 64, false>(), fna1_64x128x64_sm75_float16_cm_0); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 128, false>(), fna1_32x128x128_sm75_float16_cm_0); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 128, false>(), fna1_64x64x128_sm75_float16_cm_0); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 128, false>(), fna1_64x128x128_sm75_float16_cm_0); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 65536, false>(), fna1_32x128x65536_sm75_float16_cm_0); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 65536, false>(), fna1_64x128x65536_sm75_float16_cm_0); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 65536, false>(), fna1_64x64x65536_sm75_float16_cm_0); +} + +/////////////////////////////////////////////////////////////////// +// FNA-1D / float16 / SM75 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 32, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 32, true>::kMinBlocksPerSm) +fna1_32x128x32_sm75_float16_cm_0_rpb(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 32, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 32, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 32, true>::kMinBlocksPerSm) +fna1_64x64x32_sm75_float16_cm_0_rpb(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 32, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 32, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 32, true>::kMinBlocksPerSm) +fna1_64x128x32_sm75_float16_cm_0_rpb(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 32, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 64, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 64, true>::kMinBlocksPerSm) +fna1_32x128x64_sm75_float16_cm_0_rpb(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 64, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 64, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 64, true>::kMinBlocksPerSm) +fna1_64x64x64_sm75_float16_cm_0_rpb(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 64, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 64, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 64, true>::kMinBlocksPerSm) +fna1_64x128x64_sm75_float16_cm_0_rpb(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 64, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 128, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 128, true>::kMinBlocksPerSm) +fna1_32x128x128_sm75_float16_cm_0_rpb(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 128, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 128, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 128, true>::kMinBlocksPerSm) +fna1_64x64x128_sm75_float16_cm_0_rpb(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 128, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 128, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 128, true>::kMinBlocksPerSm) +fna1_64x128x128_sm75_float16_cm_0_rpb(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 128, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 65536, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 65536, true>::kMinBlocksPerSm) +fna1_32x128x65536_sm75_float16_cm_0_rpb(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 65536, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 65536, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 65536, true>::kMinBlocksPerSm) +fna1_64x128x65536_sm75_float16_cm_0_rpb(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 65536, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 65536, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 65536, true>::kMinBlocksPerSm) +fna1_64x64x65536_sm75_float16_cm_0_rpb(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 65536, true>::Params p); + + +template +void fna1_sm75_float16_cm_0_rpb(T cb) { + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 32, true>(), fna1_32x128x32_sm75_float16_cm_0_rpb); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 32, true>(), fna1_64x64x32_sm75_float16_cm_0_rpb); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 32, true>(), fna1_64x128x32_sm75_float16_cm_0_rpb); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 64, true>(), fna1_32x128x64_sm75_float16_cm_0_rpb); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 64, true>(), fna1_64x64x64_sm75_float16_cm_0_rpb); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 64, true>(), fna1_64x128x64_sm75_float16_cm_0_rpb); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 128, true>(), fna1_32x128x128_sm75_float16_cm_0_rpb); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 128, true>(), fna1_64x64x128_sm75_float16_cm_0_rpb); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 128, true>(), fna1_64x128x128_sm75_float16_cm_0_rpb); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 65536, true>(), fna1_32x128x65536_sm75_float16_cm_0_rpb); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 65536, true>(), fna1_64x128x65536_sm75_float16_cm_0_rpb); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 65536, true>(), fna1_64x64x65536_sm75_float16_cm_0_rpb); +} + +/////////////////////////////////////////////////////////////////// +// FNA-1D / float16 / SM75 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 32, false>::kMinBlocksPerSm) +fna1_32x128x32_sm75_float16_cm_1(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 32, false>::kMinBlocksPerSm) +fna1_64x64x32_sm75_float16_cm_1(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 32, false>::kMinBlocksPerSm) +fna1_64x128x32_sm75_float16_cm_1(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 64, false>::kMinBlocksPerSm) +fna1_32x128x64_sm75_float16_cm_1(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 64, false>::kMinBlocksPerSm) +fna1_64x64x64_sm75_float16_cm_1(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 64, false>::kMinBlocksPerSm) +fna1_64x128x64_sm75_float16_cm_1(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 128, false>::kMinBlocksPerSm) +fna1_32x128x128_sm75_float16_cm_1(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 128, false>::kMinBlocksPerSm) +fna1_64x64x128_sm75_float16_cm_1(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 128, false>::kMinBlocksPerSm) +fna1_64x128x128_sm75_float16_cm_1(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 65536, false>::kMinBlocksPerSm) +fna1_32x128x65536_sm75_float16_cm_1(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 65536, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 65536, false>::kMinBlocksPerSm) +fna1_64x128x65536_sm75_float16_cm_1(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 65536, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 65536, false>::kMinBlocksPerSm) +fna1_64x64x65536_sm75_float16_cm_1(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 65536, false>::Params p); + + +template +void fna1_sm75_float16_cm_1(T cb) { + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 32, false>(), fna1_32x128x32_sm75_float16_cm_1); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 32, false>(), fna1_64x64x32_sm75_float16_cm_1); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 32, false>(), fna1_64x128x32_sm75_float16_cm_1); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 64, false>(), fna1_32x128x64_sm75_float16_cm_1); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 64, false>(), fna1_64x64x64_sm75_float16_cm_1); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 64, false>(), fna1_64x128x64_sm75_float16_cm_1); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 128, false>(), fna1_32x128x128_sm75_float16_cm_1); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 128, false>(), fna1_64x64x128_sm75_float16_cm_1); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 128, false>(), fna1_64x128x128_sm75_float16_cm_1); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 65536, false>(), fna1_32x128x65536_sm75_float16_cm_1); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 65536, false>(), fna1_64x128x65536_sm75_float16_cm_1); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 65536, false>(), fna1_64x64x65536_sm75_float16_cm_1); +} + +/////////////////////////////////////////////////////////////////// +// FNA-1D / float32 / SM80 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 32, false>::kMinBlocksPerSm) +fna1_32x128x32_sm80_float32_cm_0(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 32, false>::kMinBlocksPerSm) +fna1_64x64x32_sm80_float32_cm_0(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 32, false>::kMinBlocksPerSm) +fna1_64x128x32_sm80_float32_cm_0(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 64, false>::kMinBlocksPerSm) +fna1_32x128x64_sm80_float32_cm_0(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 64, false>::kMinBlocksPerSm) +fna1_64x64x64_sm80_float32_cm_0(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 64, false>::kMinBlocksPerSm) +fna1_64x128x64_sm80_float32_cm_0(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 128, false>::kMinBlocksPerSm) +fna1_32x128x128_sm80_float32_cm_0(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 128, false>::kMinBlocksPerSm) +fna1_64x64x128_sm80_float32_cm_0(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 128, false>::kMinBlocksPerSm) +fna1_64x128x128_sm80_float32_cm_0(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 65536, false>::kMinBlocksPerSm) +fna1_32x128x65536_sm80_float32_cm_0(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 65536, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 65536, false>::kMinBlocksPerSm) +fna1_64x128x65536_sm80_float32_cm_0(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 65536, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 65536, false>::kMinBlocksPerSm) +fna1_64x64x65536_sm80_float32_cm_0(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 65536, false>::Params p); + + +template +void fna1_sm80_float32_cm_0(T cb) { + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 32, false>(), fna1_32x128x32_sm80_float32_cm_0); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 32, false>(), fna1_64x64x32_sm80_float32_cm_0); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 32, false>(), fna1_64x128x32_sm80_float32_cm_0); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 64, false>(), fna1_32x128x64_sm80_float32_cm_0); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 64, false>(), fna1_64x64x64_sm80_float32_cm_0); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 64, false>(), fna1_64x128x64_sm80_float32_cm_0); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 128, false>(), fna1_32x128x128_sm80_float32_cm_0); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 128, false>(), fna1_64x64x128_sm80_float32_cm_0); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 128, false>(), fna1_64x128x128_sm80_float32_cm_0); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 65536, false>(), fna1_32x128x65536_sm80_float32_cm_0); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 65536, false>(), fna1_64x128x65536_sm80_float32_cm_0); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 65536, false>(), fna1_64x64x65536_sm80_float32_cm_0); +} + +/////////////////////////////////////////////////////////////////// +// FNA-1D / float32 / SM80 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 32, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 32, true>::kMinBlocksPerSm) +fna1_32x128x32_sm80_float32_cm_0_rpb(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 32, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 32, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 32, true>::kMinBlocksPerSm) +fna1_64x64x32_sm80_float32_cm_0_rpb(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 32, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 32, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 32, true>::kMinBlocksPerSm) +fna1_64x128x32_sm80_float32_cm_0_rpb(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 32, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 64, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 64, true>::kMinBlocksPerSm) +fna1_32x128x64_sm80_float32_cm_0_rpb(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 64, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 64, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 64, true>::kMinBlocksPerSm) +fna1_64x64x64_sm80_float32_cm_0_rpb(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 64, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 64, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 64, true>::kMinBlocksPerSm) +fna1_64x128x64_sm80_float32_cm_0_rpb(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 64, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 128, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 128, true>::kMinBlocksPerSm) +fna1_32x128x128_sm80_float32_cm_0_rpb(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 128, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 128, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 128, true>::kMinBlocksPerSm) +fna1_64x64x128_sm80_float32_cm_0_rpb(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 128, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 128, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 128, true>::kMinBlocksPerSm) +fna1_64x128x128_sm80_float32_cm_0_rpb(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 128, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 65536, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 65536, true>::kMinBlocksPerSm) +fna1_32x128x65536_sm80_float32_cm_0_rpb(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 65536, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 65536, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 65536, true>::kMinBlocksPerSm) +fna1_64x128x65536_sm80_float32_cm_0_rpb(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 65536, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 65536, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 65536, true>::kMinBlocksPerSm) +fna1_64x64x65536_sm80_float32_cm_0_rpb(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 65536, true>::Params p); + + +template +void fna1_sm80_float32_cm_0_rpb(T cb) { + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 32, true>(), fna1_32x128x32_sm80_float32_cm_0_rpb); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 32, true>(), fna1_64x64x32_sm80_float32_cm_0_rpb); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 32, true>(), fna1_64x128x32_sm80_float32_cm_0_rpb); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 64, true>(), fna1_32x128x64_sm80_float32_cm_0_rpb); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 64, true>(), fna1_64x64x64_sm80_float32_cm_0_rpb); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 64, true>(), fna1_64x128x64_sm80_float32_cm_0_rpb); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 128, true>(), fna1_32x128x128_sm80_float32_cm_0_rpb); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 128, true>(), fna1_64x64x128_sm80_float32_cm_0_rpb); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 128, true>(), fna1_64x128x128_sm80_float32_cm_0_rpb); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 65536, true>(), fna1_32x128x65536_sm80_float32_cm_0_rpb); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 65536, true>(), fna1_64x128x65536_sm80_float32_cm_0_rpb); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 65536, true>(), fna1_64x64x65536_sm80_float32_cm_0_rpb); +} + +/////////////////////////////////////////////////////////////////// +// FNA-1D / float32 / SM80 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 32, false>::kMinBlocksPerSm) +fna1_32x128x32_sm80_float32_cm_1(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 32, false>::kMinBlocksPerSm) +fna1_64x64x32_sm80_float32_cm_1(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 32, false>::kMinBlocksPerSm) +fna1_64x128x32_sm80_float32_cm_1(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 64, false>::kMinBlocksPerSm) +fna1_32x128x64_sm80_float32_cm_1(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 64, false>::kMinBlocksPerSm) +fna1_64x64x64_sm80_float32_cm_1(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 64, false>::kMinBlocksPerSm) +fna1_64x128x64_sm80_float32_cm_1(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 128, false>::kMinBlocksPerSm) +fna1_32x128x128_sm80_float32_cm_1(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 128, false>::kMinBlocksPerSm) +fna1_64x64x128_sm80_float32_cm_1(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 128, false>::kMinBlocksPerSm) +fna1_64x128x128_sm80_float32_cm_1(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 65536, false>::kMinBlocksPerSm) +fna1_32x128x65536_sm80_float32_cm_1(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 65536, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 65536, false>::kMinBlocksPerSm) +fna1_64x128x65536_sm80_float32_cm_1(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 65536, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 65536, false>::kMinBlocksPerSm) +fna1_64x64x65536_sm80_float32_cm_1(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 65536, false>::Params p); + + +template +void fna1_sm80_float32_cm_1(T cb) { + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 32, false>(), fna1_32x128x32_sm80_float32_cm_1); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 32, false>(), fna1_64x64x32_sm80_float32_cm_1); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 32, false>(), fna1_64x128x32_sm80_float32_cm_1); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 64, false>(), fna1_32x128x64_sm80_float32_cm_1); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 64, false>(), fna1_64x64x64_sm80_float32_cm_1); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 64, false>(), fna1_64x128x64_sm80_float32_cm_1); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 128, false>(), fna1_32x128x128_sm80_float32_cm_1); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 128, false>(), fna1_64x64x128_sm80_float32_cm_1); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 128, false>(), fna1_64x128x128_sm80_float32_cm_1); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 65536, false>(), fna1_32x128x65536_sm80_float32_cm_1); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 65536, false>(), fna1_64x128x65536_sm80_float32_cm_1); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 65536, false>(), fna1_64x64x65536_sm80_float32_cm_1); +} + +/////////////////////////////////////////////////////////////////// +// FNA-1D / float16 / SM80 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 32, false>::kMinBlocksPerSm) +fna1_32x128x32_sm80_float16_cm_0(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 32, false>::kMinBlocksPerSm) +fna1_64x64x32_sm80_float16_cm_0(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 32, false>::kMinBlocksPerSm) +fna1_64x128x32_sm80_float16_cm_0(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 64, false>::kMinBlocksPerSm) +fna1_32x128x64_sm80_float16_cm_0(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 64, false>::kMinBlocksPerSm) +fna1_64x64x64_sm80_float16_cm_0(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 64, false>::kMinBlocksPerSm) +fna1_64x128x64_sm80_float16_cm_0(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 128, false>::kMinBlocksPerSm) +fna1_32x128x128_sm80_float16_cm_0(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 128, false>::kMinBlocksPerSm) +fna1_64x64x128_sm80_float16_cm_0(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 128, false>::kMinBlocksPerSm) +fna1_64x128x128_sm80_float16_cm_0(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 65536, false>::kMinBlocksPerSm) +fna1_32x128x65536_sm80_float16_cm_0(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 65536, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 65536, false>::kMinBlocksPerSm) +fna1_64x128x65536_sm80_float16_cm_0(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 65536, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 65536, false>::kMinBlocksPerSm) +fna1_64x64x65536_sm80_float16_cm_0(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 65536, false>::Params p); + + +template +void fna1_sm80_float16_cm_0(T cb) { + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 32, false>(), fna1_32x128x32_sm80_float16_cm_0); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 32, false>(), fna1_64x64x32_sm80_float16_cm_0); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 32, false>(), fna1_64x128x32_sm80_float16_cm_0); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 64, false>(), fna1_32x128x64_sm80_float16_cm_0); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 64, false>(), fna1_64x64x64_sm80_float16_cm_0); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 64, false>(), fna1_64x128x64_sm80_float16_cm_0); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 128, false>(), fna1_32x128x128_sm80_float16_cm_0); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 128, false>(), fna1_64x64x128_sm80_float16_cm_0); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 128, false>(), fna1_64x128x128_sm80_float16_cm_0); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 65536, false>(), fna1_32x128x65536_sm80_float16_cm_0); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 65536, false>(), fna1_64x128x65536_sm80_float16_cm_0); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 65536, false>(), fna1_64x64x65536_sm80_float16_cm_0); +} + +/////////////////////////////////////////////////////////////////// +// FNA-1D / float16 / SM80 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 32, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 32, true>::kMinBlocksPerSm) +fna1_32x128x32_sm80_float16_cm_0_rpb(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 32, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 32, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 32, true>::kMinBlocksPerSm) +fna1_64x64x32_sm80_float16_cm_0_rpb(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 32, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 32, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 32, true>::kMinBlocksPerSm) +fna1_64x128x32_sm80_float16_cm_0_rpb(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 32, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 64, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 64, true>::kMinBlocksPerSm) +fna1_32x128x64_sm80_float16_cm_0_rpb(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 64, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 64, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 64, true>::kMinBlocksPerSm) +fna1_64x64x64_sm80_float16_cm_0_rpb(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 64, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 64, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 64, true>::kMinBlocksPerSm) +fna1_64x128x64_sm80_float16_cm_0_rpb(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 64, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 128, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 128, true>::kMinBlocksPerSm) +fna1_32x128x128_sm80_float16_cm_0_rpb(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 128, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 128, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 128, true>::kMinBlocksPerSm) +fna1_64x64x128_sm80_float16_cm_0_rpb(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 128, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 128, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 128, true>::kMinBlocksPerSm) +fna1_64x128x128_sm80_float16_cm_0_rpb(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 128, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 65536, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 65536, true>::kMinBlocksPerSm) +fna1_32x128x65536_sm80_float16_cm_0_rpb(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 65536, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 65536, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 65536, true>::kMinBlocksPerSm) +fna1_64x128x65536_sm80_float16_cm_0_rpb(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 65536, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 65536, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 65536, true>::kMinBlocksPerSm) +fna1_64x64x65536_sm80_float16_cm_0_rpb(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 65536, true>::Params p); + + +template +void fna1_sm80_float16_cm_0_rpb(T cb) { + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 32, true>(), fna1_32x128x32_sm80_float16_cm_0_rpb); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 32, true>(), fna1_64x64x32_sm80_float16_cm_0_rpb); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 32, true>(), fna1_64x128x32_sm80_float16_cm_0_rpb); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 64, true>(), fna1_32x128x64_sm80_float16_cm_0_rpb); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 64, true>(), fna1_64x64x64_sm80_float16_cm_0_rpb); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 64, true>(), fna1_64x128x64_sm80_float16_cm_0_rpb); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 128, true>(), fna1_32x128x128_sm80_float16_cm_0_rpb); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 128, true>(), fna1_64x64x128_sm80_float16_cm_0_rpb); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 128, true>(), fna1_64x128x128_sm80_float16_cm_0_rpb); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 65536, true>(), fna1_32x128x65536_sm80_float16_cm_0_rpb); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 65536, true>(), fna1_64x128x65536_sm80_float16_cm_0_rpb); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 65536, true>(), fna1_64x64x65536_sm80_float16_cm_0_rpb); +} + +/////////////////////////////////////////////////////////////////// +// FNA-1D / float16 / SM80 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 32, false>::kMinBlocksPerSm) +fna1_32x128x32_sm80_float16_cm_1(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 32, false>::kMinBlocksPerSm) +fna1_64x64x32_sm80_float16_cm_1(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 32, false>::kMinBlocksPerSm) +fna1_64x128x32_sm80_float16_cm_1(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 64, false>::kMinBlocksPerSm) +fna1_32x128x64_sm80_float16_cm_1(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 64, false>::kMinBlocksPerSm) +fna1_64x64x64_sm80_float16_cm_1(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 64, false>::kMinBlocksPerSm) +fna1_64x128x64_sm80_float16_cm_1(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 128, false>::kMinBlocksPerSm) +fna1_32x128x128_sm80_float16_cm_1(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 128, false>::kMinBlocksPerSm) +fna1_64x64x128_sm80_float16_cm_1(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 128, false>::kMinBlocksPerSm) +fna1_64x128x128_sm80_float16_cm_1(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 65536, false>::kMinBlocksPerSm) +fna1_32x128x65536_sm80_float16_cm_1(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 65536, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 65536, false>::kMinBlocksPerSm) +fna1_64x128x65536_sm80_float16_cm_1(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 65536, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 65536, false>::kMinBlocksPerSm) +fna1_64x64x65536_sm80_float16_cm_1(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 65536, false>::Params p); + + +template +void fna1_sm80_float16_cm_1(T cb) { + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 32, false>(), fna1_32x128x32_sm80_float16_cm_1); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 32, false>(), fna1_64x64x32_sm80_float16_cm_1); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 32, false>(), fna1_64x128x32_sm80_float16_cm_1); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 64, false>(), fna1_32x128x64_sm80_float16_cm_1); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 64, false>(), fna1_64x64x64_sm80_float16_cm_1); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 64, false>(), fna1_64x128x64_sm80_float16_cm_1); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 128, false>(), fna1_32x128x128_sm80_float16_cm_1); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 128, false>(), fna1_64x64x128_sm80_float16_cm_1); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 128, false>(), fna1_64x128x128_sm80_float16_cm_1); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 65536, false>(), fna1_32x128x65536_sm80_float16_cm_1); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 65536, false>(), fna1_64x128x65536_sm80_float16_cm_1); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 65536, false>(), fna1_64x64x65536_sm80_float16_cm_1); +} + +/////////////////////////////////////////////////////////////////// +// FNA-1D / bfloat16 / SM80 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 32, false>::kMinBlocksPerSm) +fna1_32x128x32_sm80_bfloat16_cm_0(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 32, false>::kMinBlocksPerSm) +fna1_64x64x32_sm80_bfloat16_cm_0(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 32, false>::kMinBlocksPerSm) +fna1_64x128x32_sm80_bfloat16_cm_0(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 64, false>::kMinBlocksPerSm) +fna1_32x128x64_sm80_bfloat16_cm_0(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 64, false>::kMinBlocksPerSm) +fna1_64x64x64_sm80_bfloat16_cm_0(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 64, false>::kMinBlocksPerSm) +fna1_64x128x64_sm80_bfloat16_cm_0(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 128, false>::kMinBlocksPerSm) +fna1_32x128x128_sm80_bfloat16_cm_0(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 128, false>::kMinBlocksPerSm) +fna1_64x64x128_sm80_bfloat16_cm_0(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 128, false>::kMinBlocksPerSm) +fna1_64x128x128_sm80_bfloat16_cm_0(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 65536, false>::kMinBlocksPerSm) +fna1_32x128x65536_sm80_bfloat16_cm_0(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 65536, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 65536, false>::kMinBlocksPerSm) +fna1_64x128x65536_sm80_bfloat16_cm_0(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 65536, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 65536, false>::kMinBlocksPerSm) +fna1_64x64x65536_sm80_bfloat16_cm_0(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 65536, false>::Params p); + + +template +void fna1_sm80_bfloat16_cm_0(T cb) { + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 32, false>(), fna1_32x128x32_sm80_bfloat16_cm_0); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 32, false>(), fna1_64x64x32_sm80_bfloat16_cm_0); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 32, false>(), fna1_64x128x32_sm80_bfloat16_cm_0); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 64, false>(), fna1_32x128x64_sm80_bfloat16_cm_0); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 64, false>(), fna1_64x64x64_sm80_bfloat16_cm_0); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 64, false>(), fna1_64x128x64_sm80_bfloat16_cm_0); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 128, false>(), fna1_32x128x128_sm80_bfloat16_cm_0); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 128, false>(), fna1_64x64x128_sm80_bfloat16_cm_0); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 128, false>(), fna1_64x128x128_sm80_bfloat16_cm_0); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 65536, false>(), fna1_32x128x65536_sm80_bfloat16_cm_0); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 65536, false>(), fna1_64x128x65536_sm80_bfloat16_cm_0); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 65536, false>(), fna1_64x64x65536_sm80_bfloat16_cm_0); +} + +/////////////////////////////////////////////////////////////////// +// FNA-1D / bfloat16 / SM80 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 32, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 32, true>::kMinBlocksPerSm) +fna1_32x128x32_sm80_bfloat16_cm_0_rpb(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 32, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 32, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 32, true>::kMinBlocksPerSm) +fna1_64x64x32_sm80_bfloat16_cm_0_rpb(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 32, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 32, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 32, true>::kMinBlocksPerSm) +fna1_64x128x32_sm80_bfloat16_cm_0_rpb(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 32, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 64, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 64, true>::kMinBlocksPerSm) +fna1_32x128x64_sm80_bfloat16_cm_0_rpb(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 64, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 64, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 64, true>::kMinBlocksPerSm) +fna1_64x64x64_sm80_bfloat16_cm_0_rpb(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 64, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 64, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 64, true>::kMinBlocksPerSm) +fna1_64x128x64_sm80_bfloat16_cm_0_rpb(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 64, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 128, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 128, true>::kMinBlocksPerSm) +fna1_32x128x128_sm80_bfloat16_cm_0_rpb(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 128, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 128, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 128, true>::kMinBlocksPerSm) +fna1_64x64x128_sm80_bfloat16_cm_0_rpb(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 128, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 128, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 128, true>::kMinBlocksPerSm) +fna1_64x128x128_sm80_bfloat16_cm_0_rpb(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 128, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 65536, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 65536, true>::kMinBlocksPerSm) +fna1_32x128x65536_sm80_bfloat16_cm_0_rpb(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 65536, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 65536, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 65536, true>::kMinBlocksPerSm) +fna1_64x128x65536_sm80_bfloat16_cm_0_rpb(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 65536, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 65536, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 65536, true>::kMinBlocksPerSm) +fna1_64x64x65536_sm80_bfloat16_cm_0_rpb(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 65536, true>::Params p); + + +template +void fna1_sm80_bfloat16_cm_0_rpb(T cb) { + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 32, true>(), fna1_32x128x32_sm80_bfloat16_cm_0_rpb); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 32, true>(), fna1_64x64x32_sm80_bfloat16_cm_0_rpb); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 32, true>(), fna1_64x128x32_sm80_bfloat16_cm_0_rpb); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 64, true>(), fna1_32x128x64_sm80_bfloat16_cm_0_rpb); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 64, true>(), fna1_64x64x64_sm80_bfloat16_cm_0_rpb); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 64, true>(), fna1_64x128x64_sm80_bfloat16_cm_0_rpb); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 128, true>(), fna1_32x128x128_sm80_bfloat16_cm_0_rpb); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 128, true>(), fna1_64x64x128_sm80_bfloat16_cm_0_rpb); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 128, true>(), fna1_64x128x128_sm80_bfloat16_cm_0_rpb); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 65536, true>(), fna1_32x128x65536_sm80_bfloat16_cm_0_rpb); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 65536, true>(), fna1_64x128x65536_sm80_bfloat16_cm_0_rpb); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 65536, true>(), fna1_64x64x65536_sm80_bfloat16_cm_0_rpb); +} + +/////////////////////////////////////////////////////////////////// +// FNA-1D / bfloat16 / SM80 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 32, false>::kMinBlocksPerSm) +fna1_32x128x32_sm80_bfloat16_cm_1(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 32, false>::kMinBlocksPerSm) +fna1_64x64x32_sm80_bfloat16_cm_1(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 32, false>::kMinBlocksPerSm) +fna1_64x128x32_sm80_bfloat16_cm_1(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 64, false>::kMinBlocksPerSm) +fna1_32x128x64_sm80_bfloat16_cm_1(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 64, false>::kMinBlocksPerSm) +fna1_64x64x64_sm80_bfloat16_cm_1(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 64, false>::kMinBlocksPerSm) +fna1_64x128x64_sm80_bfloat16_cm_1(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 128, false>::kMinBlocksPerSm) +fna1_32x128x128_sm80_bfloat16_cm_1(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 128, false>::kMinBlocksPerSm) +fna1_64x64x128_sm80_bfloat16_cm_1(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 128, false>::kMinBlocksPerSm) +fna1_64x128x128_sm80_bfloat16_cm_1(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 65536, false>::kMinBlocksPerSm) +fna1_32x128x65536_sm80_bfloat16_cm_1(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 65536, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 65536, false>::kMinBlocksPerSm) +fna1_64x128x65536_sm80_bfloat16_cm_1(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 65536, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 65536, false>::kMinBlocksPerSm) +fna1_64x64x65536_sm80_bfloat16_cm_1(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 65536, false>::Params p); + + +template +void fna1_sm80_bfloat16_cm_1(T cb) { + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 32, false>(), fna1_32x128x32_sm80_bfloat16_cm_1); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 32, false>(), fna1_64x64x32_sm80_bfloat16_cm_1); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 32, false>(), fna1_64x128x32_sm80_bfloat16_cm_1); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 64, false>(), fna1_32x128x64_sm80_bfloat16_cm_1); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 64, false>(), fna1_64x64x64_sm80_bfloat16_cm_1); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 64, false>(), fna1_64x128x64_sm80_bfloat16_cm_1); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 128, false>(), fna1_32x128x128_sm80_bfloat16_cm_1); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 128, false>(), fna1_64x64x128_sm80_bfloat16_cm_1); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 128, false>(), fna1_64x128x128_sm80_bfloat16_cm_1); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 65536, false>(), fna1_32x128x65536_sm80_bfloat16_cm_1); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 65536, false>(), fna1_64x128x65536_sm80_bfloat16_cm_1); + cb(FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 65536, false>(), fna1_64x64x65536_sm80_bfloat16_cm_1); +} + +/////////////////////////////////////////////////////////////////// +// FNA-2D / float32 / SM50 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 32, false>::kMinBlocksPerSm) +fna2_32x128x32_sm50_float32_cm_0_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 32, false>::kMinBlocksPerSm) +fna2_64x64x32_sm50_float32_cm_0_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 32, false>::kMinBlocksPerSm) +fna2_64x128x32_sm50_float32_cm_0_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 64, false>::kMinBlocksPerSm) +fna2_32x128x64_sm50_float32_cm_0_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 64, false>::kMinBlocksPerSm) +fna2_64x64x64_sm50_float32_cm_0_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 64, false>::kMinBlocksPerSm) +fna2_64x128x64_sm50_float32_cm_0_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 128, false>::kMinBlocksPerSm) +fna2_32x128x128_sm50_float32_cm_0_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 128, false>::kMinBlocksPerSm) +fna2_64x64x128_sm50_float32_cm_0_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 128, false>::kMinBlocksPerSm) +fna2_64x128x128_sm50_float32_cm_0_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 65536, false>::kMinBlocksPerSm) +fna2_32x128x65536_sm50_float32_cm_0_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 65536, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 65536, false>::kMinBlocksPerSm) +fna2_64x128x65536_sm50_float32_cm_0_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 65536, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 65536, false>::kMinBlocksPerSm) +fna2_64x64x65536_sm50_float32_cm_0_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 65536, false>::Params p); + + +template +void fna2_sm50_float32_cm_0_0(T cb) { + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 32, false>(), fna2_32x128x32_sm50_float32_cm_0_0); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 32, false>(), fna2_64x64x32_sm50_float32_cm_0_0); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 32, false>(), fna2_64x128x32_sm50_float32_cm_0_0); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 64, false>(), fna2_32x128x64_sm50_float32_cm_0_0); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 64, false>(), fna2_64x64x64_sm50_float32_cm_0_0); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 64, false>(), fna2_64x128x64_sm50_float32_cm_0_0); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 128, false>(), fna2_32x128x128_sm50_float32_cm_0_0); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 128, false>(), fna2_64x64x128_sm50_float32_cm_0_0); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 128, false>(), fna2_64x128x128_sm50_float32_cm_0_0); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 65536, false>(), fna2_32x128x65536_sm50_float32_cm_0_0); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 65536, false>(), fna2_64x128x65536_sm50_float32_cm_0_0); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 65536, false>(), fna2_64x64x65536_sm50_float32_cm_0_0); +} + +/////////////////////////////////////////////////////////////////// +// FNA-2D / float32 / SM50 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 32, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 32, true>::kMinBlocksPerSm) +fna2_32x128x32_sm50_float32_cm_0_0_rpb(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 32, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 32, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 32, true>::kMinBlocksPerSm) +fna2_64x64x32_sm50_float32_cm_0_0_rpb(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 32, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 32, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 32, true>::kMinBlocksPerSm) +fna2_64x128x32_sm50_float32_cm_0_0_rpb(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 32, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 64, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 64, true>::kMinBlocksPerSm) +fna2_32x128x64_sm50_float32_cm_0_0_rpb(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 64, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 64, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 64, true>::kMinBlocksPerSm) +fna2_64x64x64_sm50_float32_cm_0_0_rpb(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 64, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 64, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 64, true>::kMinBlocksPerSm) +fna2_64x128x64_sm50_float32_cm_0_0_rpb(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 64, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 128, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 128, true>::kMinBlocksPerSm) +fna2_32x128x128_sm50_float32_cm_0_0_rpb(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 128, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 128, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 128, true>::kMinBlocksPerSm) +fna2_64x64x128_sm50_float32_cm_0_0_rpb(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 128, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 128, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 128, true>::kMinBlocksPerSm) +fna2_64x128x128_sm50_float32_cm_0_0_rpb(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 128, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 65536, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 65536, true>::kMinBlocksPerSm) +fna2_32x128x65536_sm50_float32_cm_0_0_rpb(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 65536, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 65536, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 65536, true>::kMinBlocksPerSm) +fna2_64x128x65536_sm50_float32_cm_0_0_rpb(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 65536, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 65536, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 65536, true>::kMinBlocksPerSm) +fna2_64x64x65536_sm50_float32_cm_0_0_rpb(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 65536, true>::Params p); + + +template +void fna2_sm50_float32_cm_0_0_rpb(T cb) { + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 32, true>(), fna2_32x128x32_sm50_float32_cm_0_0_rpb); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 32, true>(), fna2_64x64x32_sm50_float32_cm_0_0_rpb); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 32, true>(), fna2_64x128x32_sm50_float32_cm_0_0_rpb); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 64, true>(), fna2_32x128x64_sm50_float32_cm_0_0_rpb); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 64, true>(), fna2_64x64x64_sm50_float32_cm_0_0_rpb); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 64, true>(), fna2_64x128x64_sm50_float32_cm_0_0_rpb); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 128, true>(), fna2_32x128x128_sm50_float32_cm_0_0_rpb); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 128, true>(), fna2_64x64x128_sm50_float32_cm_0_0_rpb); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 128, true>(), fna2_64x128x128_sm50_float32_cm_0_0_rpb); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 65536, true>(), fna2_32x128x65536_sm50_float32_cm_0_0_rpb); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 65536, true>(), fna2_64x128x65536_sm50_float32_cm_0_0_rpb); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 65536, true>(), fna2_64x64x65536_sm50_float32_cm_0_0_rpb); +} + +/////////////////////////////////////////////////////////////////// +// FNA-2D / float32 / SM50 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 32, false>::kMinBlocksPerSm) +fna2_32x128x32_sm50_float32_cm_0_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 32, false>::kMinBlocksPerSm) +fna2_64x64x32_sm50_float32_cm_0_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 32, false>::kMinBlocksPerSm) +fna2_64x128x32_sm50_float32_cm_0_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 64, false>::kMinBlocksPerSm) +fna2_32x128x64_sm50_float32_cm_0_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 64, false>::kMinBlocksPerSm) +fna2_64x64x64_sm50_float32_cm_0_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 64, false>::kMinBlocksPerSm) +fna2_64x128x64_sm50_float32_cm_0_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 128, false>::kMinBlocksPerSm) +fna2_32x128x128_sm50_float32_cm_0_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 128, false>::kMinBlocksPerSm) +fna2_64x64x128_sm50_float32_cm_0_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 128, false>::kMinBlocksPerSm) +fna2_64x128x128_sm50_float32_cm_0_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 65536, false>::kMinBlocksPerSm) +fna2_32x128x65536_sm50_float32_cm_0_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 65536, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 65536, false>::kMinBlocksPerSm) +fna2_64x128x65536_sm50_float32_cm_0_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 65536, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 65536, false>::kMinBlocksPerSm) +fna2_64x64x65536_sm50_float32_cm_0_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 65536, false>::Params p); + + +template +void fna2_sm50_float32_cm_0_1(T cb) { + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 32, false>(), fna2_32x128x32_sm50_float32_cm_0_1); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 32, false>(), fna2_64x64x32_sm50_float32_cm_0_1); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 32, false>(), fna2_64x128x32_sm50_float32_cm_0_1); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 64, false>(), fna2_32x128x64_sm50_float32_cm_0_1); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 64, false>(), fna2_64x64x64_sm50_float32_cm_0_1); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 64, false>(), fna2_64x128x64_sm50_float32_cm_0_1); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 128, false>(), fna2_32x128x128_sm50_float32_cm_0_1); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 128, false>(), fna2_64x64x128_sm50_float32_cm_0_1); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 128, false>(), fna2_64x128x128_sm50_float32_cm_0_1); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 65536, false>(), fna2_32x128x65536_sm50_float32_cm_0_1); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 65536, false>(), fna2_64x128x65536_sm50_float32_cm_0_1); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 65536, false>(), fna2_64x64x65536_sm50_float32_cm_0_1); +} + +/////////////////////////////////////////////////////////////////// +// FNA-2D / float32 / SM50 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 32, false>::kMinBlocksPerSm) +fna2_32x128x32_sm50_float32_cm_1_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 32, false>::kMinBlocksPerSm) +fna2_64x64x32_sm50_float32_cm_1_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 32, false>::kMinBlocksPerSm) +fna2_64x128x32_sm50_float32_cm_1_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 64, false>::kMinBlocksPerSm) +fna2_32x128x64_sm50_float32_cm_1_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 64, false>::kMinBlocksPerSm) +fna2_64x64x64_sm50_float32_cm_1_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 64, false>::kMinBlocksPerSm) +fna2_64x128x64_sm50_float32_cm_1_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 128, false>::kMinBlocksPerSm) +fna2_32x128x128_sm50_float32_cm_1_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 128, false>::kMinBlocksPerSm) +fna2_64x64x128_sm50_float32_cm_1_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 128, false>::kMinBlocksPerSm) +fna2_64x128x128_sm50_float32_cm_1_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 65536, false>::kMinBlocksPerSm) +fna2_32x128x65536_sm50_float32_cm_1_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 65536, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 65536, false>::kMinBlocksPerSm) +fna2_64x128x65536_sm50_float32_cm_1_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 65536, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 65536, false>::kMinBlocksPerSm) +fna2_64x64x65536_sm50_float32_cm_1_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 65536, false>::Params p); + + +template +void fna2_sm50_float32_cm_1_0(T cb) { + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 32, false>(), fna2_32x128x32_sm50_float32_cm_1_0); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 32, false>(), fna2_64x64x32_sm50_float32_cm_1_0); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 32, false>(), fna2_64x128x32_sm50_float32_cm_1_0); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 64, false>(), fna2_32x128x64_sm50_float32_cm_1_0); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 64, false>(), fna2_64x64x64_sm50_float32_cm_1_0); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 64, false>(), fna2_64x128x64_sm50_float32_cm_1_0); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 128, false>(), fna2_32x128x128_sm50_float32_cm_1_0); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 128, false>(), fna2_64x64x128_sm50_float32_cm_1_0); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 128, false>(), fna2_64x128x128_sm50_float32_cm_1_0); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 65536, false>(), fna2_32x128x65536_sm50_float32_cm_1_0); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 65536, false>(), fna2_64x128x65536_sm50_float32_cm_1_0); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 65536, false>(), fna2_64x64x65536_sm50_float32_cm_1_0); +} + +/////////////////////////////////////////////////////////////////// +// FNA-2D / float32 / SM50 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 32, false>::kMinBlocksPerSm) +fna2_32x128x32_sm50_float32_cm_1_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 32, false>::kMinBlocksPerSm) +fna2_64x64x32_sm50_float32_cm_1_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 32, false>::kMinBlocksPerSm) +fna2_64x128x32_sm50_float32_cm_1_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 64, false>::kMinBlocksPerSm) +fna2_32x128x64_sm50_float32_cm_1_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 64, false>::kMinBlocksPerSm) +fna2_64x64x64_sm50_float32_cm_1_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 64, false>::kMinBlocksPerSm) +fna2_64x128x64_sm50_float32_cm_1_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 128, false>::kMinBlocksPerSm) +fna2_32x128x128_sm50_float32_cm_1_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 128, false>::kMinBlocksPerSm) +fna2_64x64x128_sm50_float32_cm_1_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 128, false>::kMinBlocksPerSm) +fna2_64x128x128_sm50_float32_cm_1_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 65536, false>::kMinBlocksPerSm) +fna2_32x128x65536_sm50_float32_cm_1_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 65536, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 65536, false>::kMinBlocksPerSm) +fna2_64x128x65536_sm50_float32_cm_1_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 65536, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 65536, false>::kMinBlocksPerSm) +fna2_64x64x65536_sm50_float32_cm_1_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 65536, false>::Params p); + + +template +void fna2_sm50_float32_cm_1_1(T cb) { + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 32, false>(), fna2_32x128x32_sm50_float32_cm_1_1); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 32, false>(), fna2_64x64x32_sm50_float32_cm_1_1); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 32, false>(), fna2_64x128x32_sm50_float32_cm_1_1); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 64, false>(), fna2_32x128x64_sm50_float32_cm_1_1); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 64, false>(), fna2_64x64x64_sm50_float32_cm_1_1); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 64, false>(), fna2_64x128x64_sm50_float32_cm_1_1); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 128, false>(), fna2_32x128x128_sm50_float32_cm_1_1); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 128, false>(), fna2_64x64x128_sm50_float32_cm_1_1); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 128, false>(), fna2_64x128x128_sm50_float32_cm_1_1); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 65536, false>(), fna2_32x128x65536_sm50_float32_cm_1_1); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 65536, false>(), fna2_64x128x65536_sm50_float32_cm_1_1); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 65536, false>(), fna2_64x64x65536_sm50_float32_cm_1_1); +} + +/////////////////////////////////////////////////////////////////// +// FNA-2D / float16 / SM50 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 32, false>::kMinBlocksPerSm) +fna2_32x128x32_sm50_float16_cm_0_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 32, false>::kMinBlocksPerSm) +fna2_64x64x32_sm50_float16_cm_0_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 32, false>::kMinBlocksPerSm) +fna2_64x128x32_sm50_float16_cm_0_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 64, false>::kMinBlocksPerSm) +fna2_32x128x64_sm50_float16_cm_0_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 64, false>::kMinBlocksPerSm) +fna2_64x64x64_sm50_float16_cm_0_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 64, false>::kMinBlocksPerSm) +fna2_64x128x64_sm50_float16_cm_0_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 128, false>::kMinBlocksPerSm) +fna2_32x128x128_sm50_float16_cm_0_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 128, false>::kMinBlocksPerSm) +fna2_64x64x128_sm50_float16_cm_0_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 128, false>::kMinBlocksPerSm) +fna2_64x128x128_sm50_float16_cm_0_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 65536, false>::kMinBlocksPerSm) +fna2_32x128x65536_sm50_float16_cm_0_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 65536, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 65536, false>::kMinBlocksPerSm) +fna2_64x128x65536_sm50_float16_cm_0_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 65536, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 65536, false>::kMinBlocksPerSm) +fna2_64x64x65536_sm50_float16_cm_0_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 65536, false>::Params p); + + +template +void fna2_sm50_float16_cm_0_0(T cb) { + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 32, false>(), fna2_32x128x32_sm50_float16_cm_0_0); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 32, false>(), fna2_64x64x32_sm50_float16_cm_0_0); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 32, false>(), fna2_64x128x32_sm50_float16_cm_0_0); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 64, false>(), fna2_32x128x64_sm50_float16_cm_0_0); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 64, false>(), fna2_64x64x64_sm50_float16_cm_0_0); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 64, false>(), fna2_64x128x64_sm50_float16_cm_0_0); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 128, false>(), fna2_32x128x128_sm50_float16_cm_0_0); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 128, false>(), fna2_64x64x128_sm50_float16_cm_0_0); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 128, false>(), fna2_64x128x128_sm50_float16_cm_0_0); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 65536, false>(), fna2_32x128x65536_sm50_float16_cm_0_0); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 65536, false>(), fna2_64x128x65536_sm50_float16_cm_0_0); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 65536, false>(), fna2_64x64x65536_sm50_float16_cm_0_0); +} + +/////////////////////////////////////////////////////////////////// +// FNA-2D / float16 / SM50 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 32, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 32, true>::kMinBlocksPerSm) +fna2_32x128x32_sm50_float16_cm_0_0_rpb(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 32, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 32, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 32, true>::kMinBlocksPerSm) +fna2_64x64x32_sm50_float16_cm_0_0_rpb(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 32, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 32, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 32, true>::kMinBlocksPerSm) +fna2_64x128x32_sm50_float16_cm_0_0_rpb(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 32, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 64, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 64, true>::kMinBlocksPerSm) +fna2_32x128x64_sm50_float16_cm_0_0_rpb(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 64, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 64, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 64, true>::kMinBlocksPerSm) +fna2_64x64x64_sm50_float16_cm_0_0_rpb(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 64, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 64, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 64, true>::kMinBlocksPerSm) +fna2_64x128x64_sm50_float16_cm_0_0_rpb(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 64, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 128, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 128, true>::kMinBlocksPerSm) +fna2_32x128x128_sm50_float16_cm_0_0_rpb(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 128, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 128, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 128, true>::kMinBlocksPerSm) +fna2_64x64x128_sm50_float16_cm_0_0_rpb(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 128, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 128, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 128, true>::kMinBlocksPerSm) +fna2_64x128x128_sm50_float16_cm_0_0_rpb(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 128, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 65536, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 65536, true>::kMinBlocksPerSm) +fna2_32x128x65536_sm50_float16_cm_0_0_rpb(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 65536, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 65536, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 65536, true>::kMinBlocksPerSm) +fna2_64x128x65536_sm50_float16_cm_0_0_rpb(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 65536, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 65536, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 65536, true>::kMinBlocksPerSm) +fna2_64x64x65536_sm50_float16_cm_0_0_rpb(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 65536, true>::Params p); + + +template +void fna2_sm50_float16_cm_0_0_rpb(T cb) { + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 32, true>(), fna2_32x128x32_sm50_float16_cm_0_0_rpb); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 32, true>(), fna2_64x64x32_sm50_float16_cm_0_0_rpb); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 32, true>(), fna2_64x128x32_sm50_float16_cm_0_0_rpb); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 64, true>(), fna2_32x128x64_sm50_float16_cm_0_0_rpb); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 64, true>(), fna2_64x64x64_sm50_float16_cm_0_0_rpb); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 64, true>(), fna2_64x128x64_sm50_float16_cm_0_0_rpb); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 128, true>(), fna2_32x128x128_sm50_float16_cm_0_0_rpb); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 128, true>(), fna2_64x64x128_sm50_float16_cm_0_0_rpb); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 128, true>(), fna2_64x128x128_sm50_float16_cm_0_0_rpb); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 65536, true>(), fna2_32x128x65536_sm50_float16_cm_0_0_rpb); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 65536, true>(), fna2_64x128x65536_sm50_float16_cm_0_0_rpb); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 65536, true>(), fna2_64x64x65536_sm50_float16_cm_0_0_rpb); +} + +/////////////////////////////////////////////////////////////////// +// FNA-2D / float16 / SM50 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 32, false>::kMinBlocksPerSm) +fna2_32x128x32_sm50_float16_cm_0_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 32, false>::kMinBlocksPerSm) +fna2_64x64x32_sm50_float16_cm_0_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 32, false>::kMinBlocksPerSm) +fna2_64x128x32_sm50_float16_cm_0_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 64, false>::kMinBlocksPerSm) +fna2_32x128x64_sm50_float16_cm_0_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 64, false>::kMinBlocksPerSm) +fna2_64x64x64_sm50_float16_cm_0_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 64, false>::kMinBlocksPerSm) +fna2_64x128x64_sm50_float16_cm_0_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 128, false>::kMinBlocksPerSm) +fna2_32x128x128_sm50_float16_cm_0_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 128, false>::kMinBlocksPerSm) +fna2_64x64x128_sm50_float16_cm_0_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 128, false>::kMinBlocksPerSm) +fna2_64x128x128_sm50_float16_cm_0_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 65536, false>::kMinBlocksPerSm) +fna2_32x128x65536_sm50_float16_cm_0_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 65536, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 65536, false>::kMinBlocksPerSm) +fna2_64x128x65536_sm50_float16_cm_0_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 65536, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 65536, false>::kMinBlocksPerSm) +fna2_64x64x65536_sm50_float16_cm_0_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 65536, false>::Params p); + + +template +void fna2_sm50_float16_cm_0_1(T cb) { + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 32, false>(), fna2_32x128x32_sm50_float16_cm_0_1); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 32, false>(), fna2_64x64x32_sm50_float16_cm_0_1); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 32, false>(), fna2_64x128x32_sm50_float16_cm_0_1); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 64, false>(), fna2_32x128x64_sm50_float16_cm_0_1); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 64, false>(), fna2_64x64x64_sm50_float16_cm_0_1); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 64, false>(), fna2_64x128x64_sm50_float16_cm_0_1); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 128, false>(), fna2_32x128x128_sm50_float16_cm_0_1); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 128, false>(), fna2_64x64x128_sm50_float16_cm_0_1); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 128, false>(), fna2_64x128x128_sm50_float16_cm_0_1); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 65536, false>(), fna2_32x128x65536_sm50_float16_cm_0_1); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 65536, false>(), fna2_64x128x65536_sm50_float16_cm_0_1); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 65536, false>(), fna2_64x64x65536_sm50_float16_cm_0_1); +} + +/////////////////////////////////////////////////////////////////// +// FNA-2D / float16 / SM50 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 32, false>::kMinBlocksPerSm) +fna2_32x128x32_sm50_float16_cm_1_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 32, false>::kMinBlocksPerSm) +fna2_64x64x32_sm50_float16_cm_1_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 32, false>::kMinBlocksPerSm) +fna2_64x128x32_sm50_float16_cm_1_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 64, false>::kMinBlocksPerSm) +fna2_32x128x64_sm50_float16_cm_1_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 64, false>::kMinBlocksPerSm) +fna2_64x64x64_sm50_float16_cm_1_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 64, false>::kMinBlocksPerSm) +fna2_64x128x64_sm50_float16_cm_1_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 128, false>::kMinBlocksPerSm) +fna2_32x128x128_sm50_float16_cm_1_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 128, false>::kMinBlocksPerSm) +fna2_64x64x128_sm50_float16_cm_1_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 128, false>::kMinBlocksPerSm) +fna2_64x128x128_sm50_float16_cm_1_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 65536, false>::kMinBlocksPerSm) +fna2_32x128x65536_sm50_float16_cm_1_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 65536, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 65536, false>::kMinBlocksPerSm) +fna2_64x128x65536_sm50_float16_cm_1_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 65536, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 65536, false>::kMinBlocksPerSm) +fna2_64x64x65536_sm50_float16_cm_1_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 65536, false>::Params p); + + +template +void fna2_sm50_float16_cm_1_0(T cb) { + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 32, false>(), fna2_32x128x32_sm50_float16_cm_1_0); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 32, false>(), fna2_64x64x32_sm50_float16_cm_1_0); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 32, false>(), fna2_64x128x32_sm50_float16_cm_1_0); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 64, false>(), fna2_32x128x64_sm50_float16_cm_1_0); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 64, false>(), fna2_64x64x64_sm50_float16_cm_1_0); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 64, false>(), fna2_64x128x64_sm50_float16_cm_1_0); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 128, false>(), fna2_32x128x128_sm50_float16_cm_1_0); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 128, false>(), fna2_64x64x128_sm50_float16_cm_1_0); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 128, false>(), fna2_64x128x128_sm50_float16_cm_1_0); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 65536, false>(), fna2_32x128x65536_sm50_float16_cm_1_0); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 65536, false>(), fna2_64x128x65536_sm50_float16_cm_1_0); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 65536, false>(), fna2_64x64x65536_sm50_float16_cm_1_0); +} + +/////////////////////////////////////////////////////////////////// +// FNA-2D / float16 / SM50 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 32, false>::kMinBlocksPerSm) +fna2_32x128x32_sm50_float16_cm_1_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 32, false>::kMinBlocksPerSm) +fna2_64x64x32_sm50_float16_cm_1_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 32, false>::kMinBlocksPerSm) +fna2_64x128x32_sm50_float16_cm_1_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 64, false>::kMinBlocksPerSm) +fna2_32x128x64_sm50_float16_cm_1_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 64, false>::kMinBlocksPerSm) +fna2_64x64x64_sm50_float16_cm_1_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 64, false>::kMinBlocksPerSm) +fna2_64x128x64_sm50_float16_cm_1_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 128, false>::kMinBlocksPerSm) +fna2_32x128x128_sm50_float16_cm_1_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 128, false>::kMinBlocksPerSm) +fna2_64x64x128_sm50_float16_cm_1_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 128, false>::kMinBlocksPerSm) +fna2_64x128x128_sm50_float16_cm_1_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 65536, false>::kMinBlocksPerSm) +fna2_32x128x65536_sm50_float16_cm_1_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 65536, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 65536, false>::kMinBlocksPerSm) +fna2_64x128x65536_sm50_float16_cm_1_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 65536, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 65536, false>::kMinBlocksPerSm) +fna2_64x64x65536_sm50_float16_cm_1_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 65536, false>::Params p); + + +template +void fna2_sm50_float16_cm_1_1(T cb) { + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 32, false>(), fna2_32x128x32_sm50_float16_cm_1_1); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 32, false>(), fna2_64x64x32_sm50_float16_cm_1_1); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 32, false>(), fna2_64x128x32_sm50_float16_cm_1_1); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 64, false>(), fna2_32x128x64_sm50_float16_cm_1_1); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 64, false>(), fna2_64x64x64_sm50_float16_cm_1_1); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 64, false>(), fna2_64x128x64_sm50_float16_cm_1_1); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 128, false>(), fna2_32x128x128_sm50_float16_cm_1_1); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 128, false>(), fna2_64x64x128_sm50_float16_cm_1_1); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 128, false>(), fna2_64x128x128_sm50_float16_cm_1_1); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 65536, false>(), fna2_32x128x65536_sm50_float16_cm_1_1); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 65536, false>(), fna2_64x128x65536_sm50_float16_cm_1_1); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 65536, false>(), fna2_64x64x65536_sm50_float16_cm_1_1); +} + +/////////////////////////////////////////////////////////////////// +// FNA-2D / float32 / SM70 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 32, false>::kMinBlocksPerSm) +fna2_32x128x32_sm70_float32_cm_0_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 32, false>::kMinBlocksPerSm) +fna2_64x64x32_sm70_float32_cm_0_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 32, false>::kMinBlocksPerSm) +fna2_64x128x32_sm70_float32_cm_0_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 64, false>::kMinBlocksPerSm) +fna2_32x128x64_sm70_float32_cm_0_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 64, false>::kMinBlocksPerSm) +fna2_64x64x64_sm70_float32_cm_0_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 64, false>::kMinBlocksPerSm) +fna2_64x128x64_sm70_float32_cm_0_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 128, false>::kMinBlocksPerSm) +fna2_32x128x128_sm70_float32_cm_0_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 128, false>::kMinBlocksPerSm) +fna2_64x64x128_sm70_float32_cm_0_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 128, false>::kMinBlocksPerSm) +fna2_64x128x128_sm70_float32_cm_0_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 65536, false>::kMinBlocksPerSm) +fna2_32x128x65536_sm70_float32_cm_0_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 65536, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 65536, false>::kMinBlocksPerSm) +fna2_64x128x65536_sm70_float32_cm_0_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 65536, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 65536, false>::kMinBlocksPerSm) +fna2_64x64x65536_sm70_float32_cm_0_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 65536, false>::Params p); + + +template +void fna2_sm70_float32_cm_0_0(T cb) { + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 32, false>(), fna2_32x128x32_sm70_float32_cm_0_0); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 32, false>(), fna2_64x64x32_sm70_float32_cm_0_0); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 32, false>(), fna2_64x128x32_sm70_float32_cm_0_0); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 64, false>(), fna2_32x128x64_sm70_float32_cm_0_0); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 64, false>(), fna2_64x64x64_sm70_float32_cm_0_0); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 64, false>(), fna2_64x128x64_sm70_float32_cm_0_0); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 128, false>(), fna2_32x128x128_sm70_float32_cm_0_0); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 128, false>(), fna2_64x64x128_sm70_float32_cm_0_0); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 128, false>(), fna2_64x128x128_sm70_float32_cm_0_0); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 65536, false>(), fna2_32x128x65536_sm70_float32_cm_0_0); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 65536, false>(), fna2_64x128x65536_sm70_float32_cm_0_0); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 65536, false>(), fna2_64x64x65536_sm70_float32_cm_0_0); +} + +/////////////////////////////////////////////////////////////////// +// FNA-2D / float32 / SM70 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 32, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 32, true>::kMinBlocksPerSm) +fna2_32x128x32_sm70_float32_cm_0_0_rpb(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 32, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 32, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 32, true>::kMinBlocksPerSm) +fna2_64x64x32_sm70_float32_cm_0_0_rpb(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 32, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 32, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 32, true>::kMinBlocksPerSm) +fna2_64x128x32_sm70_float32_cm_0_0_rpb(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 32, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 64, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 64, true>::kMinBlocksPerSm) +fna2_32x128x64_sm70_float32_cm_0_0_rpb(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 64, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 64, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 64, true>::kMinBlocksPerSm) +fna2_64x64x64_sm70_float32_cm_0_0_rpb(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 64, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 64, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 64, true>::kMinBlocksPerSm) +fna2_64x128x64_sm70_float32_cm_0_0_rpb(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 64, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 128, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 128, true>::kMinBlocksPerSm) +fna2_32x128x128_sm70_float32_cm_0_0_rpb(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 128, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 128, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 128, true>::kMinBlocksPerSm) +fna2_64x64x128_sm70_float32_cm_0_0_rpb(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 128, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 128, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 128, true>::kMinBlocksPerSm) +fna2_64x128x128_sm70_float32_cm_0_0_rpb(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 128, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 65536, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 65536, true>::kMinBlocksPerSm) +fna2_32x128x65536_sm70_float32_cm_0_0_rpb(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 65536, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 65536, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 65536, true>::kMinBlocksPerSm) +fna2_64x128x65536_sm70_float32_cm_0_0_rpb(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 65536, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 65536, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 65536, true>::kMinBlocksPerSm) +fna2_64x64x65536_sm70_float32_cm_0_0_rpb(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 65536, true>::Params p); + + +template +void fna2_sm70_float32_cm_0_0_rpb(T cb) { + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 32, true>(), fna2_32x128x32_sm70_float32_cm_0_0_rpb); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 32, true>(), fna2_64x64x32_sm70_float32_cm_0_0_rpb); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 32, true>(), fna2_64x128x32_sm70_float32_cm_0_0_rpb); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 64, true>(), fna2_32x128x64_sm70_float32_cm_0_0_rpb); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 64, true>(), fna2_64x64x64_sm70_float32_cm_0_0_rpb); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 64, true>(), fna2_64x128x64_sm70_float32_cm_0_0_rpb); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 128, true>(), fna2_32x128x128_sm70_float32_cm_0_0_rpb); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 128, true>(), fna2_64x64x128_sm70_float32_cm_0_0_rpb); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 128, true>(), fna2_64x128x128_sm70_float32_cm_0_0_rpb); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 65536, true>(), fna2_32x128x65536_sm70_float32_cm_0_0_rpb); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 65536, true>(), fna2_64x128x65536_sm70_float32_cm_0_0_rpb); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 65536, true>(), fna2_64x64x65536_sm70_float32_cm_0_0_rpb); +} + +/////////////////////////////////////////////////////////////////// +// FNA-2D / float32 / SM70 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 32, false>::kMinBlocksPerSm) +fna2_32x128x32_sm70_float32_cm_0_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 32, false>::kMinBlocksPerSm) +fna2_64x64x32_sm70_float32_cm_0_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 32, false>::kMinBlocksPerSm) +fna2_64x128x32_sm70_float32_cm_0_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 64, false>::kMinBlocksPerSm) +fna2_32x128x64_sm70_float32_cm_0_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 64, false>::kMinBlocksPerSm) +fna2_64x64x64_sm70_float32_cm_0_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 64, false>::kMinBlocksPerSm) +fna2_64x128x64_sm70_float32_cm_0_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 128, false>::kMinBlocksPerSm) +fna2_32x128x128_sm70_float32_cm_0_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 128, false>::kMinBlocksPerSm) +fna2_64x64x128_sm70_float32_cm_0_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 128, false>::kMinBlocksPerSm) +fna2_64x128x128_sm70_float32_cm_0_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 65536, false>::kMinBlocksPerSm) +fna2_32x128x65536_sm70_float32_cm_0_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 65536, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 65536, false>::kMinBlocksPerSm) +fna2_64x128x65536_sm70_float32_cm_0_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 65536, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 65536, false>::kMinBlocksPerSm) +fna2_64x64x65536_sm70_float32_cm_0_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 65536, false>::Params p); + + +template +void fna2_sm70_float32_cm_0_1(T cb) { + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 32, false>(), fna2_32x128x32_sm70_float32_cm_0_1); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 32, false>(), fna2_64x64x32_sm70_float32_cm_0_1); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 32, false>(), fna2_64x128x32_sm70_float32_cm_0_1); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 64, false>(), fna2_32x128x64_sm70_float32_cm_0_1); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 64, false>(), fna2_64x64x64_sm70_float32_cm_0_1); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 64, false>(), fna2_64x128x64_sm70_float32_cm_0_1); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 128, false>(), fna2_32x128x128_sm70_float32_cm_0_1); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 128, false>(), fna2_64x64x128_sm70_float32_cm_0_1); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 128, false>(), fna2_64x128x128_sm70_float32_cm_0_1); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 65536, false>(), fna2_32x128x65536_sm70_float32_cm_0_1); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 65536, false>(), fna2_64x128x65536_sm70_float32_cm_0_1); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 65536, false>(), fna2_64x64x65536_sm70_float32_cm_0_1); +} + +/////////////////////////////////////////////////////////////////// +// FNA-2D / float32 / SM70 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 32, false>::kMinBlocksPerSm) +fna2_32x128x32_sm70_float32_cm_1_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 32, false>::kMinBlocksPerSm) +fna2_64x64x32_sm70_float32_cm_1_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 32, false>::kMinBlocksPerSm) +fna2_64x128x32_sm70_float32_cm_1_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 64, false>::kMinBlocksPerSm) +fna2_32x128x64_sm70_float32_cm_1_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 64, false>::kMinBlocksPerSm) +fna2_64x64x64_sm70_float32_cm_1_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 64, false>::kMinBlocksPerSm) +fna2_64x128x64_sm70_float32_cm_1_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 128, false>::kMinBlocksPerSm) +fna2_32x128x128_sm70_float32_cm_1_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 128, false>::kMinBlocksPerSm) +fna2_64x64x128_sm70_float32_cm_1_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 128, false>::kMinBlocksPerSm) +fna2_64x128x128_sm70_float32_cm_1_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 65536, false>::kMinBlocksPerSm) +fna2_32x128x65536_sm70_float32_cm_1_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 65536, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 65536, false>::kMinBlocksPerSm) +fna2_64x128x65536_sm70_float32_cm_1_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 65536, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 65536, false>::kMinBlocksPerSm) +fna2_64x64x65536_sm70_float32_cm_1_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 65536, false>::Params p); + + +template +void fna2_sm70_float32_cm_1_0(T cb) { + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 32, false>(), fna2_32x128x32_sm70_float32_cm_1_0); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 32, false>(), fna2_64x64x32_sm70_float32_cm_1_0); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 32, false>(), fna2_64x128x32_sm70_float32_cm_1_0); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 64, false>(), fna2_32x128x64_sm70_float32_cm_1_0); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 64, false>(), fna2_64x64x64_sm70_float32_cm_1_0); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 64, false>(), fna2_64x128x64_sm70_float32_cm_1_0); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 128, false>(), fna2_32x128x128_sm70_float32_cm_1_0); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 128, false>(), fna2_64x64x128_sm70_float32_cm_1_0); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 128, false>(), fna2_64x128x128_sm70_float32_cm_1_0); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 65536, false>(), fna2_32x128x65536_sm70_float32_cm_1_0); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 65536, false>(), fna2_64x128x65536_sm70_float32_cm_1_0); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 65536, false>(), fna2_64x64x65536_sm70_float32_cm_1_0); +} + +/////////////////////////////////////////////////////////////////// +// FNA-2D / float32 / SM70 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 32, false>::kMinBlocksPerSm) +fna2_32x128x32_sm70_float32_cm_1_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 32, false>::kMinBlocksPerSm) +fna2_64x64x32_sm70_float32_cm_1_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 32, false>::kMinBlocksPerSm) +fna2_64x128x32_sm70_float32_cm_1_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 64, false>::kMinBlocksPerSm) +fna2_32x128x64_sm70_float32_cm_1_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 64, false>::kMinBlocksPerSm) +fna2_64x64x64_sm70_float32_cm_1_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 64, false>::kMinBlocksPerSm) +fna2_64x128x64_sm70_float32_cm_1_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 128, false>::kMinBlocksPerSm) +fna2_32x128x128_sm70_float32_cm_1_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 128, false>::kMinBlocksPerSm) +fna2_64x64x128_sm70_float32_cm_1_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 128, false>::kMinBlocksPerSm) +fna2_64x128x128_sm70_float32_cm_1_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 65536, false>::kMinBlocksPerSm) +fna2_32x128x65536_sm70_float32_cm_1_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 65536, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 65536, false>::kMinBlocksPerSm) +fna2_64x128x65536_sm70_float32_cm_1_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 65536, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 65536, false>::kMinBlocksPerSm) +fna2_64x64x65536_sm70_float32_cm_1_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 65536, false>::Params p); + + +template +void fna2_sm70_float32_cm_1_1(T cb) { + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 32, false>(), fna2_32x128x32_sm70_float32_cm_1_1); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 32, false>(), fna2_64x64x32_sm70_float32_cm_1_1); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 32, false>(), fna2_64x128x32_sm70_float32_cm_1_1); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 64, false>(), fna2_32x128x64_sm70_float32_cm_1_1); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 64, false>(), fna2_64x64x64_sm70_float32_cm_1_1); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 64, false>(), fna2_64x128x64_sm70_float32_cm_1_1); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 128, false>(), fna2_32x128x128_sm70_float32_cm_1_1); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 128, false>(), fna2_64x64x128_sm70_float32_cm_1_1); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 128, false>(), fna2_64x128x128_sm70_float32_cm_1_1); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 65536, false>(), fna2_32x128x65536_sm70_float32_cm_1_1); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 65536, false>(), fna2_64x128x65536_sm70_float32_cm_1_1); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 65536, false>(), fna2_64x64x65536_sm70_float32_cm_1_1); +} + +/////////////////////////////////////////////////////////////////// +// FNA-2D / float16 / SM70 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 32, false>::kMinBlocksPerSm) +fna2_32x128x32_sm70_float16_cm_0_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 32, false>::kMinBlocksPerSm) +fna2_64x64x32_sm70_float16_cm_0_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 32, false>::kMinBlocksPerSm) +fna2_64x128x32_sm70_float16_cm_0_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 64, false>::kMinBlocksPerSm) +fna2_32x128x64_sm70_float16_cm_0_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 64, false>::kMinBlocksPerSm) +fna2_64x64x64_sm70_float16_cm_0_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 64, false>::kMinBlocksPerSm) +fna2_64x128x64_sm70_float16_cm_0_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 128, false>::kMinBlocksPerSm) +fna2_32x128x128_sm70_float16_cm_0_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 128, false>::kMinBlocksPerSm) +fna2_64x64x128_sm70_float16_cm_0_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 128, false>::kMinBlocksPerSm) +fna2_64x128x128_sm70_float16_cm_0_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 65536, false>::kMinBlocksPerSm) +fna2_32x128x65536_sm70_float16_cm_0_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 65536, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 65536, false>::kMinBlocksPerSm) +fna2_64x128x65536_sm70_float16_cm_0_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 65536, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 65536, false>::kMinBlocksPerSm) +fna2_64x64x65536_sm70_float16_cm_0_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 65536, false>::Params p); + + +template +void fna2_sm70_float16_cm_0_0(T cb) { + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 32, false>(), fna2_32x128x32_sm70_float16_cm_0_0); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 32, false>(), fna2_64x64x32_sm70_float16_cm_0_0); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 32, false>(), fna2_64x128x32_sm70_float16_cm_0_0); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 64, false>(), fna2_32x128x64_sm70_float16_cm_0_0); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 64, false>(), fna2_64x64x64_sm70_float16_cm_0_0); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 64, false>(), fna2_64x128x64_sm70_float16_cm_0_0); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 128, false>(), fna2_32x128x128_sm70_float16_cm_0_0); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 128, false>(), fna2_64x64x128_sm70_float16_cm_0_0); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 128, false>(), fna2_64x128x128_sm70_float16_cm_0_0); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 65536, false>(), fna2_32x128x65536_sm70_float16_cm_0_0); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 65536, false>(), fna2_64x128x65536_sm70_float16_cm_0_0); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 65536, false>(), fna2_64x64x65536_sm70_float16_cm_0_0); +} + +/////////////////////////////////////////////////////////////////// +// FNA-2D / float16 / SM70 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 32, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 32, true>::kMinBlocksPerSm) +fna2_32x128x32_sm70_float16_cm_0_0_rpb(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 32, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 32, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 32, true>::kMinBlocksPerSm) +fna2_64x64x32_sm70_float16_cm_0_0_rpb(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 32, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 32, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 32, true>::kMinBlocksPerSm) +fna2_64x128x32_sm70_float16_cm_0_0_rpb(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 32, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 64, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 64, true>::kMinBlocksPerSm) +fna2_32x128x64_sm70_float16_cm_0_0_rpb(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 64, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 64, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 64, true>::kMinBlocksPerSm) +fna2_64x64x64_sm70_float16_cm_0_0_rpb(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 64, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 64, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 64, true>::kMinBlocksPerSm) +fna2_64x128x64_sm70_float16_cm_0_0_rpb(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 64, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 128, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 128, true>::kMinBlocksPerSm) +fna2_32x128x128_sm70_float16_cm_0_0_rpb(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 128, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 128, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 128, true>::kMinBlocksPerSm) +fna2_64x64x128_sm70_float16_cm_0_0_rpb(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 128, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 128, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 128, true>::kMinBlocksPerSm) +fna2_64x128x128_sm70_float16_cm_0_0_rpb(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 128, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 65536, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 65536, true>::kMinBlocksPerSm) +fna2_32x128x65536_sm70_float16_cm_0_0_rpb(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 65536, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 65536, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 65536, true>::kMinBlocksPerSm) +fna2_64x128x65536_sm70_float16_cm_0_0_rpb(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 65536, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 65536, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 65536, true>::kMinBlocksPerSm) +fna2_64x64x65536_sm70_float16_cm_0_0_rpb(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 65536, true>::Params p); + + +template +void fna2_sm70_float16_cm_0_0_rpb(T cb) { + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 32, true>(), fna2_32x128x32_sm70_float16_cm_0_0_rpb); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 32, true>(), fna2_64x64x32_sm70_float16_cm_0_0_rpb); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 32, true>(), fna2_64x128x32_sm70_float16_cm_0_0_rpb); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 64, true>(), fna2_32x128x64_sm70_float16_cm_0_0_rpb); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 64, true>(), fna2_64x64x64_sm70_float16_cm_0_0_rpb); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 64, true>(), fna2_64x128x64_sm70_float16_cm_0_0_rpb); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 128, true>(), fna2_32x128x128_sm70_float16_cm_0_0_rpb); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 128, true>(), fna2_64x64x128_sm70_float16_cm_0_0_rpb); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 128, true>(), fna2_64x128x128_sm70_float16_cm_0_0_rpb); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 65536, true>(), fna2_32x128x65536_sm70_float16_cm_0_0_rpb); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 65536, true>(), fna2_64x128x65536_sm70_float16_cm_0_0_rpb); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 65536, true>(), fna2_64x64x65536_sm70_float16_cm_0_0_rpb); +} + +/////////////////////////////////////////////////////////////////// +// FNA-2D / float16 / SM70 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 32, false>::kMinBlocksPerSm) +fna2_32x128x32_sm70_float16_cm_0_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 32, false>::kMinBlocksPerSm) +fna2_64x64x32_sm70_float16_cm_0_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 32, false>::kMinBlocksPerSm) +fna2_64x128x32_sm70_float16_cm_0_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 64, false>::kMinBlocksPerSm) +fna2_32x128x64_sm70_float16_cm_0_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 64, false>::kMinBlocksPerSm) +fna2_64x64x64_sm70_float16_cm_0_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 64, false>::kMinBlocksPerSm) +fna2_64x128x64_sm70_float16_cm_0_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 128, false>::kMinBlocksPerSm) +fna2_32x128x128_sm70_float16_cm_0_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 128, false>::kMinBlocksPerSm) +fna2_64x64x128_sm70_float16_cm_0_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 128, false>::kMinBlocksPerSm) +fna2_64x128x128_sm70_float16_cm_0_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 65536, false>::kMinBlocksPerSm) +fna2_32x128x65536_sm70_float16_cm_0_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 65536, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 65536, false>::kMinBlocksPerSm) +fna2_64x128x65536_sm70_float16_cm_0_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 65536, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 65536, false>::kMinBlocksPerSm) +fna2_64x64x65536_sm70_float16_cm_0_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 65536, false>::Params p); + + +template +void fna2_sm70_float16_cm_0_1(T cb) { + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 32, false>(), fna2_32x128x32_sm70_float16_cm_0_1); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 32, false>(), fna2_64x64x32_sm70_float16_cm_0_1); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 32, false>(), fna2_64x128x32_sm70_float16_cm_0_1); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 64, false>(), fna2_32x128x64_sm70_float16_cm_0_1); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 64, false>(), fna2_64x64x64_sm70_float16_cm_0_1); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 64, false>(), fna2_64x128x64_sm70_float16_cm_0_1); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 128, false>(), fna2_32x128x128_sm70_float16_cm_0_1); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 128, false>(), fna2_64x64x128_sm70_float16_cm_0_1); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 128, false>(), fna2_64x128x128_sm70_float16_cm_0_1); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 65536, false>(), fna2_32x128x65536_sm70_float16_cm_0_1); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 65536, false>(), fna2_64x128x65536_sm70_float16_cm_0_1); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 65536, false>(), fna2_64x64x65536_sm70_float16_cm_0_1); +} + +/////////////////////////////////////////////////////////////////// +// FNA-2D / float16 / SM70 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 32, false>::kMinBlocksPerSm) +fna2_32x128x32_sm70_float16_cm_1_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 32, false>::kMinBlocksPerSm) +fna2_64x64x32_sm70_float16_cm_1_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 32, false>::kMinBlocksPerSm) +fna2_64x128x32_sm70_float16_cm_1_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 64, false>::kMinBlocksPerSm) +fna2_32x128x64_sm70_float16_cm_1_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 64, false>::kMinBlocksPerSm) +fna2_64x64x64_sm70_float16_cm_1_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 64, false>::kMinBlocksPerSm) +fna2_64x128x64_sm70_float16_cm_1_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 128, false>::kMinBlocksPerSm) +fna2_32x128x128_sm70_float16_cm_1_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 128, false>::kMinBlocksPerSm) +fna2_64x64x128_sm70_float16_cm_1_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 128, false>::kMinBlocksPerSm) +fna2_64x128x128_sm70_float16_cm_1_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 65536, false>::kMinBlocksPerSm) +fna2_32x128x65536_sm70_float16_cm_1_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 65536, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 65536, false>::kMinBlocksPerSm) +fna2_64x128x65536_sm70_float16_cm_1_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 65536, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 65536, false>::kMinBlocksPerSm) +fna2_64x64x65536_sm70_float16_cm_1_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 65536, false>::Params p); + + +template +void fna2_sm70_float16_cm_1_0(T cb) { + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 32, false>(), fna2_32x128x32_sm70_float16_cm_1_0); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 32, false>(), fna2_64x64x32_sm70_float16_cm_1_0); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 32, false>(), fna2_64x128x32_sm70_float16_cm_1_0); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 64, false>(), fna2_32x128x64_sm70_float16_cm_1_0); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 64, false>(), fna2_64x64x64_sm70_float16_cm_1_0); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 64, false>(), fna2_64x128x64_sm70_float16_cm_1_0); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 128, false>(), fna2_32x128x128_sm70_float16_cm_1_0); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 128, false>(), fna2_64x64x128_sm70_float16_cm_1_0); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 128, false>(), fna2_64x128x128_sm70_float16_cm_1_0); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 65536, false>(), fna2_32x128x65536_sm70_float16_cm_1_0); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 65536, false>(), fna2_64x128x65536_sm70_float16_cm_1_0); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 65536, false>(), fna2_64x64x65536_sm70_float16_cm_1_0); +} + +/////////////////////////////////////////////////////////////////// +// FNA-2D / float16 / SM70 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 32, false>::kMinBlocksPerSm) +fna2_32x128x32_sm70_float16_cm_1_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 32, false>::kMinBlocksPerSm) +fna2_64x64x32_sm70_float16_cm_1_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 32, false>::kMinBlocksPerSm) +fna2_64x128x32_sm70_float16_cm_1_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 64, false>::kMinBlocksPerSm) +fna2_32x128x64_sm70_float16_cm_1_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 64, false>::kMinBlocksPerSm) +fna2_64x64x64_sm70_float16_cm_1_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 64, false>::kMinBlocksPerSm) +fna2_64x128x64_sm70_float16_cm_1_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 128, false>::kMinBlocksPerSm) +fna2_32x128x128_sm70_float16_cm_1_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 128, false>::kMinBlocksPerSm) +fna2_64x64x128_sm70_float16_cm_1_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 128, false>::kMinBlocksPerSm) +fna2_64x128x128_sm70_float16_cm_1_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 65536, false>::kMinBlocksPerSm) +fna2_32x128x65536_sm70_float16_cm_1_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 65536, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 65536, false>::kMinBlocksPerSm) +fna2_64x128x65536_sm70_float16_cm_1_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 65536, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 65536, false>::kMinBlocksPerSm) +fna2_64x64x65536_sm70_float16_cm_1_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 65536, false>::Params p); + + +template +void fna2_sm70_float16_cm_1_1(T cb) { + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 32, false>(), fna2_32x128x32_sm70_float16_cm_1_1); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 32, false>(), fna2_64x64x32_sm70_float16_cm_1_1); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 32, false>(), fna2_64x128x32_sm70_float16_cm_1_1); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 64, false>(), fna2_32x128x64_sm70_float16_cm_1_1); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 64, false>(), fna2_64x64x64_sm70_float16_cm_1_1); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 64, false>(), fna2_64x128x64_sm70_float16_cm_1_1); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 128, false>(), fna2_32x128x128_sm70_float16_cm_1_1); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 128, false>(), fna2_64x64x128_sm70_float16_cm_1_1); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 128, false>(), fna2_64x128x128_sm70_float16_cm_1_1); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 65536, false>(), fna2_32x128x65536_sm70_float16_cm_1_1); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 65536, false>(), fna2_64x128x65536_sm70_float16_cm_1_1); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 65536, false>(), fna2_64x64x65536_sm70_float16_cm_1_1); +} + +/////////////////////////////////////////////////////////////////// +// FNA-2D / float32 / SM75 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 32, false>::kMinBlocksPerSm) +fna2_32x128x32_sm75_float32_cm_0_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 32, false>::kMinBlocksPerSm) +fna2_64x64x32_sm75_float32_cm_0_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 32, false>::kMinBlocksPerSm) +fna2_64x128x32_sm75_float32_cm_0_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 64, false>::kMinBlocksPerSm) +fna2_32x128x64_sm75_float32_cm_0_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 64, false>::kMinBlocksPerSm) +fna2_64x64x64_sm75_float32_cm_0_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 64, false>::kMinBlocksPerSm) +fna2_64x128x64_sm75_float32_cm_0_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 128, false>::kMinBlocksPerSm) +fna2_32x128x128_sm75_float32_cm_0_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 128, false>::kMinBlocksPerSm) +fna2_64x64x128_sm75_float32_cm_0_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 128, false>::kMinBlocksPerSm) +fna2_64x128x128_sm75_float32_cm_0_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 65536, false>::kMinBlocksPerSm) +fna2_32x128x65536_sm75_float32_cm_0_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 65536, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 65536, false>::kMinBlocksPerSm) +fna2_64x128x65536_sm75_float32_cm_0_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 65536, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 65536, false>::kMinBlocksPerSm) +fna2_64x64x65536_sm75_float32_cm_0_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 65536, false>::Params p); + + +template +void fna2_sm75_float32_cm_0_0(T cb) { + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 32, false>(), fna2_32x128x32_sm75_float32_cm_0_0); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 32, false>(), fna2_64x64x32_sm75_float32_cm_0_0); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 32, false>(), fna2_64x128x32_sm75_float32_cm_0_0); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 64, false>(), fna2_32x128x64_sm75_float32_cm_0_0); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 64, false>(), fna2_64x64x64_sm75_float32_cm_0_0); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 64, false>(), fna2_64x128x64_sm75_float32_cm_0_0); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 128, false>(), fna2_32x128x128_sm75_float32_cm_0_0); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 128, false>(), fna2_64x64x128_sm75_float32_cm_0_0); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 128, false>(), fna2_64x128x128_sm75_float32_cm_0_0); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 65536, false>(), fna2_32x128x65536_sm75_float32_cm_0_0); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 65536, false>(), fna2_64x128x65536_sm75_float32_cm_0_0); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 65536, false>(), fna2_64x64x65536_sm75_float32_cm_0_0); +} + +/////////////////////////////////////////////////////////////////// +// FNA-2D / float32 / SM75 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 32, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 32, true>::kMinBlocksPerSm) +fna2_32x128x32_sm75_float32_cm_0_0_rpb(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 32, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 32, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 32, true>::kMinBlocksPerSm) +fna2_64x64x32_sm75_float32_cm_0_0_rpb(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 32, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 32, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 32, true>::kMinBlocksPerSm) +fna2_64x128x32_sm75_float32_cm_0_0_rpb(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 32, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 64, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 64, true>::kMinBlocksPerSm) +fna2_32x128x64_sm75_float32_cm_0_0_rpb(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 64, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 64, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 64, true>::kMinBlocksPerSm) +fna2_64x64x64_sm75_float32_cm_0_0_rpb(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 64, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 64, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 64, true>::kMinBlocksPerSm) +fna2_64x128x64_sm75_float32_cm_0_0_rpb(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 64, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 128, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 128, true>::kMinBlocksPerSm) +fna2_32x128x128_sm75_float32_cm_0_0_rpb(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 128, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 128, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 128, true>::kMinBlocksPerSm) +fna2_64x64x128_sm75_float32_cm_0_0_rpb(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 128, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 128, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 128, true>::kMinBlocksPerSm) +fna2_64x128x128_sm75_float32_cm_0_0_rpb(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 128, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 65536, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 65536, true>::kMinBlocksPerSm) +fna2_32x128x65536_sm75_float32_cm_0_0_rpb(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 65536, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 65536, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 65536, true>::kMinBlocksPerSm) +fna2_64x128x65536_sm75_float32_cm_0_0_rpb(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 65536, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 65536, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 65536, true>::kMinBlocksPerSm) +fna2_64x64x65536_sm75_float32_cm_0_0_rpb(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 65536, true>::Params p); + + +template +void fna2_sm75_float32_cm_0_0_rpb(T cb) { + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 32, true>(), fna2_32x128x32_sm75_float32_cm_0_0_rpb); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 32, true>(), fna2_64x64x32_sm75_float32_cm_0_0_rpb); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 32, true>(), fna2_64x128x32_sm75_float32_cm_0_0_rpb); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 64, true>(), fna2_32x128x64_sm75_float32_cm_0_0_rpb); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 64, true>(), fna2_64x64x64_sm75_float32_cm_0_0_rpb); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 64, true>(), fna2_64x128x64_sm75_float32_cm_0_0_rpb); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 128, true>(), fna2_32x128x128_sm75_float32_cm_0_0_rpb); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 128, true>(), fna2_64x64x128_sm75_float32_cm_0_0_rpb); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 128, true>(), fna2_64x128x128_sm75_float32_cm_0_0_rpb); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 65536, true>(), fna2_32x128x65536_sm75_float32_cm_0_0_rpb); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 65536, true>(), fna2_64x128x65536_sm75_float32_cm_0_0_rpb); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 65536, true>(), fna2_64x64x65536_sm75_float32_cm_0_0_rpb); +} + +/////////////////////////////////////////////////////////////////// +// FNA-2D / float32 / SM75 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 32, false>::kMinBlocksPerSm) +fna2_32x128x32_sm75_float32_cm_0_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 32, false>::kMinBlocksPerSm) +fna2_64x64x32_sm75_float32_cm_0_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 32, false>::kMinBlocksPerSm) +fna2_64x128x32_sm75_float32_cm_0_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 64, false>::kMinBlocksPerSm) +fna2_32x128x64_sm75_float32_cm_0_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 64, false>::kMinBlocksPerSm) +fna2_64x64x64_sm75_float32_cm_0_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 64, false>::kMinBlocksPerSm) +fna2_64x128x64_sm75_float32_cm_0_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 128, false>::kMinBlocksPerSm) +fna2_32x128x128_sm75_float32_cm_0_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 128, false>::kMinBlocksPerSm) +fna2_64x64x128_sm75_float32_cm_0_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 128, false>::kMinBlocksPerSm) +fna2_64x128x128_sm75_float32_cm_0_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 65536, false>::kMinBlocksPerSm) +fna2_32x128x65536_sm75_float32_cm_0_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 65536, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 65536, false>::kMinBlocksPerSm) +fna2_64x128x65536_sm75_float32_cm_0_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 65536, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 65536, false>::kMinBlocksPerSm) +fna2_64x64x65536_sm75_float32_cm_0_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 65536, false>::Params p); + + +template +void fna2_sm75_float32_cm_0_1(T cb) { + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 32, false>(), fna2_32x128x32_sm75_float32_cm_0_1); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 32, false>(), fna2_64x64x32_sm75_float32_cm_0_1); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 32, false>(), fna2_64x128x32_sm75_float32_cm_0_1); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 64, false>(), fna2_32x128x64_sm75_float32_cm_0_1); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 64, false>(), fna2_64x64x64_sm75_float32_cm_0_1); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 64, false>(), fna2_64x128x64_sm75_float32_cm_0_1); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 128, false>(), fna2_32x128x128_sm75_float32_cm_0_1); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 128, false>(), fna2_64x64x128_sm75_float32_cm_0_1); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 128, false>(), fna2_64x128x128_sm75_float32_cm_0_1); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 65536, false>(), fna2_32x128x65536_sm75_float32_cm_0_1); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 65536, false>(), fna2_64x128x65536_sm75_float32_cm_0_1); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 65536, false>(), fna2_64x64x65536_sm75_float32_cm_0_1); +} + +/////////////////////////////////////////////////////////////////// +// FNA-2D / float32 / SM75 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 32, false>::kMinBlocksPerSm) +fna2_32x128x32_sm75_float32_cm_1_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 32, false>::kMinBlocksPerSm) +fna2_64x64x32_sm75_float32_cm_1_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 32, false>::kMinBlocksPerSm) +fna2_64x128x32_sm75_float32_cm_1_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 64, false>::kMinBlocksPerSm) +fna2_32x128x64_sm75_float32_cm_1_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 64, false>::kMinBlocksPerSm) +fna2_64x64x64_sm75_float32_cm_1_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 64, false>::kMinBlocksPerSm) +fna2_64x128x64_sm75_float32_cm_1_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 128, false>::kMinBlocksPerSm) +fna2_32x128x128_sm75_float32_cm_1_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 128, false>::kMinBlocksPerSm) +fna2_64x64x128_sm75_float32_cm_1_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 128, false>::kMinBlocksPerSm) +fna2_64x128x128_sm75_float32_cm_1_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 65536, false>::kMinBlocksPerSm) +fna2_32x128x65536_sm75_float32_cm_1_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 65536, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 65536, false>::kMinBlocksPerSm) +fna2_64x128x65536_sm75_float32_cm_1_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 65536, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 65536, false>::kMinBlocksPerSm) +fna2_64x64x65536_sm75_float32_cm_1_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 65536, false>::Params p); + + +template +void fna2_sm75_float32_cm_1_0(T cb) { + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 32, false>(), fna2_32x128x32_sm75_float32_cm_1_0); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 32, false>(), fna2_64x64x32_sm75_float32_cm_1_0); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 32, false>(), fna2_64x128x32_sm75_float32_cm_1_0); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 64, false>(), fna2_32x128x64_sm75_float32_cm_1_0); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 64, false>(), fna2_64x64x64_sm75_float32_cm_1_0); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 64, false>(), fna2_64x128x64_sm75_float32_cm_1_0); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 128, false>(), fna2_32x128x128_sm75_float32_cm_1_0); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 128, false>(), fna2_64x64x128_sm75_float32_cm_1_0); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 128, false>(), fna2_64x128x128_sm75_float32_cm_1_0); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 65536, false>(), fna2_32x128x65536_sm75_float32_cm_1_0); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 65536, false>(), fna2_64x128x65536_sm75_float32_cm_1_0); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 65536, false>(), fna2_64x64x65536_sm75_float32_cm_1_0); +} + +/////////////////////////////////////////////////////////////////// +// FNA-2D / float32 / SM75 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 32, false>::kMinBlocksPerSm) +fna2_32x128x32_sm75_float32_cm_1_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 32, false>::kMinBlocksPerSm) +fna2_64x64x32_sm75_float32_cm_1_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 32, false>::kMinBlocksPerSm) +fna2_64x128x32_sm75_float32_cm_1_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 64, false>::kMinBlocksPerSm) +fna2_32x128x64_sm75_float32_cm_1_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 64, false>::kMinBlocksPerSm) +fna2_64x64x64_sm75_float32_cm_1_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 64, false>::kMinBlocksPerSm) +fna2_64x128x64_sm75_float32_cm_1_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 128, false>::kMinBlocksPerSm) +fna2_32x128x128_sm75_float32_cm_1_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 128, false>::kMinBlocksPerSm) +fna2_64x64x128_sm75_float32_cm_1_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 128, false>::kMinBlocksPerSm) +fna2_64x128x128_sm75_float32_cm_1_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 65536, false>::kMinBlocksPerSm) +fna2_32x128x65536_sm75_float32_cm_1_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 65536, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 65536, false>::kMinBlocksPerSm) +fna2_64x128x65536_sm75_float32_cm_1_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 65536, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 65536, false>::kMinBlocksPerSm) +fna2_64x64x65536_sm75_float32_cm_1_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 65536, false>::Params p); + + +template +void fna2_sm75_float32_cm_1_1(T cb) { + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 32, false>(), fna2_32x128x32_sm75_float32_cm_1_1); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 32, false>(), fna2_64x64x32_sm75_float32_cm_1_1); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 32, false>(), fna2_64x128x32_sm75_float32_cm_1_1); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 64, false>(), fna2_32x128x64_sm75_float32_cm_1_1); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 64, false>(), fna2_64x64x64_sm75_float32_cm_1_1); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 64, false>(), fna2_64x128x64_sm75_float32_cm_1_1); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 128, false>(), fna2_32x128x128_sm75_float32_cm_1_1); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 128, false>(), fna2_64x64x128_sm75_float32_cm_1_1); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 128, false>(), fna2_64x128x128_sm75_float32_cm_1_1); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 65536, false>(), fna2_32x128x65536_sm75_float32_cm_1_1); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 65536, false>(), fna2_64x128x65536_sm75_float32_cm_1_1); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 65536, false>(), fna2_64x64x65536_sm75_float32_cm_1_1); +} + +/////////////////////////////////////////////////////////////////// +// FNA-2D / float16 / SM75 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 32, false>::kMinBlocksPerSm) +fna2_32x128x32_sm75_float16_cm_0_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 32, false>::kMinBlocksPerSm) +fna2_64x64x32_sm75_float16_cm_0_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 32, false>::kMinBlocksPerSm) +fna2_64x128x32_sm75_float16_cm_0_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 64, false>::kMinBlocksPerSm) +fna2_32x128x64_sm75_float16_cm_0_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 64, false>::kMinBlocksPerSm) +fna2_64x64x64_sm75_float16_cm_0_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 64, false>::kMinBlocksPerSm) +fna2_64x128x64_sm75_float16_cm_0_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 128, false>::kMinBlocksPerSm) +fna2_32x128x128_sm75_float16_cm_0_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 128, false>::kMinBlocksPerSm) +fna2_64x64x128_sm75_float16_cm_0_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 128, false>::kMinBlocksPerSm) +fna2_64x128x128_sm75_float16_cm_0_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 65536, false>::kMinBlocksPerSm) +fna2_32x128x65536_sm75_float16_cm_0_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 65536, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 65536, false>::kMinBlocksPerSm) +fna2_64x128x65536_sm75_float16_cm_0_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 65536, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 65536, false>::kMinBlocksPerSm) +fna2_64x64x65536_sm75_float16_cm_0_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 65536, false>::Params p); + + +template +void fna2_sm75_float16_cm_0_0(T cb) { + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 32, false>(), fna2_32x128x32_sm75_float16_cm_0_0); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 32, false>(), fna2_64x64x32_sm75_float16_cm_0_0); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 32, false>(), fna2_64x128x32_sm75_float16_cm_0_0); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 64, false>(), fna2_32x128x64_sm75_float16_cm_0_0); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 64, false>(), fna2_64x64x64_sm75_float16_cm_0_0); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 64, false>(), fna2_64x128x64_sm75_float16_cm_0_0); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 128, false>(), fna2_32x128x128_sm75_float16_cm_0_0); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 128, false>(), fna2_64x64x128_sm75_float16_cm_0_0); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 128, false>(), fna2_64x128x128_sm75_float16_cm_0_0); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 65536, false>(), fna2_32x128x65536_sm75_float16_cm_0_0); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 65536, false>(), fna2_64x128x65536_sm75_float16_cm_0_0); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 65536, false>(), fna2_64x64x65536_sm75_float16_cm_0_0); +} + +/////////////////////////////////////////////////////////////////// +// FNA-2D / float16 / SM75 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 32, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 32, true>::kMinBlocksPerSm) +fna2_32x128x32_sm75_float16_cm_0_0_rpb(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 32, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 32, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 32, true>::kMinBlocksPerSm) +fna2_64x64x32_sm75_float16_cm_0_0_rpb(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 32, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 32, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 32, true>::kMinBlocksPerSm) +fna2_64x128x32_sm75_float16_cm_0_0_rpb(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 32, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 64, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 64, true>::kMinBlocksPerSm) +fna2_32x128x64_sm75_float16_cm_0_0_rpb(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 64, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 64, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 64, true>::kMinBlocksPerSm) +fna2_64x64x64_sm75_float16_cm_0_0_rpb(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 64, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 64, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 64, true>::kMinBlocksPerSm) +fna2_64x128x64_sm75_float16_cm_0_0_rpb(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 64, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 128, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 128, true>::kMinBlocksPerSm) +fna2_32x128x128_sm75_float16_cm_0_0_rpb(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 128, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 128, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 128, true>::kMinBlocksPerSm) +fna2_64x64x128_sm75_float16_cm_0_0_rpb(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 128, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 128, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 128, true>::kMinBlocksPerSm) +fna2_64x128x128_sm75_float16_cm_0_0_rpb(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 128, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 65536, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 65536, true>::kMinBlocksPerSm) +fna2_32x128x65536_sm75_float16_cm_0_0_rpb(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 65536, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 65536, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 65536, true>::kMinBlocksPerSm) +fna2_64x128x65536_sm75_float16_cm_0_0_rpb(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 65536, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 65536, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 65536, true>::kMinBlocksPerSm) +fna2_64x64x65536_sm75_float16_cm_0_0_rpb(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 65536, true>::Params p); + + +template +void fna2_sm75_float16_cm_0_0_rpb(T cb) { + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 32, true>(), fna2_32x128x32_sm75_float16_cm_0_0_rpb); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 32, true>(), fna2_64x64x32_sm75_float16_cm_0_0_rpb); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 32, true>(), fna2_64x128x32_sm75_float16_cm_0_0_rpb); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 64, true>(), fna2_32x128x64_sm75_float16_cm_0_0_rpb); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 64, true>(), fna2_64x64x64_sm75_float16_cm_0_0_rpb); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 64, true>(), fna2_64x128x64_sm75_float16_cm_0_0_rpb); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 128, true>(), fna2_32x128x128_sm75_float16_cm_0_0_rpb); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 128, true>(), fna2_64x64x128_sm75_float16_cm_0_0_rpb); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 128, true>(), fna2_64x128x128_sm75_float16_cm_0_0_rpb); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 65536, true>(), fna2_32x128x65536_sm75_float16_cm_0_0_rpb); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 65536, true>(), fna2_64x128x65536_sm75_float16_cm_0_0_rpb); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 65536, true>(), fna2_64x64x65536_sm75_float16_cm_0_0_rpb); +} + +/////////////////////////////////////////////////////////////////// +// FNA-2D / float16 / SM75 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 32, false>::kMinBlocksPerSm) +fna2_32x128x32_sm75_float16_cm_0_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 32, false>::kMinBlocksPerSm) +fna2_64x64x32_sm75_float16_cm_0_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 32, false>::kMinBlocksPerSm) +fna2_64x128x32_sm75_float16_cm_0_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 64, false>::kMinBlocksPerSm) +fna2_32x128x64_sm75_float16_cm_0_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 64, false>::kMinBlocksPerSm) +fna2_64x64x64_sm75_float16_cm_0_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 64, false>::kMinBlocksPerSm) +fna2_64x128x64_sm75_float16_cm_0_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 128, false>::kMinBlocksPerSm) +fna2_32x128x128_sm75_float16_cm_0_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 128, false>::kMinBlocksPerSm) +fna2_64x64x128_sm75_float16_cm_0_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 128, false>::kMinBlocksPerSm) +fna2_64x128x128_sm75_float16_cm_0_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 65536, false>::kMinBlocksPerSm) +fna2_32x128x65536_sm75_float16_cm_0_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 65536, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 65536, false>::kMinBlocksPerSm) +fna2_64x128x65536_sm75_float16_cm_0_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 65536, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 65536, false>::kMinBlocksPerSm) +fna2_64x64x65536_sm75_float16_cm_0_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 65536, false>::Params p); + + +template +void fna2_sm75_float16_cm_0_1(T cb) { + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 32, false>(), fna2_32x128x32_sm75_float16_cm_0_1); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 32, false>(), fna2_64x64x32_sm75_float16_cm_0_1); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 32, false>(), fna2_64x128x32_sm75_float16_cm_0_1); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 64, false>(), fna2_32x128x64_sm75_float16_cm_0_1); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 64, false>(), fna2_64x64x64_sm75_float16_cm_0_1); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 64, false>(), fna2_64x128x64_sm75_float16_cm_0_1); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 128, false>(), fna2_32x128x128_sm75_float16_cm_0_1); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 128, false>(), fna2_64x64x128_sm75_float16_cm_0_1); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 128, false>(), fna2_64x128x128_sm75_float16_cm_0_1); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 65536, false>(), fna2_32x128x65536_sm75_float16_cm_0_1); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 65536, false>(), fna2_64x128x65536_sm75_float16_cm_0_1); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 65536, false>(), fna2_64x64x65536_sm75_float16_cm_0_1); +} + +/////////////////////////////////////////////////////////////////// +// FNA-2D / float16 / SM75 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 32, false>::kMinBlocksPerSm) +fna2_32x128x32_sm75_float16_cm_1_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 32, false>::kMinBlocksPerSm) +fna2_64x64x32_sm75_float16_cm_1_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 32, false>::kMinBlocksPerSm) +fna2_64x128x32_sm75_float16_cm_1_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 64, false>::kMinBlocksPerSm) +fna2_32x128x64_sm75_float16_cm_1_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 64, false>::kMinBlocksPerSm) +fna2_64x64x64_sm75_float16_cm_1_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 64, false>::kMinBlocksPerSm) +fna2_64x128x64_sm75_float16_cm_1_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 128, false>::kMinBlocksPerSm) +fna2_32x128x128_sm75_float16_cm_1_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 128, false>::kMinBlocksPerSm) +fna2_64x64x128_sm75_float16_cm_1_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 128, false>::kMinBlocksPerSm) +fna2_64x128x128_sm75_float16_cm_1_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 65536, false>::kMinBlocksPerSm) +fna2_32x128x65536_sm75_float16_cm_1_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 65536, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 65536, false>::kMinBlocksPerSm) +fna2_64x128x65536_sm75_float16_cm_1_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 65536, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 65536, false>::kMinBlocksPerSm) +fna2_64x64x65536_sm75_float16_cm_1_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 65536, false>::Params p); + + +template +void fna2_sm75_float16_cm_1_0(T cb) { + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 32, false>(), fna2_32x128x32_sm75_float16_cm_1_0); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 32, false>(), fna2_64x64x32_sm75_float16_cm_1_0); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 32, false>(), fna2_64x128x32_sm75_float16_cm_1_0); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 64, false>(), fna2_32x128x64_sm75_float16_cm_1_0); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 64, false>(), fna2_64x64x64_sm75_float16_cm_1_0); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 64, false>(), fna2_64x128x64_sm75_float16_cm_1_0); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 128, false>(), fna2_32x128x128_sm75_float16_cm_1_0); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 128, false>(), fna2_64x64x128_sm75_float16_cm_1_0); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 128, false>(), fna2_64x128x128_sm75_float16_cm_1_0); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 65536, false>(), fna2_32x128x65536_sm75_float16_cm_1_0); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 65536, false>(), fna2_64x128x65536_sm75_float16_cm_1_0); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 65536, false>(), fna2_64x64x65536_sm75_float16_cm_1_0); +} + +/////////////////////////////////////////////////////////////////// +// FNA-2D / float16 / SM75 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 32, false>::kMinBlocksPerSm) +fna2_32x128x32_sm75_float16_cm_1_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 32, false>::kMinBlocksPerSm) +fna2_64x64x32_sm75_float16_cm_1_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 32, false>::kMinBlocksPerSm) +fna2_64x128x32_sm75_float16_cm_1_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 64, false>::kMinBlocksPerSm) +fna2_32x128x64_sm75_float16_cm_1_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 64, false>::kMinBlocksPerSm) +fna2_64x64x64_sm75_float16_cm_1_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 64, false>::kMinBlocksPerSm) +fna2_64x128x64_sm75_float16_cm_1_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 128, false>::kMinBlocksPerSm) +fna2_32x128x128_sm75_float16_cm_1_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 128, false>::kMinBlocksPerSm) +fna2_64x64x128_sm75_float16_cm_1_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 128, false>::kMinBlocksPerSm) +fna2_64x128x128_sm75_float16_cm_1_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 65536, false>::kMinBlocksPerSm) +fna2_32x128x65536_sm75_float16_cm_1_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 65536, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 65536, false>::kMinBlocksPerSm) +fna2_64x128x65536_sm75_float16_cm_1_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 65536, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 65536, false>::kMinBlocksPerSm) +fna2_64x64x65536_sm75_float16_cm_1_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 65536, false>::Params p); + + +template +void fna2_sm75_float16_cm_1_1(T cb) { + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 32, false>(), fna2_32x128x32_sm75_float16_cm_1_1); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 32, false>(), fna2_64x64x32_sm75_float16_cm_1_1); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 32, false>(), fna2_64x128x32_sm75_float16_cm_1_1); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 64, false>(), fna2_32x128x64_sm75_float16_cm_1_1); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 64, false>(), fna2_64x64x64_sm75_float16_cm_1_1); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 64, false>(), fna2_64x128x64_sm75_float16_cm_1_1); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 128, false>(), fna2_32x128x128_sm75_float16_cm_1_1); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 128, false>(), fna2_64x64x128_sm75_float16_cm_1_1); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 128, false>(), fna2_64x128x128_sm75_float16_cm_1_1); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 65536, false>(), fna2_32x128x65536_sm75_float16_cm_1_1); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 65536, false>(), fna2_64x128x65536_sm75_float16_cm_1_1); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 65536, false>(), fna2_64x64x65536_sm75_float16_cm_1_1); +} + +/////////////////////////////////////////////////////////////////// +// FNA-2D / float32 / SM80 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 32, false>::kMinBlocksPerSm) +fna2_32x128x32_sm80_float32_cm_0_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 32, false>::kMinBlocksPerSm) +fna2_64x64x32_sm80_float32_cm_0_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 32, false>::kMinBlocksPerSm) +fna2_64x128x32_sm80_float32_cm_0_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 64, false>::kMinBlocksPerSm) +fna2_32x128x64_sm80_float32_cm_0_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 64, false>::kMinBlocksPerSm) +fna2_64x64x64_sm80_float32_cm_0_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 64, false>::kMinBlocksPerSm) +fna2_64x128x64_sm80_float32_cm_0_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 128, false>::kMinBlocksPerSm) +fna2_32x128x128_sm80_float32_cm_0_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 128, false>::kMinBlocksPerSm) +fna2_64x64x128_sm80_float32_cm_0_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 128, false>::kMinBlocksPerSm) +fna2_64x128x128_sm80_float32_cm_0_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 65536, false>::kMinBlocksPerSm) +fna2_32x128x65536_sm80_float32_cm_0_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 65536, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 65536, false>::kMinBlocksPerSm) +fna2_64x128x65536_sm80_float32_cm_0_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 65536, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 65536, false>::kMinBlocksPerSm) +fna2_64x64x65536_sm80_float32_cm_0_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 65536, false>::Params p); + + +template +void fna2_sm80_float32_cm_0_0(T cb) { + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 32, false>(), fna2_32x128x32_sm80_float32_cm_0_0); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 32, false>(), fna2_64x64x32_sm80_float32_cm_0_0); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 32, false>(), fna2_64x128x32_sm80_float32_cm_0_0); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 64, false>(), fna2_32x128x64_sm80_float32_cm_0_0); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 64, false>(), fna2_64x64x64_sm80_float32_cm_0_0); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 64, false>(), fna2_64x128x64_sm80_float32_cm_0_0); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 128, false>(), fna2_32x128x128_sm80_float32_cm_0_0); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 128, false>(), fna2_64x64x128_sm80_float32_cm_0_0); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 128, false>(), fna2_64x128x128_sm80_float32_cm_0_0); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 65536, false>(), fna2_32x128x65536_sm80_float32_cm_0_0); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 65536, false>(), fna2_64x128x65536_sm80_float32_cm_0_0); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 65536, false>(), fna2_64x64x65536_sm80_float32_cm_0_0); +} + +/////////////////////////////////////////////////////////////////// +// FNA-2D / float32 / SM80 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 32, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 32, true>::kMinBlocksPerSm) +fna2_32x128x32_sm80_float32_cm_0_0_rpb(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 32, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 32, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 32, true>::kMinBlocksPerSm) +fna2_64x64x32_sm80_float32_cm_0_0_rpb(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 32, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 32, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 32, true>::kMinBlocksPerSm) +fna2_64x128x32_sm80_float32_cm_0_0_rpb(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 32, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 64, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 64, true>::kMinBlocksPerSm) +fna2_32x128x64_sm80_float32_cm_0_0_rpb(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 64, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 64, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 64, true>::kMinBlocksPerSm) +fna2_64x64x64_sm80_float32_cm_0_0_rpb(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 64, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 64, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 64, true>::kMinBlocksPerSm) +fna2_64x128x64_sm80_float32_cm_0_0_rpb(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 64, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 128, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 128, true>::kMinBlocksPerSm) +fna2_32x128x128_sm80_float32_cm_0_0_rpb(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 128, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 128, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 128, true>::kMinBlocksPerSm) +fna2_64x64x128_sm80_float32_cm_0_0_rpb(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 128, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 128, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 128, true>::kMinBlocksPerSm) +fna2_64x128x128_sm80_float32_cm_0_0_rpb(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 128, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 65536, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 65536, true>::kMinBlocksPerSm) +fna2_32x128x65536_sm80_float32_cm_0_0_rpb(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 65536, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 65536, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 65536, true>::kMinBlocksPerSm) +fna2_64x128x65536_sm80_float32_cm_0_0_rpb(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 65536, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 65536, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 65536, true>::kMinBlocksPerSm) +fna2_64x64x65536_sm80_float32_cm_0_0_rpb(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 65536, true>::Params p); + + +template +void fna2_sm80_float32_cm_0_0_rpb(T cb) { + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 32, true>(), fna2_32x128x32_sm80_float32_cm_0_0_rpb); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 32, true>(), fna2_64x64x32_sm80_float32_cm_0_0_rpb); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 32, true>(), fna2_64x128x32_sm80_float32_cm_0_0_rpb); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 64, true>(), fna2_32x128x64_sm80_float32_cm_0_0_rpb); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 64, true>(), fna2_64x64x64_sm80_float32_cm_0_0_rpb); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 64, true>(), fna2_64x128x64_sm80_float32_cm_0_0_rpb); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 128, true>(), fna2_32x128x128_sm80_float32_cm_0_0_rpb); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 128, true>(), fna2_64x64x128_sm80_float32_cm_0_0_rpb); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 128, true>(), fna2_64x128x128_sm80_float32_cm_0_0_rpb); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 65536, true>(), fna2_32x128x65536_sm80_float32_cm_0_0_rpb); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 65536, true>(), fna2_64x128x65536_sm80_float32_cm_0_0_rpb); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 65536, true>(), fna2_64x64x65536_sm80_float32_cm_0_0_rpb); +} + +/////////////////////////////////////////////////////////////////// +// FNA-2D / float32 / SM80 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 32, false>::kMinBlocksPerSm) +fna2_32x128x32_sm80_float32_cm_0_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 32, false>::kMinBlocksPerSm) +fna2_64x64x32_sm80_float32_cm_0_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 32, false>::kMinBlocksPerSm) +fna2_64x128x32_sm80_float32_cm_0_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 64, false>::kMinBlocksPerSm) +fna2_32x128x64_sm80_float32_cm_0_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 64, false>::kMinBlocksPerSm) +fna2_64x64x64_sm80_float32_cm_0_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 64, false>::kMinBlocksPerSm) +fna2_64x128x64_sm80_float32_cm_0_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 128, false>::kMinBlocksPerSm) +fna2_32x128x128_sm80_float32_cm_0_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 128, false>::kMinBlocksPerSm) +fna2_64x64x128_sm80_float32_cm_0_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 128, false>::kMinBlocksPerSm) +fna2_64x128x128_sm80_float32_cm_0_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 65536, false>::kMinBlocksPerSm) +fna2_32x128x65536_sm80_float32_cm_0_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 65536, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 65536, false>::kMinBlocksPerSm) +fna2_64x128x65536_sm80_float32_cm_0_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 65536, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 65536, false>::kMinBlocksPerSm) +fna2_64x64x65536_sm80_float32_cm_0_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 65536, false>::Params p); + + +template +void fna2_sm80_float32_cm_0_1(T cb) { + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 32, false>(), fna2_32x128x32_sm80_float32_cm_0_1); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 32, false>(), fna2_64x64x32_sm80_float32_cm_0_1); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 32, false>(), fna2_64x128x32_sm80_float32_cm_0_1); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 64, false>(), fna2_32x128x64_sm80_float32_cm_0_1); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 64, false>(), fna2_64x64x64_sm80_float32_cm_0_1); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 64, false>(), fna2_64x128x64_sm80_float32_cm_0_1); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 128, false>(), fna2_32x128x128_sm80_float32_cm_0_1); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 128, false>(), fna2_64x64x128_sm80_float32_cm_0_1); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 128, false>(), fna2_64x128x128_sm80_float32_cm_0_1); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 65536, false>(), fna2_32x128x65536_sm80_float32_cm_0_1); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 65536, false>(), fna2_64x128x65536_sm80_float32_cm_0_1); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 65536, false>(), fna2_64x64x65536_sm80_float32_cm_0_1); +} + +/////////////////////////////////////////////////////////////////// +// FNA-2D / float32 / SM80 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 32, false>::kMinBlocksPerSm) +fna2_32x128x32_sm80_float32_cm_1_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 32, false>::kMinBlocksPerSm) +fna2_64x64x32_sm80_float32_cm_1_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 32, false>::kMinBlocksPerSm) +fna2_64x128x32_sm80_float32_cm_1_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 64, false>::kMinBlocksPerSm) +fna2_32x128x64_sm80_float32_cm_1_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 64, false>::kMinBlocksPerSm) +fna2_64x64x64_sm80_float32_cm_1_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 64, false>::kMinBlocksPerSm) +fna2_64x128x64_sm80_float32_cm_1_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 128, false>::kMinBlocksPerSm) +fna2_32x128x128_sm80_float32_cm_1_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 128, false>::kMinBlocksPerSm) +fna2_64x64x128_sm80_float32_cm_1_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 128, false>::kMinBlocksPerSm) +fna2_64x128x128_sm80_float32_cm_1_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 65536, false>::kMinBlocksPerSm) +fna2_32x128x65536_sm80_float32_cm_1_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 65536, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 65536, false>::kMinBlocksPerSm) +fna2_64x128x65536_sm80_float32_cm_1_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 65536, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 65536, false>::kMinBlocksPerSm) +fna2_64x64x65536_sm80_float32_cm_1_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 65536, false>::Params p); + + +template +void fna2_sm80_float32_cm_1_0(T cb) { + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 32, false>(), fna2_32x128x32_sm80_float32_cm_1_0); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 32, false>(), fna2_64x64x32_sm80_float32_cm_1_0); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 32, false>(), fna2_64x128x32_sm80_float32_cm_1_0); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 64, false>(), fna2_32x128x64_sm80_float32_cm_1_0); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 64, false>(), fna2_64x64x64_sm80_float32_cm_1_0); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 64, false>(), fna2_64x128x64_sm80_float32_cm_1_0); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 128, false>(), fna2_32x128x128_sm80_float32_cm_1_0); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 128, false>(), fna2_64x64x128_sm80_float32_cm_1_0); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 128, false>(), fna2_64x128x128_sm80_float32_cm_1_0); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 65536, false>(), fna2_32x128x65536_sm80_float32_cm_1_0); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 65536, false>(), fna2_64x128x65536_sm80_float32_cm_1_0); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 65536, false>(), fna2_64x64x65536_sm80_float32_cm_1_0); +} + +/////////////////////////////////////////////////////////////////// +// FNA-2D / float32 / SM80 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 32, false>::kMinBlocksPerSm) +fna2_32x128x32_sm80_float32_cm_1_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 32, false>::kMinBlocksPerSm) +fna2_64x64x32_sm80_float32_cm_1_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 32, false>::kMinBlocksPerSm) +fna2_64x128x32_sm80_float32_cm_1_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 64, false>::kMinBlocksPerSm) +fna2_32x128x64_sm80_float32_cm_1_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 64, false>::kMinBlocksPerSm) +fna2_64x64x64_sm80_float32_cm_1_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 64, false>::kMinBlocksPerSm) +fna2_64x128x64_sm80_float32_cm_1_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 128, false>::kMinBlocksPerSm) +fna2_32x128x128_sm80_float32_cm_1_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 128, false>::kMinBlocksPerSm) +fna2_64x64x128_sm80_float32_cm_1_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 128, false>::kMinBlocksPerSm) +fna2_64x128x128_sm80_float32_cm_1_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 65536, false>::kMinBlocksPerSm) +fna2_32x128x65536_sm80_float32_cm_1_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 65536, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 65536, false>::kMinBlocksPerSm) +fna2_64x128x65536_sm80_float32_cm_1_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 65536, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 65536, false>::kMinBlocksPerSm) +fna2_64x64x65536_sm80_float32_cm_1_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 65536, false>::Params p); + + +template +void fna2_sm80_float32_cm_1_1(T cb) { + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 32, false>(), fna2_32x128x32_sm80_float32_cm_1_1); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 32, false>(), fna2_64x64x32_sm80_float32_cm_1_1); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 32, false>(), fna2_64x128x32_sm80_float32_cm_1_1); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 64, false>(), fna2_32x128x64_sm80_float32_cm_1_1); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 64, false>(), fna2_64x64x64_sm80_float32_cm_1_1); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 64, false>(), fna2_64x128x64_sm80_float32_cm_1_1); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 128, false>(), fna2_32x128x128_sm80_float32_cm_1_1); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 128, false>(), fna2_64x64x128_sm80_float32_cm_1_1); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 128, false>(), fna2_64x128x128_sm80_float32_cm_1_1); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 65536, false>(), fna2_32x128x65536_sm80_float32_cm_1_1); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 65536, false>(), fna2_64x128x65536_sm80_float32_cm_1_1); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 65536, false>(), fna2_64x64x65536_sm80_float32_cm_1_1); +} + +/////////////////////////////////////////////////////////////////// +// FNA-2D / float16 / SM80 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 32, false>::kMinBlocksPerSm) +fna2_32x128x32_sm80_float16_cm_0_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 32, false>::kMinBlocksPerSm) +fna2_64x64x32_sm80_float16_cm_0_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 32, false>::kMinBlocksPerSm) +fna2_64x128x32_sm80_float16_cm_0_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 64, false>::kMinBlocksPerSm) +fna2_32x128x64_sm80_float16_cm_0_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 64, false>::kMinBlocksPerSm) +fna2_64x64x64_sm80_float16_cm_0_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 64, false>::kMinBlocksPerSm) +fna2_64x128x64_sm80_float16_cm_0_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 128, false>::kMinBlocksPerSm) +fna2_32x128x128_sm80_float16_cm_0_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 128, false>::kMinBlocksPerSm) +fna2_64x64x128_sm80_float16_cm_0_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 128, false>::kMinBlocksPerSm) +fna2_64x128x128_sm80_float16_cm_0_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 65536, false>::kMinBlocksPerSm) +fna2_32x128x65536_sm80_float16_cm_0_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 65536, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 65536, false>::kMinBlocksPerSm) +fna2_64x128x65536_sm80_float16_cm_0_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 65536, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 65536, false>::kMinBlocksPerSm) +fna2_64x64x65536_sm80_float16_cm_0_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 65536, false>::Params p); + + +template +void fna2_sm80_float16_cm_0_0(T cb) { + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 32, false>(), fna2_32x128x32_sm80_float16_cm_0_0); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 32, false>(), fna2_64x64x32_sm80_float16_cm_0_0); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 32, false>(), fna2_64x128x32_sm80_float16_cm_0_0); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 64, false>(), fna2_32x128x64_sm80_float16_cm_0_0); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 64, false>(), fna2_64x64x64_sm80_float16_cm_0_0); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 64, false>(), fna2_64x128x64_sm80_float16_cm_0_0); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 128, false>(), fna2_32x128x128_sm80_float16_cm_0_0); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 128, false>(), fna2_64x64x128_sm80_float16_cm_0_0); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 128, false>(), fna2_64x128x128_sm80_float16_cm_0_0); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 65536, false>(), fna2_32x128x65536_sm80_float16_cm_0_0); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 65536, false>(), fna2_64x128x65536_sm80_float16_cm_0_0); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 65536, false>(), fna2_64x64x65536_sm80_float16_cm_0_0); +} + +/////////////////////////////////////////////////////////////////// +// FNA-2D / float16 / SM80 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 32, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 32, true>::kMinBlocksPerSm) +fna2_32x128x32_sm80_float16_cm_0_0_rpb(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 32, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 32, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 32, true>::kMinBlocksPerSm) +fna2_64x64x32_sm80_float16_cm_0_0_rpb(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 32, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 32, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 32, true>::kMinBlocksPerSm) +fna2_64x128x32_sm80_float16_cm_0_0_rpb(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 32, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 64, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 64, true>::kMinBlocksPerSm) +fna2_32x128x64_sm80_float16_cm_0_0_rpb(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 64, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 64, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 64, true>::kMinBlocksPerSm) +fna2_64x64x64_sm80_float16_cm_0_0_rpb(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 64, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 64, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 64, true>::kMinBlocksPerSm) +fna2_64x128x64_sm80_float16_cm_0_0_rpb(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 64, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 128, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 128, true>::kMinBlocksPerSm) +fna2_32x128x128_sm80_float16_cm_0_0_rpb(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 128, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 128, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 128, true>::kMinBlocksPerSm) +fna2_64x64x128_sm80_float16_cm_0_0_rpb(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 128, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 128, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 128, true>::kMinBlocksPerSm) +fna2_64x128x128_sm80_float16_cm_0_0_rpb(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 128, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 65536, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 65536, true>::kMinBlocksPerSm) +fna2_32x128x65536_sm80_float16_cm_0_0_rpb(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 65536, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 65536, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 65536, true>::kMinBlocksPerSm) +fna2_64x128x65536_sm80_float16_cm_0_0_rpb(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 65536, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 65536, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 65536, true>::kMinBlocksPerSm) +fna2_64x64x65536_sm80_float16_cm_0_0_rpb(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 65536, true>::Params p); + + +template +void fna2_sm80_float16_cm_0_0_rpb(T cb) { + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 32, true>(), fna2_32x128x32_sm80_float16_cm_0_0_rpb); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 32, true>(), fna2_64x64x32_sm80_float16_cm_0_0_rpb); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 32, true>(), fna2_64x128x32_sm80_float16_cm_0_0_rpb); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 64, true>(), fna2_32x128x64_sm80_float16_cm_0_0_rpb); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 64, true>(), fna2_64x64x64_sm80_float16_cm_0_0_rpb); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 64, true>(), fna2_64x128x64_sm80_float16_cm_0_0_rpb); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 128, true>(), fna2_32x128x128_sm80_float16_cm_0_0_rpb); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 128, true>(), fna2_64x64x128_sm80_float16_cm_0_0_rpb); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 128, true>(), fna2_64x128x128_sm80_float16_cm_0_0_rpb); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 65536, true>(), fna2_32x128x65536_sm80_float16_cm_0_0_rpb); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 65536, true>(), fna2_64x128x65536_sm80_float16_cm_0_0_rpb); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 65536, true>(), fna2_64x64x65536_sm80_float16_cm_0_0_rpb); +} + +/////////////////////////////////////////////////////////////////// +// FNA-2D / float16 / SM80 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 32, false>::kMinBlocksPerSm) +fna2_32x128x32_sm80_float16_cm_0_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 32, false>::kMinBlocksPerSm) +fna2_64x64x32_sm80_float16_cm_0_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 32, false>::kMinBlocksPerSm) +fna2_64x128x32_sm80_float16_cm_0_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 64, false>::kMinBlocksPerSm) +fna2_32x128x64_sm80_float16_cm_0_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 64, false>::kMinBlocksPerSm) +fna2_64x64x64_sm80_float16_cm_0_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 64, false>::kMinBlocksPerSm) +fna2_64x128x64_sm80_float16_cm_0_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 128, false>::kMinBlocksPerSm) +fna2_32x128x128_sm80_float16_cm_0_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 128, false>::kMinBlocksPerSm) +fna2_64x64x128_sm80_float16_cm_0_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 128, false>::kMinBlocksPerSm) +fna2_64x128x128_sm80_float16_cm_0_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 65536, false>::kMinBlocksPerSm) +fna2_32x128x65536_sm80_float16_cm_0_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 65536, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 65536, false>::kMinBlocksPerSm) +fna2_64x128x65536_sm80_float16_cm_0_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 65536, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 65536, false>::kMinBlocksPerSm) +fna2_64x64x65536_sm80_float16_cm_0_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 65536, false>::Params p); + + +template +void fna2_sm80_float16_cm_0_1(T cb) { + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 32, false>(), fna2_32x128x32_sm80_float16_cm_0_1); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 32, false>(), fna2_64x64x32_sm80_float16_cm_0_1); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 32, false>(), fna2_64x128x32_sm80_float16_cm_0_1); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 64, false>(), fna2_32x128x64_sm80_float16_cm_0_1); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 64, false>(), fna2_64x64x64_sm80_float16_cm_0_1); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 64, false>(), fna2_64x128x64_sm80_float16_cm_0_1); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 128, false>(), fna2_32x128x128_sm80_float16_cm_0_1); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 128, false>(), fna2_64x64x128_sm80_float16_cm_0_1); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 128, false>(), fna2_64x128x128_sm80_float16_cm_0_1); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 65536, false>(), fna2_32x128x65536_sm80_float16_cm_0_1); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 65536, false>(), fna2_64x128x65536_sm80_float16_cm_0_1); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 65536, false>(), fna2_64x64x65536_sm80_float16_cm_0_1); +} + +/////////////////////////////////////////////////////////////////// +// FNA-2D / float16 / SM80 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 32, false>::kMinBlocksPerSm) +fna2_32x128x32_sm80_float16_cm_1_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 32, false>::kMinBlocksPerSm) +fna2_64x64x32_sm80_float16_cm_1_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 32, false>::kMinBlocksPerSm) +fna2_64x128x32_sm80_float16_cm_1_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 64, false>::kMinBlocksPerSm) +fna2_32x128x64_sm80_float16_cm_1_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 64, false>::kMinBlocksPerSm) +fna2_64x64x64_sm80_float16_cm_1_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 64, false>::kMinBlocksPerSm) +fna2_64x128x64_sm80_float16_cm_1_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 128, false>::kMinBlocksPerSm) +fna2_32x128x128_sm80_float16_cm_1_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 128, false>::kMinBlocksPerSm) +fna2_64x64x128_sm80_float16_cm_1_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 128, false>::kMinBlocksPerSm) +fna2_64x128x128_sm80_float16_cm_1_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 65536, false>::kMinBlocksPerSm) +fna2_32x128x65536_sm80_float16_cm_1_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 65536, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 65536, false>::kMinBlocksPerSm) +fna2_64x128x65536_sm80_float16_cm_1_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 65536, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 65536, false>::kMinBlocksPerSm) +fna2_64x64x65536_sm80_float16_cm_1_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 65536, false>::Params p); + + +template +void fna2_sm80_float16_cm_1_0(T cb) { + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 32, false>(), fna2_32x128x32_sm80_float16_cm_1_0); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 32, false>(), fna2_64x64x32_sm80_float16_cm_1_0); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 32, false>(), fna2_64x128x32_sm80_float16_cm_1_0); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 64, false>(), fna2_32x128x64_sm80_float16_cm_1_0); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 64, false>(), fna2_64x64x64_sm80_float16_cm_1_0); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 64, false>(), fna2_64x128x64_sm80_float16_cm_1_0); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 128, false>(), fna2_32x128x128_sm80_float16_cm_1_0); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 128, false>(), fna2_64x64x128_sm80_float16_cm_1_0); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 128, false>(), fna2_64x128x128_sm80_float16_cm_1_0); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 65536, false>(), fna2_32x128x65536_sm80_float16_cm_1_0); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 65536, false>(), fna2_64x128x65536_sm80_float16_cm_1_0); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 65536, false>(), fna2_64x64x65536_sm80_float16_cm_1_0); +} + +/////////////////////////////////////////////////////////////////// +// FNA-2D / float16 / SM80 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 32, false>::kMinBlocksPerSm) +fna2_32x128x32_sm80_float16_cm_1_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 32, false>::kMinBlocksPerSm) +fna2_64x64x32_sm80_float16_cm_1_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 32, false>::kMinBlocksPerSm) +fna2_64x128x32_sm80_float16_cm_1_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 64, false>::kMinBlocksPerSm) +fna2_32x128x64_sm80_float16_cm_1_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 64, false>::kMinBlocksPerSm) +fna2_64x64x64_sm80_float16_cm_1_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 64, false>::kMinBlocksPerSm) +fna2_64x128x64_sm80_float16_cm_1_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 128, false>::kMinBlocksPerSm) +fna2_32x128x128_sm80_float16_cm_1_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 128, false>::kMinBlocksPerSm) +fna2_64x64x128_sm80_float16_cm_1_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 128, false>::kMinBlocksPerSm) +fna2_64x128x128_sm80_float16_cm_1_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 65536, false>::kMinBlocksPerSm) +fna2_32x128x65536_sm80_float16_cm_1_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 65536, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 65536, false>::kMinBlocksPerSm) +fna2_64x128x65536_sm80_float16_cm_1_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 65536, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 65536, false>::kMinBlocksPerSm) +fna2_64x64x65536_sm80_float16_cm_1_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 65536, false>::Params p); + + +template +void fna2_sm80_float16_cm_1_1(T cb) { + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 32, false>(), fna2_32x128x32_sm80_float16_cm_1_1); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 32, false>(), fna2_64x64x32_sm80_float16_cm_1_1); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 32, false>(), fna2_64x128x32_sm80_float16_cm_1_1); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 64, false>(), fna2_32x128x64_sm80_float16_cm_1_1); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 64, false>(), fna2_64x64x64_sm80_float16_cm_1_1); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 64, false>(), fna2_64x128x64_sm80_float16_cm_1_1); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 128, false>(), fna2_32x128x128_sm80_float16_cm_1_1); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 128, false>(), fna2_64x64x128_sm80_float16_cm_1_1); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 128, false>(), fna2_64x128x128_sm80_float16_cm_1_1); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 65536, false>(), fna2_32x128x65536_sm80_float16_cm_1_1); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 65536, false>(), fna2_64x128x65536_sm80_float16_cm_1_1); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 65536, false>(), fna2_64x64x65536_sm80_float16_cm_1_1); +} + +/////////////////////////////////////////////////////////////////// +// FNA-2D / bfloat16 / SM80 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 32, false>::kMinBlocksPerSm) +fna2_32x128x32_sm80_bfloat16_cm_0_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 32, false>::kMinBlocksPerSm) +fna2_64x64x32_sm80_bfloat16_cm_0_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 32, false>::kMinBlocksPerSm) +fna2_64x128x32_sm80_bfloat16_cm_0_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 64, false>::kMinBlocksPerSm) +fna2_32x128x64_sm80_bfloat16_cm_0_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 64, false>::kMinBlocksPerSm) +fna2_64x64x64_sm80_bfloat16_cm_0_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 64, false>::kMinBlocksPerSm) +fna2_64x128x64_sm80_bfloat16_cm_0_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 128, false>::kMinBlocksPerSm) +fna2_32x128x128_sm80_bfloat16_cm_0_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 128, false>::kMinBlocksPerSm) +fna2_64x64x128_sm80_bfloat16_cm_0_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 128, false>::kMinBlocksPerSm) +fna2_64x128x128_sm80_bfloat16_cm_0_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 65536, false>::kMinBlocksPerSm) +fna2_32x128x65536_sm80_bfloat16_cm_0_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 65536, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 65536, false>::kMinBlocksPerSm) +fna2_64x128x65536_sm80_bfloat16_cm_0_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 65536, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 65536, false>::kMinBlocksPerSm) +fna2_64x64x65536_sm80_bfloat16_cm_0_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 65536, false>::Params p); + + +template +void fna2_sm80_bfloat16_cm_0_0(T cb) { + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 32, false>(), fna2_32x128x32_sm80_bfloat16_cm_0_0); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 32, false>(), fna2_64x64x32_sm80_bfloat16_cm_0_0); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 32, false>(), fna2_64x128x32_sm80_bfloat16_cm_0_0); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 64, false>(), fna2_32x128x64_sm80_bfloat16_cm_0_0); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 64, false>(), fna2_64x64x64_sm80_bfloat16_cm_0_0); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 64, false>(), fna2_64x128x64_sm80_bfloat16_cm_0_0); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 128, false>(), fna2_32x128x128_sm80_bfloat16_cm_0_0); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 128, false>(), fna2_64x64x128_sm80_bfloat16_cm_0_0); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 128, false>(), fna2_64x128x128_sm80_bfloat16_cm_0_0); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 65536, false>(), fna2_32x128x65536_sm80_bfloat16_cm_0_0); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 65536, false>(), fna2_64x128x65536_sm80_bfloat16_cm_0_0); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 65536, false>(), fna2_64x64x65536_sm80_bfloat16_cm_0_0); +} + +/////////////////////////////////////////////////////////////////// +// FNA-2D / bfloat16 / SM80 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 32, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 32, true>::kMinBlocksPerSm) +fna2_32x128x32_sm80_bfloat16_cm_0_0_rpb(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 32, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 32, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 32, true>::kMinBlocksPerSm) +fna2_64x64x32_sm80_bfloat16_cm_0_0_rpb(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 32, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 32, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 32, true>::kMinBlocksPerSm) +fna2_64x128x32_sm80_bfloat16_cm_0_0_rpb(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 32, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 64, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 64, true>::kMinBlocksPerSm) +fna2_32x128x64_sm80_bfloat16_cm_0_0_rpb(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 64, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 64, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 64, true>::kMinBlocksPerSm) +fna2_64x64x64_sm80_bfloat16_cm_0_0_rpb(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 64, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 64, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 64, true>::kMinBlocksPerSm) +fna2_64x128x64_sm80_bfloat16_cm_0_0_rpb(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 64, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 128, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 128, true>::kMinBlocksPerSm) +fna2_32x128x128_sm80_bfloat16_cm_0_0_rpb(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 128, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 128, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 128, true>::kMinBlocksPerSm) +fna2_64x64x128_sm80_bfloat16_cm_0_0_rpb(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 128, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 128, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 128, true>::kMinBlocksPerSm) +fna2_64x128x128_sm80_bfloat16_cm_0_0_rpb(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 128, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 65536, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 65536, true>::kMinBlocksPerSm) +fna2_32x128x65536_sm80_bfloat16_cm_0_0_rpb(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 65536, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 65536, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 65536, true>::kMinBlocksPerSm) +fna2_64x128x65536_sm80_bfloat16_cm_0_0_rpb(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 65536, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 65536, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 65536, true>::kMinBlocksPerSm) +fna2_64x64x65536_sm80_bfloat16_cm_0_0_rpb(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 65536, true>::Params p); + + +template +void fna2_sm80_bfloat16_cm_0_0_rpb(T cb) { + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 32, true>(), fna2_32x128x32_sm80_bfloat16_cm_0_0_rpb); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 32, true>(), fna2_64x64x32_sm80_bfloat16_cm_0_0_rpb); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 32, true>(), fna2_64x128x32_sm80_bfloat16_cm_0_0_rpb); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 64, true>(), fna2_32x128x64_sm80_bfloat16_cm_0_0_rpb); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 64, true>(), fna2_64x64x64_sm80_bfloat16_cm_0_0_rpb); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 64, true>(), fna2_64x128x64_sm80_bfloat16_cm_0_0_rpb); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 128, true>(), fna2_32x128x128_sm80_bfloat16_cm_0_0_rpb); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 128, true>(), fna2_64x64x128_sm80_bfloat16_cm_0_0_rpb); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 128, true>(), fna2_64x128x128_sm80_bfloat16_cm_0_0_rpb); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 65536, true>(), fna2_32x128x65536_sm80_bfloat16_cm_0_0_rpb); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 65536, true>(), fna2_64x128x65536_sm80_bfloat16_cm_0_0_rpb); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 65536, true>(), fna2_64x64x65536_sm80_bfloat16_cm_0_0_rpb); +} + +/////////////////////////////////////////////////////////////////// +// FNA-2D / bfloat16 / SM80 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 32, false>::kMinBlocksPerSm) +fna2_32x128x32_sm80_bfloat16_cm_0_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 32, false>::kMinBlocksPerSm) +fna2_64x64x32_sm80_bfloat16_cm_0_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 32, false>::kMinBlocksPerSm) +fna2_64x128x32_sm80_bfloat16_cm_0_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 64, false>::kMinBlocksPerSm) +fna2_32x128x64_sm80_bfloat16_cm_0_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 64, false>::kMinBlocksPerSm) +fna2_64x64x64_sm80_bfloat16_cm_0_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 64, false>::kMinBlocksPerSm) +fna2_64x128x64_sm80_bfloat16_cm_0_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 128, false>::kMinBlocksPerSm) +fna2_32x128x128_sm80_bfloat16_cm_0_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 128, false>::kMinBlocksPerSm) +fna2_64x64x128_sm80_bfloat16_cm_0_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 128, false>::kMinBlocksPerSm) +fna2_64x128x128_sm80_bfloat16_cm_0_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 65536, false>::kMinBlocksPerSm) +fna2_32x128x65536_sm80_bfloat16_cm_0_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 65536, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 65536, false>::kMinBlocksPerSm) +fna2_64x128x65536_sm80_bfloat16_cm_0_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 65536, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 65536, false>::kMinBlocksPerSm) +fna2_64x64x65536_sm80_bfloat16_cm_0_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 65536, false>::Params p); + + +template +void fna2_sm80_bfloat16_cm_0_1(T cb) { + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 32, false>(), fna2_32x128x32_sm80_bfloat16_cm_0_1); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 32, false>(), fna2_64x64x32_sm80_bfloat16_cm_0_1); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 32, false>(), fna2_64x128x32_sm80_bfloat16_cm_0_1); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 64, false>(), fna2_32x128x64_sm80_bfloat16_cm_0_1); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 64, false>(), fna2_64x64x64_sm80_bfloat16_cm_0_1); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 64, false>(), fna2_64x128x64_sm80_bfloat16_cm_0_1); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 128, false>(), fna2_32x128x128_sm80_bfloat16_cm_0_1); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 128, false>(), fna2_64x64x128_sm80_bfloat16_cm_0_1); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 128, false>(), fna2_64x128x128_sm80_bfloat16_cm_0_1); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 65536, false>(), fna2_32x128x65536_sm80_bfloat16_cm_0_1); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 65536, false>(), fna2_64x128x65536_sm80_bfloat16_cm_0_1); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 65536, false>(), fna2_64x64x65536_sm80_bfloat16_cm_0_1); +} + +/////////////////////////////////////////////////////////////////// +// FNA-2D / bfloat16 / SM80 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 32, false>::kMinBlocksPerSm) +fna2_32x128x32_sm80_bfloat16_cm_1_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 32, false>::kMinBlocksPerSm) +fna2_64x64x32_sm80_bfloat16_cm_1_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 32, false>::kMinBlocksPerSm) +fna2_64x128x32_sm80_bfloat16_cm_1_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 64, false>::kMinBlocksPerSm) +fna2_32x128x64_sm80_bfloat16_cm_1_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 64, false>::kMinBlocksPerSm) +fna2_64x64x64_sm80_bfloat16_cm_1_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 64, false>::kMinBlocksPerSm) +fna2_64x128x64_sm80_bfloat16_cm_1_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 128, false>::kMinBlocksPerSm) +fna2_32x128x128_sm80_bfloat16_cm_1_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 128, false>::kMinBlocksPerSm) +fna2_64x64x128_sm80_bfloat16_cm_1_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 128, false>::kMinBlocksPerSm) +fna2_64x128x128_sm80_bfloat16_cm_1_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 65536, false>::kMinBlocksPerSm) +fna2_32x128x65536_sm80_bfloat16_cm_1_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 65536, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 65536, false>::kMinBlocksPerSm) +fna2_64x128x65536_sm80_bfloat16_cm_1_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 65536, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 65536, false>::kMinBlocksPerSm) +fna2_64x64x65536_sm80_bfloat16_cm_1_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 65536, false>::Params p); + + +template +void fna2_sm80_bfloat16_cm_1_0(T cb) { + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 32, false>(), fna2_32x128x32_sm80_bfloat16_cm_1_0); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 32, false>(), fna2_64x64x32_sm80_bfloat16_cm_1_0); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 32, false>(), fna2_64x128x32_sm80_bfloat16_cm_1_0); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 64, false>(), fna2_32x128x64_sm80_bfloat16_cm_1_0); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 64, false>(), fna2_64x64x64_sm80_bfloat16_cm_1_0); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 64, false>(), fna2_64x128x64_sm80_bfloat16_cm_1_0); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 128, false>(), fna2_32x128x128_sm80_bfloat16_cm_1_0); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 128, false>(), fna2_64x64x128_sm80_bfloat16_cm_1_0); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 128, false>(), fna2_64x128x128_sm80_bfloat16_cm_1_0); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 65536, false>(), fna2_32x128x65536_sm80_bfloat16_cm_1_0); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 65536, false>(), fna2_64x128x65536_sm80_bfloat16_cm_1_0); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 65536, false>(), fna2_64x64x65536_sm80_bfloat16_cm_1_0); +} + +/////////////////////////////////////////////////////////////////// +// FNA-2D / bfloat16 / SM80 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 32, false>::kMinBlocksPerSm) +fna2_32x128x32_sm80_bfloat16_cm_1_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 32, false>::kMinBlocksPerSm) +fna2_64x64x32_sm80_bfloat16_cm_1_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 32, false>::kMinBlocksPerSm) +fna2_64x128x32_sm80_bfloat16_cm_1_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 64, false>::kMinBlocksPerSm) +fna2_32x128x64_sm80_bfloat16_cm_1_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 64, false>::kMinBlocksPerSm) +fna2_64x64x64_sm80_bfloat16_cm_1_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 64, false>::kMinBlocksPerSm) +fna2_64x128x64_sm80_bfloat16_cm_1_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 128, false>::kMinBlocksPerSm) +fna2_32x128x128_sm80_bfloat16_cm_1_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 128, false>::kMinBlocksPerSm) +fna2_64x64x128_sm80_bfloat16_cm_1_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 128, false>::kMinBlocksPerSm) +fna2_64x128x128_sm80_bfloat16_cm_1_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 65536, false>::kMinBlocksPerSm) +fna2_32x128x65536_sm80_bfloat16_cm_1_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 65536, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 65536, false>::kMinBlocksPerSm) +fna2_64x128x65536_sm80_bfloat16_cm_1_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 65536, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 65536, false>::kMinBlocksPerSm) +fna2_64x64x65536_sm80_bfloat16_cm_1_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 65536, false>::Params p); + + +template +void fna2_sm80_bfloat16_cm_1_1(T cb) { + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 32, false>(), fna2_32x128x32_sm80_bfloat16_cm_1_1); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 32, false>(), fna2_64x64x32_sm80_bfloat16_cm_1_1); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 32, false>(), fna2_64x128x32_sm80_bfloat16_cm_1_1); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 64, false>(), fna2_32x128x64_sm80_bfloat16_cm_1_1); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 64, false>(), fna2_64x64x64_sm80_bfloat16_cm_1_1); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 64, false>(), fna2_64x128x64_sm80_bfloat16_cm_1_1); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 128, false>(), fna2_32x128x128_sm80_bfloat16_cm_1_1); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 128, false>(), fna2_64x64x128_sm80_bfloat16_cm_1_1); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 128, false>(), fna2_64x128x128_sm80_bfloat16_cm_1_1); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 65536, false>(), fna2_32x128x65536_sm80_bfloat16_cm_1_1); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 65536, false>(), fna2_64x128x65536_sm80_bfloat16_cm_1_1); + cb(FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 65536, false>(), fna2_64x64x65536_sm80_bfloat16_cm_1_1); +} + +/////////////////////////////////////////////////////////////////// +// FNA-3D / float32 / SM50 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 32, false>::kMinBlocksPerSm) +fna3_32x128x32_sm50_float32_cm_0_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 32, false>::kMinBlocksPerSm) +fna3_64x64x32_sm50_float32_cm_0_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 32, false>::kMinBlocksPerSm) +fna3_64x128x32_sm50_float32_cm_0_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 64, false>::kMinBlocksPerSm) +fna3_32x128x64_sm50_float32_cm_0_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 64, false>::kMinBlocksPerSm) +fna3_64x64x64_sm50_float32_cm_0_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 64, false>::kMinBlocksPerSm) +fna3_64x128x64_sm50_float32_cm_0_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 128, false>::kMinBlocksPerSm) +fna3_32x128x128_sm50_float32_cm_0_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 128, false>::kMinBlocksPerSm) +fna3_64x64x128_sm50_float32_cm_0_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 128, false>::kMinBlocksPerSm) +fna3_64x128x128_sm50_float32_cm_0_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 65536, false>::kMinBlocksPerSm) +fna3_32x128x65536_sm50_float32_cm_0_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 65536, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 65536, false>::kMinBlocksPerSm) +fna3_64x128x65536_sm50_float32_cm_0_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 65536, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 65536, false>::kMinBlocksPerSm) +fna3_64x64x65536_sm50_float32_cm_0_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 65536, false>::Params p); + + +template +void fna3_sm50_float32_cm_0_0_0(T cb) { + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 32, false>(), fna3_32x128x32_sm50_float32_cm_0_0_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 32, false>(), fna3_64x64x32_sm50_float32_cm_0_0_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 32, false>(), fna3_64x128x32_sm50_float32_cm_0_0_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 64, false>(), fna3_32x128x64_sm50_float32_cm_0_0_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 64, false>(), fna3_64x64x64_sm50_float32_cm_0_0_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 64, false>(), fna3_64x128x64_sm50_float32_cm_0_0_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 128, false>(), fna3_32x128x128_sm50_float32_cm_0_0_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 128, false>(), fna3_64x64x128_sm50_float32_cm_0_0_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 128, false>(), fna3_64x128x128_sm50_float32_cm_0_0_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 65536, false>(), fna3_32x128x65536_sm50_float32_cm_0_0_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 65536, false>(), fna3_64x128x65536_sm50_float32_cm_0_0_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 65536, false>(), fna3_64x64x65536_sm50_float32_cm_0_0_0); +} + +/////////////////////////////////////////////////////////////////// +// FNA-3D / float32 / SM50 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 32, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 32, true>::kMinBlocksPerSm) +fna3_32x128x32_sm50_float32_cm_0_0_0_rpb(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 32, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 32, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 32, true>::kMinBlocksPerSm) +fna3_64x64x32_sm50_float32_cm_0_0_0_rpb(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 32, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 32, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 32, true>::kMinBlocksPerSm) +fna3_64x128x32_sm50_float32_cm_0_0_0_rpb(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 32, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 64, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 64, true>::kMinBlocksPerSm) +fna3_32x128x64_sm50_float32_cm_0_0_0_rpb(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 64, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 64, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 64, true>::kMinBlocksPerSm) +fna3_64x64x64_sm50_float32_cm_0_0_0_rpb(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 64, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 64, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 64, true>::kMinBlocksPerSm) +fna3_64x128x64_sm50_float32_cm_0_0_0_rpb(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 64, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 128, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 128, true>::kMinBlocksPerSm) +fna3_32x128x128_sm50_float32_cm_0_0_0_rpb(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 128, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 128, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 128, true>::kMinBlocksPerSm) +fna3_64x64x128_sm50_float32_cm_0_0_0_rpb(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 128, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 128, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 128, true>::kMinBlocksPerSm) +fna3_64x128x128_sm50_float32_cm_0_0_0_rpb(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 128, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 65536, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 65536, true>::kMinBlocksPerSm) +fna3_32x128x65536_sm50_float32_cm_0_0_0_rpb(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 65536, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 65536, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 65536, true>::kMinBlocksPerSm) +fna3_64x128x65536_sm50_float32_cm_0_0_0_rpb(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 65536, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 65536, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 65536, true>::kMinBlocksPerSm) +fna3_64x64x65536_sm50_float32_cm_0_0_0_rpb(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 65536, true>::Params p); + + +template +void fna3_sm50_float32_cm_0_0_0_rpb(T cb) { + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 32, true>(), fna3_32x128x32_sm50_float32_cm_0_0_0_rpb); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 32, true>(), fna3_64x64x32_sm50_float32_cm_0_0_0_rpb); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 32, true>(), fna3_64x128x32_sm50_float32_cm_0_0_0_rpb); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 64, true>(), fna3_32x128x64_sm50_float32_cm_0_0_0_rpb); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 64, true>(), fna3_64x64x64_sm50_float32_cm_0_0_0_rpb); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 64, true>(), fna3_64x128x64_sm50_float32_cm_0_0_0_rpb); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 128, true>(), fna3_32x128x128_sm50_float32_cm_0_0_0_rpb); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 128, true>(), fna3_64x64x128_sm50_float32_cm_0_0_0_rpb); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 128, true>(), fna3_64x128x128_sm50_float32_cm_0_0_0_rpb); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 65536, true>(), fna3_32x128x65536_sm50_float32_cm_0_0_0_rpb); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 65536, true>(), fna3_64x128x65536_sm50_float32_cm_0_0_0_rpb); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 65536, true>(), fna3_64x64x65536_sm50_float32_cm_0_0_0_rpb); +} + +/////////////////////////////////////////////////////////////////// +// FNA-3D / float32 / SM50 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 32, false>::kMinBlocksPerSm) +fna3_32x128x32_sm50_float32_cm_0_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 32, false>::kMinBlocksPerSm) +fna3_64x64x32_sm50_float32_cm_0_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 32, false>::kMinBlocksPerSm) +fna3_64x128x32_sm50_float32_cm_0_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 64, false>::kMinBlocksPerSm) +fna3_32x128x64_sm50_float32_cm_0_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 64, false>::kMinBlocksPerSm) +fna3_64x64x64_sm50_float32_cm_0_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 64, false>::kMinBlocksPerSm) +fna3_64x128x64_sm50_float32_cm_0_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 128, false>::kMinBlocksPerSm) +fna3_32x128x128_sm50_float32_cm_0_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 128, false>::kMinBlocksPerSm) +fna3_64x64x128_sm50_float32_cm_0_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 128, false>::kMinBlocksPerSm) +fna3_64x128x128_sm50_float32_cm_0_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 65536, false>::kMinBlocksPerSm) +fna3_32x128x65536_sm50_float32_cm_0_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 65536, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 65536, false>::kMinBlocksPerSm) +fna3_64x128x65536_sm50_float32_cm_0_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 65536, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 65536, false>::kMinBlocksPerSm) +fna3_64x64x65536_sm50_float32_cm_0_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 65536, false>::Params p); + + +template +void fna3_sm50_float32_cm_0_0_1(T cb) { + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 32, false>(), fna3_32x128x32_sm50_float32_cm_0_0_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 32, false>(), fna3_64x64x32_sm50_float32_cm_0_0_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 32, false>(), fna3_64x128x32_sm50_float32_cm_0_0_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 64, false>(), fna3_32x128x64_sm50_float32_cm_0_0_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 64, false>(), fna3_64x64x64_sm50_float32_cm_0_0_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 64, false>(), fna3_64x128x64_sm50_float32_cm_0_0_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 128, false>(), fna3_32x128x128_sm50_float32_cm_0_0_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 128, false>(), fna3_64x64x128_sm50_float32_cm_0_0_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 128, false>(), fna3_64x128x128_sm50_float32_cm_0_0_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 65536, false>(), fna3_32x128x65536_sm50_float32_cm_0_0_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 65536, false>(), fna3_64x128x65536_sm50_float32_cm_0_0_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 65536, false>(), fna3_64x64x65536_sm50_float32_cm_0_0_1); +} + +/////////////////////////////////////////////////////////////////// +// FNA-3D / float32 / SM50 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 32, false>::kMinBlocksPerSm) +fna3_32x128x32_sm50_float32_cm_0_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 32, false>::kMinBlocksPerSm) +fna3_64x64x32_sm50_float32_cm_0_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 32, false>::kMinBlocksPerSm) +fna3_64x128x32_sm50_float32_cm_0_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 64, false>::kMinBlocksPerSm) +fna3_32x128x64_sm50_float32_cm_0_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 64, false>::kMinBlocksPerSm) +fna3_64x64x64_sm50_float32_cm_0_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 64, false>::kMinBlocksPerSm) +fna3_64x128x64_sm50_float32_cm_0_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 128, false>::kMinBlocksPerSm) +fna3_32x128x128_sm50_float32_cm_0_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 128, false>::kMinBlocksPerSm) +fna3_64x64x128_sm50_float32_cm_0_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 128, false>::kMinBlocksPerSm) +fna3_64x128x128_sm50_float32_cm_0_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 65536, false>::kMinBlocksPerSm) +fna3_32x128x65536_sm50_float32_cm_0_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 65536, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 65536, false>::kMinBlocksPerSm) +fna3_64x128x65536_sm50_float32_cm_0_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 65536, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 65536, false>::kMinBlocksPerSm) +fna3_64x64x65536_sm50_float32_cm_0_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 65536, false>::Params p); + + +template +void fna3_sm50_float32_cm_0_1_0(T cb) { + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 32, false>(), fna3_32x128x32_sm50_float32_cm_0_1_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 32, false>(), fna3_64x64x32_sm50_float32_cm_0_1_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 32, false>(), fna3_64x128x32_sm50_float32_cm_0_1_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 64, false>(), fna3_32x128x64_sm50_float32_cm_0_1_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 64, false>(), fna3_64x64x64_sm50_float32_cm_0_1_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 64, false>(), fna3_64x128x64_sm50_float32_cm_0_1_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 128, false>(), fna3_32x128x128_sm50_float32_cm_0_1_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 128, false>(), fna3_64x64x128_sm50_float32_cm_0_1_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 128, false>(), fna3_64x128x128_sm50_float32_cm_0_1_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 65536, false>(), fna3_32x128x65536_sm50_float32_cm_0_1_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 65536, false>(), fna3_64x128x65536_sm50_float32_cm_0_1_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 65536, false>(), fna3_64x64x65536_sm50_float32_cm_0_1_0); +} + +/////////////////////////////////////////////////////////////////// +// FNA-3D / float32 / SM50 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 32, false>::kMinBlocksPerSm) +fna3_32x128x32_sm50_float32_cm_0_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 32, false>::kMinBlocksPerSm) +fna3_64x64x32_sm50_float32_cm_0_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 32, false>::kMinBlocksPerSm) +fna3_64x128x32_sm50_float32_cm_0_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 64, false>::kMinBlocksPerSm) +fna3_32x128x64_sm50_float32_cm_0_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 64, false>::kMinBlocksPerSm) +fna3_64x64x64_sm50_float32_cm_0_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 64, false>::kMinBlocksPerSm) +fna3_64x128x64_sm50_float32_cm_0_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 128, false>::kMinBlocksPerSm) +fna3_32x128x128_sm50_float32_cm_0_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 128, false>::kMinBlocksPerSm) +fna3_64x64x128_sm50_float32_cm_0_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 128, false>::kMinBlocksPerSm) +fna3_64x128x128_sm50_float32_cm_0_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 65536, false>::kMinBlocksPerSm) +fna3_32x128x65536_sm50_float32_cm_0_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 65536, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 65536, false>::kMinBlocksPerSm) +fna3_64x128x65536_sm50_float32_cm_0_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 65536, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 65536, false>::kMinBlocksPerSm) +fna3_64x64x65536_sm50_float32_cm_0_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 65536, false>::Params p); + + +template +void fna3_sm50_float32_cm_0_1_1(T cb) { + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 32, false>(), fna3_32x128x32_sm50_float32_cm_0_1_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 32, false>(), fna3_64x64x32_sm50_float32_cm_0_1_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 32, false>(), fna3_64x128x32_sm50_float32_cm_0_1_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 64, false>(), fna3_32x128x64_sm50_float32_cm_0_1_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 64, false>(), fna3_64x64x64_sm50_float32_cm_0_1_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 64, false>(), fna3_64x128x64_sm50_float32_cm_0_1_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 128, false>(), fna3_32x128x128_sm50_float32_cm_0_1_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 128, false>(), fna3_64x64x128_sm50_float32_cm_0_1_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 128, false>(), fna3_64x128x128_sm50_float32_cm_0_1_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 65536, false>(), fna3_32x128x65536_sm50_float32_cm_0_1_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 65536, false>(), fna3_64x128x65536_sm50_float32_cm_0_1_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 65536, false>(), fna3_64x64x65536_sm50_float32_cm_0_1_1); +} + +/////////////////////////////////////////////////////////////////// +// FNA-3D / float32 / SM50 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 32, false>::kMinBlocksPerSm) +fna3_32x128x32_sm50_float32_cm_1_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 32, false>::kMinBlocksPerSm) +fna3_64x64x32_sm50_float32_cm_1_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 32, false>::kMinBlocksPerSm) +fna3_64x128x32_sm50_float32_cm_1_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 64, false>::kMinBlocksPerSm) +fna3_32x128x64_sm50_float32_cm_1_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 64, false>::kMinBlocksPerSm) +fna3_64x64x64_sm50_float32_cm_1_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 64, false>::kMinBlocksPerSm) +fna3_64x128x64_sm50_float32_cm_1_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 128, false>::kMinBlocksPerSm) +fna3_32x128x128_sm50_float32_cm_1_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 128, false>::kMinBlocksPerSm) +fna3_64x64x128_sm50_float32_cm_1_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 128, false>::kMinBlocksPerSm) +fna3_64x128x128_sm50_float32_cm_1_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 65536, false>::kMinBlocksPerSm) +fna3_32x128x65536_sm50_float32_cm_1_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 65536, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 65536, false>::kMinBlocksPerSm) +fna3_64x128x65536_sm50_float32_cm_1_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 65536, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 65536, false>::kMinBlocksPerSm) +fna3_64x64x65536_sm50_float32_cm_1_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 65536, false>::Params p); + + +template +void fna3_sm50_float32_cm_1_0_0(T cb) { + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 32, false>(), fna3_32x128x32_sm50_float32_cm_1_0_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 32, false>(), fna3_64x64x32_sm50_float32_cm_1_0_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 32, false>(), fna3_64x128x32_sm50_float32_cm_1_0_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 64, false>(), fna3_32x128x64_sm50_float32_cm_1_0_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 64, false>(), fna3_64x64x64_sm50_float32_cm_1_0_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 64, false>(), fna3_64x128x64_sm50_float32_cm_1_0_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 128, false>(), fna3_32x128x128_sm50_float32_cm_1_0_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 128, false>(), fna3_64x64x128_sm50_float32_cm_1_0_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 128, false>(), fna3_64x128x128_sm50_float32_cm_1_0_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 65536, false>(), fna3_32x128x65536_sm50_float32_cm_1_0_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 65536, false>(), fna3_64x128x65536_sm50_float32_cm_1_0_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 65536, false>(), fna3_64x64x65536_sm50_float32_cm_1_0_0); +} + +/////////////////////////////////////////////////////////////////// +// FNA-3D / float32 / SM50 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 32, false>::kMinBlocksPerSm) +fna3_32x128x32_sm50_float32_cm_1_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 32, false>::kMinBlocksPerSm) +fna3_64x64x32_sm50_float32_cm_1_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 32, false>::kMinBlocksPerSm) +fna3_64x128x32_sm50_float32_cm_1_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 64, false>::kMinBlocksPerSm) +fna3_32x128x64_sm50_float32_cm_1_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 64, false>::kMinBlocksPerSm) +fna3_64x64x64_sm50_float32_cm_1_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 64, false>::kMinBlocksPerSm) +fna3_64x128x64_sm50_float32_cm_1_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 128, false>::kMinBlocksPerSm) +fna3_32x128x128_sm50_float32_cm_1_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 128, false>::kMinBlocksPerSm) +fna3_64x64x128_sm50_float32_cm_1_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 128, false>::kMinBlocksPerSm) +fna3_64x128x128_sm50_float32_cm_1_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 65536, false>::kMinBlocksPerSm) +fna3_32x128x65536_sm50_float32_cm_1_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 65536, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 65536, false>::kMinBlocksPerSm) +fna3_64x128x65536_sm50_float32_cm_1_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 65536, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 65536, false>::kMinBlocksPerSm) +fna3_64x64x65536_sm50_float32_cm_1_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 65536, false>::Params p); + + +template +void fna3_sm50_float32_cm_1_0_1(T cb) { + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 32, false>(), fna3_32x128x32_sm50_float32_cm_1_0_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 32, false>(), fna3_64x64x32_sm50_float32_cm_1_0_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 32, false>(), fna3_64x128x32_sm50_float32_cm_1_0_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 64, false>(), fna3_32x128x64_sm50_float32_cm_1_0_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 64, false>(), fna3_64x64x64_sm50_float32_cm_1_0_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 64, false>(), fna3_64x128x64_sm50_float32_cm_1_0_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 128, false>(), fna3_32x128x128_sm50_float32_cm_1_0_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 128, false>(), fna3_64x64x128_sm50_float32_cm_1_0_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 128, false>(), fna3_64x128x128_sm50_float32_cm_1_0_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 65536, false>(), fna3_32x128x65536_sm50_float32_cm_1_0_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 65536, false>(), fna3_64x128x65536_sm50_float32_cm_1_0_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 65536, false>(), fna3_64x64x65536_sm50_float32_cm_1_0_1); +} + +/////////////////////////////////////////////////////////////////// +// FNA-3D / float32 / SM50 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 32, false>::kMinBlocksPerSm) +fna3_32x128x32_sm50_float32_cm_1_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 32, false>::kMinBlocksPerSm) +fna3_64x64x32_sm50_float32_cm_1_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 32, false>::kMinBlocksPerSm) +fna3_64x128x32_sm50_float32_cm_1_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 64, false>::kMinBlocksPerSm) +fna3_32x128x64_sm50_float32_cm_1_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 64, false>::kMinBlocksPerSm) +fna3_64x64x64_sm50_float32_cm_1_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 64, false>::kMinBlocksPerSm) +fna3_64x128x64_sm50_float32_cm_1_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 128, false>::kMinBlocksPerSm) +fna3_32x128x128_sm50_float32_cm_1_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 128, false>::kMinBlocksPerSm) +fna3_64x64x128_sm50_float32_cm_1_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 128, false>::kMinBlocksPerSm) +fna3_64x128x128_sm50_float32_cm_1_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 65536, false>::kMinBlocksPerSm) +fna3_32x128x65536_sm50_float32_cm_1_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 65536, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 65536, false>::kMinBlocksPerSm) +fna3_64x128x65536_sm50_float32_cm_1_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 65536, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 65536, false>::kMinBlocksPerSm) +fna3_64x64x65536_sm50_float32_cm_1_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 65536, false>::Params p); + + +template +void fna3_sm50_float32_cm_1_1_0(T cb) { + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 32, false>(), fna3_32x128x32_sm50_float32_cm_1_1_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 32, false>(), fna3_64x64x32_sm50_float32_cm_1_1_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 32, false>(), fna3_64x128x32_sm50_float32_cm_1_1_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 64, false>(), fna3_32x128x64_sm50_float32_cm_1_1_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 64, false>(), fna3_64x64x64_sm50_float32_cm_1_1_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 64, false>(), fna3_64x128x64_sm50_float32_cm_1_1_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 128, false>(), fna3_32x128x128_sm50_float32_cm_1_1_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 128, false>(), fna3_64x64x128_sm50_float32_cm_1_1_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 128, false>(), fna3_64x128x128_sm50_float32_cm_1_1_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 65536, false>(), fna3_32x128x65536_sm50_float32_cm_1_1_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 65536, false>(), fna3_64x128x65536_sm50_float32_cm_1_1_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 65536, false>(), fna3_64x64x65536_sm50_float32_cm_1_1_0); +} + +/////////////////////////////////////////////////////////////////// +// FNA-3D / float32 / SM50 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 32, false>::kMinBlocksPerSm) +fna3_32x128x32_sm50_float32_cm_1_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 32, false>::kMinBlocksPerSm) +fna3_64x64x32_sm50_float32_cm_1_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 32, false>::kMinBlocksPerSm) +fna3_64x128x32_sm50_float32_cm_1_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 64, false>::kMinBlocksPerSm) +fna3_32x128x64_sm50_float32_cm_1_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 64, false>::kMinBlocksPerSm) +fna3_64x64x64_sm50_float32_cm_1_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 64, false>::kMinBlocksPerSm) +fna3_64x128x64_sm50_float32_cm_1_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 128, false>::kMinBlocksPerSm) +fna3_32x128x128_sm50_float32_cm_1_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 128, false>::kMinBlocksPerSm) +fna3_64x64x128_sm50_float32_cm_1_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 128, false>::kMinBlocksPerSm) +fna3_64x128x128_sm50_float32_cm_1_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 65536, false>::kMinBlocksPerSm) +fna3_32x128x65536_sm50_float32_cm_1_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 65536, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 65536, false>::kMinBlocksPerSm) +fna3_64x128x65536_sm50_float32_cm_1_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 65536, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 65536, false>::kMinBlocksPerSm) +fna3_64x64x65536_sm50_float32_cm_1_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 65536, false>::Params p); + + +template +void fna3_sm50_float32_cm_1_1_1(T cb) { + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 32, false>(), fna3_32x128x32_sm50_float32_cm_1_1_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 32, false>(), fna3_64x64x32_sm50_float32_cm_1_1_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 32, false>(), fna3_64x128x32_sm50_float32_cm_1_1_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 64, false>(), fna3_32x128x64_sm50_float32_cm_1_1_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 64, false>(), fna3_64x64x64_sm50_float32_cm_1_1_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 64, false>(), fna3_64x128x64_sm50_float32_cm_1_1_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 128, false>(), fna3_32x128x128_sm50_float32_cm_1_1_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 128, false>(), fna3_64x64x128_sm50_float32_cm_1_1_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 128, false>(), fna3_64x128x128_sm50_float32_cm_1_1_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 65536, false>(), fna3_32x128x65536_sm50_float32_cm_1_1_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 65536, false>(), fna3_64x128x65536_sm50_float32_cm_1_1_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 65536, false>(), fna3_64x64x65536_sm50_float32_cm_1_1_1); +} + +/////////////////////////////////////////////////////////////////// +// FNA-3D / float16 / SM50 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 32, false>::kMinBlocksPerSm) +fna3_32x128x32_sm50_float16_cm_0_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 32, false>::kMinBlocksPerSm) +fna3_64x64x32_sm50_float16_cm_0_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 32, false>::kMinBlocksPerSm) +fna3_64x128x32_sm50_float16_cm_0_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 64, false>::kMinBlocksPerSm) +fna3_32x128x64_sm50_float16_cm_0_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 64, false>::kMinBlocksPerSm) +fna3_64x64x64_sm50_float16_cm_0_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 64, false>::kMinBlocksPerSm) +fna3_64x128x64_sm50_float16_cm_0_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 128, false>::kMinBlocksPerSm) +fna3_32x128x128_sm50_float16_cm_0_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 128, false>::kMinBlocksPerSm) +fna3_64x64x128_sm50_float16_cm_0_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 128, false>::kMinBlocksPerSm) +fna3_64x128x128_sm50_float16_cm_0_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 65536, false>::kMinBlocksPerSm) +fna3_32x128x65536_sm50_float16_cm_0_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 65536, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 65536, false>::kMinBlocksPerSm) +fna3_64x128x65536_sm50_float16_cm_0_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 65536, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 65536, false>::kMinBlocksPerSm) +fna3_64x64x65536_sm50_float16_cm_0_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 65536, false>::Params p); + + +template +void fna3_sm50_float16_cm_0_0_0(T cb) { + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 32, false>(), fna3_32x128x32_sm50_float16_cm_0_0_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 32, false>(), fna3_64x64x32_sm50_float16_cm_0_0_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 32, false>(), fna3_64x128x32_sm50_float16_cm_0_0_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 64, false>(), fna3_32x128x64_sm50_float16_cm_0_0_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 64, false>(), fna3_64x64x64_sm50_float16_cm_0_0_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 64, false>(), fna3_64x128x64_sm50_float16_cm_0_0_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 128, false>(), fna3_32x128x128_sm50_float16_cm_0_0_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 128, false>(), fna3_64x64x128_sm50_float16_cm_0_0_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 128, false>(), fna3_64x128x128_sm50_float16_cm_0_0_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 65536, false>(), fna3_32x128x65536_sm50_float16_cm_0_0_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 65536, false>(), fna3_64x128x65536_sm50_float16_cm_0_0_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 65536, false>(), fna3_64x64x65536_sm50_float16_cm_0_0_0); +} + +/////////////////////////////////////////////////////////////////// +// FNA-3D / float16 / SM50 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 32, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 32, true>::kMinBlocksPerSm) +fna3_32x128x32_sm50_float16_cm_0_0_0_rpb(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 32, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 32, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 32, true>::kMinBlocksPerSm) +fna3_64x64x32_sm50_float16_cm_0_0_0_rpb(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 32, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 32, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 32, true>::kMinBlocksPerSm) +fna3_64x128x32_sm50_float16_cm_0_0_0_rpb(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 32, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 64, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 64, true>::kMinBlocksPerSm) +fna3_32x128x64_sm50_float16_cm_0_0_0_rpb(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 64, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 64, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 64, true>::kMinBlocksPerSm) +fna3_64x64x64_sm50_float16_cm_0_0_0_rpb(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 64, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 64, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 64, true>::kMinBlocksPerSm) +fna3_64x128x64_sm50_float16_cm_0_0_0_rpb(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 64, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 128, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 128, true>::kMinBlocksPerSm) +fna3_32x128x128_sm50_float16_cm_0_0_0_rpb(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 128, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 128, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 128, true>::kMinBlocksPerSm) +fna3_64x64x128_sm50_float16_cm_0_0_0_rpb(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 128, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 128, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 128, true>::kMinBlocksPerSm) +fna3_64x128x128_sm50_float16_cm_0_0_0_rpb(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 128, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 65536, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 65536, true>::kMinBlocksPerSm) +fna3_32x128x65536_sm50_float16_cm_0_0_0_rpb(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 65536, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 65536, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 65536, true>::kMinBlocksPerSm) +fna3_64x128x65536_sm50_float16_cm_0_0_0_rpb(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 65536, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 65536, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 65536, true>::kMinBlocksPerSm) +fna3_64x64x65536_sm50_float16_cm_0_0_0_rpb(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 65536, true>::Params p); + + +template +void fna3_sm50_float16_cm_0_0_0_rpb(T cb) { + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 32, true>(), fna3_32x128x32_sm50_float16_cm_0_0_0_rpb); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 32, true>(), fna3_64x64x32_sm50_float16_cm_0_0_0_rpb); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 32, true>(), fna3_64x128x32_sm50_float16_cm_0_0_0_rpb); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 64, true>(), fna3_32x128x64_sm50_float16_cm_0_0_0_rpb); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 64, true>(), fna3_64x64x64_sm50_float16_cm_0_0_0_rpb); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 64, true>(), fna3_64x128x64_sm50_float16_cm_0_0_0_rpb); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 128, true>(), fna3_32x128x128_sm50_float16_cm_0_0_0_rpb); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 128, true>(), fna3_64x64x128_sm50_float16_cm_0_0_0_rpb); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 128, true>(), fna3_64x128x128_sm50_float16_cm_0_0_0_rpb); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 65536, true>(), fna3_32x128x65536_sm50_float16_cm_0_0_0_rpb); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 65536, true>(), fna3_64x128x65536_sm50_float16_cm_0_0_0_rpb); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 65536, true>(), fna3_64x64x65536_sm50_float16_cm_0_0_0_rpb); +} + +/////////////////////////////////////////////////////////////////// +// FNA-3D / float16 / SM50 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 32, false>::kMinBlocksPerSm) +fna3_32x128x32_sm50_float16_cm_0_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 32, false>::kMinBlocksPerSm) +fna3_64x64x32_sm50_float16_cm_0_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 32, false>::kMinBlocksPerSm) +fna3_64x128x32_sm50_float16_cm_0_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 64, false>::kMinBlocksPerSm) +fna3_32x128x64_sm50_float16_cm_0_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 64, false>::kMinBlocksPerSm) +fna3_64x64x64_sm50_float16_cm_0_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 64, false>::kMinBlocksPerSm) +fna3_64x128x64_sm50_float16_cm_0_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 128, false>::kMinBlocksPerSm) +fna3_32x128x128_sm50_float16_cm_0_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 128, false>::kMinBlocksPerSm) +fna3_64x64x128_sm50_float16_cm_0_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 128, false>::kMinBlocksPerSm) +fna3_64x128x128_sm50_float16_cm_0_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 65536, false>::kMinBlocksPerSm) +fna3_32x128x65536_sm50_float16_cm_0_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 65536, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 65536, false>::kMinBlocksPerSm) +fna3_64x128x65536_sm50_float16_cm_0_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 65536, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 65536, false>::kMinBlocksPerSm) +fna3_64x64x65536_sm50_float16_cm_0_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 65536, false>::Params p); + + +template +void fna3_sm50_float16_cm_0_0_1(T cb) { + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 32, false>(), fna3_32x128x32_sm50_float16_cm_0_0_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 32, false>(), fna3_64x64x32_sm50_float16_cm_0_0_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 32, false>(), fna3_64x128x32_sm50_float16_cm_0_0_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 64, false>(), fna3_32x128x64_sm50_float16_cm_0_0_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 64, false>(), fna3_64x64x64_sm50_float16_cm_0_0_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 64, false>(), fna3_64x128x64_sm50_float16_cm_0_0_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 128, false>(), fna3_32x128x128_sm50_float16_cm_0_0_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 128, false>(), fna3_64x64x128_sm50_float16_cm_0_0_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 128, false>(), fna3_64x128x128_sm50_float16_cm_0_0_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 65536, false>(), fna3_32x128x65536_sm50_float16_cm_0_0_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 65536, false>(), fna3_64x128x65536_sm50_float16_cm_0_0_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 65536, false>(), fna3_64x64x65536_sm50_float16_cm_0_0_1); +} + +/////////////////////////////////////////////////////////////////// +// FNA-3D / float16 / SM50 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 32, false>::kMinBlocksPerSm) +fna3_32x128x32_sm50_float16_cm_0_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 32, false>::kMinBlocksPerSm) +fna3_64x64x32_sm50_float16_cm_0_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 32, false>::kMinBlocksPerSm) +fna3_64x128x32_sm50_float16_cm_0_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 64, false>::kMinBlocksPerSm) +fna3_32x128x64_sm50_float16_cm_0_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 64, false>::kMinBlocksPerSm) +fna3_64x64x64_sm50_float16_cm_0_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 64, false>::kMinBlocksPerSm) +fna3_64x128x64_sm50_float16_cm_0_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 128, false>::kMinBlocksPerSm) +fna3_32x128x128_sm50_float16_cm_0_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 128, false>::kMinBlocksPerSm) +fna3_64x64x128_sm50_float16_cm_0_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 128, false>::kMinBlocksPerSm) +fna3_64x128x128_sm50_float16_cm_0_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 65536, false>::kMinBlocksPerSm) +fna3_32x128x65536_sm50_float16_cm_0_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 65536, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 65536, false>::kMinBlocksPerSm) +fna3_64x128x65536_sm50_float16_cm_0_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 65536, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 65536, false>::kMinBlocksPerSm) +fna3_64x64x65536_sm50_float16_cm_0_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 65536, false>::Params p); + + +template +void fna3_sm50_float16_cm_0_1_0(T cb) { + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 32, false>(), fna3_32x128x32_sm50_float16_cm_0_1_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 32, false>(), fna3_64x64x32_sm50_float16_cm_0_1_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 32, false>(), fna3_64x128x32_sm50_float16_cm_0_1_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 64, false>(), fna3_32x128x64_sm50_float16_cm_0_1_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 64, false>(), fna3_64x64x64_sm50_float16_cm_0_1_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 64, false>(), fna3_64x128x64_sm50_float16_cm_0_1_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 128, false>(), fna3_32x128x128_sm50_float16_cm_0_1_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 128, false>(), fna3_64x64x128_sm50_float16_cm_0_1_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 128, false>(), fna3_64x128x128_sm50_float16_cm_0_1_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 65536, false>(), fna3_32x128x65536_sm50_float16_cm_0_1_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 65536, false>(), fna3_64x128x65536_sm50_float16_cm_0_1_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 65536, false>(), fna3_64x64x65536_sm50_float16_cm_0_1_0); +} + +/////////////////////////////////////////////////////////////////// +// FNA-3D / float16 / SM50 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 32, false>::kMinBlocksPerSm) +fna3_32x128x32_sm50_float16_cm_0_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 32, false>::kMinBlocksPerSm) +fna3_64x64x32_sm50_float16_cm_0_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 32, false>::kMinBlocksPerSm) +fna3_64x128x32_sm50_float16_cm_0_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 64, false>::kMinBlocksPerSm) +fna3_32x128x64_sm50_float16_cm_0_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 64, false>::kMinBlocksPerSm) +fna3_64x64x64_sm50_float16_cm_0_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 64, false>::kMinBlocksPerSm) +fna3_64x128x64_sm50_float16_cm_0_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 128, false>::kMinBlocksPerSm) +fna3_32x128x128_sm50_float16_cm_0_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 128, false>::kMinBlocksPerSm) +fna3_64x64x128_sm50_float16_cm_0_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 128, false>::kMinBlocksPerSm) +fna3_64x128x128_sm50_float16_cm_0_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 65536, false>::kMinBlocksPerSm) +fna3_32x128x65536_sm50_float16_cm_0_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 65536, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 65536, false>::kMinBlocksPerSm) +fna3_64x128x65536_sm50_float16_cm_0_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 65536, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 65536, false>::kMinBlocksPerSm) +fna3_64x64x65536_sm50_float16_cm_0_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 65536, false>::Params p); + + +template +void fna3_sm50_float16_cm_0_1_1(T cb) { + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 32, false>(), fna3_32x128x32_sm50_float16_cm_0_1_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 32, false>(), fna3_64x64x32_sm50_float16_cm_0_1_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 32, false>(), fna3_64x128x32_sm50_float16_cm_0_1_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 64, false>(), fna3_32x128x64_sm50_float16_cm_0_1_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 64, false>(), fna3_64x64x64_sm50_float16_cm_0_1_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 64, false>(), fna3_64x128x64_sm50_float16_cm_0_1_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 128, false>(), fna3_32x128x128_sm50_float16_cm_0_1_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 128, false>(), fna3_64x64x128_sm50_float16_cm_0_1_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 128, false>(), fna3_64x128x128_sm50_float16_cm_0_1_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 65536, false>(), fna3_32x128x65536_sm50_float16_cm_0_1_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 65536, false>(), fna3_64x128x65536_sm50_float16_cm_0_1_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 65536, false>(), fna3_64x64x65536_sm50_float16_cm_0_1_1); +} + +/////////////////////////////////////////////////////////////////// +// FNA-3D / float16 / SM50 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 32, false>::kMinBlocksPerSm) +fna3_32x128x32_sm50_float16_cm_1_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 32, false>::kMinBlocksPerSm) +fna3_64x64x32_sm50_float16_cm_1_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 32, false>::kMinBlocksPerSm) +fna3_64x128x32_sm50_float16_cm_1_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 64, false>::kMinBlocksPerSm) +fna3_32x128x64_sm50_float16_cm_1_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 64, false>::kMinBlocksPerSm) +fna3_64x64x64_sm50_float16_cm_1_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 64, false>::kMinBlocksPerSm) +fna3_64x128x64_sm50_float16_cm_1_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 128, false>::kMinBlocksPerSm) +fna3_32x128x128_sm50_float16_cm_1_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 128, false>::kMinBlocksPerSm) +fna3_64x64x128_sm50_float16_cm_1_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 128, false>::kMinBlocksPerSm) +fna3_64x128x128_sm50_float16_cm_1_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 65536, false>::kMinBlocksPerSm) +fna3_32x128x65536_sm50_float16_cm_1_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 65536, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 65536, false>::kMinBlocksPerSm) +fna3_64x128x65536_sm50_float16_cm_1_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 65536, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 65536, false>::kMinBlocksPerSm) +fna3_64x64x65536_sm50_float16_cm_1_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 65536, false>::Params p); + + +template +void fna3_sm50_float16_cm_1_0_0(T cb) { + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 32, false>(), fna3_32x128x32_sm50_float16_cm_1_0_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 32, false>(), fna3_64x64x32_sm50_float16_cm_1_0_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 32, false>(), fna3_64x128x32_sm50_float16_cm_1_0_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 64, false>(), fna3_32x128x64_sm50_float16_cm_1_0_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 64, false>(), fna3_64x64x64_sm50_float16_cm_1_0_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 64, false>(), fna3_64x128x64_sm50_float16_cm_1_0_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 128, false>(), fna3_32x128x128_sm50_float16_cm_1_0_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 128, false>(), fna3_64x64x128_sm50_float16_cm_1_0_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 128, false>(), fna3_64x128x128_sm50_float16_cm_1_0_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 65536, false>(), fna3_32x128x65536_sm50_float16_cm_1_0_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 65536, false>(), fna3_64x128x65536_sm50_float16_cm_1_0_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 65536, false>(), fna3_64x64x65536_sm50_float16_cm_1_0_0); +} + +/////////////////////////////////////////////////////////////////// +// FNA-3D / float16 / SM50 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 32, false>::kMinBlocksPerSm) +fna3_32x128x32_sm50_float16_cm_1_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 32, false>::kMinBlocksPerSm) +fna3_64x64x32_sm50_float16_cm_1_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 32, false>::kMinBlocksPerSm) +fna3_64x128x32_sm50_float16_cm_1_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 64, false>::kMinBlocksPerSm) +fna3_32x128x64_sm50_float16_cm_1_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 64, false>::kMinBlocksPerSm) +fna3_64x64x64_sm50_float16_cm_1_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 64, false>::kMinBlocksPerSm) +fna3_64x128x64_sm50_float16_cm_1_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 128, false>::kMinBlocksPerSm) +fna3_32x128x128_sm50_float16_cm_1_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 128, false>::kMinBlocksPerSm) +fna3_64x64x128_sm50_float16_cm_1_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 128, false>::kMinBlocksPerSm) +fna3_64x128x128_sm50_float16_cm_1_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 65536, false>::kMinBlocksPerSm) +fna3_32x128x65536_sm50_float16_cm_1_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 65536, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 65536, false>::kMinBlocksPerSm) +fna3_64x128x65536_sm50_float16_cm_1_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 65536, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 65536, false>::kMinBlocksPerSm) +fna3_64x64x65536_sm50_float16_cm_1_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 65536, false>::Params p); + + +template +void fna3_sm50_float16_cm_1_0_1(T cb) { + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 32, false>(), fna3_32x128x32_sm50_float16_cm_1_0_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 32, false>(), fna3_64x64x32_sm50_float16_cm_1_0_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 32, false>(), fna3_64x128x32_sm50_float16_cm_1_0_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 64, false>(), fna3_32x128x64_sm50_float16_cm_1_0_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 64, false>(), fna3_64x64x64_sm50_float16_cm_1_0_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 64, false>(), fna3_64x128x64_sm50_float16_cm_1_0_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 128, false>(), fna3_32x128x128_sm50_float16_cm_1_0_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 128, false>(), fna3_64x64x128_sm50_float16_cm_1_0_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 128, false>(), fna3_64x128x128_sm50_float16_cm_1_0_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 65536, false>(), fna3_32x128x65536_sm50_float16_cm_1_0_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 65536, false>(), fna3_64x128x65536_sm50_float16_cm_1_0_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 65536, false>(), fna3_64x64x65536_sm50_float16_cm_1_0_1); +} + +/////////////////////////////////////////////////////////////////// +// FNA-3D / float16 / SM50 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 32, false>::kMinBlocksPerSm) +fna3_32x128x32_sm50_float16_cm_1_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 32, false>::kMinBlocksPerSm) +fna3_64x64x32_sm50_float16_cm_1_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 32, false>::kMinBlocksPerSm) +fna3_64x128x32_sm50_float16_cm_1_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 64, false>::kMinBlocksPerSm) +fna3_32x128x64_sm50_float16_cm_1_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 64, false>::kMinBlocksPerSm) +fna3_64x64x64_sm50_float16_cm_1_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 64, false>::kMinBlocksPerSm) +fna3_64x128x64_sm50_float16_cm_1_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 128, false>::kMinBlocksPerSm) +fna3_32x128x128_sm50_float16_cm_1_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 128, false>::kMinBlocksPerSm) +fna3_64x64x128_sm50_float16_cm_1_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 128, false>::kMinBlocksPerSm) +fna3_64x128x128_sm50_float16_cm_1_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 65536, false>::kMinBlocksPerSm) +fna3_32x128x65536_sm50_float16_cm_1_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 65536, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 65536, false>::kMinBlocksPerSm) +fna3_64x128x65536_sm50_float16_cm_1_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 65536, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 65536, false>::kMinBlocksPerSm) +fna3_64x64x65536_sm50_float16_cm_1_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 65536, false>::Params p); + + +template +void fna3_sm50_float16_cm_1_1_0(T cb) { + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 32, false>(), fna3_32x128x32_sm50_float16_cm_1_1_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 32, false>(), fna3_64x64x32_sm50_float16_cm_1_1_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 32, false>(), fna3_64x128x32_sm50_float16_cm_1_1_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 64, false>(), fna3_32x128x64_sm50_float16_cm_1_1_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 64, false>(), fna3_64x64x64_sm50_float16_cm_1_1_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 64, false>(), fna3_64x128x64_sm50_float16_cm_1_1_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 128, false>(), fna3_32x128x128_sm50_float16_cm_1_1_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 128, false>(), fna3_64x64x128_sm50_float16_cm_1_1_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 128, false>(), fna3_64x128x128_sm50_float16_cm_1_1_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 65536, false>(), fna3_32x128x65536_sm50_float16_cm_1_1_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 65536, false>(), fna3_64x128x65536_sm50_float16_cm_1_1_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 65536, false>(), fna3_64x64x65536_sm50_float16_cm_1_1_0); +} + +/////////////////////////////////////////////////////////////////// +// FNA-3D / float16 / SM50 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 32, false>::kMinBlocksPerSm) +fna3_32x128x32_sm50_float16_cm_1_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 32, false>::kMinBlocksPerSm) +fna3_64x64x32_sm50_float16_cm_1_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 32, false>::kMinBlocksPerSm) +fna3_64x128x32_sm50_float16_cm_1_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 64, false>::kMinBlocksPerSm) +fna3_32x128x64_sm50_float16_cm_1_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 64, false>::kMinBlocksPerSm) +fna3_64x64x64_sm50_float16_cm_1_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 64, false>::kMinBlocksPerSm) +fna3_64x128x64_sm50_float16_cm_1_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 128, false>::kMinBlocksPerSm) +fna3_32x128x128_sm50_float16_cm_1_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 128, false>::kMinBlocksPerSm) +fna3_64x64x128_sm50_float16_cm_1_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 128, false>::kMinBlocksPerSm) +fna3_64x128x128_sm50_float16_cm_1_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 65536, false>::kMinBlocksPerSm) +fna3_32x128x65536_sm50_float16_cm_1_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 65536, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 65536, false>::kMinBlocksPerSm) +fna3_64x128x65536_sm50_float16_cm_1_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 65536, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 65536, false>::kMinBlocksPerSm) +fna3_64x64x65536_sm50_float16_cm_1_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 65536, false>::Params p); + + +template +void fna3_sm50_float16_cm_1_1_1(T cb) { + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 32, false>(), fna3_32x128x32_sm50_float16_cm_1_1_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 32, false>(), fna3_64x64x32_sm50_float16_cm_1_1_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 32, false>(), fna3_64x128x32_sm50_float16_cm_1_1_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 64, false>(), fna3_32x128x64_sm50_float16_cm_1_1_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 64, false>(), fna3_64x64x64_sm50_float16_cm_1_1_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 64, false>(), fna3_64x128x64_sm50_float16_cm_1_1_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 128, false>(), fna3_32x128x128_sm50_float16_cm_1_1_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 128, false>(), fna3_64x64x128_sm50_float16_cm_1_1_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 128, false>(), fna3_64x128x128_sm50_float16_cm_1_1_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 65536, false>(), fna3_32x128x65536_sm50_float16_cm_1_1_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 65536, false>(), fna3_64x128x65536_sm50_float16_cm_1_1_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 65536, false>(), fna3_64x64x65536_sm50_float16_cm_1_1_1); +} + +/////////////////////////////////////////////////////////////////// +// FNA-3D / float32 / SM70 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 32, false>::kMinBlocksPerSm) +fna3_32x128x32_sm70_float32_cm_0_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 32, false>::kMinBlocksPerSm) +fna3_64x64x32_sm70_float32_cm_0_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 32, false>::kMinBlocksPerSm) +fna3_64x128x32_sm70_float32_cm_0_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 64, false>::kMinBlocksPerSm) +fna3_32x128x64_sm70_float32_cm_0_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 64, false>::kMinBlocksPerSm) +fna3_64x64x64_sm70_float32_cm_0_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 64, false>::kMinBlocksPerSm) +fna3_64x128x64_sm70_float32_cm_0_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 128, false>::kMinBlocksPerSm) +fna3_32x128x128_sm70_float32_cm_0_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 128, false>::kMinBlocksPerSm) +fna3_64x64x128_sm70_float32_cm_0_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 128, false>::kMinBlocksPerSm) +fna3_64x128x128_sm70_float32_cm_0_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 65536, false>::kMinBlocksPerSm) +fna3_32x128x65536_sm70_float32_cm_0_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 65536, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 65536, false>::kMinBlocksPerSm) +fna3_64x128x65536_sm70_float32_cm_0_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 65536, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 65536, false>::kMinBlocksPerSm) +fna3_64x64x65536_sm70_float32_cm_0_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 65536, false>::Params p); + + +template +void fna3_sm70_float32_cm_0_0_0(T cb) { + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 32, false>(), fna3_32x128x32_sm70_float32_cm_0_0_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 32, false>(), fna3_64x64x32_sm70_float32_cm_0_0_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 32, false>(), fna3_64x128x32_sm70_float32_cm_0_0_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 64, false>(), fna3_32x128x64_sm70_float32_cm_0_0_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 64, false>(), fna3_64x64x64_sm70_float32_cm_0_0_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 64, false>(), fna3_64x128x64_sm70_float32_cm_0_0_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 128, false>(), fna3_32x128x128_sm70_float32_cm_0_0_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 128, false>(), fna3_64x64x128_sm70_float32_cm_0_0_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 128, false>(), fna3_64x128x128_sm70_float32_cm_0_0_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 65536, false>(), fna3_32x128x65536_sm70_float32_cm_0_0_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 65536, false>(), fna3_64x128x65536_sm70_float32_cm_0_0_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 65536, false>(), fna3_64x64x65536_sm70_float32_cm_0_0_0); +} + +/////////////////////////////////////////////////////////////////// +// FNA-3D / float32 / SM70 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 32, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 32, true>::kMinBlocksPerSm) +fna3_32x128x32_sm70_float32_cm_0_0_0_rpb(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 32, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 32, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 32, true>::kMinBlocksPerSm) +fna3_64x64x32_sm70_float32_cm_0_0_0_rpb(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 32, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 32, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 32, true>::kMinBlocksPerSm) +fna3_64x128x32_sm70_float32_cm_0_0_0_rpb(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 32, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 64, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 64, true>::kMinBlocksPerSm) +fna3_32x128x64_sm70_float32_cm_0_0_0_rpb(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 64, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 64, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 64, true>::kMinBlocksPerSm) +fna3_64x64x64_sm70_float32_cm_0_0_0_rpb(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 64, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 64, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 64, true>::kMinBlocksPerSm) +fna3_64x128x64_sm70_float32_cm_0_0_0_rpb(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 64, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 128, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 128, true>::kMinBlocksPerSm) +fna3_32x128x128_sm70_float32_cm_0_0_0_rpb(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 128, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 128, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 128, true>::kMinBlocksPerSm) +fna3_64x64x128_sm70_float32_cm_0_0_0_rpb(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 128, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 128, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 128, true>::kMinBlocksPerSm) +fna3_64x128x128_sm70_float32_cm_0_0_0_rpb(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 128, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 65536, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 65536, true>::kMinBlocksPerSm) +fna3_32x128x65536_sm70_float32_cm_0_0_0_rpb(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 65536, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 65536, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 65536, true>::kMinBlocksPerSm) +fna3_64x128x65536_sm70_float32_cm_0_0_0_rpb(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 65536, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 65536, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 65536, true>::kMinBlocksPerSm) +fna3_64x64x65536_sm70_float32_cm_0_0_0_rpb(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 65536, true>::Params p); + + +template +void fna3_sm70_float32_cm_0_0_0_rpb(T cb) { + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 32, true>(), fna3_32x128x32_sm70_float32_cm_0_0_0_rpb); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 32, true>(), fna3_64x64x32_sm70_float32_cm_0_0_0_rpb); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 32, true>(), fna3_64x128x32_sm70_float32_cm_0_0_0_rpb); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 64, true>(), fna3_32x128x64_sm70_float32_cm_0_0_0_rpb); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 64, true>(), fna3_64x64x64_sm70_float32_cm_0_0_0_rpb); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 64, true>(), fna3_64x128x64_sm70_float32_cm_0_0_0_rpb); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 128, true>(), fna3_32x128x128_sm70_float32_cm_0_0_0_rpb); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 128, true>(), fna3_64x64x128_sm70_float32_cm_0_0_0_rpb); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 128, true>(), fna3_64x128x128_sm70_float32_cm_0_0_0_rpb); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 65536, true>(), fna3_32x128x65536_sm70_float32_cm_0_0_0_rpb); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 65536, true>(), fna3_64x128x65536_sm70_float32_cm_0_0_0_rpb); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 65536, true>(), fna3_64x64x65536_sm70_float32_cm_0_0_0_rpb); +} + +/////////////////////////////////////////////////////////////////// +// FNA-3D / float32 / SM70 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 32, false>::kMinBlocksPerSm) +fna3_32x128x32_sm70_float32_cm_0_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 32, false>::kMinBlocksPerSm) +fna3_64x64x32_sm70_float32_cm_0_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 32, false>::kMinBlocksPerSm) +fna3_64x128x32_sm70_float32_cm_0_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 64, false>::kMinBlocksPerSm) +fna3_32x128x64_sm70_float32_cm_0_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 64, false>::kMinBlocksPerSm) +fna3_64x64x64_sm70_float32_cm_0_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 64, false>::kMinBlocksPerSm) +fna3_64x128x64_sm70_float32_cm_0_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 128, false>::kMinBlocksPerSm) +fna3_32x128x128_sm70_float32_cm_0_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 128, false>::kMinBlocksPerSm) +fna3_64x64x128_sm70_float32_cm_0_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 128, false>::kMinBlocksPerSm) +fna3_64x128x128_sm70_float32_cm_0_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 65536, false>::kMinBlocksPerSm) +fna3_32x128x65536_sm70_float32_cm_0_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 65536, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 65536, false>::kMinBlocksPerSm) +fna3_64x128x65536_sm70_float32_cm_0_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 65536, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 65536, false>::kMinBlocksPerSm) +fna3_64x64x65536_sm70_float32_cm_0_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 65536, false>::Params p); + + +template +void fna3_sm70_float32_cm_0_0_1(T cb) { + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 32, false>(), fna3_32x128x32_sm70_float32_cm_0_0_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 32, false>(), fna3_64x64x32_sm70_float32_cm_0_0_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 32, false>(), fna3_64x128x32_sm70_float32_cm_0_0_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 64, false>(), fna3_32x128x64_sm70_float32_cm_0_0_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 64, false>(), fna3_64x64x64_sm70_float32_cm_0_0_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 64, false>(), fna3_64x128x64_sm70_float32_cm_0_0_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 128, false>(), fna3_32x128x128_sm70_float32_cm_0_0_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 128, false>(), fna3_64x64x128_sm70_float32_cm_0_0_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 128, false>(), fna3_64x128x128_sm70_float32_cm_0_0_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 65536, false>(), fna3_32x128x65536_sm70_float32_cm_0_0_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 65536, false>(), fna3_64x128x65536_sm70_float32_cm_0_0_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 65536, false>(), fna3_64x64x65536_sm70_float32_cm_0_0_1); +} + +/////////////////////////////////////////////////////////////////// +// FNA-3D / float32 / SM70 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 32, false>::kMinBlocksPerSm) +fna3_32x128x32_sm70_float32_cm_0_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 32, false>::kMinBlocksPerSm) +fna3_64x64x32_sm70_float32_cm_0_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 32, false>::kMinBlocksPerSm) +fna3_64x128x32_sm70_float32_cm_0_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 64, false>::kMinBlocksPerSm) +fna3_32x128x64_sm70_float32_cm_0_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 64, false>::kMinBlocksPerSm) +fna3_64x64x64_sm70_float32_cm_0_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 64, false>::kMinBlocksPerSm) +fna3_64x128x64_sm70_float32_cm_0_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 128, false>::kMinBlocksPerSm) +fna3_32x128x128_sm70_float32_cm_0_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 128, false>::kMinBlocksPerSm) +fna3_64x64x128_sm70_float32_cm_0_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 128, false>::kMinBlocksPerSm) +fna3_64x128x128_sm70_float32_cm_0_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 65536, false>::kMinBlocksPerSm) +fna3_32x128x65536_sm70_float32_cm_0_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 65536, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 65536, false>::kMinBlocksPerSm) +fna3_64x128x65536_sm70_float32_cm_0_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 65536, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 65536, false>::kMinBlocksPerSm) +fna3_64x64x65536_sm70_float32_cm_0_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 65536, false>::Params p); + + +template +void fna3_sm70_float32_cm_0_1_0(T cb) { + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 32, false>(), fna3_32x128x32_sm70_float32_cm_0_1_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 32, false>(), fna3_64x64x32_sm70_float32_cm_0_1_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 32, false>(), fna3_64x128x32_sm70_float32_cm_0_1_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 64, false>(), fna3_32x128x64_sm70_float32_cm_0_1_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 64, false>(), fna3_64x64x64_sm70_float32_cm_0_1_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 64, false>(), fna3_64x128x64_sm70_float32_cm_0_1_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 128, false>(), fna3_32x128x128_sm70_float32_cm_0_1_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 128, false>(), fna3_64x64x128_sm70_float32_cm_0_1_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 128, false>(), fna3_64x128x128_sm70_float32_cm_0_1_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 65536, false>(), fna3_32x128x65536_sm70_float32_cm_0_1_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 65536, false>(), fna3_64x128x65536_sm70_float32_cm_0_1_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 65536, false>(), fna3_64x64x65536_sm70_float32_cm_0_1_0); +} + +/////////////////////////////////////////////////////////////////// +// FNA-3D / float32 / SM70 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 32, false>::kMinBlocksPerSm) +fna3_32x128x32_sm70_float32_cm_0_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 32, false>::kMinBlocksPerSm) +fna3_64x64x32_sm70_float32_cm_0_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 32, false>::kMinBlocksPerSm) +fna3_64x128x32_sm70_float32_cm_0_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 64, false>::kMinBlocksPerSm) +fna3_32x128x64_sm70_float32_cm_0_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 64, false>::kMinBlocksPerSm) +fna3_64x64x64_sm70_float32_cm_0_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 64, false>::kMinBlocksPerSm) +fna3_64x128x64_sm70_float32_cm_0_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 128, false>::kMinBlocksPerSm) +fna3_32x128x128_sm70_float32_cm_0_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 128, false>::kMinBlocksPerSm) +fna3_64x64x128_sm70_float32_cm_0_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 128, false>::kMinBlocksPerSm) +fna3_64x128x128_sm70_float32_cm_0_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 65536, false>::kMinBlocksPerSm) +fna3_32x128x65536_sm70_float32_cm_0_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 65536, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 65536, false>::kMinBlocksPerSm) +fna3_64x128x65536_sm70_float32_cm_0_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 65536, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 65536, false>::kMinBlocksPerSm) +fna3_64x64x65536_sm70_float32_cm_0_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 65536, false>::Params p); + + +template +void fna3_sm70_float32_cm_0_1_1(T cb) { + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 32, false>(), fna3_32x128x32_sm70_float32_cm_0_1_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 32, false>(), fna3_64x64x32_sm70_float32_cm_0_1_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 32, false>(), fna3_64x128x32_sm70_float32_cm_0_1_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 64, false>(), fna3_32x128x64_sm70_float32_cm_0_1_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 64, false>(), fna3_64x64x64_sm70_float32_cm_0_1_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 64, false>(), fna3_64x128x64_sm70_float32_cm_0_1_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 128, false>(), fna3_32x128x128_sm70_float32_cm_0_1_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 128, false>(), fna3_64x64x128_sm70_float32_cm_0_1_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 128, false>(), fna3_64x128x128_sm70_float32_cm_0_1_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 65536, false>(), fna3_32x128x65536_sm70_float32_cm_0_1_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 65536, false>(), fna3_64x128x65536_sm70_float32_cm_0_1_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 65536, false>(), fna3_64x64x65536_sm70_float32_cm_0_1_1); +} + +/////////////////////////////////////////////////////////////////// +// FNA-3D / float32 / SM70 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 32, false>::kMinBlocksPerSm) +fna3_32x128x32_sm70_float32_cm_1_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 32, false>::kMinBlocksPerSm) +fna3_64x64x32_sm70_float32_cm_1_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 32, false>::kMinBlocksPerSm) +fna3_64x128x32_sm70_float32_cm_1_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 64, false>::kMinBlocksPerSm) +fna3_32x128x64_sm70_float32_cm_1_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 64, false>::kMinBlocksPerSm) +fna3_64x64x64_sm70_float32_cm_1_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 64, false>::kMinBlocksPerSm) +fna3_64x128x64_sm70_float32_cm_1_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 128, false>::kMinBlocksPerSm) +fna3_32x128x128_sm70_float32_cm_1_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 128, false>::kMinBlocksPerSm) +fna3_64x64x128_sm70_float32_cm_1_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 128, false>::kMinBlocksPerSm) +fna3_64x128x128_sm70_float32_cm_1_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 65536, false>::kMinBlocksPerSm) +fna3_32x128x65536_sm70_float32_cm_1_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 65536, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 65536, false>::kMinBlocksPerSm) +fna3_64x128x65536_sm70_float32_cm_1_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 65536, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 65536, false>::kMinBlocksPerSm) +fna3_64x64x65536_sm70_float32_cm_1_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 65536, false>::Params p); + + +template +void fna3_sm70_float32_cm_1_0_0(T cb) { + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 32, false>(), fna3_32x128x32_sm70_float32_cm_1_0_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 32, false>(), fna3_64x64x32_sm70_float32_cm_1_0_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 32, false>(), fna3_64x128x32_sm70_float32_cm_1_0_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 64, false>(), fna3_32x128x64_sm70_float32_cm_1_0_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 64, false>(), fna3_64x64x64_sm70_float32_cm_1_0_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 64, false>(), fna3_64x128x64_sm70_float32_cm_1_0_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 128, false>(), fna3_32x128x128_sm70_float32_cm_1_0_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 128, false>(), fna3_64x64x128_sm70_float32_cm_1_0_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 128, false>(), fna3_64x128x128_sm70_float32_cm_1_0_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 65536, false>(), fna3_32x128x65536_sm70_float32_cm_1_0_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 65536, false>(), fna3_64x128x65536_sm70_float32_cm_1_0_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 65536, false>(), fna3_64x64x65536_sm70_float32_cm_1_0_0); +} + +/////////////////////////////////////////////////////////////////// +// FNA-3D / float32 / SM70 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 32, false>::kMinBlocksPerSm) +fna3_32x128x32_sm70_float32_cm_1_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 32, false>::kMinBlocksPerSm) +fna3_64x64x32_sm70_float32_cm_1_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 32, false>::kMinBlocksPerSm) +fna3_64x128x32_sm70_float32_cm_1_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 64, false>::kMinBlocksPerSm) +fna3_32x128x64_sm70_float32_cm_1_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 64, false>::kMinBlocksPerSm) +fna3_64x64x64_sm70_float32_cm_1_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 64, false>::kMinBlocksPerSm) +fna3_64x128x64_sm70_float32_cm_1_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 128, false>::kMinBlocksPerSm) +fna3_32x128x128_sm70_float32_cm_1_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 128, false>::kMinBlocksPerSm) +fna3_64x64x128_sm70_float32_cm_1_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 128, false>::kMinBlocksPerSm) +fna3_64x128x128_sm70_float32_cm_1_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 65536, false>::kMinBlocksPerSm) +fna3_32x128x65536_sm70_float32_cm_1_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 65536, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 65536, false>::kMinBlocksPerSm) +fna3_64x128x65536_sm70_float32_cm_1_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 65536, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 65536, false>::kMinBlocksPerSm) +fna3_64x64x65536_sm70_float32_cm_1_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 65536, false>::Params p); + + +template +void fna3_sm70_float32_cm_1_0_1(T cb) { + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 32, false>(), fna3_32x128x32_sm70_float32_cm_1_0_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 32, false>(), fna3_64x64x32_sm70_float32_cm_1_0_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 32, false>(), fna3_64x128x32_sm70_float32_cm_1_0_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 64, false>(), fna3_32x128x64_sm70_float32_cm_1_0_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 64, false>(), fna3_64x64x64_sm70_float32_cm_1_0_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 64, false>(), fna3_64x128x64_sm70_float32_cm_1_0_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 128, false>(), fna3_32x128x128_sm70_float32_cm_1_0_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 128, false>(), fna3_64x64x128_sm70_float32_cm_1_0_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 128, false>(), fna3_64x128x128_sm70_float32_cm_1_0_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 65536, false>(), fna3_32x128x65536_sm70_float32_cm_1_0_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 65536, false>(), fna3_64x128x65536_sm70_float32_cm_1_0_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 65536, false>(), fna3_64x64x65536_sm70_float32_cm_1_0_1); +} + +/////////////////////////////////////////////////////////////////// +// FNA-3D / float32 / SM70 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 32, false>::kMinBlocksPerSm) +fna3_32x128x32_sm70_float32_cm_1_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 32, false>::kMinBlocksPerSm) +fna3_64x64x32_sm70_float32_cm_1_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 32, false>::kMinBlocksPerSm) +fna3_64x128x32_sm70_float32_cm_1_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 64, false>::kMinBlocksPerSm) +fna3_32x128x64_sm70_float32_cm_1_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 64, false>::kMinBlocksPerSm) +fna3_64x64x64_sm70_float32_cm_1_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 64, false>::kMinBlocksPerSm) +fna3_64x128x64_sm70_float32_cm_1_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 128, false>::kMinBlocksPerSm) +fna3_32x128x128_sm70_float32_cm_1_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 128, false>::kMinBlocksPerSm) +fna3_64x64x128_sm70_float32_cm_1_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 128, false>::kMinBlocksPerSm) +fna3_64x128x128_sm70_float32_cm_1_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 65536, false>::kMinBlocksPerSm) +fna3_32x128x65536_sm70_float32_cm_1_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 65536, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 65536, false>::kMinBlocksPerSm) +fna3_64x128x65536_sm70_float32_cm_1_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 65536, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 65536, false>::kMinBlocksPerSm) +fna3_64x64x65536_sm70_float32_cm_1_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 65536, false>::Params p); + + +template +void fna3_sm70_float32_cm_1_1_0(T cb) { + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 32, false>(), fna3_32x128x32_sm70_float32_cm_1_1_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 32, false>(), fna3_64x64x32_sm70_float32_cm_1_1_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 32, false>(), fna3_64x128x32_sm70_float32_cm_1_1_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 64, false>(), fna3_32x128x64_sm70_float32_cm_1_1_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 64, false>(), fna3_64x64x64_sm70_float32_cm_1_1_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 64, false>(), fna3_64x128x64_sm70_float32_cm_1_1_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 128, false>(), fna3_32x128x128_sm70_float32_cm_1_1_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 128, false>(), fna3_64x64x128_sm70_float32_cm_1_1_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 128, false>(), fna3_64x128x128_sm70_float32_cm_1_1_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 65536, false>(), fna3_32x128x65536_sm70_float32_cm_1_1_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 65536, false>(), fna3_64x128x65536_sm70_float32_cm_1_1_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 65536, false>(), fna3_64x64x65536_sm70_float32_cm_1_1_0); +} + +/////////////////////////////////////////////////////////////////// +// FNA-3D / float32 / SM70 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 32, false>::kMinBlocksPerSm) +fna3_32x128x32_sm70_float32_cm_1_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 32, false>::kMinBlocksPerSm) +fna3_64x64x32_sm70_float32_cm_1_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 32, false>::kMinBlocksPerSm) +fna3_64x128x32_sm70_float32_cm_1_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 64, false>::kMinBlocksPerSm) +fna3_32x128x64_sm70_float32_cm_1_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 64, false>::kMinBlocksPerSm) +fna3_64x64x64_sm70_float32_cm_1_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 64, false>::kMinBlocksPerSm) +fna3_64x128x64_sm70_float32_cm_1_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 128, false>::kMinBlocksPerSm) +fna3_32x128x128_sm70_float32_cm_1_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 128, false>::kMinBlocksPerSm) +fna3_64x64x128_sm70_float32_cm_1_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 128, false>::kMinBlocksPerSm) +fna3_64x128x128_sm70_float32_cm_1_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 65536, false>::kMinBlocksPerSm) +fna3_32x128x65536_sm70_float32_cm_1_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 65536, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 65536, false>::kMinBlocksPerSm) +fna3_64x128x65536_sm70_float32_cm_1_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 65536, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 65536, false>::kMinBlocksPerSm) +fna3_64x64x65536_sm70_float32_cm_1_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 65536, false>::Params p); + + +template +void fna3_sm70_float32_cm_1_1_1(T cb) { + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 32, false>(), fna3_32x128x32_sm70_float32_cm_1_1_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 32, false>(), fna3_64x64x32_sm70_float32_cm_1_1_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 32, false>(), fna3_64x128x32_sm70_float32_cm_1_1_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 64, false>(), fna3_32x128x64_sm70_float32_cm_1_1_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 64, false>(), fna3_64x64x64_sm70_float32_cm_1_1_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 64, false>(), fna3_64x128x64_sm70_float32_cm_1_1_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 128, false>(), fna3_32x128x128_sm70_float32_cm_1_1_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 128, false>(), fna3_64x64x128_sm70_float32_cm_1_1_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 128, false>(), fna3_64x128x128_sm70_float32_cm_1_1_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 65536, false>(), fna3_32x128x65536_sm70_float32_cm_1_1_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 65536, false>(), fna3_64x128x65536_sm70_float32_cm_1_1_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 65536, false>(), fna3_64x64x65536_sm70_float32_cm_1_1_1); +} + +/////////////////////////////////////////////////////////////////// +// FNA-3D / float16 / SM70 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 32, false>::kMinBlocksPerSm) +fna3_32x128x32_sm70_float16_cm_0_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 32, false>::kMinBlocksPerSm) +fna3_64x64x32_sm70_float16_cm_0_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 32, false>::kMinBlocksPerSm) +fna3_64x128x32_sm70_float16_cm_0_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 64, false>::kMinBlocksPerSm) +fna3_32x128x64_sm70_float16_cm_0_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 64, false>::kMinBlocksPerSm) +fna3_64x64x64_sm70_float16_cm_0_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 64, false>::kMinBlocksPerSm) +fna3_64x128x64_sm70_float16_cm_0_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 128, false>::kMinBlocksPerSm) +fna3_32x128x128_sm70_float16_cm_0_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 128, false>::kMinBlocksPerSm) +fna3_64x64x128_sm70_float16_cm_0_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 128, false>::kMinBlocksPerSm) +fna3_64x128x128_sm70_float16_cm_0_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 65536, false>::kMinBlocksPerSm) +fna3_32x128x65536_sm70_float16_cm_0_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 65536, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 65536, false>::kMinBlocksPerSm) +fna3_64x128x65536_sm70_float16_cm_0_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 65536, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 65536, false>::kMinBlocksPerSm) +fna3_64x64x65536_sm70_float16_cm_0_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 65536, false>::Params p); + + +template +void fna3_sm70_float16_cm_0_0_0(T cb) { + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 32, false>(), fna3_32x128x32_sm70_float16_cm_0_0_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 32, false>(), fna3_64x64x32_sm70_float16_cm_0_0_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 32, false>(), fna3_64x128x32_sm70_float16_cm_0_0_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 64, false>(), fna3_32x128x64_sm70_float16_cm_0_0_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 64, false>(), fna3_64x64x64_sm70_float16_cm_0_0_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 64, false>(), fna3_64x128x64_sm70_float16_cm_0_0_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 128, false>(), fna3_32x128x128_sm70_float16_cm_0_0_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 128, false>(), fna3_64x64x128_sm70_float16_cm_0_0_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 128, false>(), fna3_64x128x128_sm70_float16_cm_0_0_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 65536, false>(), fna3_32x128x65536_sm70_float16_cm_0_0_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 65536, false>(), fna3_64x128x65536_sm70_float16_cm_0_0_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 65536, false>(), fna3_64x64x65536_sm70_float16_cm_0_0_0); +} + +/////////////////////////////////////////////////////////////////// +// FNA-3D / float16 / SM70 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 32, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 32, true>::kMinBlocksPerSm) +fna3_32x128x32_sm70_float16_cm_0_0_0_rpb(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 32, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 32, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 32, true>::kMinBlocksPerSm) +fna3_64x64x32_sm70_float16_cm_0_0_0_rpb(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 32, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 32, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 32, true>::kMinBlocksPerSm) +fna3_64x128x32_sm70_float16_cm_0_0_0_rpb(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 32, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 64, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 64, true>::kMinBlocksPerSm) +fna3_32x128x64_sm70_float16_cm_0_0_0_rpb(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 64, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 64, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 64, true>::kMinBlocksPerSm) +fna3_64x64x64_sm70_float16_cm_0_0_0_rpb(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 64, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 64, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 64, true>::kMinBlocksPerSm) +fna3_64x128x64_sm70_float16_cm_0_0_0_rpb(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 64, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 128, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 128, true>::kMinBlocksPerSm) +fna3_32x128x128_sm70_float16_cm_0_0_0_rpb(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 128, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 128, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 128, true>::kMinBlocksPerSm) +fna3_64x64x128_sm70_float16_cm_0_0_0_rpb(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 128, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 128, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 128, true>::kMinBlocksPerSm) +fna3_64x128x128_sm70_float16_cm_0_0_0_rpb(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 128, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 65536, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 65536, true>::kMinBlocksPerSm) +fna3_32x128x65536_sm70_float16_cm_0_0_0_rpb(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 65536, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 65536, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 65536, true>::kMinBlocksPerSm) +fna3_64x128x65536_sm70_float16_cm_0_0_0_rpb(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 65536, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 65536, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 65536, true>::kMinBlocksPerSm) +fna3_64x64x65536_sm70_float16_cm_0_0_0_rpb(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 65536, true>::Params p); + + +template +void fna3_sm70_float16_cm_0_0_0_rpb(T cb) { + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 32, true>(), fna3_32x128x32_sm70_float16_cm_0_0_0_rpb); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 32, true>(), fna3_64x64x32_sm70_float16_cm_0_0_0_rpb); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 32, true>(), fna3_64x128x32_sm70_float16_cm_0_0_0_rpb); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 64, true>(), fna3_32x128x64_sm70_float16_cm_0_0_0_rpb); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 64, true>(), fna3_64x64x64_sm70_float16_cm_0_0_0_rpb); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 64, true>(), fna3_64x128x64_sm70_float16_cm_0_0_0_rpb); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 128, true>(), fna3_32x128x128_sm70_float16_cm_0_0_0_rpb); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 128, true>(), fna3_64x64x128_sm70_float16_cm_0_0_0_rpb); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 128, true>(), fna3_64x128x128_sm70_float16_cm_0_0_0_rpb); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 65536, true>(), fna3_32x128x65536_sm70_float16_cm_0_0_0_rpb); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 65536, true>(), fna3_64x128x65536_sm70_float16_cm_0_0_0_rpb); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 65536, true>(), fna3_64x64x65536_sm70_float16_cm_0_0_0_rpb); +} + +/////////////////////////////////////////////////////////////////// +// FNA-3D / float16 / SM70 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 32, false>::kMinBlocksPerSm) +fna3_32x128x32_sm70_float16_cm_0_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 32, false>::kMinBlocksPerSm) +fna3_64x64x32_sm70_float16_cm_0_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 32, false>::kMinBlocksPerSm) +fna3_64x128x32_sm70_float16_cm_0_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 64, false>::kMinBlocksPerSm) +fna3_32x128x64_sm70_float16_cm_0_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 64, false>::kMinBlocksPerSm) +fna3_64x64x64_sm70_float16_cm_0_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 64, false>::kMinBlocksPerSm) +fna3_64x128x64_sm70_float16_cm_0_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 128, false>::kMinBlocksPerSm) +fna3_32x128x128_sm70_float16_cm_0_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 128, false>::kMinBlocksPerSm) +fna3_64x64x128_sm70_float16_cm_0_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 128, false>::kMinBlocksPerSm) +fna3_64x128x128_sm70_float16_cm_0_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 65536, false>::kMinBlocksPerSm) +fna3_32x128x65536_sm70_float16_cm_0_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 65536, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 65536, false>::kMinBlocksPerSm) +fna3_64x128x65536_sm70_float16_cm_0_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 65536, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 65536, false>::kMinBlocksPerSm) +fna3_64x64x65536_sm70_float16_cm_0_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 65536, false>::Params p); + + +template +void fna3_sm70_float16_cm_0_0_1(T cb) { + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 32, false>(), fna3_32x128x32_sm70_float16_cm_0_0_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 32, false>(), fna3_64x64x32_sm70_float16_cm_0_0_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 32, false>(), fna3_64x128x32_sm70_float16_cm_0_0_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 64, false>(), fna3_32x128x64_sm70_float16_cm_0_0_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 64, false>(), fna3_64x64x64_sm70_float16_cm_0_0_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 64, false>(), fna3_64x128x64_sm70_float16_cm_0_0_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 128, false>(), fna3_32x128x128_sm70_float16_cm_0_0_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 128, false>(), fna3_64x64x128_sm70_float16_cm_0_0_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 128, false>(), fna3_64x128x128_sm70_float16_cm_0_0_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 65536, false>(), fna3_32x128x65536_sm70_float16_cm_0_0_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 65536, false>(), fna3_64x128x65536_sm70_float16_cm_0_0_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 65536, false>(), fna3_64x64x65536_sm70_float16_cm_0_0_1); +} + +/////////////////////////////////////////////////////////////////// +// FNA-3D / float16 / SM70 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 32, false>::kMinBlocksPerSm) +fna3_32x128x32_sm70_float16_cm_0_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 32, false>::kMinBlocksPerSm) +fna3_64x64x32_sm70_float16_cm_0_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 32, false>::kMinBlocksPerSm) +fna3_64x128x32_sm70_float16_cm_0_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 64, false>::kMinBlocksPerSm) +fna3_32x128x64_sm70_float16_cm_0_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 64, false>::kMinBlocksPerSm) +fna3_64x64x64_sm70_float16_cm_0_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 64, false>::kMinBlocksPerSm) +fna3_64x128x64_sm70_float16_cm_0_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 128, false>::kMinBlocksPerSm) +fna3_32x128x128_sm70_float16_cm_0_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 128, false>::kMinBlocksPerSm) +fna3_64x64x128_sm70_float16_cm_0_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 128, false>::kMinBlocksPerSm) +fna3_64x128x128_sm70_float16_cm_0_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 65536, false>::kMinBlocksPerSm) +fna3_32x128x65536_sm70_float16_cm_0_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 65536, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 65536, false>::kMinBlocksPerSm) +fna3_64x128x65536_sm70_float16_cm_0_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 65536, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 65536, false>::kMinBlocksPerSm) +fna3_64x64x65536_sm70_float16_cm_0_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 65536, false>::Params p); + + +template +void fna3_sm70_float16_cm_0_1_0(T cb) { + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 32, false>(), fna3_32x128x32_sm70_float16_cm_0_1_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 32, false>(), fna3_64x64x32_sm70_float16_cm_0_1_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 32, false>(), fna3_64x128x32_sm70_float16_cm_0_1_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 64, false>(), fna3_32x128x64_sm70_float16_cm_0_1_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 64, false>(), fna3_64x64x64_sm70_float16_cm_0_1_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 64, false>(), fna3_64x128x64_sm70_float16_cm_0_1_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 128, false>(), fna3_32x128x128_sm70_float16_cm_0_1_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 128, false>(), fna3_64x64x128_sm70_float16_cm_0_1_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 128, false>(), fna3_64x128x128_sm70_float16_cm_0_1_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 65536, false>(), fna3_32x128x65536_sm70_float16_cm_0_1_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 65536, false>(), fna3_64x128x65536_sm70_float16_cm_0_1_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 65536, false>(), fna3_64x64x65536_sm70_float16_cm_0_1_0); +} + +/////////////////////////////////////////////////////////////////// +// FNA-3D / float16 / SM70 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 32, false>::kMinBlocksPerSm) +fna3_32x128x32_sm70_float16_cm_0_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 32, false>::kMinBlocksPerSm) +fna3_64x64x32_sm70_float16_cm_0_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 32, false>::kMinBlocksPerSm) +fna3_64x128x32_sm70_float16_cm_0_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 64, false>::kMinBlocksPerSm) +fna3_32x128x64_sm70_float16_cm_0_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 64, false>::kMinBlocksPerSm) +fna3_64x64x64_sm70_float16_cm_0_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 64, false>::kMinBlocksPerSm) +fna3_64x128x64_sm70_float16_cm_0_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 128, false>::kMinBlocksPerSm) +fna3_32x128x128_sm70_float16_cm_0_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 128, false>::kMinBlocksPerSm) +fna3_64x64x128_sm70_float16_cm_0_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 128, false>::kMinBlocksPerSm) +fna3_64x128x128_sm70_float16_cm_0_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 65536, false>::kMinBlocksPerSm) +fna3_32x128x65536_sm70_float16_cm_0_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 65536, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 65536, false>::kMinBlocksPerSm) +fna3_64x128x65536_sm70_float16_cm_0_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 65536, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 65536, false>::kMinBlocksPerSm) +fna3_64x64x65536_sm70_float16_cm_0_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 65536, false>::Params p); + + +template +void fna3_sm70_float16_cm_0_1_1(T cb) { + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 32, false>(), fna3_32x128x32_sm70_float16_cm_0_1_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 32, false>(), fna3_64x64x32_sm70_float16_cm_0_1_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 32, false>(), fna3_64x128x32_sm70_float16_cm_0_1_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 64, false>(), fna3_32x128x64_sm70_float16_cm_0_1_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 64, false>(), fna3_64x64x64_sm70_float16_cm_0_1_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 64, false>(), fna3_64x128x64_sm70_float16_cm_0_1_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 128, false>(), fna3_32x128x128_sm70_float16_cm_0_1_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 128, false>(), fna3_64x64x128_sm70_float16_cm_0_1_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 128, false>(), fna3_64x128x128_sm70_float16_cm_0_1_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 65536, false>(), fna3_32x128x65536_sm70_float16_cm_0_1_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 65536, false>(), fna3_64x128x65536_sm70_float16_cm_0_1_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 65536, false>(), fna3_64x64x65536_sm70_float16_cm_0_1_1); +} + +/////////////////////////////////////////////////////////////////// +// FNA-3D / float16 / SM70 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 32, false>::kMinBlocksPerSm) +fna3_32x128x32_sm70_float16_cm_1_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 32, false>::kMinBlocksPerSm) +fna3_64x64x32_sm70_float16_cm_1_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 32, false>::kMinBlocksPerSm) +fna3_64x128x32_sm70_float16_cm_1_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 64, false>::kMinBlocksPerSm) +fna3_32x128x64_sm70_float16_cm_1_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 64, false>::kMinBlocksPerSm) +fna3_64x64x64_sm70_float16_cm_1_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 64, false>::kMinBlocksPerSm) +fna3_64x128x64_sm70_float16_cm_1_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 128, false>::kMinBlocksPerSm) +fna3_32x128x128_sm70_float16_cm_1_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 128, false>::kMinBlocksPerSm) +fna3_64x64x128_sm70_float16_cm_1_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 128, false>::kMinBlocksPerSm) +fna3_64x128x128_sm70_float16_cm_1_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 65536, false>::kMinBlocksPerSm) +fna3_32x128x65536_sm70_float16_cm_1_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 65536, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 65536, false>::kMinBlocksPerSm) +fna3_64x128x65536_sm70_float16_cm_1_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 65536, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 65536, false>::kMinBlocksPerSm) +fna3_64x64x65536_sm70_float16_cm_1_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 65536, false>::Params p); + + +template +void fna3_sm70_float16_cm_1_0_0(T cb) { + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 32, false>(), fna3_32x128x32_sm70_float16_cm_1_0_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 32, false>(), fna3_64x64x32_sm70_float16_cm_1_0_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 32, false>(), fna3_64x128x32_sm70_float16_cm_1_0_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 64, false>(), fna3_32x128x64_sm70_float16_cm_1_0_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 64, false>(), fna3_64x64x64_sm70_float16_cm_1_0_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 64, false>(), fna3_64x128x64_sm70_float16_cm_1_0_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 128, false>(), fna3_32x128x128_sm70_float16_cm_1_0_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 128, false>(), fna3_64x64x128_sm70_float16_cm_1_0_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 128, false>(), fna3_64x128x128_sm70_float16_cm_1_0_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 65536, false>(), fna3_32x128x65536_sm70_float16_cm_1_0_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 65536, false>(), fna3_64x128x65536_sm70_float16_cm_1_0_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 65536, false>(), fna3_64x64x65536_sm70_float16_cm_1_0_0); +} + +/////////////////////////////////////////////////////////////////// +// FNA-3D / float16 / SM70 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 32, false>::kMinBlocksPerSm) +fna3_32x128x32_sm70_float16_cm_1_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 32, false>::kMinBlocksPerSm) +fna3_64x64x32_sm70_float16_cm_1_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 32, false>::kMinBlocksPerSm) +fna3_64x128x32_sm70_float16_cm_1_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 64, false>::kMinBlocksPerSm) +fna3_32x128x64_sm70_float16_cm_1_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 64, false>::kMinBlocksPerSm) +fna3_64x64x64_sm70_float16_cm_1_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 64, false>::kMinBlocksPerSm) +fna3_64x128x64_sm70_float16_cm_1_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 128, false>::kMinBlocksPerSm) +fna3_32x128x128_sm70_float16_cm_1_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 128, false>::kMinBlocksPerSm) +fna3_64x64x128_sm70_float16_cm_1_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 128, false>::kMinBlocksPerSm) +fna3_64x128x128_sm70_float16_cm_1_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 65536, false>::kMinBlocksPerSm) +fna3_32x128x65536_sm70_float16_cm_1_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 65536, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 65536, false>::kMinBlocksPerSm) +fna3_64x128x65536_sm70_float16_cm_1_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 65536, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 65536, false>::kMinBlocksPerSm) +fna3_64x64x65536_sm70_float16_cm_1_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 65536, false>::Params p); + + +template +void fna3_sm70_float16_cm_1_0_1(T cb) { + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 32, false>(), fna3_32x128x32_sm70_float16_cm_1_0_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 32, false>(), fna3_64x64x32_sm70_float16_cm_1_0_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 32, false>(), fna3_64x128x32_sm70_float16_cm_1_0_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 64, false>(), fna3_32x128x64_sm70_float16_cm_1_0_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 64, false>(), fna3_64x64x64_sm70_float16_cm_1_0_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 64, false>(), fna3_64x128x64_sm70_float16_cm_1_0_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 128, false>(), fna3_32x128x128_sm70_float16_cm_1_0_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 128, false>(), fna3_64x64x128_sm70_float16_cm_1_0_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 128, false>(), fna3_64x128x128_sm70_float16_cm_1_0_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 65536, false>(), fna3_32x128x65536_sm70_float16_cm_1_0_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 65536, false>(), fna3_64x128x65536_sm70_float16_cm_1_0_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 65536, false>(), fna3_64x64x65536_sm70_float16_cm_1_0_1); +} + +/////////////////////////////////////////////////////////////////// +// FNA-3D / float16 / SM70 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 32, false>::kMinBlocksPerSm) +fna3_32x128x32_sm70_float16_cm_1_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 32, false>::kMinBlocksPerSm) +fna3_64x64x32_sm70_float16_cm_1_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 32, false>::kMinBlocksPerSm) +fna3_64x128x32_sm70_float16_cm_1_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 64, false>::kMinBlocksPerSm) +fna3_32x128x64_sm70_float16_cm_1_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 64, false>::kMinBlocksPerSm) +fna3_64x64x64_sm70_float16_cm_1_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 64, false>::kMinBlocksPerSm) +fna3_64x128x64_sm70_float16_cm_1_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 128, false>::kMinBlocksPerSm) +fna3_32x128x128_sm70_float16_cm_1_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 128, false>::kMinBlocksPerSm) +fna3_64x64x128_sm70_float16_cm_1_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 128, false>::kMinBlocksPerSm) +fna3_64x128x128_sm70_float16_cm_1_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 65536, false>::kMinBlocksPerSm) +fna3_32x128x65536_sm70_float16_cm_1_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 65536, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 65536, false>::kMinBlocksPerSm) +fna3_64x128x65536_sm70_float16_cm_1_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 65536, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 65536, false>::kMinBlocksPerSm) +fna3_64x64x65536_sm70_float16_cm_1_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 65536, false>::Params p); + + +template +void fna3_sm70_float16_cm_1_1_0(T cb) { + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 32, false>(), fna3_32x128x32_sm70_float16_cm_1_1_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 32, false>(), fna3_64x64x32_sm70_float16_cm_1_1_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 32, false>(), fna3_64x128x32_sm70_float16_cm_1_1_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 64, false>(), fna3_32x128x64_sm70_float16_cm_1_1_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 64, false>(), fna3_64x64x64_sm70_float16_cm_1_1_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 64, false>(), fna3_64x128x64_sm70_float16_cm_1_1_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 128, false>(), fna3_32x128x128_sm70_float16_cm_1_1_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 128, false>(), fna3_64x64x128_sm70_float16_cm_1_1_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 128, false>(), fna3_64x128x128_sm70_float16_cm_1_1_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 65536, false>(), fna3_32x128x65536_sm70_float16_cm_1_1_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 65536, false>(), fna3_64x128x65536_sm70_float16_cm_1_1_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 65536, false>(), fna3_64x64x65536_sm70_float16_cm_1_1_0); +} + +/////////////////////////////////////////////////////////////////// +// FNA-3D / float16 / SM70 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 32, false>::kMinBlocksPerSm) +fna3_32x128x32_sm70_float16_cm_1_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 32, false>::kMinBlocksPerSm) +fna3_64x64x32_sm70_float16_cm_1_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 32, false>::kMinBlocksPerSm) +fna3_64x128x32_sm70_float16_cm_1_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 64, false>::kMinBlocksPerSm) +fna3_32x128x64_sm70_float16_cm_1_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 64, false>::kMinBlocksPerSm) +fna3_64x64x64_sm70_float16_cm_1_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 64, false>::kMinBlocksPerSm) +fna3_64x128x64_sm70_float16_cm_1_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 128, false>::kMinBlocksPerSm) +fna3_32x128x128_sm70_float16_cm_1_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 128, false>::kMinBlocksPerSm) +fna3_64x64x128_sm70_float16_cm_1_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 128, false>::kMinBlocksPerSm) +fna3_64x128x128_sm70_float16_cm_1_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 65536, false>::kMinBlocksPerSm) +fna3_32x128x65536_sm70_float16_cm_1_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 65536, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 65536, false>::kMinBlocksPerSm) +fna3_64x128x65536_sm70_float16_cm_1_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 65536, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 65536, false>::kMinBlocksPerSm) +fna3_64x64x65536_sm70_float16_cm_1_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 65536, false>::Params p); + + +template +void fna3_sm70_float16_cm_1_1_1(T cb) { + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 32, false>(), fna3_32x128x32_sm70_float16_cm_1_1_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 32, false>(), fna3_64x64x32_sm70_float16_cm_1_1_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 32, false>(), fna3_64x128x32_sm70_float16_cm_1_1_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 64, false>(), fna3_32x128x64_sm70_float16_cm_1_1_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 64, false>(), fna3_64x64x64_sm70_float16_cm_1_1_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 64, false>(), fna3_64x128x64_sm70_float16_cm_1_1_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 128, false>(), fna3_32x128x128_sm70_float16_cm_1_1_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 128, false>(), fna3_64x64x128_sm70_float16_cm_1_1_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 128, false>(), fna3_64x128x128_sm70_float16_cm_1_1_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 65536, false>(), fna3_32x128x65536_sm70_float16_cm_1_1_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 65536, false>(), fna3_64x128x65536_sm70_float16_cm_1_1_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 65536, false>(), fna3_64x64x65536_sm70_float16_cm_1_1_1); +} + +/////////////////////////////////////////////////////////////////// +// FNA-3D / float32 / SM75 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 32, false>::kMinBlocksPerSm) +fna3_32x128x32_sm75_float32_cm_0_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 32, false>::kMinBlocksPerSm) +fna3_64x64x32_sm75_float32_cm_0_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 32, false>::kMinBlocksPerSm) +fna3_64x128x32_sm75_float32_cm_0_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 64, false>::kMinBlocksPerSm) +fna3_32x128x64_sm75_float32_cm_0_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 64, false>::kMinBlocksPerSm) +fna3_64x64x64_sm75_float32_cm_0_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 64, false>::kMinBlocksPerSm) +fna3_64x128x64_sm75_float32_cm_0_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 128, false>::kMinBlocksPerSm) +fna3_32x128x128_sm75_float32_cm_0_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 128, false>::kMinBlocksPerSm) +fna3_64x64x128_sm75_float32_cm_0_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 128, false>::kMinBlocksPerSm) +fna3_64x128x128_sm75_float32_cm_0_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 65536, false>::kMinBlocksPerSm) +fna3_32x128x65536_sm75_float32_cm_0_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 65536, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 65536, false>::kMinBlocksPerSm) +fna3_64x128x65536_sm75_float32_cm_0_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 65536, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 65536, false>::kMinBlocksPerSm) +fna3_64x64x65536_sm75_float32_cm_0_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 65536, false>::Params p); + + +template +void fna3_sm75_float32_cm_0_0_0(T cb) { + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 32, false>(), fna3_32x128x32_sm75_float32_cm_0_0_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 32, false>(), fna3_64x64x32_sm75_float32_cm_0_0_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 32, false>(), fna3_64x128x32_sm75_float32_cm_0_0_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 64, false>(), fna3_32x128x64_sm75_float32_cm_0_0_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 64, false>(), fna3_64x64x64_sm75_float32_cm_0_0_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 64, false>(), fna3_64x128x64_sm75_float32_cm_0_0_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 128, false>(), fna3_32x128x128_sm75_float32_cm_0_0_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 128, false>(), fna3_64x64x128_sm75_float32_cm_0_0_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 128, false>(), fna3_64x128x128_sm75_float32_cm_0_0_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 65536, false>(), fna3_32x128x65536_sm75_float32_cm_0_0_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 65536, false>(), fna3_64x128x65536_sm75_float32_cm_0_0_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 65536, false>(), fna3_64x64x65536_sm75_float32_cm_0_0_0); +} + +/////////////////////////////////////////////////////////////////// +// FNA-3D / float32 / SM75 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 32, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 32, true>::kMinBlocksPerSm) +fna3_32x128x32_sm75_float32_cm_0_0_0_rpb(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 32, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 32, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 32, true>::kMinBlocksPerSm) +fna3_64x64x32_sm75_float32_cm_0_0_0_rpb(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 32, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 32, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 32, true>::kMinBlocksPerSm) +fna3_64x128x32_sm75_float32_cm_0_0_0_rpb(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 32, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 64, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 64, true>::kMinBlocksPerSm) +fna3_32x128x64_sm75_float32_cm_0_0_0_rpb(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 64, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 64, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 64, true>::kMinBlocksPerSm) +fna3_64x64x64_sm75_float32_cm_0_0_0_rpb(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 64, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 64, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 64, true>::kMinBlocksPerSm) +fna3_64x128x64_sm75_float32_cm_0_0_0_rpb(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 64, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 128, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 128, true>::kMinBlocksPerSm) +fna3_32x128x128_sm75_float32_cm_0_0_0_rpb(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 128, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 128, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 128, true>::kMinBlocksPerSm) +fna3_64x64x128_sm75_float32_cm_0_0_0_rpb(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 128, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 128, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 128, true>::kMinBlocksPerSm) +fna3_64x128x128_sm75_float32_cm_0_0_0_rpb(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 128, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 65536, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 65536, true>::kMinBlocksPerSm) +fna3_32x128x65536_sm75_float32_cm_0_0_0_rpb(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 65536, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 65536, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 65536, true>::kMinBlocksPerSm) +fna3_64x128x65536_sm75_float32_cm_0_0_0_rpb(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 65536, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 65536, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 65536, true>::kMinBlocksPerSm) +fna3_64x64x65536_sm75_float32_cm_0_0_0_rpb(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 65536, true>::Params p); + + +template +void fna3_sm75_float32_cm_0_0_0_rpb(T cb) { + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 32, true>(), fna3_32x128x32_sm75_float32_cm_0_0_0_rpb); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 32, true>(), fna3_64x64x32_sm75_float32_cm_0_0_0_rpb); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 32, true>(), fna3_64x128x32_sm75_float32_cm_0_0_0_rpb); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 64, true>(), fna3_32x128x64_sm75_float32_cm_0_0_0_rpb); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 64, true>(), fna3_64x64x64_sm75_float32_cm_0_0_0_rpb); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 64, true>(), fna3_64x128x64_sm75_float32_cm_0_0_0_rpb); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 128, true>(), fna3_32x128x128_sm75_float32_cm_0_0_0_rpb); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 128, true>(), fna3_64x64x128_sm75_float32_cm_0_0_0_rpb); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 128, true>(), fna3_64x128x128_sm75_float32_cm_0_0_0_rpb); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 65536, true>(), fna3_32x128x65536_sm75_float32_cm_0_0_0_rpb); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 65536, true>(), fna3_64x128x65536_sm75_float32_cm_0_0_0_rpb); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 65536, true>(), fna3_64x64x65536_sm75_float32_cm_0_0_0_rpb); +} + +/////////////////////////////////////////////////////////////////// +// FNA-3D / float32 / SM75 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 32, false>::kMinBlocksPerSm) +fna3_32x128x32_sm75_float32_cm_0_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 32, false>::kMinBlocksPerSm) +fna3_64x64x32_sm75_float32_cm_0_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 32, false>::kMinBlocksPerSm) +fna3_64x128x32_sm75_float32_cm_0_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 64, false>::kMinBlocksPerSm) +fna3_32x128x64_sm75_float32_cm_0_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 64, false>::kMinBlocksPerSm) +fna3_64x64x64_sm75_float32_cm_0_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 64, false>::kMinBlocksPerSm) +fna3_64x128x64_sm75_float32_cm_0_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 128, false>::kMinBlocksPerSm) +fna3_32x128x128_sm75_float32_cm_0_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 128, false>::kMinBlocksPerSm) +fna3_64x64x128_sm75_float32_cm_0_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 128, false>::kMinBlocksPerSm) +fna3_64x128x128_sm75_float32_cm_0_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 65536, false>::kMinBlocksPerSm) +fna3_32x128x65536_sm75_float32_cm_0_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 65536, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 65536, false>::kMinBlocksPerSm) +fna3_64x128x65536_sm75_float32_cm_0_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 65536, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 65536, false>::kMinBlocksPerSm) +fna3_64x64x65536_sm75_float32_cm_0_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 65536, false>::Params p); + + +template +void fna3_sm75_float32_cm_0_0_1(T cb) { + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 32, false>(), fna3_32x128x32_sm75_float32_cm_0_0_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 32, false>(), fna3_64x64x32_sm75_float32_cm_0_0_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 32, false>(), fna3_64x128x32_sm75_float32_cm_0_0_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 64, false>(), fna3_32x128x64_sm75_float32_cm_0_0_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 64, false>(), fna3_64x64x64_sm75_float32_cm_0_0_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 64, false>(), fna3_64x128x64_sm75_float32_cm_0_0_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 128, false>(), fna3_32x128x128_sm75_float32_cm_0_0_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 128, false>(), fna3_64x64x128_sm75_float32_cm_0_0_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 128, false>(), fna3_64x128x128_sm75_float32_cm_0_0_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 65536, false>(), fna3_32x128x65536_sm75_float32_cm_0_0_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 65536, false>(), fna3_64x128x65536_sm75_float32_cm_0_0_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 65536, false>(), fna3_64x64x65536_sm75_float32_cm_0_0_1); +} + +/////////////////////////////////////////////////////////////////// +// FNA-3D / float32 / SM75 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 32, false>::kMinBlocksPerSm) +fna3_32x128x32_sm75_float32_cm_0_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 32, false>::kMinBlocksPerSm) +fna3_64x64x32_sm75_float32_cm_0_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 32, false>::kMinBlocksPerSm) +fna3_64x128x32_sm75_float32_cm_0_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 64, false>::kMinBlocksPerSm) +fna3_32x128x64_sm75_float32_cm_0_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 64, false>::kMinBlocksPerSm) +fna3_64x64x64_sm75_float32_cm_0_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 64, false>::kMinBlocksPerSm) +fna3_64x128x64_sm75_float32_cm_0_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 128, false>::kMinBlocksPerSm) +fna3_32x128x128_sm75_float32_cm_0_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 128, false>::kMinBlocksPerSm) +fna3_64x64x128_sm75_float32_cm_0_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 128, false>::kMinBlocksPerSm) +fna3_64x128x128_sm75_float32_cm_0_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 65536, false>::kMinBlocksPerSm) +fna3_32x128x65536_sm75_float32_cm_0_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 65536, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 65536, false>::kMinBlocksPerSm) +fna3_64x128x65536_sm75_float32_cm_0_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 65536, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 65536, false>::kMinBlocksPerSm) +fna3_64x64x65536_sm75_float32_cm_0_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 65536, false>::Params p); + + +template +void fna3_sm75_float32_cm_0_1_0(T cb) { + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 32, false>(), fna3_32x128x32_sm75_float32_cm_0_1_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 32, false>(), fna3_64x64x32_sm75_float32_cm_0_1_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 32, false>(), fna3_64x128x32_sm75_float32_cm_0_1_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 64, false>(), fna3_32x128x64_sm75_float32_cm_0_1_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 64, false>(), fna3_64x64x64_sm75_float32_cm_0_1_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 64, false>(), fna3_64x128x64_sm75_float32_cm_0_1_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 128, false>(), fna3_32x128x128_sm75_float32_cm_0_1_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 128, false>(), fna3_64x64x128_sm75_float32_cm_0_1_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 128, false>(), fna3_64x128x128_sm75_float32_cm_0_1_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 65536, false>(), fna3_32x128x65536_sm75_float32_cm_0_1_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 65536, false>(), fna3_64x128x65536_sm75_float32_cm_0_1_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 65536, false>(), fna3_64x64x65536_sm75_float32_cm_0_1_0); +} + +/////////////////////////////////////////////////////////////////// +// FNA-3D / float32 / SM75 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 32, false>::kMinBlocksPerSm) +fna3_32x128x32_sm75_float32_cm_0_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 32, false>::kMinBlocksPerSm) +fna3_64x64x32_sm75_float32_cm_0_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 32, false>::kMinBlocksPerSm) +fna3_64x128x32_sm75_float32_cm_0_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 64, false>::kMinBlocksPerSm) +fna3_32x128x64_sm75_float32_cm_0_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 64, false>::kMinBlocksPerSm) +fna3_64x64x64_sm75_float32_cm_0_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 64, false>::kMinBlocksPerSm) +fna3_64x128x64_sm75_float32_cm_0_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 128, false>::kMinBlocksPerSm) +fna3_32x128x128_sm75_float32_cm_0_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 128, false>::kMinBlocksPerSm) +fna3_64x64x128_sm75_float32_cm_0_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 128, false>::kMinBlocksPerSm) +fna3_64x128x128_sm75_float32_cm_0_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 65536, false>::kMinBlocksPerSm) +fna3_32x128x65536_sm75_float32_cm_0_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 65536, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 65536, false>::kMinBlocksPerSm) +fna3_64x128x65536_sm75_float32_cm_0_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 65536, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 65536, false>::kMinBlocksPerSm) +fna3_64x64x65536_sm75_float32_cm_0_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 65536, false>::Params p); + + +template +void fna3_sm75_float32_cm_0_1_1(T cb) { + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 32, false>(), fna3_32x128x32_sm75_float32_cm_0_1_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 32, false>(), fna3_64x64x32_sm75_float32_cm_0_1_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 32, false>(), fna3_64x128x32_sm75_float32_cm_0_1_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 64, false>(), fna3_32x128x64_sm75_float32_cm_0_1_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 64, false>(), fna3_64x64x64_sm75_float32_cm_0_1_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 64, false>(), fna3_64x128x64_sm75_float32_cm_0_1_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 128, false>(), fna3_32x128x128_sm75_float32_cm_0_1_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 128, false>(), fna3_64x64x128_sm75_float32_cm_0_1_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 128, false>(), fna3_64x128x128_sm75_float32_cm_0_1_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 65536, false>(), fna3_32x128x65536_sm75_float32_cm_0_1_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 65536, false>(), fna3_64x128x65536_sm75_float32_cm_0_1_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 65536, false>(), fna3_64x64x65536_sm75_float32_cm_0_1_1); +} + +/////////////////////////////////////////////////////////////////// +// FNA-3D / float32 / SM75 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 32, false>::kMinBlocksPerSm) +fna3_32x128x32_sm75_float32_cm_1_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 32, false>::kMinBlocksPerSm) +fna3_64x64x32_sm75_float32_cm_1_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 32, false>::kMinBlocksPerSm) +fna3_64x128x32_sm75_float32_cm_1_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 64, false>::kMinBlocksPerSm) +fna3_32x128x64_sm75_float32_cm_1_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 64, false>::kMinBlocksPerSm) +fna3_64x64x64_sm75_float32_cm_1_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 64, false>::kMinBlocksPerSm) +fna3_64x128x64_sm75_float32_cm_1_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 128, false>::kMinBlocksPerSm) +fna3_32x128x128_sm75_float32_cm_1_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 128, false>::kMinBlocksPerSm) +fna3_64x64x128_sm75_float32_cm_1_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 128, false>::kMinBlocksPerSm) +fna3_64x128x128_sm75_float32_cm_1_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 65536, false>::kMinBlocksPerSm) +fna3_32x128x65536_sm75_float32_cm_1_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 65536, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 65536, false>::kMinBlocksPerSm) +fna3_64x128x65536_sm75_float32_cm_1_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 65536, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 65536, false>::kMinBlocksPerSm) +fna3_64x64x65536_sm75_float32_cm_1_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 65536, false>::Params p); + + +template +void fna3_sm75_float32_cm_1_0_0(T cb) { + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 32, false>(), fna3_32x128x32_sm75_float32_cm_1_0_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 32, false>(), fna3_64x64x32_sm75_float32_cm_1_0_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 32, false>(), fna3_64x128x32_sm75_float32_cm_1_0_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 64, false>(), fna3_32x128x64_sm75_float32_cm_1_0_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 64, false>(), fna3_64x64x64_sm75_float32_cm_1_0_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 64, false>(), fna3_64x128x64_sm75_float32_cm_1_0_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 128, false>(), fna3_32x128x128_sm75_float32_cm_1_0_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 128, false>(), fna3_64x64x128_sm75_float32_cm_1_0_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 128, false>(), fna3_64x128x128_sm75_float32_cm_1_0_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 65536, false>(), fna3_32x128x65536_sm75_float32_cm_1_0_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 65536, false>(), fna3_64x128x65536_sm75_float32_cm_1_0_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 65536, false>(), fna3_64x64x65536_sm75_float32_cm_1_0_0); +} + +/////////////////////////////////////////////////////////////////// +// FNA-3D / float32 / SM75 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 32, false>::kMinBlocksPerSm) +fna3_32x128x32_sm75_float32_cm_1_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 32, false>::kMinBlocksPerSm) +fna3_64x64x32_sm75_float32_cm_1_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 32, false>::kMinBlocksPerSm) +fna3_64x128x32_sm75_float32_cm_1_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 64, false>::kMinBlocksPerSm) +fna3_32x128x64_sm75_float32_cm_1_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 64, false>::kMinBlocksPerSm) +fna3_64x64x64_sm75_float32_cm_1_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 64, false>::kMinBlocksPerSm) +fna3_64x128x64_sm75_float32_cm_1_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 128, false>::kMinBlocksPerSm) +fna3_32x128x128_sm75_float32_cm_1_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 128, false>::kMinBlocksPerSm) +fna3_64x64x128_sm75_float32_cm_1_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 128, false>::kMinBlocksPerSm) +fna3_64x128x128_sm75_float32_cm_1_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 65536, false>::kMinBlocksPerSm) +fna3_32x128x65536_sm75_float32_cm_1_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 65536, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 65536, false>::kMinBlocksPerSm) +fna3_64x128x65536_sm75_float32_cm_1_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 65536, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 65536, false>::kMinBlocksPerSm) +fna3_64x64x65536_sm75_float32_cm_1_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 65536, false>::Params p); + + +template +void fna3_sm75_float32_cm_1_0_1(T cb) { + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 32, false>(), fna3_32x128x32_sm75_float32_cm_1_0_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 32, false>(), fna3_64x64x32_sm75_float32_cm_1_0_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 32, false>(), fna3_64x128x32_sm75_float32_cm_1_0_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 64, false>(), fna3_32x128x64_sm75_float32_cm_1_0_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 64, false>(), fna3_64x64x64_sm75_float32_cm_1_0_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 64, false>(), fna3_64x128x64_sm75_float32_cm_1_0_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 128, false>(), fna3_32x128x128_sm75_float32_cm_1_0_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 128, false>(), fna3_64x64x128_sm75_float32_cm_1_0_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 128, false>(), fna3_64x128x128_sm75_float32_cm_1_0_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 65536, false>(), fna3_32x128x65536_sm75_float32_cm_1_0_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 65536, false>(), fna3_64x128x65536_sm75_float32_cm_1_0_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 65536, false>(), fna3_64x64x65536_sm75_float32_cm_1_0_1); +} + +/////////////////////////////////////////////////////////////////// +// FNA-3D / float32 / SM75 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 32, false>::kMinBlocksPerSm) +fna3_32x128x32_sm75_float32_cm_1_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 32, false>::kMinBlocksPerSm) +fna3_64x64x32_sm75_float32_cm_1_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 32, false>::kMinBlocksPerSm) +fna3_64x128x32_sm75_float32_cm_1_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 64, false>::kMinBlocksPerSm) +fna3_32x128x64_sm75_float32_cm_1_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 64, false>::kMinBlocksPerSm) +fna3_64x64x64_sm75_float32_cm_1_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 64, false>::kMinBlocksPerSm) +fna3_64x128x64_sm75_float32_cm_1_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 128, false>::kMinBlocksPerSm) +fna3_32x128x128_sm75_float32_cm_1_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 128, false>::kMinBlocksPerSm) +fna3_64x64x128_sm75_float32_cm_1_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 128, false>::kMinBlocksPerSm) +fna3_64x128x128_sm75_float32_cm_1_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 65536, false>::kMinBlocksPerSm) +fna3_32x128x65536_sm75_float32_cm_1_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 65536, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 65536, false>::kMinBlocksPerSm) +fna3_64x128x65536_sm75_float32_cm_1_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 65536, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 65536, false>::kMinBlocksPerSm) +fna3_64x64x65536_sm75_float32_cm_1_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 65536, false>::Params p); + + +template +void fna3_sm75_float32_cm_1_1_0(T cb) { + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 32, false>(), fna3_32x128x32_sm75_float32_cm_1_1_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 32, false>(), fna3_64x64x32_sm75_float32_cm_1_1_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 32, false>(), fna3_64x128x32_sm75_float32_cm_1_1_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 64, false>(), fna3_32x128x64_sm75_float32_cm_1_1_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 64, false>(), fna3_64x64x64_sm75_float32_cm_1_1_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 64, false>(), fna3_64x128x64_sm75_float32_cm_1_1_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 128, false>(), fna3_32x128x128_sm75_float32_cm_1_1_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 128, false>(), fna3_64x64x128_sm75_float32_cm_1_1_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 128, false>(), fna3_64x128x128_sm75_float32_cm_1_1_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 65536, false>(), fna3_32x128x65536_sm75_float32_cm_1_1_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 65536, false>(), fna3_64x128x65536_sm75_float32_cm_1_1_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 65536, false>(), fna3_64x64x65536_sm75_float32_cm_1_1_0); +} + +/////////////////////////////////////////////////////////////////// +// FNA-3D / float32 / SM75 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 32, false>::kMinBlocksPerSm) +fna3_32x128x32_sm75_float32_cm_1_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 32, false>::kMinBlocksPerSm) +fna3_64x64x32_sm75_float32_cm_1_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 32, false>::kMinBlocksPerSm) +fna3_64x128x32_sm75_float32_cm_1_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 64, false>::kMinBlocksPerSm) +fna3_32x128x64_sm75_float32_cm_1_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 64, false>::kMinBlocksPerSm) +fna3_64x64x64_sm75_float32_cm_1_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 64, false>::kMinBlocksPerSm) +fna3_64x128x64_sm75_float32_cm_1_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 128, false>::kMinBlocksPerSm) +fna3_32x128x128_sm75_float32_cm_1_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 128, false>::kMinBlocksPerSm) +fna3_64x64x128_sm75_float32_cm_1_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 128, false>::kMinBlocksPerSm) +fna3_64x128x128_sm75_float32_cm_1_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 65536, false>::kMinBlocksPerSm) +fna3_32x128x65536_sm75_float32_cm_1_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 65536, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 65536, false>::kMinBlocksPerSm) +fna3_64x128x65536_sm75_float32_cm_1_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 65536, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 65536, false>::kMinBlocksPerSm) +fna3_64x64x65536_sm75_float32_cm_1_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 65536, false>::Params p); + + +template +void fna3_sm75_float32_cm_1_1_1(T cb) { + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 32, false>(), fna3_32x128x32_sm75_float32_cm_1_1_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 32, false>(), fna3_64x64x32_sm75_float32_cm_1_1_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 32, false>(), fna3_64x128x32_sm75_float32_cm_1_1_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 64, false>(), fna3_32x128x64_sm75_float32_cm_1_1_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 64, false>(), fna3_64x64x64_sm75_float32_cm_1_1_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 64, false>(), fna3_64x128x64_sm75_float32_cm_1_1_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 128, false>(), fna3_32x128x128_sm75_float32_cm_1_1_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 128, false>(), fna3_64x64x128_sm75_float32_cm_1_1_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 128, false>(), fna3_64x128x128_sm75_float32_cm_1_1_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 65536, false>(), fna3_32x128x65536_sm75_float32_cm_1_1_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 65536, false>(), fna3_64x128x65536_sm75_float32_cm_1_1_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 65536, false>(), fna3_64x64x65536_sm75_float32_cm_1_1_1); +} + +/////////////////////////////////////////////////////////////////// +// FNA-3D / float16 / SM75 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 32, false>::kMinBlocksPerSm) +fna3_32x128x32_sm75_float16_cm_0_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 32, false>::kMinBlocksPerSm) +fna3_64x64x32_sm75_float16_cm_0_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 32, false>::kMinBlocksPerSm) +fna3_64x128x32_sm75_float16_cm_0_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 64, false>::kMinBlocksPerSm) +fna3_32x128x64_sm75_float16_cm_0_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 64, false>::kMinBlocksPerSm) +fna3_64x64x64_sm75_float16_cm_0_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 64, false>::kMinBlocksPerSm) +fna3_64x128x64_sm75_float16_cm_0_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 128, false>::kMinBlocksPerSm) +fna3_32x128x128_sm75_float16_cm_0_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 128, false>::kMinBlocksPerSm) +fna3_64x64x128_sm75_float16_cm_0_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 128, false>::kMinBlocksPerSm) +fna3_64x128x128_sm75_float16_cm_0_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 65536, false>::kMinBlocksPerSm) +fna3_32x128x65536_sm75_float16_cm_0_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 65536, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 65536, false>::kMinBlocksPerSm) +fna3_64x128x65536_sm75_float16_cm_0_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 65536, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 65536, false>::kMinBlocksPerSm) +fna3_64x64x65536_sm75_float16_cm_0_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 65536, false>::Params p); + + +template +void fna3_sm75_float16_cm_0_0_0(T cb) { + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 32, false>(), fna3_32x128x32_sm75_float16_cm_0_0_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 32, false>(), fna3_64x64x32_sm75_float16_cm_0_0_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 32, false>(), fna3_64x128x32_sm75_float16_cm_0_0_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 64, false>(), fna3_32x128x64_sm75_float16_cm_0_0_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 64, false>(), fna3_64x64x64_sm75_float16_cm_0_0_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 64, false>(), fna3_64x128x64_sm75_float16_cm_0_0_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 128, false>(), fna3_32x128x128_sm75_float16_cm_0_0_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 128, false>(), fna3_64x64x128_sm75_float16_cm_0_0_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 128, false>(), fna3_64x128x128_sm75_float16_cm_0_0_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 65536, false>(), fna3_32x128x65536_sm75_float16_cm_0_0_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 65536, false>(), fna3_64x128x65536_sm75_float16_cm_0_0_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 65536, false>(), fna3_64x64x65536_sm75_float16_cm_0_0_0); +} + +/////////////////////////////////////////////////////////////////// +// FNA-3D / float16 / SM75 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 32, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 32, true>::kMinBlocksPerSm) +fna3_32x128x32_sm75_float16_cm_0_0_0_rpb(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 32, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 32, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 32, true>::kMinBlocksPerSm) +fna3_64x64x32_sm75_float16_cm_0_0_0_rpb(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 32, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 32, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 32, true>::kMinBlocksPerSm) +fna3_64x128x32_sm75_float16_cm_0_0_0_rpb(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 32, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 64, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 64, true>::kMinBlocksPerSm) +fna3_32x128x64_sm75_float16_cm_0_0_0_rpb(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 64, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 64, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 64, true>::kMinBlocksPerSm) +fna3_64x64x64_sm75_float16_cm_0_0_0_rpb(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 64, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 64, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 64, true>::kMinBlocksPerSm) +fna3_64x128x64_sm75_float16_cm_0_0_0_rpb(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 64, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 128, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 128, true>::kMinBlocksPerSm) +fna3_32x128x128_sm75_float16_cm_0_0_0_rpb(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 128, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 128, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 128, true>::kMinBlocksPerSm) +fna3_64x64x128_sm75_float16_cm_0_0_0_rpb(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 128, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 128, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 128, true>::kMinBlocksPerSm) +fna3_64x128x128_sm75_float16_cm_0_0_0_rpb(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 128, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 65536, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 65536, true>::kMinBlocksPerSm) +fna3_32x128x65536_sm75_float16_cm_0_0_0_rpb(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 65536, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 65536, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 65536, true>::kMinBlocksPerSm) +fna3_64x128x65536_sm75_float16_cm_0_0_0_rpb(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 65536, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 65536, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 65536, true>::kMinBlocksPerSm) +fna3_64x64x65536_sm75_float16_cm_0_0_0_rpb(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 65536, true>::Params p); + + +template +void fna3_sm75_float16_cm_0_0_0_rpb(T cb) { + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 32, true>(), fna3_32x128x32_sm75_float16_cm_0_0_0_rpb); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 32, true>(), fna3_64x64x32_sm75_float16_cm_0_0_0_rpb); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 32, true>(), fna3_64x128x32_sm75_float16_cm_0_0_0_rpb); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 64, true>(), fna3_32x128x64_sm75_float16_cm_0_0_0_rpb); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 64, true>(), fna3_64x64x64_sm75_float16_cm_0_0_0_rpb); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 64, true>(), fna3_64x128x64_sm75_float16_cm_0_0_0_rpb); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 128, true>(), fna3_32x128x128_sm75_float16_cm_0_0_0_rpb); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 128, true>(), fna3_64x64x128_sm75_float16_cm_0_0_0_rpb); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 128, true>(), fna3_64x128x128_sm75_float16_cm_0_0_0_rpb); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 65536, true>(), fna3_32x128x65536_sm75_float16_cm_0_0_0_rpb); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 65536, true>(), fna3_64x128x65536_sm75_float16_cm_0_0_0_rpb); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 65536, true>(), fna3_64x64x65536_sm75_float16_cm_0_0_0_rpb); +} + +/////////////////////////////////////////////////////////////////// +// FNA-3D / float16 / SM75 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 32, false>::kMinBlocksPerSm) +fna3_32x128x32_sm75_float16_cm_0_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 32, false>::kMinBlocksPerSm) +fna3_64x64x32_sm75_float16_cm_0_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 32, false>::kMinBlocksPerSm) +fna3_64x128x32_sm75_float16_cm_0_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 64, false>::kMinBlocksPerSm) +fna3_32x128x64_sm75_float16_cm_0_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 64, false>::kMinBlocksPerSm) +fna3_64x64x64_sm75_float16_cm_0_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 64, false>::kMinBlocksPerSm) +fna3_64x128x64_sm75_float16_cm_0_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 128, false>::kMinBlocksPerSm) +fna3_32x128x128_sm75_float16_cm_0_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 128, false>::kMinBlocksPerSm) +fna3_64x64x128_sm75_float16_cm_0_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 128, false>::kMinBlocksPerSm) +fna3_64x128x128_sm75_float16_cm_0_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 65536, false>::kMinBlocksPerSm) +fna3_32x128x65536_sm75_float16_cm_0_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 65536, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 65536, false>::kMinBlocksPerSm) +fna3_64x128x65536_sm75_float16_cm_0_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 65536, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 65536, false>::kMinBlocksPerSm) +fna3_64x64x65536_sm75_float16_cm_0_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 65536, false>::Params p); + + +template +void fna3_sm75_float16_cm_0_0_1(T cb) { + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 32, false>(), fna3_32x128x32_sm75_float16_cm_0_0_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 32, false>(), fna3_64x64x32_sm75_float16_cm_0_0_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 32, false>(), fna3_64x128x32_sm75_float16_cm_0_0_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 64, false>(), fna3_32x128x64_sm75_float16_cm_0_0_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 64, false>(), fna3_64x64x64_sm75_float16_cm_0_0_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 64, false>(), fna3_64x128x64_sm75_float16_cm_0_0_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 128, false>(), fna3_32x128x128_sm75_float16_cm_0_0_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 128, false>(), fna3_64x64x128_sm75_float16_cm_0_0_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 128, false>(), fna3_64x128x128_sm75_float16_cm_0_0_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 65536, false>(), fna3_32x128x65536_sm75_float16_cm_0_0_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 65536, false>(), fna3_64x128x65536_sm75_float16_cm_0_0_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 65536, false>(), fna3_64x64x65536_sm75_float16_cm_0_0_1); +} + +/////////////////////////////////////////////////////////////////// +// FNA-3D / float16 / SM75 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 32, false>::kMinBlocksPerSm) +fna3_32x128x32_sm75_float16_cm_0_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 32, false>::kMinBlocksPerSm) +fna3_64x64x32_sm75_float16_cm_0_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 32, false>::kMinBlocksPerSm) +fna3_64x128x32_sm75_float16_cm_0_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 64, false>::kMinBlocksPerSm) +fna3_32x128x64_sm75_float16_cm_0_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 64, false>::kMinBlocksPerSm) +fna3_64x64x64_sm75_float16_cm_0_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 64, false>::kMinBlocksPerSm) +fna3_64x128x64_sm75_float16_cm_0_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 128, false>::kMinBlocksPerSm) +fna3_32x128x128_sm75_float16_cm_0_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 128, false>::kMinBlocksPerSm) +fna3_64x64x128_sm75_float16_cm_0_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 128, false>::kMinBlocksPerSm) +fna3_64x128x128_sm75_float16_cm_0_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 65536, false>::kMinBlocksPerSm) +fna3_32x128x65536_sm75_float16_cm_0_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 65536, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 65536, false>::kMinBlocksPerSm) +fna3_64x128x65536_sm75_float16_cm_0_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 65536, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 65536, false>::kMinBlocksPerSm) +fna3_64x64x65536_sm75_float16_cm_0_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 65536, false>::Params p); + + +template +void fna3_sm75_float16_cm_0_1_0(T cb) { + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 32, false>(), fna3_32x128x32_sm75_float16_cm_0_1_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 32, false>(), fna3_64x64x32_sm75_float16_cm_0_1_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 32, false>(), fna3_64x128x32_sm75_float16_cm_0_1_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 64, false>(), fna3_32x128x64_sm75_float16_cm_0_1_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 64, false>(), fna3_64x64x64_sm75_float16_cm_0_1_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 64, false>(), fna3_64x128x64_sm75_float16_cm_0_1_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 128, false>(), fna3_32x128x128_sm75_float16_cm_0_1_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 128, false>(), fna3_64x64x128_sm75_float16_cm_0_1_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 128, false>(), fna3_64x128x128_sm75_float16_cm_0_1_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 65536, false>(), fna3_32x128x65536_sm75_float16_cm_0_1_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 65536, false>(), fna3_64x128x65536_sm75_float16_cm_0_1_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 65536, false>(), fna3_64x64x65536_sm75_float16_cm_0_1_0); +} + +/////////////////////////////////////////////////////////////////// +// FNA-3D / float16 / SM75 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 32, false>::kMinBlocksPerSm) +fna3_32x128x32_sm75_float16_cm_0_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 32, false>::kMinBlocksPerSm) +fna3_64x64x32_sm75_float16_cm_0_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 32, false>::kMinBlocksPerSm) +fna3_64x128x32_sm75_float16_cm_0_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 64, false>::kMinBlocksPerSm) +fna3_32x128x64_sm75_float16_cm_0_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 64, false>::kMinBlocksPerSm) +fna3_64x64x64_sm75_float16_cm_0_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 64, false>::kMinBlocksPerSm) +fna3_64x128x64_sm75_float16_cm_0_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 128, false>::kMinBlocksPerSm) +fna3_32x128x128_sm75_float16_cm_0_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 128, false>::kMinBlocksPerSm) +fna3_64x64x128_sm75_float16_cm_0_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 128, false>::kMinBlocksPerSm) +fna3_64x128x128_sm75_float16_cm_0_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 65536, false>::kMinBlocksPerSm) +fna3_32x128x65536_sm75_float16_cm_0_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 65536, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 65536, false>::kMinBlocksPerSm) +fna3_64x128x65536_sm75_float16_cm_0_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 65536, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 65536, false>::kMinBlocksPerSm) +fna3_64x64x65536_sm75_float16_cm_0_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 65536, false>::Params p); + + +template +void fna3_sm75_float16_cm_0_1_1(T cb) { + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 32, false>(), fna3_32x128x32_sm75_float16_cm_0_1_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 32, false>(), fna3_64x64x32_sm75_float16_cm_0_1_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 32, false>(), fna3_64x128x32_sm75_float16_cm_0_1_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 64, false>(), fna3_32x128x64_sm75_float16_cm_0_1_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 64, false>(), fna3_64x64x64_sm75_float16_cm_0_1_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 64, false>(), fna3_64x128x64_sm75_float16_cm_0_1_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 128, false>(), fna3_32x128x128_sm75_float16_cm_0_1_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 128, false>(), fna3_64x64x128_sm75_float16_cm_0_1_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 128, false>(), fna3_64x128x128_sm75_float16_cm_0_1_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 65536, false>(), fna3_32x128x65536_sm75_float16_cm_0_1_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 65536, false>(), fna3_64x128x65536_sm75_float16_cm_0_1_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 65536, false>(), fna3_64x64x65536_sm75_float16_cm_0_1_1); +} + +/////////////////////////////////////////////////////////////////// +// FNA-3D / float16 / SM75 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 32, false>::kMinBlocksPerSm) +fna3_32x128x32_sm75_float16_cm_1_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 32, false>::kMinBlocksPerSm) +fna3_64x64x32_sm75_float16_cm_1_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 32, false>::kMinBlocksPerSm) +fna3_64x128x32_sm75_float16_cm_1_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 64, false>::kMinBlocksPerSm) +fna3_32x128x64_sm75_float16_cm_1_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 64, false>::kMinBlocksPerSm) +fna3_64x64x64_sm75_float16_cm_1_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 64, false>::kMinBlocksPerSm) +fna3_64x128x64_sm75_float16_cm_1_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 128, false>::kMinBlocksPerSm) +fna3_32x128x128_sm75_float16_cm_1_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 128, false>::kMinBlocksPerSm) +fna3_64x64x128_sm75_float16_cm_1_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 128, false>::kMinBlocksPerSm) +fna3_64x128x128_sm75_float16_cm_1_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 65536, false>::kMinBlocksPerSm) +fna3_32x128x65536_sm75_float16_cm_1_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 65536, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 65536, false>::kMinBlocksPerSm) +fna3_64x128x65536_sm75_float16_cm_1_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 65536, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 65536, false>::kMinBlocksPerSm) +fna3_64x64x65536_sm75_float16_cm_1_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 65536, false>::Params p); + + +template +void fna3_sm75_float16_cm_1_0_0(T cb) { + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 32, false>(), fna3_32x128x32_sm75_float16_cm_1_0_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 32, false>(), fna3_64x64x32_sm75_float16_cm_1_0_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 32, false>(), fna3_64x128x32_sm75_float16_cm_1_0_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 64, false>(), fna3_32x128x64_sm75_float16_cm_1_0_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 64, false>(), fna3_64x64x64_sm75_float16_cm_1_0_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 64, false>(), fna3_64x128x64_sm75_float16_cm_1_0_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 128, false>(), fna3_32x128x128_sm75_float16_cm_1_0_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 128, false>(), fna3_64x64x128_sm75_float16_cm_1_0_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 128, false>(), fna3_64x128x128_sm75_float16_cm_1_0_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 65536, false>(), fna3_32x128x65536_sm75_float16_cm_1_0_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 65536, false>(), fna3_64x128x65536_sm75_float16_cm_1_0_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 65536, false>(), fna3_64x64x65536_sm75_float16_cm_1_0_0); +} + +/////////////////////////////////////////////////////////////////// +// FNA-3D / float16 / SM75 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 32, false>::kMinBlocksPerSm) +fna3_32x128x32_sm75_float16_cm_1_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 32, false>::kMinBlocksPerSm) +fna3_64x64x32_sm75_float16_cm_1_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 32, false>::kMinBlocksPerSm) +fna3_64x128x32_sm75_float16_cm_1_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 64, false>::kMinBlocksPerSm) +fna3_32x128x64_sm75_float16_cm_1_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 64, false>::kMinBlocksPerSm) +fna3_64x64x64_sm75_float16_cm_1_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 64, false>::kMinBlocksPerSm) +fna3_64x128x64_sm75_float16_cm_1_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 128, false>::kMinBlocksPerSm) +fna3_32x128x128_sm75_float16_cm_1_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 128, false>::kMinBlocksPerSm) +fna3_64x64x128_sm75_float16_cm_1_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 128, false>::kMinBlocksPerSm) +fna3_64x128x128_sm75_float16_cm_1_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 65536, false>::kMinBlocksPerSm) +fna3_32x128x65536_sm75_float16_cm_1_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 65536, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 65536, false>::kMinBlocksPerSm) +fna3_64x128x65536_sm75_float16_cm_1_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 65536, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 65536, false>::kMinBlocksPerSm) +fna3_64x64x65536_sm75_float16_cm_1_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 65536, false>::Params p); + + +template +void fna3_sm75_float16_cm_1_0_1(T cb) { + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 32, false>(), fna3_32x128x32_sm75_float16_cm_1_0_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 32, false>(), fna3_64x64x32_sm75_float16_cm_1_0_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 32, false>(), fna3_64x128x32_sm75_float16_cm_1_0_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 64, false>(), fna3_32x128x64_sm75_float16_cm_1_0_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 64, false>(), fna3_64x64x64_sm75_float16_cm_1_0_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 64, false>(), fna3_64x128x64_sm75_float16_cm_1_0_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 128, false>(), fna3_32x128x128_sm75_float16_cm_1_0_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 128, false>(), fna3_64x64x128_sm75_float16_cm_1_0_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 128, false>(), fna3_64x128x128_sm75_float16_cm_1_0_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 65536, false>(), fna3_32x128x65536_sm75_float16_cm_1_0_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 65536, false>(), fna3_64x128x65536_sm75_float16_cm_1_0_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 65536, false>(), fna3_64x64x65536_sm75_float16_cm_1_0_1); +} + +/////////////////////////////////////////////////////////////////// +// FNA-3D / float16 / SM75 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 32, false>::kMinBlocksPerSm) +fna3_32x128x32_sm75_float16_cm_1_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 32, false>::kMinBlocksPerSm) +fna3_64x64x32_sm75_float16_cm_1_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 32, false>::kMinBlocksPerSm) +fna3_64x128x32_sm75_float16_cm_1_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 64, false>::kMinBlocksPerSm) +fna3_32x128x64_sm75_float16_cm_1_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 64, false>::kMinBlocksPerSm) +fna3_64x64x64_sm75_float16_cm_1_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 64, false>::kMinBlocksPerSm) +fna3_64x128x64_sm75_float16_cm_1_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 128, false>::kMinBlocksPerSm) +fna3_32x128x128_sm75_float16_cm_1_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 128, false>::kMinBlocksPerSm) +fna3_64x64x128_sm75_float16_cm_1_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 128, false>::kMinBlocksPerSm) +fna3_64x128x128_sm75_float16_cm_1_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 65536, false>::kMinBlocksPerSm) +fna3_32x128x65536_sm75_float16_cm_1_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 65536, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 65536, false>::kMinBlocksPerSm) +fna3_64x128x65536_sm75_float16_cm_1_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 65536, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 65536, false>::kMinBlocksPerSm) +fna3_64x64x65536_sm75_float16_cm_1_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 65536, false>::Params p); + + +template +void fna3_sm75_float16_cm_1_1_0(T cb) { + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 32, false>(), fna3_32x128x32_sm75_float16_cm_1_1_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 32, false>(), fna3_64x64x32_sm75_float16_cm_1_1_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 32, false>(), fna3_64x128x32_sm75_float16_cm_1_1_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 64, false>(), fna3_32x128x64_sm75_float16_cm_1_1_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 64, false>(), fna3_64x64x64_sm75_float16_cm_1_1_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 64, false>(), fna3_64x128x64_sm75_float16_cm_1_1_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 128, false>(), fna3_32x128x128_sm75_float16_cm_1_1_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 128, false>(), fna3_64x64x128_sm75_float16_cm_1_1_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 128, false>(), fna3_64x128x128_sm75_float16_cm_1_1_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 65536, false>(), fna3_32x128x65536_sm75_float16_cm_1_1_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 65536, false>(), fna3_64x128x65536_sm75_float16_cm_1_1_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 65536, false>(), fna3_64x64x65536_sm75_float16_cm_1_1_0); +} + +/////////////////////////////////////////////////////////////////// +// FNA-3D / float16 / SM75 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 32, false>::kMinBlocksPerSm) +fna3_32x128x32_sm75_float16_cm_1_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 32, false>::kMinBlocksPerSm) +fna3_64x64x32_sm75_float16_cm_1_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 32, false>::kMinBlocksPerSm) +fna3_64x128x32_sm75_float16_cm_1_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 64, false>::kMinBlocksPerSm) +fna3_32x128x64_sm75_float16_cm_1_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 64, false>::kMinBlocksPerSm) +fna3_64x64x64_sm75_float16_cm_1_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 64, false>::kMinBlocksPerSm) +fna3_64x128x64_sm75_float16_cm_1_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 128, false>::kMinBlocksPerSm) +fna3_32x128x128_sm75_float16_cm_1_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 128, false>::kMinBlocksPerSm) +fna3_64x64x128_sm75_float16_cm_1_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 128, false>::kMinBlocksPerSm) +fna3_64x128x128_sm75_float16_cm_1_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 65536, false>::kMinBlocksPerSm) +fna3_32x128x65536_sm75_float16_cm_1_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 65536, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 65536, false>::kMinBlocksPerSm) +fna3_64x128x65536_sm75_float16_cm_1_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 65536, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 65536, false>::kMinBlocksPerSm) +fna3_64x64x65536_sm75_float16_cm_1_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 65536, false>::Params p); + + +template +void fna3_sm75_float16_cm_1_1_1(T cb) { + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 32, false>(), fna3_32x128x32_sm75_float16_cm_1_1_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 32, false>(), fna3_64x64x32_sm75_float16_cm_1_1_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 32, false>(), fna3_64x128x32_sm75_float16_cm_1_1_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 64, false>(), fna3_32x128x64_sm75_float16_cm_1_1_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 64, false>(), fna3_64x64x64_sm75_float16_cm_1_1_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 64, false>(), fna3_64x128x64_sm75_float16_cm_1_1_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 128, false>(), fna3_32x128x128_sm75_float16_cm_1_1_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 128, false>(), fna3_64x64x128_sm75_float16_cm_1_1_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 128, false>(), fna3_64x128x128_sm75_float16_cm_1_1_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 65536, false>(), fna3_32x128x65536_sm75_float16_cm_1_1_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 65536, false>(), fna3_64x128x65536_sm75_float16_cm_1_1_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 65536, false>(), fna3_64x64x65536_sm75_float16_cm_1_1_1); +} + +/////////////////////////////////////////////////////////////////// +// FNA-3D / float32 / SM80 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 32, false>::kMinBlocksPerSm) +fna3_32x128x32_sm80_float32_cm_0_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 32, false>::kMinBlocksPerSm) +fna3_64x64x32_sm80_float32_cm_0_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 32, false>::kMinBlocksPerSm) +fna3_64x128x32_sm80_float32_cm_0_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 64, false>::kMinBlocksPerSm) +fna3_32x128x64_sm80_float32_cm_0_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 64, false>::kMinBlocksPerSm) +fna3_64x64x64_sm80_float32_cm_0_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 64, false>::kMinBlocksPerSm) +fna3_64x128x64_sm80_float32_cm_0_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 128, false>::kMinBlocksPerSm) +fna3_32x128x128_sm80_float32_cm_0_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 128, false>::kMinBlocksPerSm) +fna3_64x64x128_sm80_float32_cm_0_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 128, false>::kMinBlocksPerSm) +fna3_64x128x128_sm80_float32_cm_0_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 65536, false>::kMinBlocksPerSm) +fna3_32x128x65536_sm80_float32_cm_0_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 65536, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 65536, false>::kMinBlocksPerSm) +fna3_64x128x65536_sm80_float32_cm_0_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 65536, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 65536, false>::kMinBlocksPerSm) +fna3_64x64x65536_sm80_float32_cm_0_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 65536, false>::Params p); + + +template +void fna3_sm80_float32_cm_0_0_0(T cb) { + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 32, false>(), fna3_32x128x32_sm80_float32_cm_0_0_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 32, false>(), fna3_64x64x32_sm80_float32_cm_0_0_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 32, false>(), fna3_64x128x32_sm80_float32_cm_0_0_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 64, false>(), fna3_32x128x64_sm80_float32_cm_0_0_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 64, false>(), fna3_64x64x64_sm80_float32_cm_0_0_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 64, false>(), fna3_64x128x64_sm80_float32_cm_0_0_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 128, false>(), fna3_32x128x128_sm80_float32_cm_0_0_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 128, false>(), fna3_64x64x128_sm80_float32_cm_0_0_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 128, false>(), fna3_64x128x128_sm80_float32_cm_0_0_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 65536, false>(), fna3_32x128x65536_sm80_float32_cm_0_0_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 65536, false>(), fna3_64x128x65536_sm80_float32_cm_0_0_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 65536, false>(), fna3_64x64x65536_sm80_float32_cm_0_0_0); +} + +/////////////////////////////////////////////////////////////////// +// FNA-3D / float32 / SM80 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 32, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 32, true>::kMinBlocksPerSm) +fna3_32x128x32_sm80_float32_cm_0_0_0_rpb(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 32, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 32, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 32, true>::kMinBlocksPerSm) +fna3_64x64x32_sm80_float32_cm_0_0_0_rpb(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 32, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 32, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 32, true>::kMinBlocksPerSm) +fna3_64x128x32_sm80_float32_cm_0_0_0_rpb(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 32, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 64, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 64, true>::kMinBlocksPerSm) +fna3_32x128x64_sm80_float32_cm_0_0_0_rpb(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 64, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 64, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 64, true>::kMinBlocksPerSm) +fna3_64x64x64_sm80_float32_cm_0_0_0_rpb(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 64, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 64, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 64, true>::kMinBlocksPerSm) +fna3_64x128x64_sm80_float32_cm_0_0_0_rpb(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 64, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 128, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 128, true>::kMinBlocksPerSm) +fna3_32x128x128_sm80_float32_cm_0_0_0_rpb(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 128, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 128, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 128, true>::kMinBlocksPerSm) +fna3_64x64x128_sm80_float32_cm_0_0_0_rpb(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 128, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 128, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 128, true>::kMinBlocksPerSm) +fna3_64x128x128_sm80_float32_cm_0_0_0_rpb(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 128, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 65536, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 65536, true>::kMinBlocksPerSm) +fna3_32x128x65536_sm80_float32_cm_0_0_0_rpb(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 65536, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 65536, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 65536, true>::kMinBlocksPerSm) +fna3_64x128x65536_sm80_float32_cm_0_0_0_rpb(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 65536, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 65536, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 65536, true>::kMinBlocksPerSm) +fna3_64x64x65536_sm80_float32_cm_0_0_0_rpb(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 65536, true>::Params p); + + +template +void fna3_sm80_float32_cm_0_0_0_rpb(T cb) { + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 32, true>(), fna3_32x128x32_sm80_float32_cm_0_0_0_rpb); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 32, true>(), fna3_64x64x32_sm80_float32_cm_0_0_0_rpb); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 32, true>(), fna3_64x128x32_sm80_float32_cm_0_0_0_rpb); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 64, true>(), fna3_32x128x64_sm80_float32_cm_0_0_0_rpb); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 64, true>(), fna3_64x64x64_sm80_float32_cm_0_0_0_rpb); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 64, true>(), fna3_64x128x64_sm80_float32_cm_0_0_0_rpb); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 128, true>(), fna3_32x128x128_sm80_float32_cm_0_0_0_rpb); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 128, true>(), fna3_64x64x128_sm80_float32_cm_0_0_0_rpb); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 128, true>(), fna3_64x128x128_sm80_float32_cm_0_0_0_rpb); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 65536, true>(), fna3_32x128x65536_sm80_float32_cm_0_0_0_rpb); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 65536, true>(), fna3_64x128x65536_sm80_float32_cm_0_0_0_rpb); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 65536, true>(), fna3_64x64x65536_sm80_float32_cm_0_0_0_rpb); +} + +/////////////////////////////////////////////////////////////////// +// FNA-3D / float32 / SM80 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 32, false>::kMinBlocksPerSm) +fna3_32x128x32_sm80_float32_cm_0_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 32, false>::kMinBlocksPerSm) +fna3_64x64x32_sm80_float32_cm_0_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 32, false>::kMinBlocksPerSm) +fna3_64x128x32_sm80_float32_cm_0_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 64, false>::kMinBlocksPerSm) +fna3_32x128x64_sm80_float32_cm_0_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 64, false>::kMinBlocksPerSm) +fna3_64x64x64_sm80_float32_cm_0_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 64, false>::kMinBlocksPerSm) +fna3_64x128x64_sm80_float32_cm_0_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 128, false>::kMinBlocksPerSm) +fna3_32x128x128_sm80_float32_cm_0_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 128, false>::kMinBlocksPerSm) +fna3_64x64x128_sm80_float32_cm_0_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 128, false>::kMinBlocksPerSm) +fna3_64x128x128_sm80_float32_cm_0_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 65536, false>::kMinBlocksPerSm) +fna3_32x128x65536_sm80_float32_cm_0_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 65536, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 65536, false>::kMinBlocksPerSm) +fna3_64x128x65536_sm80_float32_cm_0_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 65536, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 65536, false>::kMinBlocksPerSm) +fna3_64x64x65536_sm80_float32_cm_0_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 65536, false>::Params p); + + +template +void fna3_sm80_float32_cm_0_0_1(T cb) { + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 32, false>(), fna3_32x128x32_sm80_float32_cm_0_0_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 32, false>(), fna3_64x64x32_sm80_float32_cm_0_0_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 32, false>(), fna3_64x128x32_sm80_float32_cm_0_0_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 64, false>(), fna3_32x128x64_sm80_float32_cm_0_0_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 64, false>(), fna3_64x64x64_sm80_float32_cm_0_0_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 64, false>(), fna3_64x128x64_sm80_float32_cm_0_0_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 128, false>(), fna3_32x128x128_sm80_float32_cm_0_0_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 128, false>(), fna3_64x64x128_sm80_float32_cm_0_0_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 128, false>(), fna3_64x128x128_sm80_float32_cm_0_0_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 65536, false>(), fna3_32x128x65536_sm80_float32_cm_0_0_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 65536, false>(), fna3_64x128x65536_sm80_float32_cm_0_0_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 65536, false>(), fna3_64x64x65536_sm80_float32_cm_0_0_1); +} + +/////////////////////////////////////////////////////////////////// +// FNA-3D / float32 / SM80 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 32, false>::kMinBlocksPerSm) +fna3_32x128x32_sm80_float32_cm_0_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 32, false>::kMinBlocksPerSm) +fna3_64x64x32_sm80_float32_cm_0_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 32, false>::kMinBlocksPerSm) +fna3_64x128x32_sm80_float32_cm_0_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 64, false>::kMinBlocksPerSm) +fna3_32x128x64_sm80_float32_cm_0_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 64, false>::kMinBlocksPerSm) +fna3_64x64x64_sm80_float32_cm_0_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 64, false>::kMinBlocksPerSm) +fna3_64x128x64_sm80_float32_cm_0_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 128, false>::kMinBlocksPerSm) +fna3_32x128x128_sm80_float32_cm_0_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 128, false>::kMinBlocksPerSm) +fna3_64x64x128_sm80_float32_cm_0_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 128, false>::kMinBlocksPerSm) +fna3_64x128x128_sm80_float32_cm_0_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 65536, false>::kMinBlocksPerSm) +fna3_32x128x65536_sm80_float32_cm_0_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 65536, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 65536, false>::kMinBlocksPerSm) +fna3_64x128x65536_sm80_float32_cm_0_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 65536, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 65536, false>::kMinBlocksPerSm) +fna3_64x64x65536_sm80_float32_cm_0_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 65536, false>::Params p); + + +template +void fna3_sm80_float32_cm_0_1_0(T cb) { + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 32, false>(), fna3_32x128x32_sm80_float32_cm_0_1_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 32, false>(), fna3_64x64x32_sm80_float32_cm_0_1_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 32, false>(), fna3_64x128x32_sm80_float32_cm_0_1_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 64, false>(), fna3_32x128x64_sm80_float32_cm_0_1_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 64, false>(), fna3_64x64x64_sm80_float32_cm_0_1_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 64, false>(), fna3_64x128x64_sm80_float32_cm_0_1_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 128, false>(), fna3_32x128x128_sm80_float32_cm_0_1_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 128, false>(), fna3_64x64x128_sm80_float32_cm_0_1_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 128, false>(), fna3_64x128x128_sm80_float32_cm_0_1_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 65536, false>(), fna3_32x128x65536_sm80_float32_cm_0_1_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 65536, false>(), fna3_64x128x65536_sm80_float32_cm_0_1_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 65536, false>(), fna3_64x64x65536_sm80_float32_cm_0_1_0); +} + +/////////////////////////////////////////////////////////////////// +// FNA-3D / float32 / SM80 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 32, false>::kMinBlocksPerSm) +fna3_32x128x32_sm80_float32_cm_0_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 32, false>::kMinBlocksPerSm) +fna3_64x64x32_sm80_float32_cm_0_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 32, false>::kMinBlocksPerSm) +fna3_64x128x32_sm80_float32_cm_0_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 64, false>::kMinBlocksPerSm) +fna3_32x128x64_sm80_float32_cm_0_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 64, false>::kMinBlocksPerSm) +fna3_64x64x64_sm80_float32_cm_0_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 64, false>::kMinBlocksPerSm) +fna3_64x128x64_sm80_float32_cm_0_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 128, false>::kMinBlocksPerSm) +fna3_32x128x128_sm80_float32_cm_0_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 128, false>::kMinBlocksPerSm) +fna3_64x64x128_sm80_float32_cm_0_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 128, false>::kMinBlocksPerSm) +fna3_64x128x128_sm80_float32_cm_0_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 65536, false>::kMinBlocksPerSm) +fna3_32x128x65536_sm80_float32_cm_0_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 65536, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 65536, false>::kMinBlocksPerSm) +fna3_64x128x65536_sm80_float32_cm_0_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 65536, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 65536, false>::kMinBlocksPerSm) +fna3_64x64x65536_sm80_float32_cm_0_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 65536, false>::Params p); + + +template +void fna3_sm80_float32_cm_0_1_1(T cb) { + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 32, false>(), fna3_32x128x32_sm80_float32_cm_0_1_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 32, false>(), fna3_64x64x32_sm80_float32_cm_0_1_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 32, false>(), fna3_64x128x32_sm80_float32_cm_0_1_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 64, false>(), fna3_32x128x64_sm80_float32_cm_0_1_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 64, false>(), fna3_64x64x64_sm80_float32_cm_0_1_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 64, false>(), fna3_64x128x64_sm80_float32_cm_0_1_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 128, false>(), fna3_32x128x128_sm80_float32_cm_0_1_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 128, false>(), fna3_64x64x128_sm80_float32_cm_0_1_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 128, false>(), fna3_64x128x128_sm80_float32_cm_0_1_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 65536, false>(), fna3_32x128x65536_sm80_float32_cm_0_1_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 65536, false>(), fna3_64x128x65536_sm80_float32_cm_0_1_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 65536, false>(), fna3_64x64x65536_sm80_float32_cm_0_1_1); +} + +/////////////////////////////////////////////////////////////////// +// FNA-3D / float32 / SM80 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 32, false>::kMinBlocksPerSm) +fna3_32x128x32_sm80_float32_cm_1_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 32, false>::kMinBlocksPerSm) +fna3_64x64x32_sm80_float32_cm_1_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 32, false>::kMinBlocksPerSm) +fna3_64x128x32_sm80_float32_cm_1_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 64, false>::kMinBlocksPerSm) +fna3_32x128x64_sm80_float32_cm_1_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 64, false>::kMinBlocksPerSm) +fna3_64x64x64_sm80_float32_cm_1_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 64, false>::kMinBlocksPerSm) +fna3_64x128x64_sm80_float32_cm_1_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 128, false>::kMinBlocksPerSm) +fna3_32x128x128_sm80_float32_cm_1_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 128, false>::kMinBlocksPerSm) +fna3_64x64x128_sm80_float32_cm_1_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 128, false>::kMinBlocksPerSm) +fna3_64x128x128_sm80_float32_cm_1_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 65536, false>::kMinBlocksPerSm) +fna3_32x128x65536_sm80_float32_cm_1_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 65536, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 65536, false>::kMinBlocksPerSm) +fna3_64x128x65536_sm80_float32_cm_1_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 65536, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 65536, false>::kMinBlocksPerSm) +fna3_64x64x65536_sm80_float32_cm_1_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 65536, false>::Params p); + + +template +void fna3_sm80_float32_cm_1_0_0(T cb) { + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 32, false>(), fna3_32x128x32_sm80_float32_cm_1_0_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 32, false>(), fna3_64x64x32_sm80_float32_cm_1_0_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 32, false>(), fna3_64x128x32_sm80_float32_cm_1_0_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 64, false>(), fna3_32x128x64_sm80_float32_cm_1_0_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 64, false>(), fna3_64x64x64_sm80_float32_cm_1_0_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 64, false>(), fna3_64x128x64_sm80_float32_cm_1_0_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 128, false>(), fna3_32x128x128_sm80_float32_cm_1_0_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 128, false>(), fna3_64x64x128_sm80_float32_cm_1_0_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 128, false>(), fna3_64x128x128_sm80_float32_cm_1_0_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 65536, false>(), fna3_32x128x65536_sm80_float32_cm_1_0_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 65536, false>(), fna3_64x128x65536_sm80_float32_cm_1_0_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 65536, false>(), fna3_64x64x65536_sm80_float32_cm_1_0_0); +} + +/////////////////////////////////////////////////////////////////// +// FNA-3D / float32 / SM80 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 32, false>::kMinBlocksPerSm) +fna3_32x128x32_sm80_float32_cm_1_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 32, false>::kMinBlocksPerSm) +fna3_64x64x32_sm80_float32_cm_1_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 32, false>::kMinBlocksPerSm) +fna3_64x128x32_sm80_float32_cm_1_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 64, false>::kMinBlocksPerSm) +fna3_32x128x64_sm80_float32_cm_1_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 64, false>::kMinBlocksPerSm) +fna3_64x64x64_sm80_float32_cm_1_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 64, false>::kMinBlocksPerSm) +fna3_64x128x64_sm80_float32_cm_1_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 128, false>::kMinBlocksPerSm) +fna3_32x128x128_sm80_float32_cm_1_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 128, false>::kMinBlocksPerSm) +fna3_64x64x128_sm80_float32_cm_1_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 128, false>::kMinBlocksPerSm) +fna3_64x128x128_sm80_float32_cm_1_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 65536, false>::kMinBlocksPerSm) +fna3_32x128x65536_sm80_float32_cm_1_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 65536, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 65536, false>::kMinBlocksPerSm) +fna3_64x128x65536_sm80_float32_cm_1_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 65536, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 65536, false>::kMinBlocksPerSm) +fna3_64x64x65536_sm80_float32_cm_1_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 65536, false>::Params p); + + +template +void fna3_sm80_float32_cm_1_0_1(T cb) { + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 32, false>(), fna3_32x128x32_sm80_float32_cm_1_0_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 32, false>(), fna3_64x64x32_sm80_float32_cm_1_0_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 32, false>(), fna3_64x128x32_sm80_float32_cm_1_0_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 64, false>(), fna3_32x128x64_sm80_float32_cm_1_0_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 64, false>(), fna3_64x64x64_sm80_float32_cm_1_0_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 64, false>(), fna3_64x128x64_sm80_float32_cm_1_0_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 128, false>(), fna3_32x128x128_sm80_float32_cm_1_0_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 128, false>(), fna3_64x64x128_sm80_float32_cm_1_0_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 128, false>(), fna3_64x128x128_sm80_float32_cm_1_0_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 65536, false>(), fna3_32x128x65536_sm80_float32_cm_1_0_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 65536, false>(), fna3_64x128x65536_sm80_float32_cm_1_0_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 65536, false>(), fna3_64x64x65536_sm80_float32_cm_1_0_1); +} + +/////////////////////////////////////////////////////////////////// +// FNA-3D / float32 / SM80 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 32, false>::kMinBlocksPerSm) +fna3_32x128x32_sm80_float32_cm_1_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 32, false>::kMinBlocksPerSm) +fna3_64x64x32_sm80_float32_cm_1_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 32, false>::kMinBlocksPerSm) +fna3_64x128x32_sm80_float32_cm_1_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 64, false>::kMinBlocksPerSm) +fna3_32x128x64_sm80_float32_cm_1_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 64, false>::kMinBlocksPerSm) +fna3_64x64x64_sm80_float32_cm_1_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 64, false>::kMinBlocksPerSm) +fna3_64x128x64_sm80_float32_cm_1_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 128, false>::kMinBlocksPerSm) +fna3_32x128x128_sm80_float32_cm_1_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 128, false>::kMinBlocksPerSm) +fna3_64x64x128_sm80_float32_cm_1_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 128, false>::kMinBlocksPerSm) +fna3_64x128x128_sm80_float32_cm_1_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 65536, false>::kMinBlocksPerSm) +fna3_32x128x65536_sm80_float32_cm_1_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 65536, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 65536, false>::kMinBlocksPerSm) +fna3_64x128x65536_sm80_float32_cm_1_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 65536, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 65536, false>::kMinBlocksPerSm) +fna3_64x64x65536_sm80_float32_cm_1_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 65536, false>::Params p); + + +template +void fna3_sm80_float32_cm_1_1_0(T cb) { + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 32, false>(), fna3_32x128x32_sm80_float32_cm_1_1_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 32, false>(), fna3_64x64x32_sm80_float32_cm_1_1_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 32, false>(), fna3_64x128x32_sm80_float32_cm_1_1_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 64, false>(), fna3_32x128x64_sm80_float32_cm_1_1_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 64, false>(), fna3_64x64x64_sm80_float32_cm_1_1_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 64, false>(), fna3_64x128x64_sm80_float32_cm_1_1_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 128, false>(), fna3_32x128x128_sm80_float32_cm_1_1_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 128, false>(), fna3_64x64x128_sm80_float32_cm_1_1_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 128, false>(), fna3_64x128x128_sm80_float32_cm_1_1_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 65536, false>(), fna3_32x128x65536_sm80_float32_cm_1_1_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 65536, false>(), fna3_64x128x65536_sm80_float32_cm_1_1_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 65536, false>(), fna3_64x64x65536_sm80_float32_cm_1_1_0); +} + +/////////////////////////////////////////////////////////////////// +// FNA-3D / float32 / SM80 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 32, false>::kMinBlocksPerSm) +fna3_32x128x32_sm80_float32_cm_1_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 32, false>::kMinBlocksPerSm) +fna3_64x64x32_sm80_float32_cm_1_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 32, false>::kMinBlocksPerSm) +fna3_64x128x32_sm80_float32_cm_1_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 64, false>::kMinBlocksPerSm) +fna3_32x128x64_sm80_float32_cm_1_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 64, false>::kMinBlocksPerSm) +fna3_64x64x64_sm80_float32_cm_1_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 64, false>::kMinBlocksPerSm) +fna3_64x128x64_sm80_float32_cm_1_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 128, false>::kMinBlocksPerSm) +fna3_32x128x128_sm80_float32_cm_1_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 128, false>::kMinBlocksPerSm) +fna3_64x64x128_sm80_float32_cm_1_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 128, false>::kMinBlocksPerSm) +fna3_64x128x128_sm80_float32_cm_1_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 65536, false>::kMinBlocksPerSm) +fna3_32x128x65536_sm80_float32_cm_1_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 65536, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 65536, false>::kMinBlocksPerSm) +fna3_64x128x65536_sm80_float32_cm_1_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 65536, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 65536, false>::kMinBlocksPerSm) +fna3_64x64x65536_sm80_float32_cm_1_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 65536, false>::Params p); + + +template +void fna3_sm80_float32_cm_1_1_1(T cb) { + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 32, false>(), fna3_32x128x32_sm80_float32_cm_1_1_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 32, false>(), fna3_64x64x32_sm80_float32_cm_1_1_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 32, false>(), fna3_64x128x32_sm80_float32_cm_1_1_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 64, false>(), fna3_32x128x64_sm80_float32_cm_1_1_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 64, false>(), fna3_64x64x64_sm80_float32_cm_1_1_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 64, false>(), fna3_64x128x64_sm80_float32_cm_1_1_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 128, false>(), fna3_32x128x128_sm80_float32_cm_1_1_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 128, false>(), fna3_64x64x128_sm80_float32_cm_1_1_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 128, false>(), fna3_64x128x128_sm80_float32_cm_1_1_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 65536, false>(), fna3_32x128x65536_sm80_float32_cm_1_1_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 65536, false>(), fna3_64x128x65536_sm80_float32_cm_1_1_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 65536, false>(), fna3_64x64x65536_sm80_float32_cm_1_1_1); +} + +/////////////////////////////////////////////////////////////////// +// FNA-3D / float16 / SM80 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 32, false>::kMinBlocksPerSm) +fna3_32x128x32_sm80_float16_cm_0_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 32, false>::kMinBlocksPerSm) +fna3_64x64x32_sm80_float16_cm_0_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 32, false>::kMinBlocksPerSm) +fna3_64x128x32_sm80_float16_cm_0_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 64, false>::kMinBlocksPerSm) +fna3_32x128x64_sm80_float16_cm_0_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 64, false>::kMinBlocksPerSm) +fna3_64x64x64_sm80_float16_cm_0_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 64, false>::kMinBlocksPerSm) +fna3_64x128x64_sm80_float16_cm_0_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 128, false>::kMinBlocksPerSm) +fna3_32x128x128_sm80_float16_cm_0_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 128, false>::kMinBlocksPerSm) +fna3_64x64x128_sm80_float16_cm_0_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 128, false>::kMinBlocksPerSm) +fna3_64x128x128_sm80_float16_cm_0_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 65536, false>::kMinBlocksPerSm) +fna3_32x128x65536_sm80_float16_cm_0_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 65536, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 65536, false>::kMinBlocksPerSm) +fna3_64x128x65536_sm80_float16_cm_0_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 65536, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 65536, false>::kMinBlocksPerSm) +fna3_64x64x65536_sm80_float16_cm_0_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 65536, false>::Params p); + + +template +void fna3_sm80_float16_cm_0_0_0(T cb) { + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 32, false>(), fna3_32x128x32_sm80_float16_cm_0_0_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 32, false>(), fna3_64x64x32_sm80_float16_cm_0_0_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 32, false>(), fna3_64x128x32_sm80_float16_cm_0_0_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 64, false>(), fna3_32x128x64_sm80_float16_cm_0_0_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 64, false>(), fna3_64x64x64_sm80_float16_cm_0_0_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 64, false>(), fna3_64x128x64_sm80_float16_cm_0_0_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 128, false>(), fna3_32x128x128_sm80_float16_cm_0_0_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 128, false>(), fna3_64x64x128_sm80_float16_cm_0_0_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 128, false>(), fna3_64x128x128_sm80_float16_cm_0_0_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 65536, false>(), fna3_32x128x65536_sm80_float16_cm_0_0_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 65536, false>(), fna3_64x128x65536_sm80_float16_cm_0_0_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 65536, false>(), fna3_64x64x65536_sm80_float16_cm_0_0_0); +} + +/////////////////////////////////////////////////////////////////// +// FNA-3D / float16 / SM80 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 32, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 32, true>::kMinBlocksPerSm) +fna3_32x128x32_sm80_float16_cm_0_0_0_rpb(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 32, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 32, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 32, true>::kMinBlocksPerSm) +fna3_64x64x32_sm80_float16_cm_0_0_0_rpb(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 32, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 32, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 32, true>::kMinBlocksPerSm) +fna3_64x128x32_sm80_float16_cm_0_0_0_rpb(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 32, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 64, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 64, true>::kMinBlocksPerSm) +fna3_32x128x64_sm80_float16_cm_0_0_0_rpb(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 64, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 64, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 64, true>::kMinBlocksPerSm) +fna3_64x64x64_sm80_float16_cm_0_0_0_rpb(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 64, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 64, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 64, true>::kMinBlocksPerSm) +fna3_64x128x64_sm80_float16_cm_0_0_0_rpb(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 64, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 128, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 128, true>::kMinBlocksPerSm) +fna3_32x128x128_sm80_float16_cm_0_0_0_rpb(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 128, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 128, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 128, true>::kMinBlocksPerSm) +fna3_64x64x128_sm80_float16_cm_0_0_0_rpb(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 128, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 128, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 128, true>::kMinBlocksPerSm) +fna3_64x128x128_sm80_float16_cm_0_0_0_rpb(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 128, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 65536, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 65536, true>::kMinBlocksPerSm) +fna3_32x128x65536_sm80_float16_cm_0_0_0_rpb(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 65536, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 65536, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 65536, true>::kMinBlocksPerSm) +fna3_64x128x65536_sm80_float16_cm_0_0_0_rpb(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 65536, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 65536, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 65536, true>::kMinBlocksPerSm) +fna3_64x64x65536_sm80_float16_cm_0_0_0_rpb(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 65536, true>::Params p); + + +template +void fna3_sm80_float16_cm_0_0_0_rpb(T cb) { + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 32, true>(), fna3_32x128x32_sm80_float16_cm_0_0_0_rpb); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 32, true>(), fna3_64x64x32_sm80_float16_cm_0_0_0_rpb); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 32, true>(), fna3_64x128x32_sm80_float16_cm_0_0_0_rpb); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 64, true>(), fna3_32x128x64_sm80_float16_cm_0_0_0_rpb); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 64, true>(), fna3_64x64x64_sm80_float16_cm_0_0_0_rpb); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 64, true>(), fna3_64x128x64_sm80_float16_cm_0_0_0_rpb); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 128, true>(), fna3_32x128x128_sm80_float16_cm_0_0_0_rpb); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 128, true>(), fna3_64x64x128_sm80_float16_cm_0_0_0_rpb); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 128, true>(), fna3_64x128x128_sm80_float16_cm_0_0_0_rpb); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 65536, true>(), fna3_32x128x65536_sm80_float16_cm_0_0_0_rpb); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 65536, true>(), fna3_64x128x65536_sm80_float16_cm_0_0_0_rpb); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 65536, true>(), fna3_64x64x65536_sm80_float16_cm_0_0_0_rpb); +} + +/////////////////////////////////////////////////////////////////// +// FNA-3D / float16 / SM80 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 32, false>::kMinBlocksPerSm) +fna3_32x128x32_sm80_float16_cm_0_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 32, false>::kMinBlocksPerSm) +fna3_64x64x32_sm80_float16_cm_0_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 32, false>::kMinBlocksPerSm) +fna3_64x128x32_sm80_float16_cm_0_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 64, false>::kMinBlocksPerSm) +fna3_32x128x64_sm80_float16_cm_0_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 64, false>::kMinBlocksPerSm) +fna3_64x64x64_sm80_float16_cm_0_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 64, false>::kMinBlocksPerSm) +fna3_64x128x64_sm80_float16_cm_0_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 128, false>::kMinBlocksPerSm) +fna3_32x128x128_sm80_float16_cm_0_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 128, false>::kMinBlocksPerSm) +fna3_64x64x128_sm80_float16_cm_0_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 128, false>::kMinBlocksPerSm) +fna3_64x128x128_sm80_float16_cm_0_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 65536, false>::kMinBlocksPerSm) +fna3_32x128x65536_sm80_float16_cm_0_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 65536, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 65536, false>::kMinBlocksPerSm) +fna3_64x128x65536_sm80_float16_cm_0_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 65536, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 65536, false>::kMinBlocksPerSm) +fna3_64x64x65536_sm80_float16_cm_0_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 65536, false>::Params p); + + +template +void fna3_sm80_float16_cm_0_0_1(T cb) { + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 32, false>(), fna3_32x128x32_sm80_float16_cm_0_0_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 32, false>(), fna3_64x64x32_sm80_float16_cm_0_0_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 32, false>(), fna3_64x128x32_sm80_float16_cm_0_0_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 64, false>(), fna3_32x128x64_sm80_float16_cm_0_0_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 64, false>(), fna3_64x64x64_sm80_float16_cm_0_0_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 64, false>(), fna3_64x128x64_sm80_float16_cm_0_0_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 128, false>(), fna3_32x128x128_sm80_float16_cm_0_0_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 128, false>(), fna3_64x64x128_sm80_float16_cm_0_0_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 128, false>(), fna3_64x128x128_sm80_float16_cm_0_0_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 65536, false>(), fna3_32x128x65536_sm80_float16_cm_0_0_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 65536, false>(), fna3_64x128x65536_sm80_float16_cm_0_0_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 65536, false>(), fna3_64x64x65536_sm80_float16_cm_0_0_1); +} + +/////////////////////////////////////////////////////////////////// +// FNA-3D / float16 / SM80 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 32, false>::kMinBlocksPerSm) +fna3_32x128x32_sm80_float16_cm_0_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 32, false>::kMinBlocksPerSm) +fna3_64x64x32_sm80_float16_cm_0_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 32, false>::kMinBlocksPerSm) +fna3_64x128x32_sm80_float16_cm_0_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 64, false>::kMinBlocksPerSm) +fna3_32x128x64_sm80_float16_cm_0_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 64, false>::kMinBlocksPerSm) +fna3_64x64x64_sm80_float16_cm_0_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 64, false>::kMinBlocksPerSm) +fna3_64x128x64_sm80_float16_cm_0_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 128, false>::kMinBlocksPerSm) +fna3_32x128x128_sm80_float16_cm_0_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 128, false>::kMinBlocksPerSm) +fna3_64x64x128_sm80_float16_cm_0_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 128, false>::kMinBlocksPerSm) +fna3_64x128x128_sm80_float16_cm_0_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 65536, false>::kMinBlocksPerSm) +fna3_32x128x65536_sm80_float16_cm_0_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 65536, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 65536, false>::kMinBlocksPerSm) +fna3_64x128x65536_sm80_float16_cm_0_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 65536, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 65536, false>::kMinBlocksPerSm) +fna3_64x64x65536_sm80_float16_cm_0_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 65536, false>::Params p); + + +template +void fna3_sm80_float16_cm_0_1_0(T cb) { + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 32, false>(), fna3_32x128x32_sm80_float16_cm_0_1_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 32, false>(), fna3_64x64x32_sm80_float16_cm_0_1_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 32, false>(), fna3_64x128x32_sm80_float16_cm_0_1_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 64, false>(), fna3_32x128x64_sm80_float16_cm_0_1_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 64, false>(), fna3_64x64x64_sm80_float16_cm_0_1_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 64, false>(), fna3_64x128x64_sm80_float16_cm_0_1_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 128, false>(), fna3_32x128x128_sm80_float16_cm_0_1_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 128, false>(), fna3_64x64x128_sm80_float16_cm_0_1_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 128, false>(), fna3_64x128x128_sm80_float16_cm_0_1_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 65536, false>(), fna3_32x128x65536_sm80_float16_cm_0_1_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 65536, false>(), fna3_64x128x65536_sm80_float16_cm_0_1_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 65536, false>(), fna3_64x64x65536_sm80_float16_cm_0_1_0); +} + +/////////////////////////////////////////////////////////////////// +// FNA-3D / float16 / SM80 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 32, false>::kMinBlocksPerSm) +fna3_32x128x32_sm80_float16_cm_0_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 32, false>::kMinBlocksPerSm) +fna3_64x64x32_sm80_float16_cm_0_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 32, false>::kMinBlocksPerSm) +fna3_64x128x32_sm80_float16_cm_0_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 64, false>::kMinBlocksPerSm) +fna3_32x128x64_sm80_float16_cm_0_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 64, false>::kMinBlocksPerSm) +fna3_64x64x64_sm80_float16_cm_0_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 64, false>::kMinBlocksPerSm) +fna3_64x128x64_sm80_float16_cm_0_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 128, false>::kMinBlocksPerSm) +fna3_32x128x128_sm80_float16_cm_0_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 128, false>::kMinBlocksPerSm) +fna3_64x64x128_sm80_float16_cm_0_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 128, false>::kMinBlocksPerSm) +fna3_64x128x128_sm80_float16_cm_0_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 65536, false>::kMinBlocksPerSm) +fna3_32x128x65536_sm80_float16_cm_0_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 65536, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 65536, false>::kMinBlocksPerSm) +fna3_64x128x65536_sm80_float16_cm_0_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 65536, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 65536, false>::kMinBlocksPerSm) +fna3_64x64x65536_sm80_float16_cm_0_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 65536, false>::Params p); + + +template +void fna3_sm80_float16_cm_0_1_1(T cb) { + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 32, false>(), fna3_32x128x32_sm80_float16_cm_0_1_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 32, false>(), fna3_64x64x32_sm80_float16_cm_0_1_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 32, false>(), fna3_64x128x32_sm80_float16_cm_0_1_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 64, false>(), fna3_32x128x64_sm80_float16_cm_0_1_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 64, false>(), fna3_64x64x64_sm80_float16_cm_0_1_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 64, false>(), fna3_64x128x64_sm80_float16_cm_0_1_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 128, false>(), fna3_32x128x128_sm80_float16_cm_0_1_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 128, false>(), fna3_64x64x128_sm80_float16_cm_0_1_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 128, false>(), fna3_64x128x128_sm80_float16_cm_0_1_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 65536, false>(), fna3_32x128x65536_sm80_float16_cm_0_1_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 65536, false>(), fna3_64x128x65536_sm80_float16_cm_0_1_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 65536, false>(), fna3_64x64x65536_sm80_float16_cm_0_1_1); +} + +/////////////////////////////////////////////////////////////////// +// FNA-3D / float16 / SM80 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 32, false>::kMinBlocksPerSm) +fna3_32x128x32_sm80_float16_cm_1_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 32, false>::kMinBlocksPerSm) +fna3_64x64x32_sm80_float16_cm_1_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 32, false>::kMinBlocksPerSm) +fna3_64x128x32_sm80_float16_cm_1_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 64, false>::kMinBlocksPerSm) +fna3_32x128x64_sm80_float16_cm_1_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 64, false>::kMinBlocksPerSm) +fna3_64x64x64_sm80_float16_cm_1_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 64, false>::kMinBlocksPerSm) +fna3_64x128x64_sm80_float16_cm_1_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 128, false>::kMinBlocksPerSm) +fna3_32x128x128_sm80_float16_cm_1_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 128, false>::kMinBlocksPerSm) +fna3_64x64x128_sm80_float16_cm_1_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 128, false>::kMinBlocksPerSm) +fna3_64x128x128_sm80_float16_cm_1_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 65536, false>::kMinBlocksPerSm) +fna3_32x128x65536_sm80_float16_cm_1_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 65536, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 65536, false>::kMinBlocksPerSm) +fna3_64x128x65536_sm80_float16_cm_1_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 65536, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 65536, false>::kMinBlocksPerSm) +fna3_64x64x65536_sm80_float16_cm_1_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 65536, false>::Params p); + + +template +void fna3_sm80_float16_cm_1_0_0(T cb) { + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 32, false>(), fna3_32x128x32_sm80_float16_cm_1_0_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 32, false>(), fna3_64x64x32_sm80_float16_cm_1_0_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 32, false>(), fna3_64x128x32_sm80_float16_cm_1_0_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 64, false>(), fna3_32x128x64_sm80_float16_cm_1_0_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 64, false>(), fna3_64x64x64_sm80_float16_cm_1_0_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 64, false>(), fna3_64x128x64_sm80_float16_cm_1_0_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 128, false>(), fna3_32x128x128_sm80_float16_cm_1_0_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 128, false>(), fna3_64x64x128_sm80_float16_cm_1_0_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 128, false>(), fna3_64x128x128_sm80_float16_cm_1_0_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 65536, false>(), fna3_32x128x65536_sm80_float16_cm_1_0_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 65536, false>(), fna3_64x128x65536_sm80_float16_cm_1_0_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 65536, false>(), fna3_64x64x65536_sm80_float16_cm_1_0_0); +} + +/////////////////////////////////////////////////////////////////// +// FNA-3D / float16 / SM80 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 32, false>::kMinBlocksPerSm) +fna3_32x128x32_sm80_float16_cm_1_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 32, false>::kMinBlocksPerSm) +fna3_64x64x32_sm80_float16_cm_1_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 32, false>::kMinBlocksPerSm) +fna3_64x128x32_sm80_float16_cm_1_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 64, false>::kMinBlocksPerSm) +fna3_32x128x64_sm80_float16_cm_1_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 64, false>::kMinBlocksPerSm) +fna3_64x64x64_sm80_float16_cm_1_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 64, false>::kMinBlocksPerSm) +fna3_64x128x64_sm80_float16_cm_1_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 128, false>::kMinBlocksPerSm) +fna3_32x128x128_sm80_float16_cm_1_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 128, false>::kMinBlocksPerSm) +fna3_64x64x128_sm80_float16_cm_1_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 128, false>::kMinBlocksPerSm) +fna3_64x128x128_sm80_float16_cm_1_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 65536, false>::kMinBlocksPerSm) +fna3_32x128x65536_sm80_float16_cm_1_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 65536, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 65536, false>::kMinBlocksPerSm) +fna3_64x128x65536_sm80_float16_cm_1_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 65536, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 65536, false>::kMinBlocksPerSm) +fna3_64x64x65536_sm80_float16_cm_1_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 65536, false>::Params p); + + +template +void fna3_sm80_float16_cm_1_0_1(T cb) { + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 32, false>(), fna3_32x128x32_sm80_float16_cm_1_0_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 32, false>(), fna3_64x64x32_sm80_float16_cm_1_0_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 32, false>(), fna3_64x128x32_sm80_float16_cm_1_0_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 64, false>(), fna3_32x128x64_sm80_float16_cm_1_0_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 64, false>(), fna3_64x64x64_sm80_float16_cm_1_0_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 64, false>(), fna3_64x128x64_sm80_float16_cm_1_0_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 128, false>(), fna3_32x128x128_sm80_float16_cm_1_0_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 128, false>(), fna3_64x64x128_sm80_float16_cm_1_0_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 128, false>(), fna3_64x128x128_sm80_float16_cm_1_0_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 65536, false>(), fna3_32x128x65536_sm80_float16_cm_1_0_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 65536, false>(), fna3_64x128x65536_sm80_float16_cm_1_0_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 65536, false>(), fna3_64x64x65536_sm80_float16_cm_1_0_1); +} + +/////////////////////////////////////////////////////////////////// +// FNA-3D / float16 / SM80 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 32, false>::kMinBlocksPerSm) +fna3_32x128x32_sm80_float16_cm_1_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 32, false>::kMinBlocksPerSm) +fna3_64x64x32_sm80_float16_cm_1_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 32, false>::kMinBlocksPerSm) +fna3_64x128x32_sm80_float16_cm_1_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 64, false>::kMinBlocksPerSm) +fna3_32x128x64_sm80_float16_cm_1_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 64, false>::kMinBlocksPerSm) +fna3_64x64x64_sm80_float16_cm_1_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 64, false>::kMinBlocksPerSm) +fna3_64x128x64_sm80_float16_cm_1_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 128, false>::kMinBlocksPerSm) +fna3_32x128x128_sm80_float16_cm_1_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 128, false>::kMinBlocksPerSm) +fna3_64x64x128_sm80_float16_cm_1_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 128, false>::kMinBlocksPerSm) +fna3_64x128x128_sm80_float16_cm_1_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 65536, false>::kMinBlocksPerSm) +fna3_32x128x65536_sm80_float16_cm_1_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 65536, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 65536, false>::kMinBlocksPerSm) +fna3_64x128x65536_sm80_float16_cm_1_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 65536, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 65536, false>::kMinBlocksPerSm) +fna3_64x64x65536_sm80_float16_cm_1_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 65536, false>::Params p); + + +template +void fna3_sm80_float16_cm_1_1_0(T cb) { + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 32, false>(), fna3_32x128x32_sm80_float16_cm_1_1_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 32, false>(), fna3_64x64x32_sm80_float16_cm_1_1_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 32, false>(), fna3_64x128x32_sm80_float16_cm_1_1_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 64, false>(), fna3_32x128x64_sm80_float16_cm_1_1_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 64, false>(), fna3_64x64x64_sm80_float16_cm_1_1_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 64, false>(), fna3_64x128x64_sm80_float16_cm_1_1_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 128, false>(), fna3_32x128x128_sm80_float16_cm_1_1_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 128, false>(), fna3_64x64x128_sm80_float16_cm_1_1_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 128, false>(), fna3_64x128x128_sm80_float16_cm_1_1_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 65536, false>(), fna3_32x128x65536_sm80_float16_cm_1_1_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 65536, false>(), fna3_64x128x65536_sm80_float16_cm_1_1_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 65536, false>(), fna3_64x64x65536_sm80_float16_cm_1_1_0); +} + +/////////////////////////////////////////////////////////////////// +// FNA-3D / float16 / SM80 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 32, false>::kMinBlocksPerSm) +fna3_32x128x32_sm80_float16_cm_1_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 32, false>::kMinBlocksPerSm) +fna3_64x64x32_sm80_float16_cm_1_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 32, false>::kMinBlocksPerSm) +fna3_64x128x32_sm80_float16_cm_1_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 64, false>::kMinBlocksPerSm) +fna3_32x128x64_sm80_float16_cm_1_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 64, false>::kMinBlocksPerSm) +fna3_64x64x64_sm80_float16_cm_1_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 64, false>::kMinBlocksPerSm) +fna3_64x128x64_sm80_float16_cm_1_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 128, false>::kMinBlocksPerSm) +fna3_32x128x128_sm80_float16_cm_1_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 128, false>::kMinBlocksPerSm) +fna3_64x64x128_sm80_float16_cm_1_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 128, false>::kMinBlocksPerSm) +fna3_64x128x128_sm80_float16_cm_1_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 65536, false>::kMinBlocksPerSm) +fna3_32x128x65536_sm80_float16_cm_1_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 65536, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 65536, false>::kMinBlocksPerSm) +fna3_64x128x65536_sm80_float16_cm_1_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 65536, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 65536, false>::kMinBlocksPerSm) +fna3_64x64x65536_sm80_float16_cm_1_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 65536, false>::Params p); + + +template +void fna3_sm80_float16_cm_1_1_1(T cb) { + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 32, false>(), fna3_32x128x32_sm80_float16_cm_1_1_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 32, false>(), fna3_64x64x32_sm80_float16_cm_1_1_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 32, false>(), fna3_64x128x32_sm80_float16_cm_1_1_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 64, false>(), fna3_32x128x64_sm80_float16_cm_1_1_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 64, false>(), fna3_64x64x64_sm80_float16_cm_1_1_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 64, false>(), fna3_64x128x64_sm80_float16_cm_1_1_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 128, false>(), fna3_32x128x128_sm80_float16_cm_1_1_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 128, false>(), fna3_64x64x128_sm80_float16_cm_1_1_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 128, false>(), fna3_64x128x128_sm80_float16_cm_1_1_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 65536, false>(), fna3_32x128x65536_sm80_float16_cm_1_1_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 65536, false>(), fna3_64x128x65536_sm80_float16_cm_1_1_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 65536, false>(), fna3_64x64x65536_sm80_float16_cm_1_1_1); +} + +/////////////////////////////////////////////////////////////////// +// FNA-3D / bfloat16 / SM80 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 32, false>::kMinBlocksPerSm) +fna3_32x128x32_sm80_bfloat16_cm_0_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 32, false>::kMinBlocksPerSm) +fna3_64x64x32_sm80_bfloat16_cm_0_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 32, false>::kMinBlocksPerSm) +fna3_64x128x32_sm80_bfloat16_cm_0_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 64, false>::kMinBlocksPerSm) +fna3_32x128x64_sm80_bfloat16_cm_0_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 64, false>::kMinBlocksPerSm) +fna3_64x64x64_sm80_bfloat16_cm_0_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 64, false>::kMinBlocksPerSm) +fna3_64x128x64_sm80_bfloat16_cm_0_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 128, false>::kMinBlocksPerSm) +fna3_32x128x128_sm80_bfloat16_cm_0_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 128, false>::kMinBlocksPerSm) +fna3_64x64x128_sm80_bfloat16_cm_0_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 128, false>::kMinBlocksPerSm) +fna3_64x128x128_sm80_bfloat16_cm_0_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 65536, false>::kMinBlocksPerSm) +fna3_32x128x65536_sm80_bfloat16_cm_0_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 65536, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 65536, false>::kMinBlocksPerSm) +fna3_64x128x65536_sm80_bfloat16_cm_0_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 65536, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 65536, false>::kMinBlocksPerSm) +fna3_64x64x65536_sm80_bfloat16_cm_0_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 65536, false>::Params p); + + +template +void fna3_sm80_bfloat16_cm_0_0_0(T cb) { + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 32, false>(), fna3_32x128x32_sm80_bfloat16_cm_0_0_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 32, false>(), fna3_64x64x32_sm80_bfloat16_cm_0_0_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 32, false>(), fna3_64x128x32_sm80_bfloat16_cm_0_0_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 64, false>(), fna3_32x128x64_sm80_bfloat16_cm_0_0_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 64, false>(), fna3_64x64x64_sm80_bfloat16_cm_0_0_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 64, false>(), fna3_64x128x64_sm80_bfloat16_cm_0_0_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 128, false>(), fna3_32x128x128_sm80_bfloat16_cm_0_0_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 128, false>(), fna3_64x64x128_sm80_bfloat16_cm_0_0_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 128, false>(), fna3_64x128x128_sm80_bfloat16_cm_0_0_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 65536, false>(), fna3_32x128x65536_sm80_bfloat16_cm_0_0_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 65536, false>(), fna3_64x128x65536_sm80_bfloat16_cm_0_0_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 65536, false>(), fna3_64x64x65536_sm80_bfloat16_cm_0_0_0); +} + +/////////////////////////////////////////////////////////////////// +// FNA-3D / bfloat16 / SM80 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 32, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 32, true>::kMinBlocksPerSm) +fna3_32x128x32_sm80_bfloat16_cm_0_0_0_rpb(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 32, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 32, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 32, true>::kMinBlocksPerSm) +fna3_64x64x32_sm80_bfloat16_cm_0_0_0_rpb(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 32, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 32, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 32, true>::kMinBlocksPerSm) +fna3_64x128x32_sm80_bfloat16_cm_0_0_0_rpb(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 32, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 64, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 64, true>::kMinBlocksPerSm) +fna3_32x128x64_sm80_bfloat16_cm_0_0_0_rpb(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 64, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 64, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 64, true>::kMinBlocksPerSm) +fna3_64x64x64_sm80_bfloat16_cm_0_0_0_rpb(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 64, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 64, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 64, true>::kMinBlocksPerSm) +fna3_64x128x64_sm80_bfloat16_cm_0_0_0_rpb(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 64, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 128, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 128, true>::kMinBlocksPerSm) +fna3_32x128x128_sm80_bfloat16_cm_0_0_0_rpb(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 128, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 128, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 128, true>::kMinBlocksPerSm) +fna3_64x64x128_sm80_bfloat16_cm_0_0_0_rpb(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 128, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 128, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 128, true>::kMinBlocksPerSm) +fna3_64x128x128_sm80_bfloat16_cm_0_0_0_rpb(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 128, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 65536, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 65536, true>::kMinBlocksPerSm) +fna3_32x128x65536_sm80_bfloat16_cm_0_0_0_rpb(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 65536, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 65536, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 65536, true>::kMinBlocksPerSm) +fna3_64x128x65536_sm80_bfloat16_cm_0_0_0_rpb(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 65536, true>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 65536, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 65536, true>::kMinBlocksPerSm) +fna3_64x64x65536_sm80_bfloat16_cm_0_0_0_rpb(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 65536, true>::Params p); + + +template +void fna3_sm80_bfloat16_cm_0_0_0_rpb(T cb) { + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 32, true>(), fna3_32x128x32_sm80_bfloat16_cm_0_0_0_rpb); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 32, true>(), fna3_64x64x32_sm80_bfloat16_cm_0_0_0_rpb); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 32, true>(), fna3_64x128x32_sm80_bfloat16_cm_0_0_0_rpb); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 64, true>(), fna3_32x128x64_sm80_bfloat16_cm_0_0_0_rpb); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 64, true>(), fna3_64x64x64_sm80_bfloat16_cm_0_0_0_rpb); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 64, true>(), fna3_64x128x64_sm80_bfloat16_cm_0_0_0_rpb); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 128, true>(), fna3_32x128x128_sm80_bfloat16_cm_0_0_0_rpb); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 128, true>(), fna3_64x64x128_sm80_bfloat16_cm_0_0_0_rpb); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 128, true>(), fna3_64x128x128_sm80_bfloat16_cm_0_0_0_rpb); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 65536, true>(), fna3_32x128x65536_sm80_bfloat16_cm_0_0_0_rpb); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 65536, true>(), fna3_64x128x65536_sm80_bfloat16_cm_0_0_0_rpb); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 65536, true>(), fna3_64x64x65536_sm80_bfloat16_cm_0_0_0_rpb); +} + +/////////////////////////////////////////////////////////////////// +// FNA-3D / bfloat16 / SM80 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 32, false>::kMinBlocksPerSm) +fna3_32x128x32_sm80_bfloat16_cm_0_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 32, false>::kMinBlocksPerSm) +fna3_64x64x32_sm80_bfloat16_cm_0_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 32, false>::kMinBlocksPerSm) +fna3_64x128x32_sm80_bfloat16_cm_0_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 64, false>::kMinBlocksPerSm) +fna3_32x128x64_sm80_bfloat16_cm_0_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 64, false>::kMinBlocksPerSm) +fna3_64x64x64_sm80_bfloat16_cm_0_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 64, false>::kMinBlocksPerSm) +fna3_64x128x64_sm80_bfloat16_cm_0_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 128, false>::kMinBlocksPerSm) +fna3_32x128x128_sm80_bfloat16_cm_0_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 128, false>::kMinBlocksPerSm) +fna3_64x64x128_sm80_bfloat16_cm_0_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 128, false>::kMinBlocksPerSm) +fna3_64x128x128_sm80_bfloat16_cm_0_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 65536, false>::kMinBlocksPerSm) +fna3_32x128x65536_sm80_bfloat16_cm_0_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 65536, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 65536, false>::kMinBlocksPerSm) +fna3_64x128x65536_sm80_bfloat16_cm_0_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 65536, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 65536, false>::kMinBlocksPerSm) +fna3_64x64x65536_sm80_bfloat16_cm_0_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 65536, false>::Params p); + + +template +void fna3_sm80_bfloat16_cm_0_0_1(T cb) { + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 32, false>(), fna3_32x128x32_sm80_bfloat16_cm_0_0_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 32, false>(), fna3_64x64x32_sm80_bfloat16_cm_0_0_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 32, false>(), fna3_64x128x32_sm80_bfloat16_cm_0_0_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 64, false>(), fna3_32x128x64_sm80_bfloat16_cm_0_0_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 64, false>(), fna3_64x64x64_sm80_bfloat16_cm_0_0_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 64, false>(), fna3_64x128x64_sm80_bfloat16_cm_0_0_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 128, false>(), fna3_32x128x128_sm80_bfloat16_cm_0_0_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 128, false>(), fna3_64x64x128_sm80_bfloat16_cm_0_0_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 128, false>(), fna3_64x128x128_sm80_bfloat16_cm_0_0_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 65536, false>(), fna3_32x128x65536_sm80_bfloat16_cm_0_0_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 65536, false>(), fna3_64x128x65536_sm80_bfloat16_cm_0_0_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 65536, false>(), fna3_64x64x65536_sm80_bfloat16_cm_0_0_1); +} + +/////////////////////////////////////////////////////////////////// +// FNA-3D / bfloat16 / SM80 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 32, false>::kMinBlocksPerSm) +fna3_32x128x32_sm80_bfloat16_cm_0_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 32, false>::kMinBlocksPerSm) +fna3_64x64x32_sm80_bfloat16_cm_0_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 32, false>::kMinBlocksPerSm) +fna3_64x128x32_sm80_bfloat16_cm_0_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 64, false>::kMinBlocksPerSm) +fna3_32x128x64_sm80_bfloat16_cm_0_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 64, false>::kMinBlocksPerSm) +fna3_64x64x64_sm80_bfloat16_cm_0_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 64, false>::kMinBlocksPerSm) +fna3_64x128x64_sm80_bfloat16_cm_0_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 128, false>::kMinBlocksPerSm) +fna3_32x128x128_sm80_bfloat16_cm_0_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 128, false>::kMinBlocksPerSm) +fna3_64x64x128_sm80_bfloat16_cm_0_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 128, false>::kMinBlocksPerSm) +fna3_64x128x128_sm80_bfloat16_cm_0_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 65536, false>::kMinBlocksPerSm) +fna3_32x128x65536_sm80_bfloat16_cm_0_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 65536, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 65536, false>::kMinBlocksPerSm) +fna3_64x128x65536_sm80_bfloat16_cm_0_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 65536, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 65536, false>::kMinBlocksPerSm) +fna3_64x64x65536_sm80_bfloat16_cm_0_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 65536, false>::Params p); + + +template +void fna3_sm80_bfloat16_cm_0_1_0(T cb) { + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 32, false>(), fna3_32x128x32_sm80_bfloat16_cm_0_1_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 32, false>(), fna3_64x64x32_sm80_bfloat16_cm_0_1_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 32, false>(), fna3_64x128x32_sm80_bfloat16_cm_0_1_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 64, false>(), fna3_32x128x64_sm80_bfloat16_cm_0_1_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 64, false>(), fna3_64x64x64_sm80_bfloat16_cm_0_1_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 64, false>(), fna3_64x128x64_sm80_bfloat16_cm_0_1_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 128, false>(), fna3_32x128x128_sm80_bfloat16_cm_0_1_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 128, false>(), fna3_64x64x128_sm80_bfloat16_cm_0_1_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 128, false>(), fna3_64x128x128_sm80_bfloat16_cm_0_1_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 65536, false>(), fna3_32x128x65536_sm80_bfloat16_cm_0_1_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 65536, false>(), fna3_64x128x65536_sm80_bfloat16_cm_0_1_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 65536, false>(), fna3_64x64x65536_sm80_bfloat16_cm_0_1_0); +} + +/////////////////////////////////////////////////////////////////// +// FNA-3D / bfloat16 / SM80 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 32, false>::kMinBlocksPerSm) +fna3_32x128x32_sm80_bfloat16_cm_0_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 32, false>::kMinBlocksPerSm) +fna3_64x64x32_sm80_bfloat16_cm_0_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 32, false>::kMinBlocksPerSm) +fna3_64x128x32_sm80_bfloat16_cm_0_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 64, false>::kMinBlocksPerSm) +fna3_32x128x64_sm80_bfloat16_cm_0_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 64, false>::kMinBlocksPerSm) +fna3_64x64x64_sm80_bfloat16_cm_0_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 64, false>::kMinBlocksPerSm) +fna3_64x128x64_sm80_bfloat16_cm_0_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 128, false>::kMinBlocksPerSm) +fna3_32x128x128_sm80_bfloat16_cm_0_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 128, false>::kMinBlocksPerSm) +fna3_64x64x128_sm80_bfloat16_cm_0_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 128, false>::kMinBlocksPerSm) +fna3_64x128x128_sm80_bfloat16_cm_0_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 65536, false>::kMinBlocksPerSm) +fna3_32x128x65536_sm80_bfloat16_cm_0_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 65536, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 65536, false>::kMinBlocksPerSm) +fna3_64x128x65536_sm80_bfloat16_cm_0_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 65536, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 65536, false>::kMinBlocksPerSm) +fna3_64x64x65536_sm80_bfloat16_cm_0_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 65536, false>::Params p); + + +template +void fna3_sm80_bfloat16_cm_0_1_1(T cb) { + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 32, false>(), fna3_32x128x32_sm80_bfloat16_cm_0_1_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 32, false>(), fna3_64x64x32_sm80_bfloat16_cm_0_1_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 32, false>(), fna3_64x128x32_sm80_bfloat16_cm_0_1_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 64, false>(), fna3_32x128x64_sm80_bfloat16_cm_0_1_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 64, false>(), fna3_64x64x64_sm80_bfloat16_cm_0_1_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 64, false>(), fna3_64x128x64_sm80_bfloat16_cm_0_1_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 128, false>(), fna3_32x128x128_sm80_bfloat16_cm_0_1_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 128, false>(), fna3_64x64x128_sm80_bfloat16_cm_0_1_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 128, false>(), fna3_64x128x128_sm80_bfloat16_cm_0_1_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 65536, false>(), fna3_32x128x65536_sm80_bfloat16_cm_0_1_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 65536, false>(), fna3_64x128x65536_sm80_bfloat16_cm_0_1_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 65536, false>(), fna3_64x64x65536_sm80_bfloat16_cm_0_1_1); +} + +/////////////////////////////////////////////////////////////////// +// FNA-3D / bfloat16 / SM80 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 32, false>::kMinBlocksPerSm) +fna3_32x128x32_sm80_bfloat16_cm_1_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 32, false>::kMinBlocksPerSm) +fna3_64x64x32_sm80_bfloat16_cm_1_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 32, false>::kMinBlocksPerSm) +fna3_64x128x32_sm80_bfloat16_cm_1_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 64, false>::kMinBlocksPerSm) +fna3_32x128x64_sm80_bfloat16_cm_1_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 64, false>::kMinBlocksPerSm) +fna3_64x64x64_sm80_bfloat16_cm_1_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 64, false>::kMinBlocksPerSm) +fna3_64x128x64_sm80_bfloat16_cm_1_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 128, false>::kMinBlocksPerSm) +fna3_32x128x128_sm80_bfloat16_cm_1_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 128, false>::kMinBlocksPerSm) +fna3_64x64x128_sm80_bfloat16_cm_1_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 128, false>::kMinBlocksPerSm) +fna3_64x128x128_sm80_bfloat16_cm_1_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 65536, false>::kMinBlocksPerSm) +fna3_32x128x65536_sm80_bfloat16_cm_1_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 65536, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 65536, false>::kMinBlocksPerSm) +fna3_64x128x65536_sm80_bfloat16_cm_1_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 65536, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 65536, false>::kMinBlocksPerSm) +fna3_64x64x65536_sm80_bfloat16_cm_1_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 65536, false>::Params p); + + +template +void fna3_sm80_bfloat16_cm_1_0_0(T cb) { + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 32, false>(), fna3_32x128x32_sm80_bfloat16_cm_1_0_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 32, false>(), fna3_64x64x32_sm80_bfloat16_cm_1_0_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 32, false>(), fna3_64x128x32_sm80_bfloat16_cm_1_0_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 64, false>(), fna3_32x128x64_sm80_bfloat16_cm_1_0_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 64, false>(), fna3_64x64x64_sm80_bfloat16_cm_1_0_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 64, false>(), fna3_64x128x64_sm80_bfloat16_cm_1_0_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 128, false>(), fna3_32x128x128_sm80_bfloat16_cm_1_0_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 128, false>(), fna3_64x64x128_sm80_bfloat16_cm_1_0_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 128, false>(), fna3_64x128x128_sm80_bfloat16_cm_1_0_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 65536, false>(), fna3_32x128x65536_sm80_bfloat16_cm_1_0_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 65536, false>(), fna3_64x128x65536_sm80_bfloat16_cm_1_0_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 65536, false>(), fna3_64x64x65536_sm80_bfloat16_cm_1_0_0); +} + +/////////////////////////////////////////////////////////////////// +// FNA-3D / bfloat16 / SM80 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 32, false>::kMinBlocksPerSm) +fna3_32x128x32_sm80_bfloat16_cm_1_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 32, false>::kMinBlocksPerSm) +fna3_64x64x32_sm80_bfloat16_cm_1_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 32, false>::kMinBlocksPerSm) +fna3_64x128x32_sm80_bfloat16_cm_1_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 64, false>::kMinBlocksPerSm) +fna3_32x128x64_sm80_bfloat16_cm_1_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 64, false>::kMinBlocksPerSm) +fna3_64x64x64_sm80_bfloat16_cm_1_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 64, false>::kMinBlocksPerSm) +fna3_64x128x64_sm80_bfloat16_cm_1_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 128, false>::kMinBlocksPerSm) +fna3_32x128x128_sm80_bfloat16_cm_1_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 128, false>::kMinBlocksPerSm) +fna3_64x64x128_sm80_bfloat16_cm_1_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 128, false>::kMinBlocksPerSm) +fna3_64x128x128_sm80_bfloat16_cm_1_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 65536, false>::kMinBlocksPerSm) +fna3_32x128x65536_sm80_bfloat16_cm_1_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 65536, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 65536, false>::kMinBlocksPerSm) +fna3_64x128x65536_sm80_bfloat16_cm_1_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 65536, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 65536, false>::kMinBlocksPerSm) +fna3_64x64x65536_sm80_bfloat16_cm_1_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 65536, false>::Params p); + + +template +void fna3_sm80_bfloat16_cm_1_0_1(T cb) { + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 32, false>(), fna3_32x128x32_sm80_bfloat16_cm_1_0_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 32, false>(), fna3_64x64x32_sm80_bfloat16_cm_1_0_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 32, false>(), fna3_64x128x32_sm80_bfloat16_cm_1_0_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 64, false>(), fna3_32x128x64_sm80_bfloat16_cm_1_0_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 64, false>(), fna3_64x64x64_sm80_bfloat16_cm_1_0_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 64, false>(), fna3_64x128x64_sm80_bfloat16_cm_1_0_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 128, false>(), fna3_32x128x128_sm80_bfloat16_cm_1_0_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 128, false>(), fna3_64x64x128_sm80_bfloat16_cm_1_0_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 128, false>(), fna3_64x128x128_sm80_bfloat16_cm_1_0_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 65536, false>(), fna3_32x128x65536_sm80_bfloat16_cm_1_0_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 65536, false>(), fna3_64x128x65536_sm80_bfloat16_cm_1_0_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 65536, false>(), fna3_64x64x65536_sm80_bfloat16_cm_1_0_1); +} + +/////////////////////////////////////////////////////////////////// +// FNA-3D / bfloat16 / SM80 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 32, false>::kMinBlocksPerSm) +fna3_32x128x32_sm80_bfloat16_cm_1_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 32, false>::kMinBlocksPerSm) +fna3_64x64x32_sm80_bfloat16_cm_1_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 32, false>::kMinBlocksPerSm) +fna3_64x128x32_sm80_bfloat16_cm_1_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 64, false>::kMinBlocksPerSm) +fna3_32x128x64_sm80_bfloat16_cm_1_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 64, false>::kMinBlocksPerSm) +fna3_64x64x64_sm80_bfloat16_cm_1_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 64, false>::kMinBlocksPerSm) +fna3_64x128x64_sm80_bfloat16_cm_1_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 128, false>::kMinBlocksPerSm) +fna3_32x128x128_sm80_bfloat16_cm_1_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 128, false>::kMinBlocksPerSm) +fna3_64x64x128_sm80_bfloat16_cm_1_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 128, false>::kMinBlocksPerSm) +fna3_64x128x128_sm80_bfloat16_cm_1_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 65536, false>::kMinBlocksPerSm) +fna3_32x128x65536_sm80_bfloat16_cm_1_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 65536, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 65536, false>::kMinBlocksPerSm) +fna3_64x128x65536_sm80_bfloat16_cm_1_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 65536, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 65536, false>::kMinBlocksPerSm) +fna3_64x64x65536_sm80_bfloat16_cm_1_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 65536, false>::Params p); + + +template +void fna3_sm80_bfloat16_cm_1_1_0(T cb) { + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 32, false>(), fna3_32x128x32_sm80_bfloat16_cm_1_1_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 32, false>(), fna3_64x64x32_sm80_bfloat16_cm_1_1_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 32, false>(), fna3_64x128x32_sm80_bfloat16_cm_1_1_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 64, false>(), fna3_32x128x64_sm80_bfloat16_cm_1_1_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 64, false>(), fna3_64x64x64_sm80_bfloat16_cm_1_1_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 64, false>(), fna3_64x128x64_sm80_bfloat16_cm_1_1_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 128, false>(), fna3_32x128x128_sm80_bfloat16_cm_1_1_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 128, false>(), fna3_64x64x128_sm80_bfloat16_cm_1_1_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 128, false>(), fna3_64x128x128_sm80_bfloat16_cm_1_1_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 65536, false>(), fna3_32x128x65536_sm80_bfloat16_cm_1_1_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 65536, false>(), fna3_64x128x65536_sm80_bfloat16_cm_1_1_0); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 65536, false>(), fna3_64x64x65536_sm80_bfloat16_cm_1_1_0); +} + +/////////////////////////////////////////////////////////////////// +// FNA-3D / bfloat16 / SM80 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 32, false>::kMinBlocksPerSm) +fna3_32x128x32_sm80_bfloat16_cm_1_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 32, false>::kMinBlocksPerSm) +fna3_64x64x32_sm80_bfloat16_cm_1_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 32, false>::kMinBlocksPerSm) +fna3_64x128x32_sm80_bfloat16_cm_1_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 32, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 64, false>::kMinBlocksPerSm) +fna3_32x128x64_sm80_bfloat16_cm_1_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 64, false>::kMinBlocksPerSm) +fna3_64x64x64_sm80_bfloat16_cm_1_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 64, false>::kMinBlocksPerSm) +fna3_64x128x64_sm80_bfloat16_cm_1_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 64, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 128, false>::kMinBlocksPerSm) +fna3_32x128x128_sm80_bfloat16_cm_1_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 128, false>::kMinBlocksPerSm) +fna3_64x64x128_sm80_bfloat16_cm_1_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 128, false>::kMinBlocksPerSm) +fna3_64x128x128_sm80_bfloat16_cm_1_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 128, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 65536, false>::kMinBlocksPerSm) +fna3_32x128x65536_sm80_bfloat16_cm_1_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 65536, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 65536, false>::kMinBlocksPerSm) +fna3_64x128x65536_sm80_bfloat16_cm_1_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 65536, false>::Params p); + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 65536, false>::kMinBlocksPerSm) +fna3_64x64x65536_sm80_bfloat16_cm_1_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 65536, false>::Params p); + + +template +void fna3_sm80_bfloat16_cm_1_1_1(T cb) { + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 32, false>(), fna3_32x128x32_sm80_bfloat16_cm_1_1_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 32, false>(), fna3_64x64x32_sm80_bfloat16_cm_1_1_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 32, false>(), fna3_64x128x32_sm80_bfloat16_cm_1_1_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 64, false>(), fna3_32x128x64_sm80_bfloat16_cm_1_1_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 64, false>(), fna3_64x64x64_sm80_bfloat16_cm_1_1_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 64, false>(), fna3_64x128x64_sm80_bfloat16_cm_1_1_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 128, false>(), fna3_32x128x128_sm80_bfloat16_cm_1_1_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 128, false>(), fna3_64x64x128_sm80_bfloat16_cm_1_1_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 128, false>(), fna3_64x128x128_sm80_bfloat16_cm_1_1_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 65536, false>(), fna3_32x128x65536_sm80_bfloat16_cm_1_1_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 65536, false>(), fna3_64x128x65536_sm80_bfloat16_cm_1_1_1); + cb(FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 65536, false>(), fna3_64x64x65536_sm80_bfloat16_cm_1_1_1); +} + +} // namespace natten +} // namespace cuda +} // namespace fna + diff --git a/csrc/autogen/include/natten_autogen/cuda/gemm/2d/sm70/dispatch_align.h b/csrc/autogen/include/natten_autogen/cuda/gemm/2d/sm70/dispatch_align.h index ddefa77..0fe1169 100644 --- a/csrc/autogen/include/natten_autogen/cuda/gemm/2d/sm70/dispatch_align.h +++ b/csrc/autogen/include/natten_autogen/cuda/gemm/2d/sm70/dispatch_align.h @@ -330,640 +330,1540 @@ namespace gemm { } \ }(); +#define DISPATCH_ALIGNMENT_na2d_pn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks35(dim, ...) \ + [&] { \ + if (dim % 8 == 0) { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks35_align8(__VA_ARGS__); \ + } \ + else if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks35_align4(__VA_ARGS__); \ + } \ + else if (dim % 2 == 0) { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks35_align2(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na2d_pn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks35 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=half. " \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_pn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks37(dim, ...) \ + [&] { \ + if (dim % 8 == 0) { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks37_align8(__VA_ARGS__); \ + } \ + else if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks37_align4(__VA_ARGS__); \ + } \ + else if (dim % 2 == 0) { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks37_align2(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na2d_pn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks37 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=half. " \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_pn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks39(dim, ...) \ + [&] { \ + if (dim % 8 == 0) { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks39_align8(__VA_ARGS__); \ + } \ + else if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks39_align4(__VA_ARGS__); \ + } \ + else if (dim % 2 == 0) { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks39_align2(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na2d_pn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks39 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=half. " \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_pn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks41(dim, ...) \ + [&] { \ + if (dim % 8 == 0) { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks41_align8(__VA_ARGS__); \ + } \ + else if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks41_align4(__VA_ARGS__); \ + } \ + else if (dim % 2 == 0) { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks41_align2(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na2d_pn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks41 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=half. " \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_pn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks43(dim, ...) \ + [&] { \ + if (dim % 8 == 0) { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks43_align8(__VA_ARGS__); \ + } \ + else if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks43_align4(__VA_ARGS__); \ + } \ + else if (dim % 2 == 0) { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks43_align2(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na2d_pn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks43 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=half. " \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_pn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks45(dim, ...) \ + [&] { \ + if (dim % 8 == 0) { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks45_align8(__VA_ARGS__); \ + } \ + else if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks45_align4(__VA_ARGS__); \ + } \ + else if (dim % 2 == 0) { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks45_align2(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na2d_pn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks45 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=half. " \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_pn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks47(dim, ...) \ + [&] { \ + if (dim % 8 == 0) { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks47_align8(__VA_ARGS__); \ + } \ + else if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks47_align4(__VA_ARGS__); \ + } \ + else if (dim % 2 == 0) { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks47_align2(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na2d_pn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks47 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=half. " \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_pn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks49(dim, ...) \ + [&] { \ + if (dim % 8 == 0) { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks49_align8(__VA_ARGS__); \ + } \ + else if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks49_align4(__VA_ARGS__); \ + } \ + else if (dim % 2 == 0) { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks49_align2(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na2d_pn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks49 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=half. " \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_pn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks51(dim, ...) \ + [&] { \ + if (dim % 8 == 0) { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks51_align8(__VA_ARGS__); \ + } \ + else if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks51_align4(__VA_ARGS__); \ + } \ + else if (dim % 2 == 0) { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks51_align2(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na2d_pn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks51 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=half. " \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_pn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks53(dim, ...) \ + [&] { \ + if (dim % 8 == 0) { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks53_align8(__VA_ARGS__); \ + } \ + else if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks53_align4(__VA_ARGS__); \ + } \ + else if (dim % 2 == 0) { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks53_align2(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na2d_pn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks53 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=half. " \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_pn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks55(dim, ...) \ + [&] { \ + if (dim % 8 == 0) { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks55_align8(__VA_ARGS__); \ + } \ + else if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks55_align4(__VA_ARGS__); \ + } \ + else if (dim % 2 == 0) { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks55_align2(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na2d_pn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks55 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=half. " \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_pn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks57(dim, ...) \ + [&] { \ + if (dim % 8 == 0) { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks57_align8(__VA_ARGS__); \ + } \ + else if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks57_align4(__VA_ARGS__); \ + } \ + else if (dim % 2 == 0) { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks57_align2(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na2d_pn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks57 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=half. " \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_pn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks59(dim, ...) \ + [&] { \ + if (dim % 8 == 0) { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks59_align8(__VA_ARGS__); \ + } \ + else if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks59_align4(__VA_ARGS__); \ + } \ + else if (dim % 2 == 0) { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks59_align2(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na2d_pn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks59 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=half. " \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_pn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks61(dim, ...) \ + [&] { \ + if (dim % 8 == 0) { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks61_align8(__VA_ARGS__); \ + } \ + else if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks61_align4(__VA_ARGS__); \ + } \ + else if (dim % 2 == 0) { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks61_align2(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na2d_pn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks61 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=half. " \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_pn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks63(dim, ...) \ + [&] { \ + if (dim % 8 == 0) { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks63_align8(__VA_ARGS__); \ + } \ + else if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks63_align4(__VA_ARGS__); \ + } \ + else if (dim % 2 == 0) { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks63_align2(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na2d_pn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks63 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=half. " \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + #define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks3(dim, ...) \ [&] { \ if (dim % 8 == 0) { \ - natten::cuda::gemm::na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks3_align8(__VA_ARGS__); \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks3_align8(__VA_ARGS__); \ + } \ + else if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks3_align4(__VA_ARGS__); \ + } \ + else if (dim % 2 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks3_align2(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks3 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=half. " \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks5(dim, ...) \ + [&] { \ + if (dim % 8 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks5_align8(__VA_ARGS__); \ + } \ + else if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks5_align4(__VA_ARGS__); \ + } \ + else if (dim % 2 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks5_align2(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks5 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=half. " \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks7(dim, ...) \ + [&] { \ + if (dim % 8 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks7_align8(__VA_ARGS__); \ + } \ + else if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks7_align4(__VA_ARGS__); \ + } \ + else if (dim % 2 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks7_align2(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks7 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=half. " \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks9(dim, ...) \ + [&] { \ + if (dim % 8 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks9_align8(__VA_ARGS__); \ + } \ + else if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks9_align4(__VA_ARGS__); \ + } \ + else if (dim % 2 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks9_align2(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks9 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=half. " \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks11(dim, ...) \ + [&] { \ + if (dim % 8 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks11_align8(__VA_ARGS__); \ + } \ + else if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks11_align4(__VA_ARGS__); \ + } \ + else if (dim % 2 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks11_align2(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks11 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=half. " \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks13(dim, ...) \ + [&] { \ + if (dim % 8 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks13_align8(__VA_ARGS__); \ + } \ + else if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks13_align4(__VA_ARGS__); \ + } \ + else if (dim % 2 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks13_align2(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks13 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=half. " \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks15(dim, ...) \ + [&] { \ + if (dim % 8 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks15_align8(__VA_ARGS__); \ + } \ + else if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks15_align4(__VA_ARGS__); \ + } \ + else if (dim % 2 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks15_align2(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks15 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=half. " \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks17(dim, ...) \ + [&] { \ + if (dim % 8 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks17_align8(__VA_ARGS__); \ + } \ + else if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks17_align4(__VA_ARGS__); \ + } \ + else if (dim % 2 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks17_align2(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks17 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=half. " \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks19(dim, ...) \ + [&] { \ + if (dim % 8 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks19_align8(__VA_ARGS__); \ + } \ + else if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks19_align4(__VA_ARGS__); \ + } \ + else if (dim % 2 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks19_align2(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks19 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=half. " \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks21(dim, ...) \ + [&] { \ + if (dim % 8 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks21_align8(__VA_ARGS__); \ + } \ + else if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks21_align4(__VA_ARGS__); \ + } \ + else if (dim % 2 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks21_align2(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks21 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=half. " \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks23(dim, ...) \ + [&] { \ + if (dim % 8 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks23_align8(__VA_ARGS__); \ + } \ + else if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks23_align4(__VA_ARGS__); \ + } \ + else if (dim % 2 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks23_align2(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks23 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=half. " \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks25(dim, ...) \ + [&] { \ + if (dim % 8 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks25_align8(__VA_ARGS__); \ + } \ + else if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks25_align4(__VA_ARGS__); \ + } \ + else if (dim % 2 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks25_align2(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks25 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=half. " \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks27(dim, ...) \ + [&] { \ + if (dim % 8 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks27_align8(__VA_ARGS__); \ + } \ + else if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks27_align4(__VA_ARGS__); \ + } \ + else if (dim % 2 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks27_align2(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks27 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=half. " \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks29(dim, ...) \ + [&] { \ + if (dim % 8 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks29_align8(__VA_ARGS__); \ + } \ + else if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks29_align4(__VA_ARGS__); \ + } \ + else if (dim % 2 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks29_align2(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks29 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=half. " \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks31(dim, ...) \ + [&] { \ + if (dim % 8 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks31_align8(__VA_ARGS__); \ + } \ + else if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks31_align4(__VA_ARGS__); \ + } \ + else if (dim % 2 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks31_align2(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks31 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=half. " \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks33(dim, ...) \ + [&] { \ + if (dim % 8 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks33_align8(__VA_ARGS__); \ + } \ + else if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks33_align4(__VA_ARGS__); \ + } \ + else if (dim % 2 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks33_align2(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks33 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=half. " \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks35(dim, ...) \ + [&] { \ + if (dim % 8 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks35_align8(__VA_ARGS__); \ + } \ + else if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks35_align4(__VA_ARGS__); \ + } \ + else if (dim % 2 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks35_align2(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks35 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=half. " \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks37(dim, ...) \ + [&] { \ + if (dim % 8 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks37_align8(__VA_ARGS__); \ + } \ + else if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks37_align4(__VA_ARGS__); \ + } \ + else if (dim % 2 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks37_align2(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks37 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=half. " \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks39(dim, ...) \ + [&] { \ + if (dim % 8 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks39_align8(__VA_ARGS__); \ + } \ + else if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks39_align4(__VA_ARGS__); \ + } \ + else if (dim % 2 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks39_align2(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks39 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=half. " \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks41(dim, ...) \ + [&] { \ + if (dim % 8 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks41_align8(__VA_ARGS__); \ + } \ + else if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks41_align4(__VA_ARGS__); \ + } \ + else if (dim % 2 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks41_align2(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks41 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=half. " \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks43(dim, ...) \ + [&] { \ + if (dim % 8 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks43_align8(__VA_ARGS__); \ + } \ + else if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks43_align4(__VA_ARGS__); \ + } \ + else if (dim % 2 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks43_align2(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks43 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=half. " \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks45(dim, ...) \ + [&] { \ + if (dim % 8 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks45_align8(__VA_ARGS__); \ + } \ + else if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks45_align4(__VA_ARGS__); \ + } \ + else if (dim % 2 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks45_align2(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks45 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=half. " \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks47(dim, ...) \ + [&] { \ + if (dim % 8 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks47_align8(__VA_ARGS__); \ + } \ + else if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks47_align4(__VA_ARGS__); \ + } \ + else if (dim % 2 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks47_align2(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks47 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=half. " \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks49(dim, ...) \ + [&] { \ + if (dim % 8 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks49_align8(__VA_ARGS__); \ + } \ + else if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks49_align4(__VA_ARGS__); \ + } \ + else if (dim % 2 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks49_align2(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks49 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=half. " \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks51(dim, ...) \ + [&] { \ + if (dim % 8 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks51_align8(__VA_ARGS__); \ + } \ + else if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks51_align4(__VA_ARGS__); \ + } \ + else if (dim % 2 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks51_align2(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks51 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=half. " \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks53(dim, ...) \ + [&] { \ + if (dim % 8 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks53_align8(__VA_ARGS__); \ + } \ + else if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks53_align4(__VA_ARGS__); \ + } \ + else if (dim % 2 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks53_align2(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks53 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=half. " \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks55(dim, ...) \ + [&] { \ + if (dim % 8 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks55_align8(__VA_ARGS__); \ + } \ + else if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks55_align4(__VA_ARGS__); \ + } \ + else if (dim % 2 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks55_align2(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks55 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=half. " \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks57(dim, ...) \ + [&] { \ + if (dim % 8 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks57_align8(__VA_ARGS__); \ + } \ + else if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks57_align4(__VA_ARGS__); \ + } \ + else if (dim % 2 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks57_align2(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks57 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=half. " \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks59(dim, ...) \ + [&] { \ + if (dim % 8 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks59_align8(__VA_ARGS__); \ + } \ + else if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks59_align4(__VA_ARGS__); \ + } \ + else if (dim % 2 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks59_align2(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks59 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=half. " \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks61(dim, ...) \ + [&] { \ + if (dim % 8 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks61_align8(__VA_ARGS__); \ } \ else if (dim % 4 == 0) { \ - natten::cuda::gemm::na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks3_align4(__VA_ARGS__); \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks61_align4(__VA_ARGS__); \ } \ else if (dim % 2 == 0) { \ - natten::cuda::gemm::na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks3_align2(__VA_ARGS__); \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks61_align2(__VA_ARGS__); \ } \ else { \ std::cerr << "NATTEN kernel launch failed!" \ - << "na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks3 requires at least 32-bit alignment." \ + << "na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks61 requires at least 32-bit alignment." \ << "Got dim=" << dim << ", dtype=half. " \ << std::endl; \ exit(EXIT_FAILURE); \ } \ }(); -#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks5(dim, ...) \ +#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks63(dim, ...) \ [&] { \ if (dim % 8 == 0) { \ - natten::cuda::gemm::na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks5_align8(__VA_ARGS__); \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks63_align8(__VA_ARGS__); \ } \ else if (dim % 4 == 0) { \ - natten::cuda::gemm::na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks5_align4(__VA_ARGS__); \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks63_align4(__VA_ARGS__); \ } \ else if (dim % 2 == 0) { \ - natten::cuda::gemm::na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks5_align2(__VA_ARGS__); \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks63_align2(__VA_ARGS__); \ } \ else { \ std::cerr << "NATTEN kernel launch failed!" \ - << "na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks5 requires at least 32-bit alignment." \ + << "na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks63 requires at least 32-bit alignment." \ << "Got dim=" << dim << ", dtype=half. " \ << std::endl; \ exit(EXIT_FAILURE); \ } \ }(); -#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks7(dim, ...) \ +#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks3(dim, ...) \ [&] { \ if (dim % 8 == 0) { \ - natten::cuda::gemm::na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks7_align8(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks3_align8(__VA_ARGS__); \ } \ else if (dim % 4 == 0) { \ - natten::cuda::gemm::na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks7_align4(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks3_align4(__VA_ARGS__); \ } \ else if (dim % 2 == 0) { \ - natten::cuda::gemm::na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks7_align2(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks3_align2(__VA_ARGS__); \ } \ else { \ std::cerr << "NATTEN kernel launch failed!" \ - << "na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks7 requires at least 32-bit alignment." \ + << "na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks3 requires at least 32-bit alignment." \ << "Got dim=" << dim << ", dtype=half. " \ << std::endl; \ exit(EXIT_FAILURE); \ } \ }(); -#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks9(dim, ...) \ +#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks5(dim, ...) \ [&] { \ if (dim % 8 == 0) { \ - natten::cuda::gemm::na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks9_align8(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks5_align8(__VA_ARGS__); \ } \ else if (dim % 4 == 0) { \ - natten::cuda::gemm::na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks9_align4(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks5_align4(__VA_ARGS__); \ } \ else if (dim % 2 == 0) { \ - natten::cuda::gemm::na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks9_align2(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks5_align2(__VA_ARGS__); \ } \ else { \ std::cerr << "NATTEN kernel launch failed!" \ - << "na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks9 requires at least 32-bit alignment." \ + << "na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks5 requires at least 32-bit alignment." \ << "Got dim=" << dim << ", dtype=half. " \ << std::endl; \ exit(EXIT_FAILURE); \ } \ }(); -#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks11(dim, ...) \ +#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks7(dim, ...) \ [&] { \ if (dim % 8 == 0) { \ - natten::cuda::gemm::na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks11_align8(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks7_align8(__VA_ARGS__); \ } \ else if (dim % 4 == 0) { \ - natten::cuda::gemm::na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks11_align4(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks7_align4(__VA_ARGS__); \ } \ else if (dim % 2 == 0) { \ - natten::cuda::gemm::na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks11_align2(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks7_align2(__VA_ARGS__); \ } \ else { \ std::cerr << "NATTEN kernel launch failed!" \ - << "na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks11 requires at least 32-bit alignment." \ + << "na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks7 requires at least 32-bit alignment." \ << "Got dim=" << dim << ", dtype=half. " \ << std::endl; \ exit(EXIT_FAILURE); \ } \ }(); -#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks13(dim, ...) \ +#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks9(dim, ...) \ [&] { \ if (dim % 8 == 0) { \ - natten::cuda::gemm::na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks13_align8(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks9_align8(__VA_ARGS__); \ } \ else if (dim % 4 == 0) { \ - natten::cuda::gemm::na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks13_align4(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks9_align4(__VA_ARGS__); \ } \ else if (dim % 2 == 0) { \ - natten::cuda::gemm::na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks13_align2(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks9_align2(__VA_ARGS__); \ } \ else { \ std::cerr << "NATTEN kernel launch failed!" \ - << "na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks13 requires at least 32-bit alignment." \ + << "na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks9 requires at least 32-bit alignment." \ << "Got dim=" << dim << ", dtype=half. " \ << std::endl; \ exit(EXIT_FAILURE); \ } \ }(); -#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks15(dim, ...) \ +#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks11(dim, ...) \ [&] { \ if (dim % 8 == 0) { \ - natten::cuda::gemm::na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks15_align8(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks11_align8(__VA_ARGS__); \ } \ else if (dim % 4 == 0) { \ - natten::cuda::gemm::na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks15_align4(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks11_align4(__VA_ARGS__); \ } \ else if (dim % 2 == 0) { \ - natten::cuda::gemm::na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks15_align2(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks11_align2(__VA_ARGS__); \ } \ else { \ std::cerr << "NATTEN kernel launch failed!" \ - << "na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks15 requires at least 32-bit alignment." \ + << "na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks11 requires at least 32-bit alignment." \ << "Got dim=" << dim << ", dtype=half. " \ << std::endl; \ exit(EXIT_FAILURE); \ } \ }(); -#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks17(dim, ...) \ +#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks13(dim, ...) \ [&] { \ if (dim % 8 == 0) { \ - natten::cuda::gemm::na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks17_align8(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks13_align8(__VA_ARGS__); \ } \ else if (dim % 4 == 0) { \ - natten::cuda::gemm::na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks17_align4(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks13_align4(__VA_ARGS__); \ } \ else if (dim % 2 == 0) { \ - natten::cuda::gemm::na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks17_align2(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks13_align2(__VA_ARGS__); \ } \ else { \ std::cerr << "NATTEN kernel launch failed!" \ - << "na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks17 requires at least 32-bit alignment." \ + << "na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks13 requires at least 32-bit alignment." \ << "Got dim=" << dim << ", dtype=half. " \ << std::endl; \ exit(EXIT_FAILURE); \ } \ }(); -#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks19(dim, ...) \ +#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks15(dim, ...) \ [&] { \ if (dim % 8 == 0) { \ - natten::cuda::gemm::na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks19_align8(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks15_align8(__VA_ARGS__); \ } \ else if (dim % 4 == 0) { \ - natten::cuda::gemm::na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks19_align4(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks15_align4(__VA_ARGS__); \ } \ else if (dim % 2 == 0) { \ - natten::cuda::gemm::na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks19_align2(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks15_align2(__VA_ARGS__); \ } \ else { \ std::cerr << "NATTEN kernel launch failed!" \ - << "na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks19 requires at least 32-bit alignment." \ + << "na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks15 requires at least 32-bit alignment." \ << "Got dim=" << dim << ", dtype=half. " \ << std::endl; \ exit(EXIT_FAILURE); \ } \ }(); -#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks21(dim, ...) \ +#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks17(dim, ...) \ [&] { \ if (dim % 8 == 0) { \ - natten::cuda::gemm::na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks21_align8(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks17_align8(__VA_ARGS__); \ } \ else if (dim % 4 == 0) { \ - natten::cuda::gemm::na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks21_align4(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks17_align4(__VA_ARGS__); \ } \ else if (dim % 2 == 0) { \ - natten::cuda::gemm::na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks21_align2(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks17_align2(__VA_ARGS__); \ } \ else { \ std::cerr << "NATTEN kernel launch failed!" \ - << "na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks21 requires at least 32-bit alignment." \ + << "na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks17 requires at least 32-bit alignment." \ << "Got dim=" << dim << ", dtype=half. " \ << std::endl; \ exit(EXIT_FAILURE); \ } \ }(); -#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks23(dim, ...) \ +#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks19(dim, ...) \ [&] { \ if (dim % 8 == 0) { \ - natten::cuda::gemm::na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks23_align8(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks19_align8(__VA_ARGS__); \ } \ else if (dim % 4 == 0) { \ - natten::cuda::gemm::na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks23_align4(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks19_align4(__VA_ARGS__); \ } \ else if (dim % 2 == 0) { \ - natten::cuda::gemm::na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks23_align2(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks19_align2(__VA_ARGS__); \ } \ else { \ std::cerr << "NATTEN kernel launch failed!" \ - << "na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks23 requires at least 32-bit alignment." \ + << "na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks19 requires at least 32-bit alignment." \ << "Got dim=" << dim << ", dtype=half. " \ << std::endl; \ exit(EXIT_FAILURE); \ } \ }(); -#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks25(dim, ...) \ +#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks21(dim, ...) \ [&] { \ if (dim % 8 == 0) { \ - natten::cuda::gemm::na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks25_align8(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks21_align8(__VA_ARGS__); \ } \ else if (dim % 4 == 0) { \ - natten::cuda::gemm::na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks25_align4(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks21_align4(__VA_ARGS__); \ } \ else if (dim % 2 == 0) { \ - natten::cuda::gemm::na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks25_align2(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks21_align2(__VA_ARGS__); \ } \ else { \ std::cerr << "NATTEN kernel launch failed!" \ - << "na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks25 requires at least 32-bit alignment." \ + << "na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks21 requires at least 32-bit alignment." \ << "Got dim=" << dim << ", dtype=half. " \ << std::endl; \ exit(EXIT_FAILURE); \ } \ }(); -#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks27(dim, ...) \ +#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks23(dim, ...) \ [&] { \ if (dim % 8 == 0) { \ - natten::cuda::gemm::na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks27_align8(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks23_align8(__VA_ARGS__); \ } \ else if (dim % 4 == 0) { \ - natten::cuda::gemm::na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks27_align4(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks23_align4(__VA_ARGS__); \ } \ else if (dim % 2 == 0) { \ - natten::cuda::gemm::na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks27_align2(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks23_align2(__VA_ARGS__); \ } \ else { \ std::cerr << "NATTEN kernel launch failed!" \ - << "na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks27 requires at least 32-bit alignment." \ + << "na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks23 requires at least 32-bit alignment." \ << "Got dim=" << dim << ", dtype=half. " \ << std::endl; \ exit(EXIT_FAILURE); \ } \ }(); -#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks29(dim, ...) \ +#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks25(dim, ...) \ [&] { \ if (dim % 8 == 0) { \ - natten::cuda::gemm::na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks29_align8(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks25_align8(__VA_ARGS__); \ } \ else if (dim % 4 == 0) { \ - natten::cuda::gemm::na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks29_align4(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks25_align4(__VA_ARGS__); \ } \ else if (dim % 2 == 0) { \ - natten::cuda::gemm::na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks29_align2(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks25_align2(__VA_ARGS__); \ } \ else { \ std::cerr << "NATTEN kernel launch failed!" \ - << "na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks29 requires at least 32-bit alignment." \ + << "na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks25 requires at least 32-bit alignment." \ << "Got dim=" << dim << ", dtype=half. " \ << std::endl; \ exit(EXIT_FAILURE); \ } \ }(); -#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks31(dim, ...) \ +#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks27(dim, ...) \ [&] { \ if (dim % 8 == 0) { \ - natten::cuda::gemm::na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks31_align8(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks27_align8(__VA_ARGS__); \ } \ else if (dim % 4 == 0) { \ - natten::cuda::gemm::na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks31_align4(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks27_align4(__VA_ARGS__); \ } \ else if (dim % 2 == 0) { \ - natten::cuda::gemm::na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks31_align2(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks27_align2(__VA_ARGS__); \ } \ else { \ std::cerr << "NATTEN kernel launch failed!" \ - << "na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks31 requires at least 32-bit alignment." \ + << "na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks27 requires at least 32-bit alignment." \ << "Got dim=" << dim << ", dtype=half. " \ << std::endl; \ exit(EXIT_FAILURE); \ } \ }(); -#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks33(dim, ...) \ +#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks29(dim, ...) \ [&] { \ if (dim % 8 == 0) { \ - natten::cuda::gemm::na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks33_align8(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks29_align8(__VA_ARGS__); \ } \ else if (dim % 4 == 0) { \ - natten::cuda::gemm::na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks33_align4(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks29_align4(__VA_ARGS__); \ } \ else if (dim % 2 == 0) { \ - natten::cuda::gemm::na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks33_align2(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks29_align2(__VA_ARGS__); \ } \ else { \ std::cerr << "NATTEN kernel launch failed!" \ - << "na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks33 requires at least 32-bit alignment." \ + << "na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks29 requires at least 32-bit alignment." \ << "Got dim=" << dim << ", dtype=half. " \ << std::endl; \ exit(EXIT_FAILURE); \ } \ }(); -#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks3(dim, ...) \ +#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks31(dim, ...) \ [&] { \ if (dim % 8 == 0) { \ - natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks3_align8(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks31_align8(__VA_ARGS__); \ } \ else if (dim % 4 == 0) { \ - natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks3_align4(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks31_align4(__VA_ARGS__); \ } \ else if (dim % 2 == 0) { \ - natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks3_align2(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks31_align2(__VA_ARGS__); \ } \ else { \ std::cerr << "NATTEN kernel launch failed!" \ - << "na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks3 requires at least 32-bit alignment." \ + << "na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks31 requires at least 32-bit alignment." \ << "Got dim=" << dim << ", dtype=half. " \ << std::endl; \ exit(EXIT_FAILURE); \ } \ }(); -#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks5(dim, ...) \ +#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks33(dim, ...) \ [&] { \ if (dim % 8 == 0) { \ - natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks5_align8(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks33_align8(__VA_ARGS__); \ } \ else if (dim % 4 == 0) { \ - natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks5_align4(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks33_align4(__VA_ARGS__); \ } \ else if (dim % 2 == 0) { \ - natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks5_align2(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks33_align2(__VA_ARGS__); \ } \ else { \ std::cerr << "NATTEN kernel launch failed!" \ - << "na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks5 requires at least 32-bit alignment." \ + << "na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks33 requires at least 32-bit alignment." \ << "Got dim=" << dim << ", dtype=half. " \ << std::endl; \ exit(EXIT_FAILURE); \ } \ }(); -#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks7(dim, ...) \ +#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks35(dim, ...) \ [&] { \ if (dim % 8 == 0) { \ - natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks7_align8(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks35_align8(__VA_ARGS__); \ } \ else if (dim % 4 == 0) { \ - natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks7_align4(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks35_align4(__VA_ARGS__); \ } \ else if (dim % 2 == 0) { \ - natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks7_align2(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks35_align2(__VA_ARGS__); \ } \ else { \ std::cerr << "NATTEN kernel launch failed!" \ - << "na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks7 requires at least 32-bit alignment." \ + << "na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks35 requires at least 32-bit alignment." \ << "Got dim=" << dim << ", dtype=half. " \ << std::endl; \ exit(EXIT_FAILURE); \ } \ }(); -#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks9(dim, ...) \ +#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks37(dim, ...) \ [&] { \ if (dim % 8 == 0) { \ - natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks9_align8(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks37_align8(__VA_ARGS__); \ } \ else if (dim % 4 == 0) { \ - natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks9_align4(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks37_align4(__VA_ARGS__); \ } \ else if (dim % 2 == 0) { \ - natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks9_align2(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks37_align2(__VA_ARGS__); \ } \ else { \ std::cerr << "NATTEN kernel launch failed!" \ - << "na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks9 requires at least 32-bit alignment." \ + << "na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks37 requires at least 32-bit alignment." \ << "Got dim=" << dim << ", dtype=half. " \ << std::endl; \ exit(EXIT_FAILURE); \ } \ }(); -#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks11(dim, ...) \ +#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks39(dim, ...) \ [&] { \ if (dim % 8 == 0) { \ - natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks11_align8(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks39_align8(__VA_ARGS__); \ } \ else if (dim % 4 == 0) { \ - natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks11_align4(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks39_align4(__VA_ARGS__); \ } \ else if (dim % 2 == 0) { \ - natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks11_align2(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks39_align2(__VA_ARGS__); \ } \ else { \ std::cerr << "NATTEN kernel launch failed!" \ - << "na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks11 requires at least 32-bit alignment." \ + << "na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks39 requires at least 32-bit alignment." \ << "Got dim=" << dim << ", dtype=half. " \ << std::endl; \ exit(EXIT_FAILURE); \ } \ }(); -#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks13(dim, ...) \ +#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks41(dim, ...) \ [&] { \ if (dim % 8 == 0) { \ - natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks13_align8(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks41_align8(__VA_ARGS__); \ } \ else if (dim % 4 == 0) { \ - natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks13_align4(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks41_align4(__VA_ARGS__); \ } \ else if (dim % 2 == 0) { \ - natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks13_align2(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks41_align2(__VA_ARGS__); \ } \ else { \ std::cerr << "NATTEN kernel launch failed!" \ - << "na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks13 requires at least 32-bit alignment." \ + << "na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks41 requires at least 32-bit alignment." \ << "Got dim=" << dim << ", dtype=half. " \ << std::endl; \ exit(EXIT_FAILURE); \ } \ }(); -#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks15(dim, ...) \ +#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks43(dim, ...) \ [&] { \ if (dim % 8 == 0) { \ - natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks15_align8(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks43_align8(__VA_ARGS__); \ } \ else if (dim % 4 == 0) { \ - natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks15_align4(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks43_align4(__VA_ARGS__); \ } \ else if (dim % 2 == 0) { \ - natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks15_align2(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks43_align2(__VA_ARGS__); \ } \ else { \ std::cerr << "NATTEN kernel launch failed!" \ - << "na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks15 requires at least 32-bit alignment." \ + << "na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks43 requires at least 32-bit alignment." \ << "Got dim=" << dim << ", dtype=half. " \ << std::endl; \ exit(EXIT_FAILURE); \ } \ }(); -#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks17(dim, ...) \ +#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks45(dim, ...) \ [&] { \ if (dim % 8 == 0) { \ - natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks17_align8(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks45_align8(__VA_ARGS__); \ } \ else if (dim % 4 == 0) { \ - natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks17_align4(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks45_align4(__VA_ARGS__); \ } \ else if (dim % 2 == 0) { \ - natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks17_align2(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks45_align2(__VA_ARGS__); \ } \ else { \ std::cerr << "NATTEN kernel launch failed!" \ - << "na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks17 requires at least 32-bit alignment." \ + << "na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks45 requires at least 32-bit alignment." \ << "Got dim=" << dim << ", dtype=half. " \ << std::endl; \ exit(EXIT_FAILURE); \ } \ }(); -#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks19(dim, ...) \ +#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks47(dim, ...) \ [&] { \ if (dim % 8 == 0) { \ - natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks19_align8(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks47_align8(__VA_ARGS__); \ } \ else if (dim % 4 == 0) { \ - natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks19_align4(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks47_align4(__VA_ARGS__); \ } \ else if (dim % 2 == 0) { \ - natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks19_align2(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks47_align2(__VA_ARGS__); \ } \ else { \ std::cerr << "NATTEN kernel launch failed!" \ - << "na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks19 requires at least 32-bit alignment." \ + << "na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks47 requires at least 32-bit alignment." \ << "Got dim=" << dim << ", dtype=half. " \ << std::endl; \ exit(EXIT_FAILURE); \ } \ }(); -#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks21(dim, ...) \ +#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks49(dim, ...) \ [&] { \ if (dim % 8 == 0) { \ - natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks21_align8(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks49_align8(__VA_ARGS__); \ } \ else if (dim % 4 == 0) { \ - natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks21_align4(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks49_align4(__VA_ARGS__); \ } \ else if (dim % 2 == 0) { \ - natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks21_align2(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks49_align2(__VA_ARGS__); \ } \ else { \ std::cerr << "NATTEN kernel launch failed!" \ - << "na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks21 requires at least 32-bit alignment." \ + << "na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks49 requires at least 32-bit alignment." \ << "Got dim=" << dim << ", dtype=half. " \ << std::endl; \ exit(EXIT_FAILURE); \ } \ }(); -#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks23(dim, ...) \ +#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks51(dim, ...) \ [&] { \ if (dim % 8 == 0) { \ - natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks23_align8(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks51_align8(__VA_ARGS__); \ } \ else if (dim % 4 == 0) { \ - natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks23_align4(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks51_align4(__VA_ARGS__); \ } \ else if (dim % 2 == 0) { \ - natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks23_align2(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks51_align2(__VA_ARGS__); \ } \ else { \ std::cerr << "NATTEN kernel launch failed!" \ - << "na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks23 requires at least 32-bit alignment." \ + << "na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks51 requires at least 32-bit alignment." \ << "Got dim=" << dim << ", dtype=half. " \ << std::endl; \ exit(EXIT_FAILURE); \ } \ }(); -#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks25(dim, ...) \ +#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks53(dim, ...) \ [&] { \ if (dim % 8 == 0) { \ - natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks25_align8(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks53_align8(__VA_ARGS__); \ } \ else if (dim % 4 == 0) { \ - natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks25_align4(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks53_align4(__VA_ARGS__); \ } \ else if (dim % 2 == 0) { \ - natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks25_align2(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks53_align2(__VA_ARGS__); \ } \ else { \ std::cerr << "NATTEN kernel launch failed!" \ - << "na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks25 requires at least 32-bit alignment." \ + << "na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks53 requires at least 32-bit alignment." \ << "Got dim=" << dim << ", dtype=half. " \ << std::endl; \ exit(EXIT_FAILURE); \ } \ }(); -#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks27(dim, ...) \ +#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks55(dim, ...) \ [&] { \ if (dim % 8 == 0) { \ - natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks27_align8(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks55_align8(__VA_ARGS__); \ } \ else if (dim % 4 == 0) { \ - natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks27_align4(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks55_align4(__VA_ARGS__); \ } \ else if (dim % 2 == 0) { \ - natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks27_align2(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks55_align2(__VA_ARGS__); \ } \ else { \ std::cerr << "NATTEN kernel launch failed!" \ - << "na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks27 requires at least 32-bit alignment." \ + << "na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks55 requires at least 32-bit alignment." \ << "Got dim=" << dim << ", dtype=half. " \ << std::endl; \ exit(EXIT_FAILURE); \ } \ }(); -#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks29(dim, ...) \ +#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks57(dim, ...) \ [&] { \ if (dim % 8 == 0) { \ - natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks29_align8(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks57_align8(__VA_ARGS__); \ } \ else if (dim % 4 == 0) { \ - natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks29_align4(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks57_align4(__VA_ARGS__); \ } \ else if (dim % 2 == 0) { \ - natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks29_align2(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks57_align2(__VA_ARGS__); \ } \ else { \ std::cerr << "NATTEN kernel launch failed!" \ - << "na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks29 requires at least 32-bit alignment." \ + << "na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks57 requires at least 32-bit alignment." \ << "Got dim=" << dim << ", dtype=half. " \ << std::endl; \ exit(EXIT_FAILURE); \ } \ }(); -#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks31(dim, ...) \ +#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks59(dim, ...) \ [&] { \ if (dim % 8 == 0) { \ - natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks31_align8(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks59_align8(__VA_ARGS__); \ } \ else if (dim % 4 == 0) { \ - natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks31_align4(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks59_align4(__VA_ARGS__); \ } \ else if (dim % 2 == 0) { \ - natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks31_align2(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks59_align2(__VA_ARGS__); \ } \ else { \ std::cerr << "NATTEN kernel launch failed!" \ - << "na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks31 requires at least 32-bit alignment." \ + << "na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks59 requires at least 32-bit alignment." \ << "Got dim=" << dim << ", dtype=half. " \ << std::endl; \ exit(EXIT_FAILURE); \ } \ }(); -#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks33(dim, ...) \ +#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks61(dim, ...) \ [&] { \ if (dim % 8 == 0) { \ - natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks33_align8(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks61_align8(__VA_ARGS__); \ } \ else if (dim % 4 == 0) { \ - natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks33_align4(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks61_align4(__VA_ARGS__); \ } \ else if (dim % 2 == 0) { \ - natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks33_align2(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks61_align2(__VA_ARGS__); \ } \ else { \ std::cerr << "NATTEN kernel launch failed!" \ - << "na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks33 requires at least 32-bit alignment." \ + << "na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks61 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=half. " \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks63(dim, ...) \ + [&] { \ + if (dim % 8 == 0) { \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks63_align8(__VA_ARGS__); \ + } \ + else if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks63_align4(__VA_ARGS__); \ + } \ + else if (dim % 2 == 0) { \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks63_align2(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks63 requires at least 32-bit alignment." \ << "Got dim=" << dim << ", dtype=half. " \ << std::endl; \ exit(EXIT_FAILURE); \ diff --git a/csrc/autogen/include/natten_autogen/cuda/gemm/2d/sm70/dispatch_kernel_size.h b/csrc/autogen/include/natten_autogen/cuda/gemm/2d/sm70/dispatch_kernel_size.h index 4d693a9..81996ed 100644 --- a/csrc/autogen/include/natten_autogen/cuda/gemm/2d/sm70/dispatch_kernel_size.h +++ b/csrc/autogen/include/natten_autogen/cuda/gemm/2d/sm70/dispatch_kernel_size.h @@ -60,6 +60,51 @@ namespace gemm { else if (kernel_size == 33) { \ DISPATCH_ALIGNMENT_na2d_pn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks33(dim, __VA_ARGS__); \ } \ + else if (kernel_size == 35) { \ + DISPATCH_ALIGNMENT_na2d_pn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks35(dim, __VA_ARGS__); \ + } \ + else if (kernel_size == 37) { \ + DISPATCH_ALIGNMENT_na2d_pn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks37(dim, __VA_ARGS__); \ + } \ + else if (kernel_size == 39) { \ + DISPATCH_ALIGNMENT_na2d_pn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks39(dim, __VA_ARGS__); \ + } \ + else if (kernel_size == 41) { \ + DISPATCH_ALIGNMENT_na2d_pn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks41(dim, __VA_ARGS__); \ + } \ + else if (kernel_size == 43) { \ + DISPATCH_ALIGNMENT_na2d_pn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks43(dim, __VA_ARGS__); \ + } \ + else if (kernel_size == 45) { \ + DISPATCH_ALIGNMENT_na2d_pn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks45(dim, __VA_ARGS__); \ + } \ + else if (kernel_size == 47) { \ + DISPATCH_ALIGNMENT_na2d_pn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks47(dim, __VA_ARGS__); \ + } \ + else if (kernel_size == 49) { \ + DISPATCH_ALIGNMENT_na2d_pn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks49(dim, __VA_ARGS__); \ + } \ + else if (kernel_size == 51) { \ + DISPATCH_ALIGNMENT_na2d_pn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks51(dim, __VA_ARGS__); \ + } \ + else if (kernel_size == 53) { \ + DISPATCH_ALIGNMENT_na2d_pn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks53(dim, __VA_ARGS__); \ + } \ + else if (kernel_size == 55) { \ + DISPATCH_ALIGNMENT_na2d_pn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks55(dim, __VA_ARGS__); \ + } \ + else if (kernel_size == 57) { \ + DISPATCH_ALIGNMENT_na2d_pn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks57(dim, __VA_ARGS__); \ + } \ + else if (kernel_size == 59) { \ + DISPATCH_ALIGNMENT_na2d_pn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks59(dim, __VA_ARGS__); \ + } \ + else if (kernel_size == 61) { \ + DISPATCH_ALIGNMENT_na2d_pn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks61(dim, __VA_ARGS__); \ + } \ + else if (kernel_size == 63) { \ + DISPATCH_ALIGNMENT_na2d_pn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks63(dim, __VA_ARGS__); \ + } \ else { \ std::cerr << "NATTEN kernel launch failed! " \ << "na2d_pn_cuda_gemm_sm70_half does not support implement " \ @@ -121,6 +166,51 @@ namespace gemm { else if (kernel_size == 33) { \ DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks33(dim, __VA_ARGS__); \ } \ + else if (kernel_size == 35) { \ + DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks35(dim, __VA_ARGS__); \ + } \ + else if (kernel_size == 37) { \ + DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks37(dim, __VA_ARGS__); \ + } \ + else if (kernel_size == 39) { \ + DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks39(dim, __VA_ARGS__); \ + } \ + else if (kernel_size == 41) { \ + DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks41(dim, __VA_ARGS__); \ + } \ + else if (kernel_size == 43) { \ + DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks43(dim, __VA_ARGS__); \ + } \ + else if (kernel_size == 45) { \ + DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks45(dim, __VA_ARGS__); \ + } \ + else if (kernel_size == 47) { \ + DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks47(dim, __VA_ARGS__); \ + } \ + else if (kernel_size == 49) { \ + DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks49(dim, __VA_ARGS__); \ + } \ + else if (kernel_size == 51) { \ + DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks51(dim, __VA_ARGS__); \ + } \ + else if (kernel_size == 53) { \ + DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks53(dim, __VA_ARGS__); \ + } \ + else if (kernel_size == 55) { \ + DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks55(dim, __VA_ARGS__); \ + } \ + else if (kernel_size == 57) { \ + DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks57(dim, __VA_ARGS__); \ + } \ + else if (kernel_size == 59) { \ + DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks59(dim, __VA_ARGS__); \ + } \ + else if (kernel_size == 61) { \ + DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks61(dim, __VA_ARGS__); \ + } \ + else if (kernel_size == 63) { \ + DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks63(dim, __VA_ARGS__); \ + } \ else { \ std::cerr << "NATTEN kernel launch failed! " \ << "na2d_nn_cuda_gemm_sm70_half does not support implement " \ @@ -182,6 +272,51 @@ namespace gemm { else if (kernel_size == 33) { \ DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks33(dim, __VA_ARGS__); \ } \ + else if (kernel_size == 35) { \ + DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks35(dim, __VA_ARGS__); \ + } \ + else if (kernel_size == 37) { \ + DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks37(dim, __VA_ARGS__); \ + } \ + else if (kernel_size == 39) { \ + DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks39(dim, __VA_ARGS__); \ + } \ + else if (kernel_size == 41) { \ + DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks41(dim, __VA_ARGS__); \ + } \ + else if (kernel_size == 43) { \ + DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks43(dim, __VA_ARGS__); \ + } \ + else if (kernel_size == 45) { \ + DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks45(dim, __VA_ARGS__); \ + } \ + else if (kernel_size == 47) { \ + DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks47(dim, __VA_ARGS__); \ + } \ + else if (kernel_size == 49) { \ + DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks49(dim, __VA_ARGS__); \ + } \ + else if (kernel_size == 51) { \ + DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks51(dim, __VA_ARGS__); \ + } \ + else if (kernel_size == 53) { \ + DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks53(dim, __VA_ARGS__); \ + } \ + else if (kernel_size == 55) { \ + DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks55(dim, __VA_ARGS__); \ + } \ + else if (kernel_size == 57) { \ + DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks57(dim, __VA_ARGS__); \ + } \ + else if (kernel_size == 59) { \ + DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks59(dim, __VA_ARGS__); \ + } \ + else if (kernel_size == 61) { \ + DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks61(dim, __VA_ARGS__); \ + } \ + else if (kernel_size == 63) { \ + DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks63(dim, __VA_ARGS__); \ + } \ else { \ std::cerr << "NATTEN kernel launch failed! " \ << "na2d_in_cuda_gemm_sm70_half does not support implement " \ diff --git a/csrc/autogen/include/natten_autogen/cuda/gemm/2d/sm70/kernels.h b/csrc/autogen/include/natten_autogen/cuda/gemm/2d/sm70/kernels.h index c69cc1f..188bbea 100644 --- a/csrc/autogen/include/natten_autogen/cuda/gemm/2d/sm70/kernels.h +++ b/csrc/autogen/include/natten_autogen/cuda/gemm/2d/sm70/kernels.h @@ -921,6 +921,861 @@ void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks33_align2( float scale, cudaStream_t stream); +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks35_align8( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks35_align4( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks35_align2( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks37_align8( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks37_align4( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks37_align2( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks39_align8( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks39_align4( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks39_align2( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks41_align8( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks41_align4( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks41_align2( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks43_align8( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks43_align4( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks43_align2( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks45_align8( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks45_align4( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks45_align2( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks47_align8( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks47_align4( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks47_align2( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks49_align8( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks49_align4( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks49_align2( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks51_align8( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks51_align4( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks51_align2( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks53_align8( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks53_align4( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks53_align2( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks55_align8( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks55_align4( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks55_align2( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks57_align8( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks57_align4( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks57_align2( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks59_align8( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks59_align4( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks59_align2( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks61_align8( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks61_align4( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks61_align2( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks63_align8( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks63_align4( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks63_align2( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks3_align8( void * attn_ptr, void * value_ptr, @@ -939,7 +1794,1393 @@ void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks3_align8( float scale, cudaStream_t stream); -void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks3_align4( +void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks3_align4( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks3_align2( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks5_align8( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks5_align4( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks5_align2( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks7_align8( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks7_align4( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks7_align2( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks9_align8( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks9_align4( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks9_align2( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks11_align8( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks11_align4( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks11_align2( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks13_align8( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks13_align4( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks13_align2( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks15_align8( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks15_align4( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks15_align2( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks17_align8( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks17_align4( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks17_align2( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks19_align8( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks19_align4( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks19_align2( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks21_align8( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks21_align4( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks21_align2( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks23_align8( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks23_align4( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks23_align2( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks25_align8( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks25_align4( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks25_align2( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks27_align8( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks27_align4( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks27_align2( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks29_align8( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks29_align4( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks29_align2( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks31_align8( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks31_align4( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks31_align2( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks33_align8( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks33_align4( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks33_align2( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks35_align8( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks35_align4( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks35_align2( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks37_align8( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks37_align4( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks37_align2( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks39_align8( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks39_align4( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks39_align2( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks41_align8( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks41_align4( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks41_align2( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks43_align8( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks43_align4( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks43_align2( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks45_align8( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks45_align4( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks45_align2( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks47_align8( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks47_align4( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks47_align2( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks49_align8( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks49_align4( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks49_align2( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks51_align8( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks51_align4( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks51_align2( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks53_align8( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks53_align4( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks53_align2( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks55_align8( void * attn_ptr, void * value_ptr, void * output_ptr, @@ -957,7 +3198,7 @@ void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks3_align4( float scale, cudaStream_t stream); -void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks3_align2( +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks55_align4( void * attn_ptr, void * value_ptr, void * output_ptr, @@ -975,7 +3216,7 @@ void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks3_align2( float scale, cudaStream_t stream); -void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks5_align8( +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks55_align2( void * attn_ptr, void * value_ptr, void * output_ptr, @@ -993,7 +3234,7 @@ void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks5_align8( float scale, cudaStream_t stream); -void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks5_align4( +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks57_align8( void * attn_ptr, void * value_ptr, void * output_ptr, @@ -1011,7 +3252,7 @@ void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks5_align4( float scale, cudaStream_t stream); -void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks5_align2( +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks57_align4( void * attn_ptr, void * value_ptr, void * output_ptr, @@ -1029,7 +3270,7 @@ void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks5_align2( float scale, cudaStream_t stream); -void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks7_align8( +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks57_align2( void * attn_ptr, void * value_ptr, void * output_ptr, @@ -1047,7 +3288,7 @@ void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks7_align8( float scale, cudaStream_t stream); -void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks7_align4( +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks59_align8( void * attn_ptr, void * value_ptr, void * output_ptr, @@ -1065,7 +3306,7 @@ void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks7_align4( float scale, cudaStream_t stream); -void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks7_align2( +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks59_align4( void * attn_ptr, void * value_ptr, void * output_ptr, @@ -1083,7 +3324,7 @@ void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks7_align2( float scale, cudaStream_t stream); -void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks9_align8( +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks59_align2( void * attn_ptr, void * value_ptr, void * output_ptr, @@ -1101,7 +3342,7 @@ void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks9_align8( float scale, cudaStream_t stream); -void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks9_align4( +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks61_align8( void * attn_ptr, void * value_ptr, void * output_ptr, @@ -1119,7 +3360,7 @@ void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks9_align4( float scale, cudaStream_t stream); -void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks9_align2( +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks61_align4( void * attn_ptr, void * value_ptr, void * output_ptr, @@ -1137,7 +3378,7 @@ void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks9_align2( float scale, cudaStream_t stream); -void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks11_align8( +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks61_align2( void * attn_ptr, void * value_ptr, void * output_ptr, @@ -1155,7 +3396,43 @@ void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks11_align8( float scale, cudaStream_t stream); -void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks11_align4( +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks63_align8( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks63_align4( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks63_align2( void * attn_ptr, void * value_ptr, void * output_ptr, @@ -1173,10 +3450,208 @@ void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks11_align4( float scale, cudaStream_t stream); -void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks11_align2( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks3_align8( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks3_align4( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks3_align2( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks5_align8( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks5_align4( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks5_align2( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks7_align8( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks7_align4( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks7_align2( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks9_align8( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks9_align4( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks9_align2( void * attn_ptr, - void * value_ptr, - void * output_ptr, + void * d_output_ptr, + void * d_value_ptr, int batch_size, int heads, int height, @@ -1191,10 +3666,10 @@ void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks11_align2( float scale, cudaStream_t stream); -void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks13_align8( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks11_align8( void * attn_ptr, - void * value_ptr, - void * output_ptr, + void * d_output_ptr, + void * d_value_ptr, int batch_size, int heads, int height, @@ -1209,10 +3684,10 @@ void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks13_align8( float scale, cudaStream_t stream); -void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks13_align4( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks11_align4( void * attn_ptr, - void * value_ptr, - void * output_ptr, + void * d_output_ptr, + void * d_value_ptr, int batch_size, int heads, int height, @@ -1227,10 +3702,10 @@ void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks13_align4( float scale, cudaStream_t stream); -void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks13_align2( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks11_align2( void * attn_ptr, - void * value_ptr, - void * output_ptr, + void * d_output_ptr, + void * d_value_ptr, int batch_size, int heads, int height, @@ -1245,10 +3720,10 @@ void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks13_align2( float scale, cudaStream_t stream); -void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks15_align8( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks13_align8( void * attn_ptr, - void * value_ptr, - void * output_ptr, + void * d_output_ptr, + void * d_value_ptr, int batch_size, int heads, int height, @@ -1263,10 +3738,10 @@ void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks15_align8( float scale, cudaStream_t stream); -void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks15_align4( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks13_align4( void * attn_ptr, - void * value_ptr, - void * output_ptr, + void * d_output_ptr, + void * d_value_ptr, int batch_size, int heads, int height, @@ -1281,10 +3756,10 @@ void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks15_align4( float scale, cudaStream_t stream); -void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks15_align2( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks13_align2( void * attn_ptr, - void * value_ptr, - void * output_ptr, + void * d_output_ptr, + void * d_value_ptr, int batch_size, int heads, int height, @@ -1299,10 +3774,10 @@ void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks15_align2( float scale, cudaStream_t stream); -void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks17_align8( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks15_align8( void * attn_ptr, - void * value_ptr, - void * output_ptr, + void * d_output_ptr, + void * d_value_ptr, int batch_size, int heads, int height, @@ -1317,10 +3792,10 @@ void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks17_align8( float scale, cudaStream_t stream); -void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks17_align4( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks15_align4( void * attn_ptr, - void * value_ptr, - void * output_ptr, + void * d_output_ptr, + void * d_value_ptr, int batch_size, int heads, int height, @@ -1335,10 +3810,10 @@ void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks17_align4( float scale, cudaStream_t stream); -void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks17_align2( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks15_align2( void * attn_ptr, - void * value_ptr, - void * output_ptr, + void * d_output_ptr, + void * d_value_ptr, int batch_size, int heads, int height, @@ -1353,10 +3828,10 @@ void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks17_align2( float scale, cudaStream_t stream); -void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks19_align8( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks17_align8( void * attn_ptr, - void * value_ptr, - void * output_ptr, + void * d_output_ptr, + void * d_value_ptr, int batch_size, int heads, int height, @@ -1371,10 +3846,10 @@ void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks19_align8( float scale, cudaStream_t stream); -void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks19_align4( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks17_align4( void * attn_ptr, - void * value_ptr, - void * output_ptr, + void * d_output_ptr, + void * d_value_ptr, int batch_size, int heads, int height, @@ -1389,10 +3864,10 @@ void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks19_align4( float scale, cudaStream_t stream); -void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks19_align2( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks17_align2( void * attn_ptr, - void * value_ptr, - void * output_ptr, + void * d_output_ptr, + void * d_value_ptr, int batch_size, int heads, int height, @@ -1407,10 +3882,10 @@ void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks19_align2( float scale, cudaStream_t stream); -void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks21_align8( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks19_align8( void * attn_ptr, - void * value_ptr, - void * output_ptr, + void * d_output_ptr, + void * d_value_ptr, int batch_size, int heads, int height, @@ -1425,10 +3900,10 @@ void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks21_align8( float scale, cudaStream_t stream); -void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks21_align4( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks19_align4( void * attn_ptr, - void * value_ptr, - void * output_ptr, + void * d_output_ptr, + void * d_value_ptr, int batch_size, int heads, int height, @@ -1443,10 +3918,10 @@ void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks21_align4( float scale, cudaStream_t stream); -void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks21_align2( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks19_align2( void * attn_ptr, - void * value_ptr, - void * output_ptr, + void * d_output_ptr, + void * d_value_ptr, int batch_size, int heads, int height, @@ -1461,10 +3936,10 @@ void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks21_align2( float scale, cudaStream_t stream); -void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks23_align8( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks21_align8( void * attn_ptr, - void * value_ptr, - void * output_ptr, + void * d_output_ptr, + void * d_value_ptr, int batch_size, int heads, int height, @@ -1479,10 +3954,10 @@ void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks23_align8( float scale, cudaStream_t stream); -void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks23_align4( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks21_align4( void * attn_ptr, - void * value_ptr, - void * output_ptr, + void * d_output_ptr, + void * d_value_ptr, int batch_size, int heads, int height, @@ -1497,10 +3972,10 @@ void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks23_align4( float scale, cudaStream_t stream); -void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks23_align2( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks21_align2( void * attn_ptr, - void * value_ptr, - void * output_ptr, + void * d_output_ptr, + void * d_value_ptr, int batch_size, int heads, int height, @@ -1515,10 +3990,10 @@ void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks23_align2( float scale, cudaStream_t stream); -void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks25_align8( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks23_align8( void * attn_ptr, - void * value_ptr, - void * output_ptr, + void * d_output_ptr, + void * d_value_ptr, int batch_size, int heads, int height, @@ -1533,10 +4008,10 @@ void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks25_align8( float scale, cudaStream_t stream); -void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks25_align4( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks23_align4( void * attn_ptr, - void * value_ptr, - void * output_ptr, + void * d_output_ptr, + void * d_value_ptr, int batch_size, int heads, int height, @@ -1551,10 +4026,10 @@ void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks25_align4( float scale, cudaStream_t stream); -void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks25_align2( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks23_align2( void * attn_ptr, - void * value_ptr, - void * output_ptr, + void * d_output_ptr, + void * d_value_ptr, int batch_size, int heads, int height, @@ -1569,10 +4044,10 @@ void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks25_align2( float scale, cudaStream_t stream); -void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks27_align8( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks25_align8( void * attn_ptr, - void * value_ptr, - void * output_ptr, + void * d_output_ptr, + void * d_value_ptr, int batch_size, int heads, int height, @@ -1587,10 +4062,10 @@ void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks27_align8( float scale, cudaStream_t stream); -void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks27_align4( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks25_align4( void * attn_ptr, - void * value_ptr, - void * output_ptr, + void * d_output_ptr, + void * d_value_ptr, int batch_size, int heads, int height, @@ -1605,10 +4080,10 @@ void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks27_align4( float scale, cudaStream_t stream); -void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks27_align2( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks25_align2( void * attn_ptr, - void * value_ptr, - void * output_ptr, + void * d_output_ptr, + void * d_value_ptr, int batch_size, int heads, int height, @@ -1623,10 +4098,10 @@ void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks27_align2( float scale, cudaStream_t stream); -void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks29_align8( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks27_align8( void * attn_ptr, - void * value_ptr, - void * output_ptr, + void * d_output_ptr, + void * d_value_ptr, int batch_size, int heads, int height, @@ -1641,10 +4116,10 @@ void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks29_align8( float scale, cudaStream_t stream); -void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks29_align4( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks27_align4( void * attn_ptr, - void * value_ptr, - void * output_ptr, + void * d_output_ptr, + void * d_value_ptr, int batch_size, int heads, int height, @@ -1659,10 +4134,10 @@ void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks29_align4( float scale, cudaStream_t stream); -void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks29_align2( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks27_align2( void * attn_ptr, - void * value_ptr, - void * output_ptr, + void * d_output_ptr, + void * d_value_ptr, int batch_size, int heads, int height, @@ -1677,10 +4152,10 @@ void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks29_align2( float scale, cudaStream_t stream); -void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks31_align8( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks29_align8( void * attn_ptr, - void * value_ptr, - void * output_ptr, + void * d_output_ptr, + void * d_value_ptr, int batch_size, int heads, int height, @@ -1695,10 +4170,10 @@ void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks31_align8( float scale, cudaStream_t stream); -void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks31_align4( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks29_align4( void * attn_ptr, - void * value_ptr, - void * output_ptr, + void * d_output_ptr, + void * d_value_ptr, int batch_size, int heads, int height, @@ -1713,10 +4188,10 @@ void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks31_align4( float scale, cudaStream_t stream); -void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks31_align2( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks29_align2( void * attn_ptr, - void * value_ptr, - void * output_ptr, + void * d_output_ptr, + void * d_value_ptr, int batch_size, int heads, int height, @@ -1731,10 +4206,10 @@ void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks31_align2( float scale, cudaStream_t stream); -void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks33_align8( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks31_align8( void * attn_ptr, - void * value_ptr, - void * output_ptr, + void * d_output_ptr, + void * d_value_ptr, int batch_size, int heads, int height, @@ -1749,10 +4224,10 @@ void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks33_align8( float scale, cudaStream_t stream); -void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks33_align4( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks31_align4( void * attn_ptr, - void * value_ptr, - void * output_ptr, + void * d_output_ptr, + void * d_value_ptr, int batch_size, int heads, int height, @@ -1767,10 +4242,10 @@ void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks33_align4( float scale, cudaStream_t stream); -void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks33_align2( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks31_align2( void * attn_ptr, - void * value_ptr, - void * output_ptr, + void * d_output_ptr, + void * d_value_ptr, int batch_size, int heads, int height, @@ -1785,7 +4260,7 @@ void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks33_align2( float scale, cudaStream_t stream); -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks3_align8( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks33_align8( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -1803,7 +4278,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks3_align8( float scale, cudaStream_t stream); -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks3_align4( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks33_align4( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -1821,7 +4296,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks3_align4( float scale, cudaStream_t stream); -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks3_align2( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks33_align2( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -1839,7 +4314,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks3_align2( float scale, cudaStream_t stream); -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks5_align8( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks35_align8( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -1857,7 +4332,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks5_align8( float scale, cudaStream_t stream); -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks5_align4( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks35_align4( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -1875,7 +4350,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks5_align4( float scale, cudaStream_t stream); -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks5_align2( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks35_align2( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -1893,7 +4368,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks5_align2( float scale, cudaStream_t stream); -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks7_align8( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks37_align8( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -1911,7 +4386,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks7_align8( float scale, cudaStream_t stream); -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks7_align4( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks37_align4( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -1929,7 +4404,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks7_align4( float scale, cudaStream_t stream); -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks7_align2( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks37_align2( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -1947,7 +4422,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks7_align2( float scale, cudaStream_t stream); -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks9_align8( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks39_align8( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -1965,7 +4440,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks9_align8( float scale, cudaStream_t stream); -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks9_align4( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks39_align4( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -1983,7 +4458,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks9_align4( float scale, cudaStream_t stream); -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks9_align2( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks39_align2( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -2001,7 +4476,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks9_align2( float scale, cudaStream_t stream); -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks11_align8( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks41_align8( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -2019,7 +4494,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks11_align8( float scale, cudaStream_t stream); -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks11_align4( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks41_align4( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -2037,7 +4512,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks11_align4( float scale, cudaStream_t stream); -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks11_align2( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks41_align2( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -2055,7 +4530,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks11_align2( float scale, cudaStream_t stream); -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks13_align8( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks43_align8( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -2073,7 +4548,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks13_align8( float scale, cudaStream_t stream); -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks13_align4( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks43_align4( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -2091,7 +4566,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks13_align4( float scale, cudaStream_t stream); -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks13_align2( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks43_align2( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -2109,7 +4584,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks13_align2( float scale, cudaStream_t stream); -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks15_align8( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks45_align8( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -2127,7 +4602,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks15_align8( float scale, cudaStream_t stream); -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks15_align4( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks45_align4( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -2145,7 +4620,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks15_align4( float scale, cudaStream_t stream); -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks15_align2( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks45_align2( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -2163,7 +4638,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks15_align2( float scale, cudaStream_t stream); -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks17_align8( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks47_align8( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -2181,7 +4656,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks17_align8( float scale, cudaStream_t stream); -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks17_align4( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks47_align4( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -2199,7 +4674,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks17_align4( float scale, cudaStream_t stream); -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks17_align2( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks47_align2( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -2217,7 +4692,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks17_align2( float scale, cudaStream_t stream); -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks19_align8( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks49_align8( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -2235,7 +4710,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks19_align8( float scale, cudaStream_t stream); -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks19_align4( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks49_align4( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -2253,7 +4728,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks19_align4( float scale, cudaStream_t stream); -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks19_align2( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks49_align2( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -2271,7 +4746,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks19_align2( float scale, cudaStream_t stream); -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks21_align8( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks51_align8( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -2289,7 +4764,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks21_align8( float scale, cudaStream_t stream); -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks21_align4( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks51_align4( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -2307,7 +4782,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks21_align4( float scale, cudaStream_t stream); -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks21_align2( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks51_align2( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -2325,7 +4800,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks21_align2( float scale, cudaStream_t stream); -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks23_align8( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks53_align8( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -2343,7 +4818,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks23_align8( float scale, cudaStream_t stream); -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks23_align4( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks53_align4( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -2361,7 +4836,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks23_align4( float scale, cudaStream_t stream); -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks23_align2( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks53_align2( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -2379,7 +4854,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks23_align2( float scale, cudaStream_t stream); -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks25_align8( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks55_align8( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -2397,7 +4872,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks25_align8( float scale, cudaStream_t stream); -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks25_align4( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks55_align4( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -2415,7 +4890,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks25_align4( float scale, cudaStream_t stream); -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks25_align2( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks55_align2( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -2433,7 +4908,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks25_align2( float scale, cudaStream_t stream); -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks27_align8( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks57_align8( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -2451,7 +4926,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks27_align8( float scale, cudaStream_t stream); -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks27_align4( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks57_align4( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -2469,7 +4944,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks27_align4( float scale, cudaStream_t stream); -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks27_align2( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks57_align2( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -2487,7 +4962,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks27_align2( float scale, cudaStream_t stream); -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks29_align8( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks59_align8( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -2505,7 +4980,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks29_align8( float scale, cudaStream_t stream); -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks29_align4( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks59_align4( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -2523,7 +4998,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks29_align4( float scale, cudaStream_t stream); -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks29_align2( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks59_align2( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -2541,7 +5016,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks29_align2( float scale, cudaStream_t stream); -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks31_align8( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks61_align8( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -2559,7 +5034,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks31_align8( float scale, cudaStream_t stream); -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks31_align4( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks61_align4( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -2577,7 +5052,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks31_align4( float scale, cudaStream_t stream); -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks31_align2( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks61_align2( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -2595,7 +5070,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks31_align2( float scale, cudaStream_t stream); -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks33_align8( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks63_align8( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -2613,7 +5088,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks33_align8( float scale, cudaStream_t stream); -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks33_align4( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks63_align4( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -2631,7 +5106,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks33_align4( float scale, cudaStream_t stream); -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks33_align2( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks63_align2( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, diff --git a/csrc/autogen/include/natten_autogen/cuda/gemm/2d/sm75/dispatch_align.h b/csrc/autogen/include/natten_autogen/cuda/gemm/2d/sm75/dispatch_align.h index 92f5574..6d4ff21 100644 --- a/csrc/autogen/include/natten_autogen/cuda/gemm/2d/sm75/dispatch_align.h +++ b/csrc/autogen/include/natten_autogen/cuda/gemm/2d/sm75/dispatch_align.h @@ -330,640 +330,1540 @@ namespace gemm { } \ }(); +#define DISPATCH_ALIGNMENT_na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks35(dim, ...) \ + [&] { \ + if (dim % 8 == 0) { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks35_align8(__VA_ARGS__); \ + } \ + else if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks35_align4(__VA_ARGS__); \ + } \ + else if (dim % 2 == 0) { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks35_align2(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks35 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=half. " \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks37(dim, ...) \ + [&] { \ + if (dim % 8 == 0) { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks37_align8(__VA_ARGS__); \ + } \ + else if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks37_align4(__VA_ARGS__); \ + } \ + else if (dim % 2 == 0) { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks37_align2(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks37 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=half. " \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks39(dim, ...) \ + [&] { \ + if (dim % 8 == 0) { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks39_align8(__VA_ARGS__); \ + } \ + else if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks39_align4(__VA_ARGS__); \ + } \ + else if (dim % 2 == 0) { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks39_align2(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks39 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=half. " \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks41(dim, ...) \ + [&] { \ + if (dim % 8 == 0) { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks41_align8(__VA_ARGS__); \ + } \ + else if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks41_align4(__VA_ARGS__); \ + } \ + else if (dim % 2 == 0) { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks41_align2(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks41 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=half. " \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks43(dim, ...) \ + [&] { \ + if (dim % 8 == 0) { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks43_align8(__VA_ARGS__); \ + } \ + else if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks43_align4(__VA_ARGS__); \ + } \ + else if (dim % 2 == 0) { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks43_align2(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks43 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=half. " \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks45(dim, ...) \ + [&] { \ + if (dim % 8 == 0) { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks45_align8(__VA_ARGS__); \ + } \ + else if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks45_align4(__VA_ARGS__); \ + } \ + else if (dim % 2 == 0) { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks45_align2(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks45 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=half. " \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks47(dim, ...) \ + [&] { \ + if (dim % 8 == 0) { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks47_align8(__VA_ARGS__); \ + } \ + else if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks47_align4(__VA_ARGS__); \ + } \ + else if (dim % 2 == 0) { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks47_align2(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks47 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=half. " \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks49(dim, ...) \ + [&] { \ + if (dim % 8 == 0) { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks49_align8(__VA_ARGS__); \ + } \ + else if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks49_align4(__VA_ARGS__); \ + } \ + else if (dim % 2 == 0) { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks49_align2(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks49 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=half. " \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks51(dim, ...) \ + [&] { \ + if (dim % 8 == 0) { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks51_align8(__VA_ARGS__); \ + } \ + else if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks51_align4(__VA_ARGS__); \ + } \ + else if (dim % 2 == 0) { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks51_align2(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks51 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=half. " \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks53(dim, ...) \ + [&] { \ + if (dim % 8 == 0) { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks53_align8(__VA_ARGS__); \ + } \ + else if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks53_align4(__VA_ARGS__); \ + } \ + else if (dim % 2 == 0) { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks53_align2(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks53 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=half. " \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks55(dim, ...) \ + [&] { \ + if (dim % 8 == 0) { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks55_align8(__VA_ARGS__); \ + } \ + else if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks55_align4(__VA_ARGS__); \ + } \ + else if (dim % 2 == 0) { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks55_align2(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks55 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=half. " \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks57(dim, ...) \ + [&] { \ + if (dim % 8 == 0) { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks57_align8(__VA_ARGS__); \ + } \ + else if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks57_align4(__VA_ARGS__); \ + } \ + else if (dim % 2 == 0) { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks57_align2(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks57 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=half. " \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks59(dim, ...) \ + [&] { \ + if (dim % 8 == 0) { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks59_align8(__VA_ARGS__); \ + } \ + else if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks59_align4(__VA_ARGS__); \ + } \ + else if (dim % 2 == 0) { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks59_align2(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks59 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=half. " \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks61(dim, ...) \ + [&] { \ + if (dim % 8 == 0) { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks61_align8(__VA_ARGS__); \ + } \ + else if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks61_align4(__VA_ARGS__); \ + } \ + else if (dim % 2 == 0) { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks61_align2(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks61 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=half. " \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks63(dim, ...) \ + [&] { \ + if (dim % 8 == 0) { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks63_align8(__VA_ARGS__); \ + } \ + else if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks63_align4(__VA_ARGS__); \ + } \ + else if (dim % 2 == 0) { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks63_align2(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks63 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=half. " \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + #define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks3(dim, ...) \ [&] { \ if (dim % 8 == 0) { \ - natten::cuda::gemm::na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks3_align8(__VA_ARGS__); \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks3_align8(__VA_ARGS__); \ + } \ + else if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks3_align4(__VA_ARGS__); \ + } \ + else if (dim % 2 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks3_align2(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks3 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=half. " \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks5(dim, ...) \ + [&] { \ + if (dim % 8 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks5_align8(__VA_ARGS__); \ + } \ + else if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks5_align4(__VA_ARGS__); \ + } \ + else if (dim % 2 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks5_align2(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks5 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=half. " \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks7(dim, ...) \ + [&] { \ + if (dim % 8 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks7_align8(__VA_ARGS__); \ + } \ + else if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks7_align4(__VA_ARGS__); \ + } \ + else if (dim % 2 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks7_align2(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks7 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=half. " \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks9(dim, ...) \ + [&] { \ + if (dim % 8 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks9_align8(__VA_ARGS__); \ + } \ + else if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks9_align4(__VA_ARGS__); \ + } \ + else if (dim % 2 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks9_align2(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks9 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=half. " \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks11(dim, ...) \ + [&] { \ + if (dim % 8 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks11_align8(__VA_ARGS__); \ + } \ + else if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks11_align4(__VA_ARGS__); \ + } \ + else if (dim % 2 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks11_align2(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks11 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=half. " \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks13(dim, ...) \ + [&] { \ + if (dim % 8 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks13_align8(__VA_ARGS__); \ + } \ + else if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks13_align4(__VA_ARGS__); \ + } \ + else if (dim % 2 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks13_align2(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks13 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=half. " \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks15(dim, ...) \ + [&] { \ + if (dim % 8 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks15_align8(__VA_ARGS__); \ + } \ + else if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks15_align4(__VA_ARGS__); \ + } \ + else if (dim % 2 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks15_align2(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks15 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=half. " \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks17(dim, ...) \ + [&] { \ + if (dim % 8 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks17_align8(__VA_ARGS__); \ + } \ + else if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks17_align4(__VA_ARGS__); \ + } \ + else if (dim % 2 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks17_align2(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks17 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=half. " \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks19(dim, ...) \ + [&] { \ + if (dim % 8 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks19_align8(__VA_ARGS__); \ + } \ + else if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks19_align4(__VA_ARGS__); \ + } \ + else if (dim % 2 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks19_align2(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks19 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=half. " \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks21(dim, ...) \ + [&] { \ + if (dim % 8 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks21_align8(__VA_ARGS__); \ + } \ + else if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks21_align4(__VA_ARGS__); \ + } \ + else if (dim % 2 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks21_align2(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks21 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=half. " \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks23(dim, ...) \ + [&] { \ + if (dim % 8 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks23_align8(__VA_ARGS__); \ + } \ + else if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks23_align4(__VA_ARGS__); \ + } \ + else if (dim % 2 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks23_align2(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks23 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=half. " \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks25(dim, ...) \ + [&] { \ + if (dim % 8 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks25_align8(__VA_ARGS__); \ + } \ + else if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks25_align4(__VA_ARGS__); \ + } \ + else if (dim % 2 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks25_align2(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks25 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=half. " \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks27(dim, ...) \ + [&] { \ + if (dim % 8 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks27_align8(__VA_ARGS__); \ + } \ + else if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks27_align4(__VA_ARGS__); \ + } \ + else if (dim % 2 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks27_align2(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks27 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=half. " \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks29(dim, ...) \ + [&] { \ + if (dim % 8 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks29_align8(__VA_ARGS__); \ + } \ + else if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks29_align4(__VA_ARGS__); \ + } \ + else if (dim % 2 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks29_align2(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks29 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=half. " \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks31(dim, ...) \ + [&] { \ + if (dim % 8 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks31_align8(__VA_ARGS__); \ + } \ + else if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks31_align4(__VA_ARGS__); \ + } \ + else if (dim % 2 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks31_align2(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks31 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=half. " \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks33(dim, ...) \ + [&] { \ + if (dim % 8 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks33_align8(__VA_ARGS__); \ + } \ + else if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks33_align4(__VA_ARGS__); \ + } \ + else if (dim % 2 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks33_align2(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks33 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=half. " \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks35(dim, ...) \ + [&] { \ + if (dim % 8 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks35_align8(__VA_ARGS__); \ + } \ + else if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks35_align4(__VA_ARGS__); \ + } \ + else if (dim % 2 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks35_align2(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks35 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=half. " \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks37(dim, ...) \ + [&] { \ + if (dim % 8 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks37_align8(__VA_ARGS__); \ + } \ + else if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks37_align4(__VA_ARGS__); \ + } \ + else if (dim % 2 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks37_align2(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks37 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=half. " \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks39(dim, ...) \ + [&] { \ + if (dim % 8 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks39_align8(__VA_ARGS__); \ + } \ + else if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks39_align4(__VA_ARGS__); \ + } \ + else if (dim % 2 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks39_align2(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks39 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=half. " \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks41(dim, ...) \ + [&] { \ + if (dim % 8 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks41_align8(__VA_ARGS__); \ + } \ + else if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks41_align4(__VA_ARGS__); \ + } \ + else if (dim % 2 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks41_align2(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks41 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=half. " \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks43(dim, ...) \ + [&] { \ + if (dim % 8 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks43_align8(__VA_ARGS__); \ + } \ + else if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks43_align4(__VA_ARGS__); \ + } \ + else if (dim % 2 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks43_align2(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks43 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=half. " \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks45(dim, ...) \ + [&] { \ + if (dim % 8 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks45_align8(__VA_ARGS__); \ + } \ + else if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks45_align4(__VA_ARGS__); \ + } \ + else if (dim % 2 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks45_align2(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks45 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=half. " \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks47(dim, ...) \ + [&] { \ + if (dim % 8 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks47_align8(__VA_ARGS__); \ + } \ + else if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks47_align4(__VA_ARGS__); \ + } \ + else if (dim % 2 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks47_align2(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks47 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=half. " \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks49(dim, ...) \ + [&] { \ + if (dim % 8 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks49_align8(__VA_ARGS__); \ + } \ + else if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks49_align4(__VA_ARGS__); \ + } \ + else if (dim % 2 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks49_align2(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks49 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=half. " \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks51(dim, ...) \ + [&] { \ + if (dim % 8 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks51_align8(__VA_ARGS__); \ + } \ + else if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks51_align4(__VA_ARGS__); \ + } \ + else if (dim % 2 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks51_align2(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks51 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=half. " \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks53(dim, ...) \ + [&] { \ + if (dim % 8 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks53_align8(__VA_ARGS__); \ + } \ + else if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks53_align4(__VA_ARGS__); \ + } \ + else if (dim % 2 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks53_align2(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks53 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=half. " \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks55(dim, ...) \ + [&] { \ + if (dim % 8 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks55_align8(__VA_ARGS__); \ + } \ + else if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks55_align4(__VA_ARGS__); \ + } \ + else if (dim % 2 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks55_align2(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks55 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=half. " \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks57(dim, ...) \ + [&] { \ + if (dim % 8 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks57_align8(__VA_ARGS__); \ + } \ + else if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks57_align4(__VA_ARGS__); \ + } \ + else if (dim % 2 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks57_align2(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks57 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=half. " \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks59(dim, ...) \ + [&] { \ + if (dim % 8 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks59_align8(__VA_ARGS__); \ + } \ + else if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks59_align4(__VA_ARGS__); \ + } \ + else if (dim % 2 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks59_align2(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks59 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=half. " \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks61(dim, ...) \ + [&] { \ + if (dim % 8 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks61_align8(__VA_ARGS__); \ } \ else if (dim % 4 == 0) { \ - natten::cuda::gemm::na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks3_align4(__VA_ARGS__); \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks61_align4(__VA_ARGS__); \ } \ else if (dim % 2 == 0) { \ - natten::cuda::gemm::na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks3_align2(__VA_ARGS__); \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks61_align2(__VA_ARGS__); \ } \ else { \ std::cerr << "NATTEN kernel launch failed!" \ - << "na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks3 requires at least 32-bit alignment." \ + << "na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks61 requires at least 32-bit alignment." \ << "Got dim=" << dim << ", dtype=half. " \ << std::endl; \ exit(EXIT_FAILURE); \ } \ }(); -#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks5(dim, ...) \ +#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks63(dim, ...) \ [&] { \ if (dim % 8 == 0) { \ - natten::cuda::gemm::na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks5_align8(__VA_ARGS__); \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks63_align8(__VA_ARGS__); \ } \ else if (dim % 4 == 0) { \ - natten::cuda::gemm::na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks5_align4(__VA_ARGS__); \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks63_align4(__VA_ARGS__); \ } \ else if (dim % 2 == 0) { \ - natten::cuda::gemm::na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks5_align2(__VA_ARGS__); \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks63_align2(__VA_ARGS__); \ } \ else { \ std::cerr << "NATTEN kernel launch failed!" \ - << "na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks5 requires at least 32-bit alignment." \ + << "na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks63 requires at least 32-bit alignment." \ << "Got dim=" << dim << ", dtype=half. " \ << std::endl; \ exit(EXIT_FAILURE); \ } \ }(); -#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks7(dim, ...) \ +#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks3(dim, ...) \ [&] { \ if (dim % 8 == 0) { \ - natten::cuda::gemm::na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks7_align8(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks3_align8(__VA_ARGS__); \ } \ else if (dim % 4 == 0) { \ - natten::cuda::gemm::na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks7_align4(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks3_align4(__VA_ARGS__); \ } \ else if (dim % 2 == 0) { \ - natten::cuda::gemm::na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks7_align2(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks3_align2(__VA_ARGS__); \ } \ else { \ std::cerr << "NATTEN kernel launch failed!" \ - << "na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks7 requires at least 32-bit alignment." \ + << "na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks3 requires at least 32-bit alignment." \ << "Got dim=" << dim << ", dtype=half. " \ << std::endl; \ exit(EXIT_FAILURE); \ } \ }(); -#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks9(dim, ...) \ +#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks5(dim, ...) \ [&] { \ if (dim % 8 == 0) { \ - natten::cuda::gemm::na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks9_align8(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks5_align8(__VA_ARGS__); \ } \ else if (dim % 4 == 0) { \ - natten::cuda::gemm::na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks9_align4(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks5_align4(__VA_ARGS__); \ } \ else if (dim % 2 == 0) { \ - natten::cuda::gemm::na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks9_align2(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks5_align2(__VA_ARGS__); \ } \ else { \ std::cerr << "NATTEN kernel launch failed!" \ - << "na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks9 requires at least 32-bit alignment." \ + << "na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks5 requires at least 32-bit alignment." \ << "Got dim=" << dim << ", dtype=half. " \ << std::endl; \ exit(EXIT_FAILURE); \ } \ }(); -#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks11(dim, ...) \ +#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks7(dim, ...) \ [&] { \ if (dim % 8 == 0) { \ - natten::cuda::gemm::na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks11_align8(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks7_align8(__VA_ARGS__); \ } \ else if (dim % 4 == 0) { \ - natten::cuda::gemm::na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks11_align4(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks7_align4(__VA_ARGS__); \ } \ else if (dim % 2 == 0) { \ - natten::cuda::gemm::na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks11_align2(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks7_align2(__VA_ARGS__); \ } \ else { \ std::cerr << "NATTEN kernel launch failed!" \ - << "na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks11 requires at least 32-bit alignment." \ + << "na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks7 requires at least 32-bit alignment." \ << "Got dim=" << dim << ", dtype=half. " \ << std::endl; \ exit(EXIT_FAILURE); \ } \ }(); -#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks13(dim, ...) \ +#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks9(dim, ...) \ [&] { \ if (dim % 8 == 0) { \ - natten::cuda::gemm::na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks13_align8(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks9_align8(__VA_ARGS__); \ } \ else if (dim % 4 == 0) { \ - natten::cuda::gemm::na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks13_align4(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks9_align4(__VA_ARGS__); \ } \ else if (dim % 2 == 0) { \ - natten::cuda::gemm::na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks13_align2(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks9_align2(__VA_ARGS__); \ } \ else { \ std::cerr << "NATTEN kernel launch failed!" \ - << "na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks13 requires at least 32-bit alignment." \ + << "na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks9 requires at least 32-bit alignment." \ << "Got dim=" << dim << ", dtype=half. " \ << std::endl; \ exit(EXIT_FAILURE); \ } \ }(); -#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks15(dim, ...) \ +#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks11(dim, ...) \ [&] { \ if (dim % 8 == 0) { \ - natten::cuda::gemm::na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks15_align8(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks11_align8(__VA_ARGS__); \ } \ else if (dim % 4 == 0) { \ - natten::cuda::gemm::na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks15_align4(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks11_align4(__VA_ARGS__); \ } \ else if (dim % 2 == 0) { \ - natten::cuda::gemm::na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks15_align2(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks11_align2(__VA_ARGS__); \ } \ else { \ std::cerr << "NATTEN kernel launch failed!" \ - << "na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks15 requires at least 32-bit alignment." \ + << "na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks11 requires at least 32-bit alignment." \ << "Got dim=" << dim << ", dtype=half. " \ << std::endl; \ exit(EXIT_FAILURE); \ } \ }(); -#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks17(dim, ...) \ +#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks13(dim, ...) \ [&] { \ if (dim % 8 == 0) { \ - natten::cuda::gemm::na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks17_align8(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks13_align8(__VA_ARGS__); \ } \ else if (dim % 4 == 0) { \ - natten::cuda::gemm::na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks17_align4(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks13_align4(__VA_ARGS__); \ } \ else if (dim % 2 == 0) { \ - natten::cuda::gemm::na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks17_align2(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks13_align2(__VA_ARGS__); \ } \ else { \ std::cerr << "NATTEN kernel launch failed!" \ - << "na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks17 requires at least 32-bit alignment." \ + << "na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks13 requires at least 32-bit alignment." \ << "Got dim=" << dim << ", dtype=half. " \ << std::endl; \ exit(EXIT_FAILURE); \ } \ }(); -#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks19(dim, ...) \ +#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks15(dim, ...) \ [&] { \ if (dim % 8 == 0) { \ - natten::cuda::gemm::na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks19_align8(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks15_align8(__VA_ARGS__); \ } \ else if (dim % 4 == 0) { \ - natten::cuda::gemm::na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks19_align4(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks15_align4(__VA_ARGS__); \ } \ else if (dim % 2 == 0) { \ - natten::cuda::gemm::na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks19_align2(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks15_align2(__VA_ARGS__); \ } \ else { \ std::cerr << "NATTEN kernel launch failed!" \ - << "na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks19 requires at least 32-bit alignment." \ + << "na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks15 requires at least 32-bit alignment." \ << "Got dim=" << dim << ", dtype=half. " \ << std::endl; \ exit(EXIT_FAILURE); \ } \ }(); -#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks21(dim, ...) \ +#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks17(dim, ...) \ [&] { \ if (dim % 8 == 0) { \ - natten::cuda::gemm::na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks21_align8(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks17_align8(__VA_ARGS__); \ } \ else if (dim % 4 == 0) { \ - natten::cuda::gemm::na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks21_align4(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks17_align4(__VA_ARGS__); \ } \ else if (dim % 2 == 0) { \ - natten::cuda::gemm::na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks21_align2(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks17_align2(__VA_ARGS__); \ } \ else { \ std::cerr << "NATTEN kernel launch failed!" \ - << "na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks21 requires at least 32-bit alignment." \ + << "na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks17 requires at least 32-bit alignment." \ << "Got dim=" << dim << ", dtype=half. " \ << std::endl; \ exit(EXIT_FAILURE); \ } \ }(); -#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks23(dim, ...) \ +#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks19(dim, ...) \ [&] { \ if (dim % 8 == 0) { \ - natten::cuda::gemm::na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks23_align8(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks19_align8(__VA_ARGS__); \ } \ else if (dim % 4 == 0) { \ - natten::cuda::gemm::na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks23_align4(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks19_align4(__VA_ARGS__); \ } \ else if (dim % 2 == 0) { \ - natten::cuda::gemm::na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks23_align2(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks19_align2(__VA_ARGS__); \ } \ else { \ std::cerr << "NATTEN kernel launch failed!" \ - << "na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks23 requires at least 32-bit alignment." \ + << "na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks19 requires at least 32-bit alignment." \ << "Got dim=" << dim << ", dtype=half. " \ << std::endl; \ exit(EXIT_FAILURE); \ } \ }(); -#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks25(dim, ...) \ +#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks21(dim, ...) \ [&] { \ if (dim % 8 == 0) { \ - natten::cuda::gemm::na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks25_align8(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks21_align8(__VA_ARGS__); \ } \ else if (dim % 4 == 0) { \ - natten::cuda::gemm::na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks25_align4(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks21_align4(__VA_ARGS__); \ } \ else if (dim % 2 == 0) { \ - natten::cuda::gemm::na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks25_align2(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks21_align2(__VA_ARGS__); \ } \ else { \ std::cerr << "NATTEN kernel launch failed!" \ - << "na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks25 requires at least 32-bit alignment." \ + << "na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks21 requires at least 32-bit alignment." \ << "Got dim=" << dim << ", dtype=half. " \ << std::endl; \ exit(EXIT_FAILURE); \ } \ }(); -#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks27(dim, ...) \ +#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks23(dim, ...) \ [&] { \ if (dim % 8 == 0) { \ - natten::cuda::gemm::na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks27_align8(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks23_align8(__VA_ARGS__); \ } \ else if (dim % 4 == 0) { \ - natten::cuda::gemm::na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks27_align4(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks23_align4(__VA_ARGS__); \ } \ else if (dim % 2 == 0) { \ - natten::cuda::gemm::na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks27_align2(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks23_align2(__VA_ARGS__); \ } \ else { \ std::cerr << "NATTEN kernel launch failed!" \ - << "na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks27 requires at least 32-bit alignment." \ + << "na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks23 requires at least 32-bit alignment." \ << "Got dim=" << dim << ", dtype=half. " \ << std::endl; \ exit(EXIT_FAILURE); \ } \ }(); -#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks29(dim, ...) \ +#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks25(dim, ...) \ [&] { \ if (dim % 8 == 0) { \ - natten::cuda::gemm::na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks29_align8(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks25_align8(__VA_ARGS__); \ } \ else if (dim % 4 == 0) { \ - natten::cuda::gemm::na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks29_align4(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks25_align4(__VA_ARGS__); \ } \ else if (dim % 2 == 0) { \ - natten::cuda::gemm::na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks29_align2(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks25_align2(__VA_ARGS__); \ } \ else { \ std::cerr << "NATTEN kernel launch failed!" \ - << "na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks29 requires at least 32-bit alignment." \ + << "na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks25 requires at least 32-bit alignment." \ << "Got dim=" << dim << ", dtype=half. " \ << std::endl; \ exit(EXIT_FAILURE); \ } \ }(); -#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks31(dim, ...) \ +#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks27(dim, ...) \ [&] { \ if (dim % 8 == 0) { \ - natten::cuda::gemm::na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks31_align8(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks27_align8(__VA_ARGS__); \ } \ else if (dim % 4 == 0) { \ - natten::cuda::gemm::na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks31_align4(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks27_align4(__VA_ARGS__); \ } \ else if (dim % 2 == 0) { \ - natten::cuda::gemm::na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks31_align2(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks27_align2(__VA_ARGS__); \ } \ else { \ std::cerr << "NATTEN kernel launch failed!" \ - << "na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks31 requires at least 32-bit alignment." \ + << "na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks27 requires at least 32-bit alignment." \ << "Got dim=" << dim << ", dtype=half. " \ << std::endl; \ exit(EXIT_FAILURE); \ } \ }(); -#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks33(dim, ...) \ +#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks29(dim, ...) \ [&] { \ if (dim % 8 == 0) { \ - natten::cuda::gemm::na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks33_align8(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks29_align8(__VA_ARGS__); \ } \ else if (dim % 4 == 0) { \ - natten::cuda::gemm::na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks33_align4(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks29_align4(__VA_ARGS__); \ } \ else if (dim % 2 == 0) { \ - natten::cuda::gemm::na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks33_align2(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks29_align2(__VA_ARGS__); \ } \ else { \ std::cerr << "NATTEN kernel launch failed!" \ - << "na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks33 requires at least 32-bit alignment." \ + << "na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks29 requires at least 32-bit alignment." \ << "Got dim=" << dim << ", dtype=half. " \ << std::endl; \ exit(EXIT_FAILURE); \ } \ }(); -#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks3(dim, ...) \ +#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks31(dim, ...) \ [&] { \ if (dim % 8 == 0) { \ - natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks3_align8(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks31_align8(__VA_ARGS__); \ } \ else if (dim % 4 == 0) { \ - natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks3_align4(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks31_align4(__VA_ARGS__); \ } \ else if (dim % 2 == 0) { \ - natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks3_align2(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks31_align2(__VA_ARGS__); \ } \ else { \ std::cerr << "NATTEN kernel launch failed!" \ - << "na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks3 requires at least 32-bit alignment." \ + << "na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks31 requires at least 32-bit alignment." \ << "Got dim=" << dim << ", dtype=half. " \ << std::endl; \ exit(EXIT_FAILURE); \ } \ }(); -#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks5(dim, ...) \ +#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks33(dim, ...) \ [&] { \ if (dim % 8 == 0) { \ - natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks5_align8(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks33_align8(__VA_ARGS__); \ } \ else if (dim % 4 == 0) { \ - natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks5_align4(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks33_align4(__VA_ARGS__); \ } \ else if (dim % 2 == 0) { \ - natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks5_align2(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks33_align2(__VA_ARGS__); \ } \ else { \ std::cerr << "NATTEN kernel launch failed!" \ - << "na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks5 requires at least 32-bit alignment." \ + << "na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks33 requires at least 32-bit alignment." \ << "Got dim=" << dim << ", dtype=half. " \ << std::endl; \ exit(EXIT_FAILURE); \ } \ }(); -#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks7(dim, ...) \ +#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks35(dim, ...) \ [&] { \ if (dim % 8 == 0) { \ - natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks7_align8(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks35_align8(__VA_ARGS__); \ } \ else if (dim % 4 == 0) { \ - natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks7_align4(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks35_align4(__VA_ARGS__); \ } \ else if (dim % 2 == 0) { \ - natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks7_align2(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks35_align2(__VA_ARGS__); \ } \ else { \ std::cerr << "NATTEN kernel launch failed!" \ - << "na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks7 requires at least 32-bit alignment." \ + << "na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks35 requires at least 32-bit alignment." \ << "Got dim=" << dim << ", dtype=half. " \ << std::endl; \ exit(EXIT_FAILURE); \ } \ }(); -#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks9(dim, ...) \ +#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks37(dim, ...) \ [&] { \ if (dim % 8 == 0) { \ - natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks9_align8(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks37_align8(__VA_ARGS__); \ } \ else if (dim % 4 == 0) { \ - natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks9_align4(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks37_align4(__VA_ARGS__); \ } \ else if (dim % 2 == 0) { \ - natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks9_align2(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks37_align2(__VA_ARGS__); \ } \ else { \ std::cerr << "NATTEN kernel launch failed!" \ - << "na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks9 requires at least 32-bit alignment." \ + << "na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks37 requires at least 32-bit alignment." \ << "Got dim=" << dim << ", dtype=half. " \ << std::endl; \ exit(EXIT_FAILURE); \ } \ }(); -#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks11(dim, ...) \ +#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks39(dim, ...) \ [&] { \ if (dim % 8 == 0) { \ - natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks11_align8(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks39_align8(__VA_ARGS__); \ } \ else if (dim % 4 == 0) { \ - natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks11_align4(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks39_align4(__VA_ARGS__); \ } \ else if (dim % 2 == 0) { \ - natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks11_align2(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks39_align2(__VA_ARGS__); \ } \ else { \ std::cerr << "NATTEN kernel launch failed!" \ - << "na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks11 requires at least 32-bit alignment." \ + << "na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks39 requires at least 32-bit alignment." \ << "Got dim=" << dim << ", dtype=half. " \ << std::endl; \ exit(EXIT_FAILURE); \ } \ }(); -#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks13(dim, ...) \ +#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks41(dim, ...) \ [&] { \ if (dim % 8 == 0) { \ - natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks13_align8(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks41_align8(__VA_ARGS__); \ } \ else if (dim % 4 == 0) { \ - natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks13_align4(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks41_align4(__VA_ARGS__); \ } \ else if (dim % 2 == 0) { \ - natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks13_align2(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks41_align2(__VA_ARGS__); \ } \ else { \ std::cerr << "NATTEN kernel launch failed!" \ - << "na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks13 requires at least 32-bit alignment." \ + << "na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks41 requires at least 32-bit alignment." \ << "Got dim=" << dim << ", dtype=half. " \ << std::endl; \ exit(EXIT_FAILURE); \ } \ }(); -#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks15(dim, ...) \ +#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks43(dim, ...) \ [&] { \ if (dim % 8 == 0) { \ - natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks15_align8(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks43_align8(__VA_ARGS__); \ } \ else if (dim % 4 == 0) { \ - natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks15_align4(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks43_align4(__VA_ARGS__); \ } \ else if (dim % 2 == 0) { \ - natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks15_align2(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks43_align2(__VA_ARGS__); \ } \ else { \ std::cerr << "NATTEN kernel launch failed!" \ - << "na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks15 requires at least 32-bit alignment." \ + << "na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks43 requires at least 32-bit alignment." \ << "Got dim=" << dim << ", dtype=half. " \ << std::endl; \ exit(EXIT_FAILURE); \ } \ }(); -#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks17(dim, ...) \ +#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks45(dim, ...) \ [&] { \ if (dim % 8 == 0) { \ - natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks17_align8(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks45_align8(__VA_ARGS__); \ } \ else if (dim % 4 == 0) { \ - natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks17_align4(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks45_align4(__VA_ARGS__); \ } \ else if (dim % 2 == 0) { \ - natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks17_align2(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks45_align2(__VA_ARGS__); \ } \ else { \ std::cerr << "NATTEN kernel launch failed!" \ - << "na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks17 requires at least 32-bit alignment." \ + << "na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks45 requires at least 32-bit alignment." \ << "Got dim=" << dim << ", dtype=half. " \ << std::endl; \ exit(EXIT_FAILURE); \ } \ }(); -#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks19(dim, ...) \ +#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks47(dim, ...) \ [&] { \ if (dim % 8 == 0) { \ - natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks19_align8(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks47_align8(__VA_ARGS__); \ } \ else if (dim % 4 == 0) { \ - natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks19_align4(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks47_align4(__VA_ARGS__); \ } \ else if (dim % 2 == 0) { \ - natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks19_align2(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks47_align2(__VA_ARGS__); \ } \ else { \ std::cerr << "NATTEN kernel launch failed!" \ - << "na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks19 requires at least 32-bit alignment." \ + << "na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks47 requires at least 32-bit alignment." \ << "Got dim=" << dim << ", dtype=half. " \ << std::endl; \ exit(EXIT_FAILURE); \ } \ }(); -#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks21(dim, ...) \ +#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks49(dim, ...) \ [&] { \ if (dim % 8 == 0) { \ - natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks21_align8(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks49_align8(__VA_ARGS__); \ } \ else if (dim % 4 == 0) { \ - natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks21_align4(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks49_align4(__VA_ARGS__); \ } \ else if (dim % 2 == 0) { \ - natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks21_align2(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks49_align2(__VA_ARGS__); \ } \ else { \ std::cerr << "NATTEN kernel launch failed!" \ - << "na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks21 requires at least 32-bit alignment." \ + << "na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks49 requires at least 32-bit alignment." \ << "Got dim=" << dim << ", dtype=half. " \ << std::endl; \ exit(EXIT_FAILURE); \ } \ }(); -#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks23(dim, ...) \ +#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks51(dim, ...) \ [&] { \ if (dim % 8 == 0) { \ - natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks23_align8(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks51_align8(__VA_ARGS__); \ } \ else if (dim % 4 == 0) { \ - natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks23_align4(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks51_align4(__VA_ARGS__); \ } \ else if (dim % 2 == 0) { \ - natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks23_align2(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks51_align2(__VA_ARGS__); \ } \ else { \ std::cerr << "NATTEN kernel launch failed!" \ - << "na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks23 requires at least 32-bit alignment." \ + << "na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks51 requires at least 32-bit alignment." \ << "Got dim=" << dim << ", dtype=half. " \ << std::endl; \ exit(EXIT_FAILURE); \ } \ }(); -#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks25(dim, ...) \ +#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks53(dim, ...) \ [&] { \ if (dim % 8 == 0) { \ - natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks25_align8(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks53_align8(__VA_ARGS__); \ } \ else if (dim % 4 == 0) { \ - natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks25_align4(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks53_align4(__VA_ARGS__); \ } \ else if (dim % 2 == 0) { \ - natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks25_align2(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks53_align2(__VA_ARGS__); \ } \ else { \ std::cerr << "NATTEN kernel launch failed!" \ - << "na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks25 requires at least 32-bit alignment." \ + << "na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks53 requires at least 32-bit alignment." \ << "Got dim=" << dim << ", dtype=half. " \ << std::endl; \ exit(EXIT_FAILURE); \ } \ }(); -#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks27(dim, ...) \ +#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks55(dim, ...) \ [&] { \ if (dim % 8 == 0) { \ - natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks27_align8(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks55_align8(__VA_ARGS__); \ } \ else if (dim % 4 == 0) { \ - natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks27_align4(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks55_align4(__VA_ARGS__); \ } \ else if (dim % 2 == 0) { \ - natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks27_align2(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks55_align2(__VA_ARGS__); \ } \ else { \ std::cerr << "NATTEN kernel launch failed!" \ - << "na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks27 requires at least 32-bit alignment." \ + << "na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks55 requires at least 32-bit alignment." \ << "Got dim=" << dim << ", dtype=half. " \ << std::endl; \ exit(EXIT_FAILURE); \ } \ }(); -#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks29(dim, ...) \ +#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks57(dim, ...) \ [&] { \ if (dim % 8 == 0) { \ - natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks29_align8(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks57_align8(__VA_ARGS__); \ } \ else if (dim % 4 == 0) { \ - natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks29_align4(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks57_align4(__VA_ARGS__); \ } \ else if (dim % 2 == 0) { \ - natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks29_align2(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks57_align2(__VA_ARGS__); \ } \ else { \ std::cerr << "NATTEN kernel launch failed!" \ - << "na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks29 requires at least 32-bit alignment." \ + << "na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks57 requires at least 32-bit alignment." \ << "Got dim=" << dim << ", dtype=half. " \ << std::endl; \ exit(EXIT_FAILURE); \ } \ }(); -#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks31(dim, ...) \ +#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks59(dim, ...) \ [&] { \ if (dim % 8 == 0) { \ - natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks31_align8(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks59_align8(__VA_ARGS__); \ } \ else if (dim % 4 == 0) { \ - natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks31_align4(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks59_align4(__VA_ARGS__); \ } \ else if (dim % 2 == 0) { \ - natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks31_align2(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks59_align2(__VA_ARGS__); \ } \ else { \ std::cerr << "NATTEN kernel launch failed!" \ - << "na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks31 requires at least 32-bit alignment." \ + << "na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks59 requires at least 32-bit alignment." \ << "Got dim=" << dim << ", dtype=half. " \ << std::endl; \ exit(EXIT_FAILURE); \ } \ }(); -#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks33(dim, ...) \ +#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks61(dim, ...) \ [&] { \ if (dim % 8 == 0) { \ - natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks33_align8(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks61_align8(__VA_ARGS__); \ } \ else if (dim % 4 == 0) { \ - natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks33_align4(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks61_align4(__VA_ARGS__); \ } \ else if (dim % 2 == 0) { \ - natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks33_align2(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks61_align2(__VA_ARGS__); \ } \ else { \ std::cerr << "NATTEN kernel launch failed!" \ - << "na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks33 requires at least 32-bit alignment." \ + << "na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks61 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=half. " \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks63(dim, ...) \ + [&] { \ + if (dim % 8 == 0) { \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks63_align8(__VA_ARGS__); \ + } \ + else if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks63_align4(__VA_ARGS__); \ + } \ + else if (dim % 2 == 0) { \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks63_align2(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks63 requires at least 32-bit alignment." \ << "Got dim=" << dim << ", dtype=half. " \ << std::endl; \ exit(EXIT_FAILURE); \ diff --git a/csrc/autogen/include/natten_autogen/cuda/gemm/2d/sm75/dispatch_kernel_size.h b/csrc/autogen/include/natten_autogen/cuda/gemm/2d/sm75/dispatch_kernel_size.h index 5b17f90..fdbc7bb 100644 --- a/csrc/autogen/include/natten_autogen/cuda/gemm/2d/sm75/dispatch_kernel_size.h +++ b/csrc/autogen/include/natten_autogen/cuda/gemm/2d/sm75/dispatch_kernel_size.h @@ -60,6 +60,51 @@ namespace gemm { else if (kernel_size == 33) { \ DISPATCH_ALIGNMENT_na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks33(dim, __VA_ARGS__); \ } \ + else if (kernel_size == 35) { \ + DISPATCH_ALIGNMENT_na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks35(dim, __VA_ARGS__); \ + } \ + else if (kernel_size == 37) { \ + DISPATCH_ALIGNMENT_na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks37(dim, __VA_ARGS__); \ + } \ + else if (kernel_size == 39) { \ + DISPATCH_ALIGNMENT_na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks39(dim, __VA_ARGS__); \ + } \ + else if (kernel_size == 41) { \ + DISPATCH_ALIGNMENT_na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks41(dim, __VA_ARGS__); \ + } \ + else if (kernel_size == 43) { \ + DISPATCH_ALIGNMENT_na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks43(dim, __VA_ARGS__); \ + } \ + else if (kernel_size == 45) { \ + DISPATCH_ALIGNMENT_na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks45(dim, __VA_ARGS__); \ + } \ + else if (kernel_size == 47) { \ + DISPATCH_ALIGNMENT_na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks47(dim, __VA_ARGS__); \ + } \ + else if (kernel_size == 49) { \ + DISPATCH_ALIGNMENT_na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks49(dim, __VA_ARGS__); \ + } \ + else if (kernel_size == 51) { \ + DISPATCH_ALIGNMENT_na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks51(dim, __VA_ARGS__); \ + } \ + else if (kernel_size == 53) { \ + DISPATCH_ALIGNMENT_na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks53(dim, __VA_ARGS__); \ + } \ + else if (kernel_size == 55) { \ + DISPATCH_ALIGNMENT_na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks55(dim, __VA_ARGS__); \ + } \ + else if (kernel_size == 57) { \ + DISPATCH_ALIGNMENT_na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks57(dim, __VA_ARGS__); \ + } \ + else if (kernel_size == 59) { \ + DISPATCH_ALIGNMENT_na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks59(dim, __VA_ARGS__); \ + } \ + else if (kernel_size == 61) { \ + DISPATCH_ALIGNMENT_na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks61(dim, __VA_ARGS__); \ + } \ + else if (kernel_size == 63) { \ + DISPATCH_ALIGNMENT_na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks63(dim, __VA_ARGS__); \ + } \ else { \ std::cerr << "NATTEN kernel launch failed! " \ << "na2d_pn_cuda_gemm_sm75_half does not support implement " \ @@ -121,6 +166,51 @@ namespace gemm { else if (kernel_size == 33) { \ DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks33(dim, __VA_ARGS__); \ } \ + else if (kernel_size == 35) { \ + DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks35(dim, __VA_ARGS__); \ + } \ + else if (kernel_size == 37) { \ + DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks37(dim, __VA_ARGS__); \ + } \ + else if (kernel_size == 39) { \ + DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks39(dim, __VA_ARGS__); \ + } \ + else if (kernel_size == 41) { \ + DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks41(dim, __VA_ARGS__); \ + } \ + else if (kernel_size == 43) { \ + DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks43(dim, __VA_ARGS__); \ + } \ + else if (kernel_size == 45) { \ + DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks45(dim, __VA_ARGS__); \ + } \ + else if (kernel_size == 47) { \ + DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks47(dim, __VA_ARGS__); \ + } \ + else if (kernel_size == 49) { \ + DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks49(dim, __VA_ARGS__); \ + } \ + else if (kernel_size == 51) { \ + DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks51(dim, __VA_ARGS__); \ + } \ + else if (kernel_size == 53) { \ + DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks53(dim, __VA_ARGS__); \ + } \ + else if (kernel_size == 55) { \ + DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks55(dim, __VA_ARGS__); \ + } \ + else if (kernel_size == 57) { \ + DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks57(dim, __VA_ARGS__); \ + } \ + else if (kernel_size == 59) { \ + DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks59(dim, __VA_ARGS__); \ + } \ + else if (kernel_size == 61) { \ + DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks61(dim, __VA_ARGS__); \ + } \ + else if (kernel_size == 63) { \ + DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks63(dim, __VA_ARGS__); \ + } \ else { \ std::cerr << "NATTEN kernel launch failed! " \ << "na2d_nn_cuda_gemm_sm75_half does not support implement " \ @@ -182,6 +272,51 @@ namespace gemm { else if (kernel_size == 33) { \ DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks33(dim, __VA_ARGS__); \ } \ + else if (kernel_size == 35) { \ + DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks35(dim, __VA_ARGS__); \ + } \ + else if (kernel_size == 37) { \ + DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks37(dim, __VA_ARGS__); \ + } \ + else if (kernel_size == 39) { \ + DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks39(dim, __VA_ARGS__); \ + } \ + else if (kernel_size == 41) { \ + DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks41(dim, __VA_ARGS__); \ + } \ + else if (kernel_size == 43) { \ + DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks43(dim, __VA_ARGS__); \ + } \ + else if (kernel_size == 45) { \ + DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks45(dim, __VA_ARGS__); \ + } \ + else if (kernel_size == 47) { \ + DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks47(dim, __VA_ARGS__); \ + } \ + else if (kernel_size == 49) { \ + DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks49(dim, __VA_ARGS__); \ + } \ + else if (kernel_size == 51) { \ + DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks51(dim, __VA_ARGS__); \ + } \ + else if (kernel_size == 53) { \ + DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks53(dim, __VA_ARGS__); \ + } \ + else if (kernel_size == 55) { \ + DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks55(dim, __VA_ARGS__); \ + } \ + else if (kernel_size == 57) { \ + DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks57(dim, __VA_ARGS__); \ + } \ + else if (kernel_size == 59) { \ + DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks59(dim, __VA_ARGS__); \ + } \ + else if (kernel_size == 61) { \ + DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks61(dim, __VA_ARGS__); \ + } \ + else if (kernel_size == 63) { \ + DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks63(dim, __VA_ARGS__); \ + } \ else { \ std::cerr << "NATTEN kernel launch failed! " \ << "na2d_in_cuda_gemm_sm75_half does not support implement " \ diff --git a/csrc/autogen/include/natten_autogen/cuda/gemm/2d/sm75/kernels.h b/csrc/autogen/include/natten_autogen/cuda/gemm/2d/sm75/kernels.h index ecbebd2..4adb35c 100644 --- a/csrc/autogen/include/natten_autogen/cuda/gemm/2d/sm75/kernels.h +++ b/csrc/autogen/include/natten_autogen/cuda/gemm/2d/sm75/kernels.h @@ -921,6 +921,861 @@ void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks33_align2( float scale, cudaStream_t stream); +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks35_align8( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks35_align4( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks35_align2( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks37_align8( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks37_align4( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks37_align2( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks39_align8( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks39_align4( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks39_align2( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks41_align8( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks41_align4( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks41_align2( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks43_align8( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks43_align4( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks43_align2( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks45_align8( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks45_align4( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks45_align2( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks47_align8( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks47_align4( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks47_align2( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks49_align8( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks49_align4( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks49_align2( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks51_align8( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks51_align4( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks51_align2( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks53_align8( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks53_align4( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks53_align2( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks55_align8( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks55_align4( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks55_align2( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks57_align8( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks57_align4( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks57_align2( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks59_align8( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks59_align4( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks59_align2( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks61_align8( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks61_align4( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks61_align2( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks63_align8( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks63_align4( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks63_align2( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks3_align8( void * attn_ptr, void * value_ptr, @@ -939,7 +1794,1393 @@ void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks3_align8( float scale, cudaStream_t stream); -void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks3_align4( +void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks3_align4( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks3_align2( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks5_align8( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks5_align4( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks5_align2( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks7_align8( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks7_align4( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks7_align2( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks9_align8( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks9_align4( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks9_align2( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks11_align8( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks11_align4( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks11_align2( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks13_align8( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks13_align4( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks13_align2( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks15_align8( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks15_align4( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks15_align2( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks17_align8( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks17_align4( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks17_align2( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks19_align8( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks19_align4( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks19_align2( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks21_align8( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks21_align4( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks21_align2( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks23_align8( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks23_align4( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks23_align2( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks25_align8( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks25_align4( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks25_align2( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks27_align8( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks27_align4( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks27_align2( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks29_align8( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks29_align4( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks29_align2( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks31_align8( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks31_align4( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks31_align2( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks33_align8( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks33_align4( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks33_align2( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks35_align8( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks35_align4( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks35_align2( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks37_align8( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks37_align4( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks37_align2( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks39_align8( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks39_align4( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks39_align2( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks41_align8( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks41_align4( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks41_align2( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks43_align8( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks43_align4( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks43_align2( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks45_align8( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks45_align4( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks45_align2( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks47_align8( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks47_align4( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks47_align2( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks49_align8( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks49_align4( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks49_align2( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks51_align8( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks51_align4( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks51_align2( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks53_align8( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks53_align4( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks53_align2( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks55_align8( void * attn_ptr, void * value_ptr, void * output_ptr, @@ -957,7 +3198,7 @@ void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks3_align4( float scale, cudaStream_t stream); -void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks3_align2( +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks55_align4( void * attn_ptr, void * value_ptr, void * output_ptr, @@ -975,7 +3216,7 @@ void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks3_align2( float scale, cudaStream_t stream); -void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks5_align8( +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks55_align2( void * attn_ptr, void * value_ptr, void * output_ptr, @@ -993,7 +3234,7 @@ void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks5_align8( float scale, cudaStream_t stream); -void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks5_align4( +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks57_align8( void * attn_ptr, void * value_ptr, void * output_ptr, @@ -1011,7 +3252,7 @@ void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks5_align4( float scale, cudaStream_t stream); -void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks5_align2( +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks57_align4( void * attn_ptr, void * value_ptr, void * output_ptr, @@ -1029,7 +3270,7 @@ void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks5_align2( float scale, cudaStream_t stream); -void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks7_align8( +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks57_align2( void * attn_ptr, void * value_ptr, void * output_ptr, @@ -1047,7 +3288,7 @@ void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks7_align8( float scale, cudaStream_t stream); -void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks7_align4( +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks59_align8( void * attn_ptr, void * value_ptr, void * output_ptr, @@ -1065,7 +3306,7 @@ void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks7_align4( float scale, cudaStream_t stream); -void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks7_align2( +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks59_align4( void * attn_ptr, void * value_ptr, void * output_ptr, @@ -1083,7 +3324,7 @@ void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks7_align2( float scale, cudaStream_t stream); -void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks9_align8( +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks59_align2( void * attn_ptr, void * value_ptr, void * output_ptr, @@ -1101,7 +3342,7 @@ void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks9_align8( float scale, cudaStream_t stream); -void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks9_align4( +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks61_align8( void * attn_ptr, void * value_ptr, void * output_ptr, @@ -1119,7 +3360,7 @@ void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks9_align4( float scale, cudaStream_t stream); -void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks9_align2( +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks61_align4( void * attn_ptr, void * value_ptr, void * output_ptr, @@ -1137,7 +3378,7 @@ void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks9_align2( float scale, cudaStream_t stream); -void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks11_align8( +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks61_align2( void * attn_ptr, void * value_ptr, void * output_ptr, @@ -1155,7 +3396,43 @@ void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks11_align8( float scale, cudaStream_t stream); -void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks11_align4( +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks63_align8( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks63_align4( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks63_align2( void * attn_ptr, void * value_ptr, void * output_ptr, @@ -1173,10 +3450,208 @@ void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks11_align4( float scale, cudaStream_t stream); -void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks11_align2( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks3_align8( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks3_align4( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks3_align2( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks5_align8( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks5_align4( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks5_align2( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks7_align8( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks7_align4( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks7_align2( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks9_align8( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks9_align4( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks9_align2( void * attn_ptr, - void * value_ptr, - void * output_ptr, + void * d_output_ptr, + void * d_value_ptr, int batch_size, int heads, int height, @@ -1191,10 +3666,10 @@ void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks11_align2( float scale, cudaStream_t stream); -void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks13_align8( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks11_align8( void * attn_ptr, - void * value_ptr, - void * output_ptr, + void * d_output_ptr, + void * d_value_ptr, int batch_size, int heads, int height, @@ -1209,10 +3684,10 @@ void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks13_align8( float scale, cudaStream_t stream); -void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks13_align4( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks11_align4( void * attn_ptr, - void * value_ptr, - void * output_ptr, + void * d_output_ptr, + void * d_value_ptr, int batch_size, int heads, int height, @@ -1227,10 +3702,10 @@ void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks13_align4( float scale, cudaStream_t stream); -void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks13_align2( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks11_align2( void * attn_ptr, - void * value_ptr, - void * output_ptr, + void * d_output_ptr, + void * d_value_ptr, int batch_size, int heads, int height, @@ -1245,10 +3720,10 @@ void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks13_align2( float scale, cudaStream_t stream); -void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks15_align8( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks13_align8( void * attn_ptr, - void * value_ptr, - void * output_ptr, + void * d_output_ptr, + void * d_value_ptr, int batch_size, int heads, int height, @@ -1263,10 +3738,10 @@ void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks15_align8( float scale, cudaStream_t stream); -void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks15_align4( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks13_align4( void * attn_ptr, - void * value_ptr, - void * output_ptr, + void * d_output_ptr, + void * d_value_ptr, int batch_size, int heads, int height, @@ -1281,10 +3756,10 @@ void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks15_align4( float scale, cudaStream_t stream); -void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks15_align2( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks13_align2( void * attn_ptr, - void * value_ptr, - void * output_ptr, + void * d_output_ptr, + void * d_value_ptr, int batch_size, int heads, int height, @@ -1299,10 +3774,10 @@ void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks15_align2( float scale, cudaStream_t stream); -void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks17_align8( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks15_align8( void * attn_ptr, - void * value_ptr, - void * output_ptr, + void * d_output_ptr, + void * d_value_ptr, int batch_size, int heads, int height, @@ -1317,10 +3792,10 @@ void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks17_align8( float scale, cudaStream_t stream); -void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks17_align4( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks15_align4( void * attn_ptr, - void * value_ptr, - void * output_ptr, + void * d_output_ptr, + void * d_value_ptr, int batch_size, int heads, int height, @@ -1335,10 +3810,10 @@ void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks17_align4( float scale, cudaStream_t stream); -void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks17_align2( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks15_align2( void * attn_ptr, - void * value_ptr, - void * output_ptr, + void * d_output_ptr, + void * d_value_ptr, int batch_size, int heads, int height, @@ -1353,10 +3828,10 @@ void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks17_align2( float scale, cudaStream_t stream); -void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks19_align8( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks17_align8( void * attn_ptr, - void * value_ptr, - void * output_ptr, + void * d_output_ptr, + void * d_value_ptr, int batch_size, int heads, int height, @@ -1371,10 +3846,10 @@ void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks19_align8( float scale, cudaStream_t stream); -void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks19_align4( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks17_align4( void * attn_ptr, - void * value_ptr, - void * output_ptr, + void * d_output_ptr, + void * d_value_ptr, int batch_size, int heads, int height, @@ -1389,10 +3864,10 @@ void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks19_align4( float scale, cudaStream_t stream); -void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks19_align2( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks17_align2( void * attn_ptr, - void * value_ptr, - void * output_ptr, + void * d_output_ptr, + void * d_value_ptr, int batch_size, int heads, int height, @@ -1407,10 +3882,10 @@ void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks19_align2( float scale, cudaStream_t stream); -void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks21_align8( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks19_align8( void * attn_ptr, - void * value_ptr, - void * output_ptr, + void * d_output_ptr, + void * d_value_ptr, int batch_size, int heads, int height, @@ -1425,10 +3900,10 @@ void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks21_align8( float scale, cudaStream_t stream); -void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks21_align4( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks19_align4( void * attn_ptr, - void * value_ptr, - void * output_ptr, + void * d_output_ptr, + void * d_value_ptr, int batch_size, int heads, int height, @@ -1443,10 +3918,10 @@ void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks21_align4( float scale, cudaStream_t stream); -void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks21_align2( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks19_align2( void * attn_ptr, - void * value_ptr, - void * output_ptr, + void * d_output_ptr, + void * d_value_ptr, int batch_size, int heads, int height, @@ -1461,10 +3936,10 @@ void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks21_align2( float scale, cudaStream_t stream); -void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks23_align8( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks21_align8( void * attn_ptr, - void * value_ptr, - void * output_ptr, + void * d_output_ptr, + void * d_value_ptr, int batch_size, int heads, int height, @@ -1479,10 +3954,10 @@ void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks23_align8( float scale, cudaStream_t stream); -void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks23_align4( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks21_align4( void * attn_ptr, - void * value_ptr, - void * output_ptr, + void * d_output_ptr, + void * d_value_ptr, int batch_size, int heads, int height, @@ -1497,10 +3972,10 @@ void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks23_align4( float scale, cudaStream_t stream); -void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks23_align2( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks21_align2( void * attn_ptr, - void * value_ptr, - void * output_ptr, + void * d_output_ptr, + void * d_value_ptr, int batch_size, int heads, int height, @@ -1515,10 +3990,10 @@ void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks23_align2( float scale, cudaStream_t stream); -void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks25_align8( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks23_align8( void * attn_ptr, - void * value_ptr, - void * output_ptr, + void * d_output_ptr, + void * d_value_ptr, int batch_size, int heads, int height, @@ -1533,10 +4008,10 @@ void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks25_align8( float scale, cudaStream_t stream); -void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks25_align4( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks23_align4( void * attn_ptr, - void * value_ptr, - void * output_ptr, + void * d_output_ptr, + void * d_value_ptr, int batch_size, int heads, int height, @@ -1551,10 +4026,10 @@ void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks25_align4( float scale, cudaStream_t stream); -void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks25_align2( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks23_align2( void * attn_ptr, - void * value_ptr, - void * output_ptr, + void * d_output_ptr, + void * d_value_ptr, int batch_size, int heads, int height, @@ -1569,10 +4044,10 @@ void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks25_align2( float scale, cudaStream_t stream); -void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks27_align8( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks25_align8( void * attn_ptr, - void * value_ptr, - void * output_ptr, + void * d_output_ptr, + void * d_value_ptr, int batch_size, int heads, int height, @@ -1587,10 +4062,10 @@ void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks27_align8( float scale, cudaStream_t stream); -void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks27_align4( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks25_align4( void * attn_ptr, - void * value_ptr, - void * output_ptr, + void * d_output_ptr, + void * d_value_ptr, int batch_size, int heads, int height, @@ -1605,10 +4080,10 @@ void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks27_align4( float scale, cudaStream_t stream); -void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks27_align2( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks25_align2( void * attn_ptr, - void * value_ptr, - void * output_ptr, + void * d_output_ptr, + void * d_value_ptr, int batch_size, int heads, int height, @@ -1623,10 +4098,10 @@ void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks27_align2( float scale, cudaStream_t stream); -void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks29_align8( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks27_align8( void * attn_ptr, - void * value_ptr, - void * output_ptr, + void * d_output_ptr, + void * d_value_ptr, int batch_size, int heads, int height, @@ -1641,10 +4116,10 @@ void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks29_align8( float scale, cudaStream_t stream); -void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks29_align4( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks27_align4( void * attn_ptr, - void * value_ptr, - void * output_ptr, + void * d_output_ptr, + void * d_value_ptr, int batch_size, int heads, int height, @@ -1659,10 +4134,10 @@ void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks29_align4( float scale, cudaStream_t stream); -void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks29_align2( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks27_align2( void * attn_ptr, - void * value_ptr, - void * output_ptr, + void * d_output_ptr, + void * d_value_ptr, int batch_size, int heads, int height, @@ -1677,10 +4152,10 @@ void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks29_align2( float scale, cudaStream_t stream); -void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks31_align8( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks29_align8( void * attn_ptr, - void * value_ptr, - void * output_ptr, + void * d_output_ptr, + void * d_value_ptr, int batch_size, int heads, int height, @@ -1695,10 +4170,10 @@ void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks31_align8( float scale, cudaStream_t stream); -void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks31_align4( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks29_align4( void * attn_ptr, - void * value_ptr, - void * output_ptr, + void * d_output_ptr, + void * d_value_ptr, int batch_size, int heads, int height, @@ -1713,10 +4188,10 @@ void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks31_align4( float scale, cudaStream_t stream); -void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks31_align2( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks29_align2( void * attn_ptr, - void * value_ptr, - void * output_ptr, + void * d_output_ptr, + void * d_value_ptr, int batch_size, int heads, int height, @@ -1731,10 +4206,10 @@ void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks31_align2( float scale, cudaStream_t stream); -void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks33_align8( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks31_align8( void * attn_ptr, - void * value_ptr, - void * output_ptr, + void * d_output_ptr, + void * d_value_ptr, int batch_size, int heads, int height, @@ -1749,10 +4224,10 @@ void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks33_align8( float scale, cudaStream_t stream); -void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks33_align4( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks31_align4( void * attn_ptr, - void * value_ptr, - void * output_ptr, + void * d_output_ptr, + void * d_value_ptr, int batch_size, int heads, int height, @@ -1767,10 +4242,10 @@ void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks33_align4( float scale, cudaStream_t stream); -void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks33_align2( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks31_align2( void * attn_ptr, - void * value_ptr, - void * output_ptr, + void * d_output_ptr, + void * d_value_ptr, int batch_size, int heads, int height, @@ -1785,7 +4260,7 @@ void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks33_align2( float scale, cudaStream_t stream); -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks3_align8( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks33_align8( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -1803,7 +4278,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks3_align8( float scale, cudaStream_t stream); -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks3_align4( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks33_align4( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -1821,7 +4296,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks3_align4( float scale, cudaStream_t stream); -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks3_align2( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks33_align2( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -1839,7 +4314,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks3_align2( float scale, cudaStream_t stream); -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks5_align8( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks35_align8( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -1857,7 +4332,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks5_align8( float scale, cudaStream_t stream); -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks5_align4( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks35_align4( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -1875,7 +4350,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks5_align4( float scale, cudaStream_t stream); -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks5_align2( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks35_align2( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -1893,7 +4368,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks5_align2( float scale, cudaStream_t stream); -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks7_align8( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks37_align8( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -1911,7 +4386,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks7_align8( float scale, cudaStream_t stream); -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks7_align4( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks37_align4( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -1929,7 +4404,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks7_align4( float scale, cudaStream_t stream); -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks7_align2( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks37_align2( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -1947,7 +4422,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks7_align2( float scale, cudaStream_t stream); -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks9_align8( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks39_align8( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -1965,7 +4440,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks9_align8( float scale, cudaStream_t stream); -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks9_align4( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks39_align4( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -1983,7 +4458,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks9_align4( float scale, cudaStream_t stream); -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks9_align2( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks39_align2( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -2001,7 +4476,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks9_align2( float scale, cudaStream_t stream); -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks11_align8( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks41_align8( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -2019,7 +4494,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks11_align8( float scale, cudaStream_t stream); -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks11_align4( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks41_align4( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -2037,7 +4512,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks11_align4( float scale, cudaStream_t stream); -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks11_align2( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks41_align2( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -2055,7 +4530,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks11_align2( float scale, cudaStream_t stream); -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks13_align8( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks43_align8( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -2073,7 +4548,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks13_align8( float scale, cudaStream_t stream); -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks13_align4( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks43_align4( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -2091,7 +4566,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks13_align4( float scale, cudaStream_t stream); -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks13_align2( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks43_align2( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -2109,7 +4584,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks13_align2( float scale, cudaStream_t stream); -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks15_align8( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks45_align8( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -2127,7 +4602,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks15_align8( float scale, cudaStream_t stream); -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks15_align4( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks45_align4( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -2145,7 +4620,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks15_align4( float scale, cudaStream_t stream); -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks15_align2( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks45_align2( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -2163,7 +4638,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks15_align2( float scale, cudaStream_t stream); -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks17_align8( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks47_align8( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -2181,7 +4656,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks17_align8( float scale, cudaStream_t stream); -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks17_align4( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks47_align4( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -2199,7 +4674,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks17_align4( float scale, cudaStream_t stream); -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks17_align2( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks47_align2( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -2217,7 +4692,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks17_align2( float scale, cudaStream_t stream); -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks19_align8( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks49_align8( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -2235,7 +4710,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks19_align8( float scale, cudaStream_t stream); -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks19_align4( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks49_align4( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -2253,7 +4728,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks19_align4( float scale, cudaStream_t stream); -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks19_align2( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks49_align2( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -2271,7 +4746,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks19_align2( float scale, cudaStream_t stream); -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks21_align8( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks51_align8( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -2289,7 +4764,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks21_align8( float scale, cudaStream_t stream); -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks21_align4( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks51_align4( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -2307,7 +4782,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks21_align4( float scale, cudaStream_t stream); -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks21_align2( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks51_align2( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -2325,7 +4800,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks21_align2( float scale, cudaStream_t stream); -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks23_align8( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks53_align8( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -2343,7 +4818,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks23_align8( float scale, cudaStream_t stream); -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks23_align4( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks53_align4( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -2361,7 +4836,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks23_align4( float scale, cudaStream_t stream); -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks23_align2( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks53_align2( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -2379,7 +4854,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks23_align2( float scale, cudaStream_t stream); -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks25_align8( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks55_align8( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -2397,7 +4872,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks25_align8( float scale, cudaStream_t stream); -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks25_align4( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks55_align4( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -2415,7 +4890,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks25_align4( float scale, cudaStream_t stream); -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks25_align2( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks55_align2( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -2433,7 +4908,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks25_align2( float scale, cudaStream_t stream); -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks27_align8( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks57_align8( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -2451,7 +4926,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks27_align8( float scale, cudaStream_t stream); -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks27_align4( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks57_align4( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -2469,7 +4944,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks27_align4( float scale, cudaStream_t stream); -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks27_align2( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks57_align2( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -2487,7 +4962,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks27_align2( float scale, cudaStream_t stream); -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks29_align8( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks59_align8( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -2505,7 +4980,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks29_align8( float scale, cudaStream_t stream); -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks29_align4( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks59_align4( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -2523,7 +4998,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks29_align4( float scale, cudaStream_t stream); -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks29_align2( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks59_align2( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -2541,7 +5016,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks29_align2( float scale, cudaStream_t stream); -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks31_align8( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks61_align8( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -2559,7 +5034,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks31_align8( float scale, cudaStream_t stream); -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks31_align4( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks61_align4( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -2577,7 +5052,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks31_align4( float scale, cudaStream_t stream); -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks31_align2( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks61_align2( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -2595,7 +5070,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks31_align2( float scale, cudaStream_t stream); -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks33_align8( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks63_align8( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -2613,7 +5088,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks33_align8( float scale, cudaStream_t stream); -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks33_align4( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks63_align4( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -2631,7 +5106,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks33_align4( float scale, cudaStream_t stream); -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks33_align2( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks63_align2( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, diff --git a/csrc/autogen/include/natten_autogen/cuda/gemm/2d/sm80/dispatch_align.h b/csrc/autogen/include/natten_autogen/cuda/gemm/2d/sm80/dispatch_align.h index 1859edf..61efe6f 100644 --- a/csrc/autogen/include/natten_autogen/cuda/gemm/2d/sm80/dispatch_align.h +++ b/csrc/autogen/include/natten_autogen/cuda/gemm/2d/sm80/dispatch_align.h @@ -90,6 +90,81 @@ namespace gemm { natten::cuda::gemm::na2d_pn_cuda_gemm_double_128x128x16_64x64x16_8x8x4_3_sm80_ks33_align1(__VA_ARGS__); \ }(); +#define DISPATCH_ALIGNMENT_na2d_pn_cuda_gemm_double_128x128x16_64x64x16_8x8x4_3_sm80_ks35(dim, ...) \ + [&] { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_double_128x128x16_64x64x16_8x8x4_3_sm80_ks35_align1(__VA_ARGS__); \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_pn_cuda_gemm_double_128x128x16_64x64x16_8x8x4_3_sm80_ks37(dim, ...) \ + [&] { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_double_128x128x16_64x64x16_8x8x4_3_sm80_ks37_align1(__VA_ARGS__); \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_pn_cuda_gemm_double_128x128x16_64x64x16_8x8x4_3_sm80_ks39(dim, ...) \ + [&] { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_double_128x128x16_64x64x16_8x8x4_3_sm80_ks39_align1(__VA_ARGS__); \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_pn_cuda_gemm_double_128x128x16_64x64x16_8x8x4_3_sm80_ks41(dim, ...) \ + [&] { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_double_128x128x16_64x64x16_8x8x4_3_sm80_ks41_align1(__VA_ARGS__); \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_pn_cuda_gemm_double_128x128x16_64x64x16_8x8x4_3_sm80_ks43(dim, ...) \ + [&] { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_double_128x128x16_64x64x16_8x8x4_3_sm80_ks43_align1(__VA_ARGS__); \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_pn_cuda_gemm_double_128x128x16_64x64x16_8x8x4_3_sm80_ks45(dim, ...) \ + [&] { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_double_128x128x16_64x64x16_8x8x4_3_sm80_ks45_align1(__VA_ARGS__); \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_pn_cuda_gemm_double_128x128x16_64x64x16_8x8x4_3_sm80_ks47(dim, ...) \ + [&] { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_double_128x128x16_64x64x16_8x8x4_3_sm80_ks47_align1(__VA_ARGS__); \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_pn_cuda_gemm_double_128x128x16_64x64x16_8x8x4_3_sm80_ks49(dim, ...) \ + [&] { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_double_128x128x16_64x64x16_8x8x4_3_sm80_ks49_align1(__VA_ARGS__); \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_pn_cuda_gemm_double_128x128x16_64x64x16_8x8x4_3_sm80_ks51(dim, ...) \ + [&] { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_double_128x128x16_64x64x16_8x8x4_3_sm80_ks51_align1(__VA_ARGS__); \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_pn_cuda_gemm_double_128x128x16_64x64x16_8x8x4_3_sm80_ks53(dim, ...) \ + [&] { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_double_128x128x16_64x64x16_8x8x4_3_sm80_ks53_align1(__VA_ARGS__); \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_pn_cuda_gemm_double_128x128x16_64x64x16_8x8x4_3_sm80_ks55(dim, ...) \ + [&] { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_double_128x128x16_64x64x16_8x8x4_3_sm80_ks55_align1(__VA_ARGS__); \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_pn_cuda_gemm_double_128x128x16_64x64x16_8x8x4_3_sm80_ks57(dim, ...) \ + [&] { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_double_128x128x16_64x64x16_8x8x4_3_sm80_ks57_align1(__VA_ARGS__); \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_pn_cuda_gemm_double_128x128x16_64x64x16_8x8x4_3_sm80_ks59(dim, ...) \ + [&] { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_double_128x128x16_64x64x16_8x8x4_3_sm80_ks59_align1(__VA_ARGS__); \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_pn_cuda_gemm_double_128x128x16_64x64x16_8x8x4_3_sm80_ks61(dim, ...) \ + [&] { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_double_128x128x16_64x64x16_8x8x4_3_sm80_ks61_align1(__VA_ARGS__); \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_pn_cuda_gemm_double_128x128x16_64x64x16_8x8x4_3_sm80_ks63(dim, ...) \ + [&] { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_double_128x128x16_64x64x16_8x8x4_3_sm80_ks63_align1(__VA_ARGS__); \ +}(); + #define DISPATCH_ALIGNMENT_na2d_pn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks3(dim, ...) \ [&] { \ if (dim % 4 == 0) { \ @@ -410,2720 +485,5570 @@ namespace gemm { } \ }(); -#define DISPATCH_ALIGNMENT_na2d_pn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks3(dim, ...) \ +#define DISPATCH_ALIGNMENT_na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks35(dim, ...) \ [&] { \ - if (dim % 8 == 0) { \ - natten::cuda::gemm::na2d_pn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks3_align8(__VA_ARGS__); \ - } \ - else if (dim % 4 == 0) { \ - natten::cuda::gemm::na2d_pn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks3_align4(__VA_ARGS__); \ + if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks35_align4(__VA_ARGS__); \ } \ else if (dim % 2 == 0) { \ - natten::cuda::gemm::na2d_pn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks3_align2(__VA_ARGS__); \ + natten::cuda::gemm::na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks35_align2(__VA_ARGS__); \ + } \ + else if (dim % 1 == 0) { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks35_align1(__VA_ARGS__); \ } \ else { \ std::cerr << "NATTEN kernel launch failed!" \ - << "na2d_pn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks3 requires at least 32-bit alignment." \ - << "Got dim=" << dim << ", dtype=half. " \ + << "na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks35 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=float. " \ << std::endl; \ exit(EXIT_FAILURE); \ } \ }(); -#define DISPATCH_ALIGNMENT_na2d_pn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks5(dim, ...) \ +#define DISPATCH_ALIGNMENT_na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks37(dim, ...) \ [&] { \ - if (dim % 8 == 0) { \ - natten::cuda::gemm::na2d_pn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks5_align8(__VA_ARGS__); \ - } \ - else if (dim % 4 == 0) { \ - natten::cuda::gemm::na2d_pn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks5_align4(__VA_ARGS__); \ + if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks37_align4(__VA_ARGS__); \ } \ else if (dim % 2 == 0) { \ - natten::cuda::gemm::na2d_pn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks5_align2(__VA_ARGS__); \ + natten::cuda::gemm::na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks37_align2(__VA_ARGS__); \ + } \ + else if (dim % 1 == 0) { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks37_align1(__VA_ARGS__); \ } \ else { \ std::cerr << "NATTEN kernel launch failed!" \ - << "na2d_pn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks5 requires at least 32-bit alignment." \ - << "Got dim=" << dim << ", dtype=half. " \ + << "na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks37 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=float. " \ << std::endl; \ exit(EXIT_FAILURE); \ } \ }(); -#define DISPATCH_ALIGNMENT_na2d_pn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks7(dim, ...) \ +#define DISPATCH_ALIGNMENT_na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks39(dim, ...) \ [&] { \ - if (dim % 8 == 0) { \ - natten::cuda::gemm::na2d_pn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks7_align8(__VA_ARGS__); \ - } \ - else if (dim % 4 == 0) { \ - natten::cuda::gemm::na2d_pn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks7_align4(__VA_ARGS__); \ + if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks39_align4(__VA_ARGS__); \ } \ else if (dim % 2 == 0) { \ - natten::cuda::gemm::na2d_pn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks7_align2(__VA_ARGS__); \ + natten::cuda::gemm::na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks39_align2(__VA_ARGS__); \ + } \ + else if (dim % 1 == 0) { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks39_align1(__VA_ARGS__); \ } \ else { \ std::cerr << "NATTEN kernel launch failed!" \ - << "na2d_pn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks7 requires at least 32-bit alignment." \ - << "Got dim=" << dim << ", dtype=half. " \ + << "na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks39 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=float. " \ << std::endl; \ exit(EXIT_FAILURE); \ } \ }(); -#define DISPATCH_ALIGNMENT_na2d_pn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks9(dim, ...) \ +#define DISPATCH_ALIGNMENT_na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks41(dim, ...) \ [&] { \ - if (dim % 8 == 0) { \ - natten::cuda::gemm::na2d_pn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks9_align8(__VA_ARGS__); \ - } \ - else if (dim % 4 == 0) { \ - natten::cuda::gemm::na2d_pn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks9_align4(__VA_ARGS__); \ + if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks41_align4(__VA_ARGS__); \ } \ else if (dim % 2 == 0) { \ - natten::cuda::gemm::na2d_pn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks9_align2(__VA_ARGS__); \ + natten::cuda::gemm::na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks41_align2(__VA_ARGS__); \ + } \ + else if (dim % 1 == 0) { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks41_align1(__VA_ARGS__); \ } \ else { \ std::cerr << "NATTEN kernel launch failed!" \ - << "na2d_pn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks9 requires at least 32-bit alignment." \ - << "Got dim=" << dim << ", dtype=half. " \ + << "na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks41 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=float. " \ << std::endl; \ exit(EXIT_FAILURE); \ } \ }(); -#define DISPATCH_ALIGNMENT_na2d_pn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks11(dim, ...) \ +#define DISPATCH_ALIGNMENT_na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks43(dim, ...) \ [&] { \ - if (dim % 8 == 0) { \ - natten::cuda::gemm::na2d_pn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks11_align8(__VA_ARGS__); \ - } \ - else if (dim % 4 == 0) { \ - natten::cuda::gemm::na2d_pn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks11_align4(__VA_ARGS__); \ + if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks43_align4(__VA_ARGS__); \ } \ else if (dim % 2 == 0) { \ - natten::cuda::gemm::na2d_pn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks11_align2(__VA_ARGS__); \ + natten::cuda::gemm::na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks43_align2(__VA_ARGS__); \ + } \ + else if (dim % 1 == 0) { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks43_align1(__VA_ARGS__); \ } \ else { \ std::cerr << "NATTEN kernel launch failed!" \ - << "na2d_pn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks11 requires at least 32-bit alignment." \ - << "Got dim=" << dim << ", dtype=half. " \ + << "na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks43 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=float. " \ << std::endl; \ exit(EXIT_FAILURE); \ } \ }(); -#define DISPATCH_ALIGNMENT_na2d_pn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks13(dim, ...) \ +#define DISPATCH_ALIGNMENT_na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks45(dim, ...) \ [&] { \ - if (dim % 8 == 0) { \ - natten::cuda::gemm::na2d_pn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks13_align8(__VA_ARGS__); \ - } \ - else if (dim % 4 == 0) { \ - natten::cuda::gemm::na2d_pn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks13_align4(__VA_ARGS__); \ + if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks45_align4(__VA_ARGS__); \ } \ else if (dim % 2 == 0) { \ - natten::cuda::gemm::na2d_pn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks13_align2(__VA_ARGS__); \ + natten::cuda::gemm::na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks45_align2(__VA_ARGS__); \ + } \ + else if (dim % 1 == 0) { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks45_align1(__VA_ARGS__); \ } \ else { \ std::cerr << "NATTEN kernel launch failed!" \ - << "na2d_pn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks13 requires at least 32-bit alignment." \ - << "Got dim=" << dim << ", dtype=half. " \ + << "na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks45 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=float. " \ << std::endl; \ exit(EXIT_FAILURE); \ } \ }(); -#define DISPATCH_ALIGNMENT_na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks15(dim, ...) \ +#define DISPATCH_ALIGNMENT_na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks47(dim, ...) \ [&] { \ - if (dim % 8 == 0) { \ - natten::cuda::gemm::na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks15_align8(__VA_ARGS__); \ - } \ - else if (dim % 4 == 0) { \ - natten::cuda::gemm::na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks15_align4(__VA_ARGS__); \ + if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks47_align4(__VA_ARGS__); \ } \ else if (dim % 2 == 0) { \ - natten::cuda::gemm::na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks15_align2(__VA_ARGS__); \ + natten::cuda::gemm::na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks47_align2(__VA_ARGS__); \ + } \ + else if (dim % 1 == 0) { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks47_align1(__VA_ARGS__); \ } \ else { \ std::cerr << "NATTEN kernel launch failed!" \ - << "na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks15 requires at least 32-bit alignment." \ - << "Got dim=" << dim << ", dtype=half. " \ + << "na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks47 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=float. " \ << std::endl; \ exit(EXIT_FAILURE); \ } \ }(); -#define DISPATCH_ALIGNMENT_na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks17(dim, ...) \ +#define DISPATCH_ALIGNMENT_na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks49(dim, ...) \ [&] { \ - if (dim % 8 == 0) { \ - natten::cuda::gemm::na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks17_align8(__VA_ARGS__); \ - } \ - else if (dim % 4 == 0) { \ - natten::cuda::gemm::na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks17_align4(__VA_ARGS__); \ + if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks49_align4(__VA_ARGS__); \ } \ else if (dim % 2 == 0) { \ - natten::cuda::gemm::na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks17_align2(__VA_ARGS__); \ + natten::cuda::gemm::na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks49_align2(__VA_ARGS__); \ + } \ + else if (dim % 1 == 0) { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks49_align1(__VA_ARGS__); \ } \ else { \ std::cerr << "NATTEN kernel launch failed!" \ - << "na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks17 requires at least 32-bit alignment." \ - << "Got dim=" << dim << ", dtype=half. " \ + << "na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks49 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=float. " \ << std::endl; \ exit(EXIT_FAILURE); \ } \ }(); -#define DISPATCH_ALIGNMENT_na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks19(dim, ...) \ +#define DISPATCH_ALIGNMENT_na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks51(dim, ...) \ [&] { \ - if (dim % 8 == 0) { \ - natten::cuda::gemm::na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks19_align8(__VA_ARGS__); \ - } \ - else if (dim % 4 == 0) { \ - natten::cuda::gemm::na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks19_align4(__VA_ARGS__); \ + if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks51_align4(__VA_ARGS__); \ } \ else if (dim % 2 == 0) { \ - natten::cuda::gemm::na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks19_align2(__VA_ARGS__); \ + natten::cuda::gemm::na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks51_align2(__VA_ARGS__); \ + } \ + else if (dim % 1 == 0) { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks51_align1(__VA_ARGS__); \ } \ else { \ std::cerr << "NATTEN kernel launch failed!" \ - << "na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks19 requires at least 32-bit alignment." \ - << "Got dim=" << dim << ", dtype=half. " \ + << "na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks51 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=float. " \ << std::endl; \ exit(EXIT_FAILURE); \ } \ }(); -#define DISPATCH_ALIGNMENT_na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks21(dim, ...) \ +#define DISPATCH_ALIGNMENT_na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks53(dim, ...) \ [&] { \ - if (dim % 8 == 0) { \ - natten::cuda::gemm::na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks21_align8(__VA_ARGS__); \ - } \ - else if (dim % 4 == 0) { \ - natten::cuda::gemm::na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks21_align4(__VA_ARGS__); \ + if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks53_align4(__VA_ARGS__); \ } \ else if (dim % 2 == 0) { \ - natten::cuda::gemm::na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks21_align2(__VA_ARGS__); \ + natten::cuda::gemm::na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks53_align2(__VA_ARGS__); \ + } \ + else if (dim % 1 == 0) { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks53_align1(__VA_ARGS__); \ } \ else { \ std::cerr << "NATTEN kernel launch failed!" \ - << "na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks21 requires at least 32-bit alignment." \ - << "Got dim=" << dim << ", dtype=half. " \ + << "na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks53 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=float. " \ << std::endl; \ exit(EXIT_FAILURE); \ } \ }(); -#define DISPATCH_ALIGNMENT_na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks23(dim, ...) \ +#define DISPATCH_ALIGNMENT_na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks55(dim, ...) \ [&] { \ - if (dim % 8 == 0) { \ - natten::cuda::gemm::na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks23_align8(__VA_ARGS__); \ - } \ - else if (dim % 4 == 0) { \ - natten::cuda::gemm::na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks23_align4(__VA_ARGS__); \ + if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks55_align4(__VA_ARGS__); \ } \ else if (dim % 2 == 0) { \ - natten::cuda::gemm::na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks23_align2(__VA_ARGS__); \ + natten::cuda::gemm::na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks55_align2(__VA_ARGS__); \ + } \ + else if (dim % 1 == 0) { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks55_align1(__VA_ARGS__); \ } \ else { \ std::cerr << "NATTEN kernel launch failed!" \ - << "na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks23 requires at least 32-bit alignment." \ - << "Got dim=" << dim << ", dtype=half. " \ + << "na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks55 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=float. " \ << std::endl; \ exit(EXIT_FAILURE); \ } \ }(); -#define DISPATCH_ALIGNMENT_na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks25(dim, ...) \ +#define DISPATCH_ALIGNMENT_na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks57(dim, ...) \ [&] { \ - if (dim % 8 == 0) { \ - natten::cuda::gemm::na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks25_align8(__VA_ARGS__); \ - } \ - else if (dim % 4 == 0) { \ - natten::cuda::gemm::na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks25_align4(__VA_ARGS__); \ + if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks57_align4(__VA_ARGS__); \ } \ else if (dim % 2 == 0) { \ - natten::cuda::gemm::na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks25_align2(__VA_ARGS__); \ + natten::cuda::gemm::na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks57_align2(__VA_ARGS__); \ + } \ + else if (dim % 1 == 0) { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks57_align1(__VA_ARGS__); \ } \ else { \ std::cerr << "NATTEN kernel launch failed!" \ - << "na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks25 requires at least 32-bit alignment." \ - << "Got dim=" << dim << ", dtype=half. " \ + << "na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks57 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=float. " \ << std::endl; \ exit(EXIT_FAILURE); \ } \ }(); -#define DISPATCH_ALIGNMENT_na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks27(dim, ...) \ +#define DISPATCH_ALIGNMENT_na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks59(dim, ...) \ [&] { \ - if (dim % 8 == 0) { \ - natten::cuda::gemm::na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks27_align8(__VA_ARGS__); \ - } \ - else if (dim % 4 == 0) { \ - natten::cuda::gemm::na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks27_align4(__VA_ARGS__); \ + if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks59_align4(__VA_ARGS__); \ } \ else if (dim % 2 == 0) { \ - natten::cuda::gemm::na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks27_align2(__VA_ARGS__); \ + natten::cuda::gemm::na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks59_align2(__VA_ARGS__); \ + } \ + else if (dim % 1 == 0) { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks59_align1(__VA_ARGS__); \ } \ else { \ std::cerr << "NATTEN kernel launch failed!" \ - << "na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks27 requires at least 32-bit alignment." \ - << "Got dim=" << dim << ", dtype=half. " \ + << "na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks59 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=float. " \ << std::endl; \ exit(EXIT_FAILURE); \ } \ }(); -#define DISPATCH_ALIGNMENT_na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks29(dim, ...) \ +#define DISPATCH_ALIGNMENT_na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks61(dim, ...) \ [&] { \ - if (dim % 8 == 0) { \ - natten::cuda::gemm::na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks29_align8(__VA_ARGS__); \ - } \ - else if (dim % 4 == 0) { \ - natten::cuda::gemm::na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks29_align4(__VA_ARGS__); \ + if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks61_align4(__VA_ARGS__); \ } \ else if (dim % 2 == 0) { \ - natten::cuda::gemm::na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks29_align2(__VA_ARGS__); \ + natten::cuda::gemm::na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks61_align2(__VA_ARGS__); \ + } \ + else if (dim % 1 == 0) { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks61_align1(__VA_ARGS__); \ } \ else { \ std::cerr << "NATTEN kernel launch failed!" \ - << "na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks29 requires at least 32-bit alignment." \ - << "Got dim=" << dim << ", dtype=half. " \ + << "na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks61 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=float. " \ << std::endl; \ exit(EXIT_FAILURE); \ } \ }(); -#define DISPATCH_ALIGNMENT_na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks31(dim, ...) \ +#define DISPATCH_ALIGNMENT_na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks63(dim, ...) \ [&] { \ - if (dim % 8 == 0) { \ - natten::cuda::gemm::na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks31_align8(__VA_ARGS__); \ - } \ - else if (dim % 4 == 0) { \ - natten::cuda::gemm::na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks31_align4(__VA_ARGS__); \ + if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks63_align4(__VA_ARGS__); \ } \ else if (dim % 2 == 0) { \ - natten::cuda::gemm::na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks31_align2(__VA_ARGS__); \ + natten::cuda::gemm::na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks63_align2(__VA_ARGS__); \ + } \ + else if (dim % 1 == 0) { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks63_align1(__VA_ARGS__); \ } \ else { \ std::cerr << "NATTEN kernel launch failed!" \ - << "na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks31 requires at least 32-bit alignment." \ - << "Got dim=" << dim << ", dtype=half. " \ + << "na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks63 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=float. " \ << std::endl; \ exit(EXIT_FAILURE); \ } \ }(); -#define DISPATCH_ALIGNMENT_na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks33(dim, ...) \ +#define DISPATCH_ALIGNMENT_na2d_pn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks3(dim, ...) \ [&] { \ if (dim % 8 == 0) { \ - natten::cuda::gemm::na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks33_align8(__VA_ARGS__); \ + natten::cuda::gemm::na2d_pn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks3_align8(__VA_ARGS__); \ } \ else if (dim % 4 == 0) { \ - natten::cuda::gemm::na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks33_align4(__VA_ARGS__); \ + natten::cuda::gemm::na2d_pn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks3_align4(__VA_ARGS__); \ } \ else if (dim % 2 == 0) { \ - natten::cuda::gemm::na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks33_align2(__VA_ARGS__); \ + natten::cuda::gemm::na2d_pn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks3_align2(__VA_ARGS__); \ } \ else { \ std::cerr << "NATTEN kernel launch failed!" \ - << "na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks33 requires at least 32-bit alignment." \ + << "na2d_pn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks3 requires at least 32-bit alignment." \ << "Got dim=" << dim << ", dtype=half. " \ << std::endl; \ exit(EXIT_FAILURE); \ } \ }(); -#define DISPATCH_ALIGNMENT_na2d_pn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks3(dim, ...) \ +#define DISPATCH_ALIGNMENT_na2d_pn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks5(dim, ...) \ [&] { \ if (dim % 8 == 0) { \ - natten::cuda::gemm::na2d_pn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks3_align8(__VA_ARGS__); \ + natten::cuda::gemm::na2d_pn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks5_align8(__VA_ARGS__); \ } \ else if (dim % 4 == 0) { \ - natten::cuda::gemm::na2d_pn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks3_align4(__VA_ARGS__); \ + natten::cuda::gemm::na2d_pn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks5_align4(__VA_ARGS__); \ } \ else if (dim % 2 == 0) { \ - natten::cuda::gemm::na2d_pn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks3_align2(__VA_ARGS__); \ + natten::cuda::gemm::na2d_pn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks5_align2(__VA_ARGS__); \ } \ else { \ std::cerr << "NATTEN kernel launch failed!" \ - << "na2d_pn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks3 requires at least 32-bit alignment." \ - << "Got dim=" << dim << ", dtype=bfloat16. " \ + << "na2d_pn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks5 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=half. " \ << std::endl; \ exit(EXIT_FAILURE); \ } \ }(); -#define DISPATCH_ALIGNMENT_na2d_pn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks5(dim, ...) \ +#define DISPATCH_ALIGNMENT_na2d_pn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks7(dim, ...) \ [&] { \ if (dim % 8 == 0) { \ - natten::cuda::gemm::na2d_pn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks5_align8(__VA_ARGS__); \ + natten::cuda::gemm::na2d_pn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks7_align8(__VA_ARGS__); \ } \ else if (dim % 4 == 0) { \ - natten::cuda::gemm::na2d_pn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks5_align4(__VA_ARGS__); \ + natten::cuda::gemm::na2d_pn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks7_align4(__VA_ARGS__); \ } \ else if (dim % 2 == 0) { \ - natten::cuda::gemm::na2d_pn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks5_align2(__VA_ARGS__); \ + natten::cuda::gemm::na2d_pn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks7_align2(__VA_ARGS__); \ } \ else { \ std::cerr << "NATTEN kernel launch failed!" \ - << "na2d_pn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks5 requires at least 32-bit alignment." \ - << "Got dim=" << dim << ", dtype=bfloat16. " \ + << "na2d_pn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks7 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=half. " \ << std::endl; \ exit(EXIT_FAILURE); \ } \ }(); -#define DISPATCH_ALIGNMENT_na2d_pn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks7(dim, ...) \ +#define DISPATCH_ALIGNMENT_na2d_pn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks9(dim, ...) \ [&] { \ if (dim % 8 == 0) { \ - natten::cuda::gemm::na2d_pn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks7_align8(__VA_ARGS__); \ + natten::cuda::gemm::na2d_pn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks9_align8(__VA_ARGS__); \ } \ else if (dim % 4 == 0) { \ - natten::cuda::gemm::na2d_pn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks7_align4(__VA_ARGS__); \ + natten::cuda::gemm::na2d_pn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks9_align4(__VA_ARGS__); \ } \ else if (dim % 2 == 0) { \ - natten::cuda::gemm::na2d_pn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks7_align2(__VA_ARGS__); \ + natten::cuda::gemm::na2d_pn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks9_align2(__VA_ARGS__); \ } \ else { \ std::cerr << "NATTEN kernel launch failed!" \ - << "na2d_pn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks7 requires at least 32-bit alignment." \ - << "Got dim=" << dim << ", dtype=bfloat16. " \ + << "na2d_pn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks9 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=half. " \ << std::endl; \ exit(EXIT_FAILURE); \ } \ }(); -#define DISPATCH_ALIGNMENT_na2d_pn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks9(dim, ...) \ +#define DISPATCH_ALIGNMENT_na2d_pn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks11(dim, ...) \ [&] { \ if (dim % 8 == 0) { \ - natten::cuda::gemm::na2d_pn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks9_align8(__VA_ARGS__); \ + natten::cuda::gemm::na2d_pn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks11_align8(__VA_ARGS__); \ } \ else if (dim % 4 == 0) { \ - natten::cuda::gemm::na2d_pn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks9_align4(__VA_ARGS__); \ + natten::cuda::gemm::na2d_pn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks11_align4(__VA_ARGS__); \ } \ else if (dim % 2 == 0) { \ - natten::cuda::gemm::na2d_pn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks9_align2(__VA_ARGS__); \ + natten::cuda::gemm::na2d_pn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks11_align2(__VA_ARGS__); \ } \ else { \ std::cerr << "NATTEN kernel launch failed!" \ - << "na2d_pn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks9 requires at least 32-bit alignment." \ - << "Got dim=" << dim << ", dtype=bfloat16. " \ + << "na2d_pn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks11 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=half. " \ << std::endl; \ exit(EXIT_FAILURE); \ } \ }(); -#define DISPATCH_ALIGNMENT_na2d_pn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks11(dim, ...) \ +#define DISPATCH_ALIGNMENT_na2d_pn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks13(dim, ...) \ [&] { \ if (dim % 8 == 0) { \ - natten::cuda::gemm::na2d_pn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks11_align8(__VA_ARGS__); \ + natten::cuda::gemm::na2d_pn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks13_align8(__VA_ARGS__); \ } \ else if (dim % 4 == 0) { \ - natten::cuda::gemm::na2d_pn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks11_align4(__VA_ARGS__); \ + natten::cuda::gemm::na2d_pn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks13_align4(__VA_ARGS__); \ } \ else if (dim % 2 == 0) { \ - natten::cuda::gemm::na2d_pn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks11_align2(__VA_ARGS__); \ + natten::cuda::gemm::na2d_pn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks13_align2(__VA_ARGS__); \ } \ else { \ std::cerr << "NATTEN kernel launch failed!" \ - << "na2d_pn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks11 requires at least 32-bit alignment." \ - << "Got dim=" << dim << ", dtype=bfloat16. " \ + << "na2d_pn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks13 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=half. " \ << std::endl; \ exit(EXIT_FAILURE); \ } \ }(); -#define DISPATCH_ALIGNMENT_na2d_pn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks13(dim, ...) \ +#define DISPATCH_ALIGNMENT_na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks15(dim, ...) \ [&] { \ if (dim % 8 == 0) { \ - natten::cuda::gemm::na2d_pn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks13_align8(__VA_ARGS__); \ + natten::cuda::gemm::na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks15_align8(__VA_ARGS__); \ } \ else if (dim % 4 == 0) { \ - natten::cuda::gemm::na2d_pn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks13_align4(__VA_ARGS__); \ + natten::cuda::gemm::na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks15_align4(__VA_ARGS__); \ } \ else if (dim % 2 == 0) { \ - natten::cuda::gemm::na2d_pn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks13_align2(__VA_ARGS__); \ + natten::cuda::gemm::na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks15_align2(__VA_ARGS__); \ } \ else { \ std::cerr << "NATTEN kernel launch failed!" \ - << "na2d_pn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks13 requires at least 32-bit alignment." \ - << "Got dim=" << dim << ", dtype=bfloat16. " \ + << "na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks15 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=half. " \ << std::endl; \ exit(EXIT_FAILURE); \ } \ }(); -#define DISPATCH_ALIGNMENT_na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks15(dim, ...) \ +#define DISPATCH_ALIGNMENT_na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks17(dim, ...) \ [&] { \ if (dim % 8 == 0) { \ - natten::cuda::gemm::na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks15_align8(__VA_ARGS__); \ + natten::cuda::gemm::na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks17_align8(__VA_ARGS__); \ } \ else if (dim % 4 == 0) { \ - natten::cuda::gemm::na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks15_align4(__VA_ARGS__); \ + natten::cuda::gemm::na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks17_align4(__VA_ARGS__); \ } \ else if (dim % 2 == 0) { \ - natten::cuda::gemm::na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks15_align2(__VA_ARGS__); \ + natten::cuda::gemm::na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks17_align2(__VA_ARGS__); \ } \ else { \ std::cerr << "NATTEN kernel launch failed!" \ - << "na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks15 requires at least 32-bit alignment." \ - << "Got dim=" << dim << ", dtype=bfloat16. " \ + << "na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks17 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=half. " \ << std::endl; \ exit(EXIT_FAILURE); \ } \ }(); -#define DISPATCH_ALIGNMENT_na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks17(dim, ...) \ +#define DISPATCH_ALIGNMENT_na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks19(dim, ...) \ [&] { \ if (dim % 8 == 0) { \ - natten::cuda::gemm::na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks17_align8(__VA_ARGS__); \ + natten::cuda::gemm::na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks19_align8(__VA_ARGS__); \ } \ else if (dim % 4 == 0) { \ - natten::cuda::gemm::na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks17_align4(__VA_ARGS__); \ + natten::cuda::gemm::na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks19_align4(__VA_ARGS__); \ } \ else if (dim % 2 == 0) { \ - natten::cuda::gemm::na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks17_align2(__VA_ARGS__); \ + natten::cuda::gemm::na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks19_align2(__VA_ARGS__); \ } \ else { \ std::cerr << "NATTEN kernel launch failed!" \ - << "na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks17 requires at least 32-bit alignment." \ - << "Got dim=" << dim << ", dtype=bfloat16. " \ + << "na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks19 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=half. " \ << std::endl; \ exit(EXIT_FAILURE); \ } \ }(); -#define DISPATCH_ALIGNMENT_na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks19(dim, ...) \ +#define DISPATCH_ALIGNMENT_na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks21(dim, ...) \ [&] { \ if (dim % 8 == 0) { \ - natten::cuda::gemm::na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks19_align8(__VA_ARGS__); \ + natten::cuda::gemm::na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks21_align8(__VA_ARGS__); \ } \ else if (dim % 4 == 0) { \ - natten::cuda::gemm::na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks19_align4(__VA_ARGS__); \ + natten::cuda::gemm::na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks21_align4(__VA_ARGS__); \ } \ else if (dim % 2 == 0) { \ - natten::cuda::gemm::na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks19_align2(__VA_ARGS__); \ + natten::cuda::gemm::na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks21_align2(__VA_ARGS__); \ } \ else { \ std::cerr << "NATTEN kernel launch failed!" \ - << "na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks19 requires at least 32-bit alignment." \ - << "Got dim=" << dim << ", dtype=bfloat16. " \ + << "na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks21 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=half. " \ << std::endl; \ exit(EXIT_FAILURE); \ } \ }(); -#define DISPATCH_ALIGNMENT_na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks21(dim, ...) \ +#define DISPATCH_ALIGNMENT_na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks23(dim, ...) \ [&] { \ if (dim % 8 == 0) { \ - natten::cuda::gemm::na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks21_align8(__VA_ARGS__); \ + natten::cuda::gemm::na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks23_align8(__VA_ARGS__); \ } \ else if (dim % 4 == 0) { \ - natten::cuda::gemm::na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks21_align4(__VA_ARGS__); \ + natten::cuda::gemm::na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks23_align4(__VA_ARGS__); \ } \ else if (dim % 2 == 0) { \ - natten::cuda::gemm::na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks21_align2(__VA_ARGS__); \ + natten::cuda::gemm::na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks23_align2(__VA_ARGS__); \ } \ else { \ std::cerr << "NATTEN kernel launch failed!" \ - << "na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks21 requires at least 32-bit alignment." \ - << "Got dim=" << dim << ", dtype=bfloat16. " \ + << "na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks23 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=half. " \ << std::endl; \ exit(EXIT_FAILURE); \ } \ }(); -#define DISPATCH_ALIGNMENT_na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks23(dim, ...) \ +#define DISPATCH_ALIGNMENT_na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks25(dim, ...) \ [&] { \ if (dim % 8 == 0) { \ - natten::cuda::gemm::na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks23_align8(__VA_ARGS__); \ + natten::cuda::gemm::na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks25_align8(__VA_ARGS__); \ } \ else if (dim % 4 == 0) { \ - natten::cuda::gemm::na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks23_align4(__VA_ARGS__); \ + natten::cuda::gemm::na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks25_align4(__VA_ARGS__); \ } \ else if (dim % 2 == 0) { \ - natten::cuda::gemm::na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks23_align2(__VA_ARGS__); \ + natten::cuda::gemm::na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks25_align2(__VA_ARGS__); \ } \ else { \ std::cerr << "NATTEN kernel launch failed!" \ - << "na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks23 requires at least 32-bit alignment." \ - << "Got dim=" << dim << ", dtype=bfloat16. " \ + << "na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks25 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=half. " \ << std::endl; \ exit(EXIT_FAILURE); \ } \ }(); -#define DISPATCH_ALIGNMENT_na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks25(dim, ...) \ +#define DISPATCH_ALIGNMENT_na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks27(dim, ...) \ [&] { \ if (dim % 8 == 0) { \ - natten::cuda::gemm::na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks25_align8(__VA_ARGS__); \ + natten::cuda::gemm::na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks27_align8(__VA_ARGS__); \ } \ else if (dim % 4 == 0) { \ - natten::cuda::gemm::na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks25_align4(__VA_ARGS__); \ + natten::cuda::gemm::na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks27_align4(__VA_ARGS__); \ } \ else if (dim % 2 == 0) { \ - natten::cuda::gemm::na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks25_align2(__VA_ARGS__); \ + natten::cuda::gemm::na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks27_align2(__VA_ARGS__); \ } \ else { \ std::cerr << "NATTEN kernel launch failed!" \ - << "na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks25 requires at least 32-bit alignment." \ - << "Got dim=" << dim << ", dtype=bfloat16. " \ + << "na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks27 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=half. " \ << std::endl; \ exit(EXIT_FAILURE); \ } \ }(); -#define DISPATCH_ALIGNMENT_na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks27(dim, ...) \ +#define DISPATCH_ALIGNMENT_na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks29(dim, ...) \ [&] { \ if (dim % 8 == 0) { \ - natten::cuda::gemm::na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks27_align8(__VA_ARGS__); \ + natten::cuda::gemm::na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks29_align8(__VA_ARGS__); \ } \ else if (dim % 4 == 0) { \ - natten::cuda::gemm::na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks27_align4(__VA_ARGS__); \ + natten::cuda::gemm::na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks29_align4(__VA_ARGS__); \ } \ else if (dim % 2 == 0) { \ - natten::cuda::gemm::na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks27_align2(__VA_ARGS__); \ + natten::cuda::gemm::na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks29_align2(__VA_ARGS__); \ } \ else { \ std::cerr << "NATTEN kernel launch failed!" \ - << "na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks27 requires at least 32-bit alignment." \ - << "Got dim=" << dim << ", dtype=bfloat16. " \ + << "na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks29 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=half. " \ << std::endl; \ exit(EXIT_FAILURE); \ } \ }(); -#define DISPATCH_ALIGNMENT_na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks29(dim, ...) \ +#define DISPATCH_ALIGNMENT_na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks31(dim, ...) \ [&] { \ if (dim % 8 == 0) { \ - natten::cuda::gemm::na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks29_align8(__VA_ARGS__); \ + natten::cuda::gemm::na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks31_align8(__VA_ARGS__); \ } \ else if (dim % 4 == 0) { \ - natten::cuda::gemm::na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks29_align4(__VA_ARGS__); \ + natten::cuda::gemm::na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks31_align4(__VA_ARGS__); \ } \ else if (dim % 2 == 0) { \ - natten::cuda::gemm::na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks29_align2(__VA_ARGS__); \ + natten::cuda::gemm::na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks31_align2(__VA_ARGS__); \ } \ else { \ std::cerr << "NATTEN kernel launch failed!" \ - << "na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks29 requires at least 32-bit alignment." \ - << "Got dim=" << dim << ", dtype=bfloat16. " \ + << "na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks31 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=half. " \ << std::endl; \ exit(EXIT_FAILURE); \ } \ }(); -#define DISPATCH_ALIGNMENT_na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks31(dim, ...) \ +#define DISPATCH_ALIGNMENT_na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks33(dim, ...) \ [&] { \ if (dim % 8 == 0) { \ - natten::cuda::gemm::na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks31_align8(__VA_ARGS__); \ + natten::cuda::gemm::na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks33_align8(__VA_ARGS__); \ } \ else if (dim % 4 == 0) { \ - natten::cuda::gemm::na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks31_align4(__VA_ARGS__); \ + natten::cuda::gemm::na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks33_align4(__VA_ARGS__); \ } \ else if (dim % 2 == 0) { \ - natten::cuda::gemm::na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks31_align2(__VA_ARGS__); \ + natten::cuda::gemm::na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks33_align2(__VA_ARGS__); \ } \ else { \ std::cerr << "NATTEN kernel launch failed!" \ - << "na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks31 requires at least 32-bit alignment." \ - << "Got dim=" << dim << ", dtype=bfloat16. " \ + << "na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks33 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=half. " \ << std::endl; \ exit(EXIT_FAILURE); \ } \ }(); -#define DISPATCH_ALIGNMENT_na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks33(dim, ...) \ +#define DISPATCH_ALIGNMENT_na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks35(dim, ...) \ [&] { \ if (dim % 8 == 0) { \ - natten::cuda::gemm::na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks33_align8(__VA_ARGS__); \ + natten::cuda::gemm::na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks35_align8(__VA_ARGS__); \ } \ else if (dim % 4 == 0) { \ - natten::cuda::gemm::na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks33_align4(__VA_ARGS__); \ + natten::cuda::gemm::na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks35_align4(__VA_ARGS__); \ } \ else if (dim % 2 == 0) { \ - natten::cuda::gemm::na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks33_align2(__VA_ARGS__); \ + natten::cuda::gemm::na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks35_align2(__VA_ARGS__); \ } \ else { \ std::cerr << "NATTEN kernel launch failed!" \ - << "na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks33 requires at least 32-bit alignment." \ - << "Got dim=" << dim << ", dtype=bfloat16. " \ + << "na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks35 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=half. " \ << std::endl; \ exit(EXIT_FAILURE); \ } \ }(); -#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks3(dim, ...) \ +#define DISPATCH_ALIGNMENT_na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks37(dim, ...) \ [&] { \ - natten::cuda::gemm::na2d_nn_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks3_align1(__VA_ARGS__); \ + if (dim % 8 == 0) { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks37_align8(__VA_ARGS__); \ + } \ + else if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks37_align4(__VA_ARGS__); \ + } \ + else if (dim % 2 == 0) { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks37_align2(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks37 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=half. " \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ }(); -#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks5(dim, ...) \ +#define DISPATCH_ALIGNMENT_na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks39(dim, ...) \ [&] { \ - natten::cuda::gemm::na2d_nn_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks5_align1(__VA_ARGS__); \ + if (dim % 8 == 0) { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks39_align8(__VA_ARGS__); \ + } \ + else if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks39_align4(__VA_ARGS__); \ + } \ + else if (dim % 2 == 0) { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks39_align2(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks39 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=half. " \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ }(); -#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks7(dim, ...) \ +#define DISPATCH_ALIGNMENT_na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks41(dim, ...) \ [&] { \ - natten::cuda::gemm::na2d_nn_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks7_align1(__VA_ARGS__); \ + if (dim % 8 == 0) { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks41_align8(__VA_ARGS__); \ + } \ + else if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks41_align4(__VA_ARGS__); \ + } \ + else if (dim % 2 == 0) { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks41_align2(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks41 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=half. " \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ }(); -#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks9(dim, ...) \ +#define DISPATCH_ALIGNMENT_na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks43(dim, ...) \ [&] { \ - natten::cuda::gemm::na2d_nn_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks9_align1(__VA_ARGS__); \ + if (dim % 8 == 0) { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks43_align8(__VA_ARGS__); \ + } \ + else if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks43_align4(__VA_ARGS__); \ + } \ + else if (dim % 2 == 0) { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks43_align2(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks43 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=half. " \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ }(); -#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks11(dim, ...) \ +#define DISPATCH_ALIGNMENT_na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks45(dim, ...) \ [&] { \ - natten::cuda::gemm::na2d_nn_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks11_align1(__VA_ARGS__); \ + if (dim % 8 == 0) { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks45_align8(__VA_ARGS__); \ + } \ + else if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks45_align4(__VA_ARGS__); \ + } \ + else if (dim % 2 == 0) { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks45_align2(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks45 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=half. " \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ }(); -#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks13(dim, ...) \ +#define DISPATCH_ALIGNMENT_na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks47(dim, ...) \ [&] { \ - natten::cuda::gemm::na2d_nn_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks13_align1(__VA_ARGS__); \ -}(); - -#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks15(dim, ...) \ + if (dim % 8 == 0) { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks47_align8(__VA_ARGS__); \ + } \ + else if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks47_align4(__VA_ARGS__); \ + } \ + else if (dim % 2 == 0) { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks47_align2(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks47 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=half. " \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks49(dim, ...) \ + [&] { \ + if (dim % 8 == 0) { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks49_align8(__VA_ARGS__); \ + } \ + else if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks49_align4(__VA_ARGS__); \ + } \ + else if (dim % 2 == 0) { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks49_align2(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks49 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=half. " \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks51(dim, ...) \ + [&] { \ + if (dim % 8 == 0) { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks51_align8(__VA_ARGS__); \ + } \ + else if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks51_align4(__VA_ARGS__); \ + } \ + else if (dim % 2 == 0) { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks51_align2(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks51 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=half. " \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks53(dim, ...) \ + [&] { \ + if (dim % 8 == 0) { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks53_align8(__VA_ARGS__); \ + } \ + else if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks53_align4(__VA_ARGS__); \ + } \ + else if (dim % 2 == 0) { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks53_align2(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks53 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=half. " \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks55(dim, ...) \ + [&] { \ + if (dim % 8 == 0) { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks55_align8(__VA_ARGS__); \ + } \ + else if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks55_align4(__VA_ARGS__); \ + } \ + else if (dim % 2 == 0) { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks55_align2(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks55 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=half. " \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks57(dim, ...) \ + [&] { \ + if (dim % 8 == 0) { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks57_align8(__VA_ARGS__); \ + } \ + else if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks57_align4(__VA_ARGS__); \ + } \ + else if (dim % 2 == 0) { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks57_align2(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks57 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=half. " \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks59(dim, ...) \ + [&] { \ + if (dim % 8 == 0) { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks59_align8(__VA_ARGS__); \ + } \ + else if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks59_align4(__VA_ARGS__); \ + } \ + else if (dim % 2 == 0) { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks59_align2(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks59 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=half. " \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks61(dim, ...) \ + [&] { \ + if (dim % 8 == 0) { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks61_align8(__VA_ARGS__); \ + } \ + else if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks61_align4(__VA_ARGS__); \ + } \ + else if (dim % 2 == 0) { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks61_align2(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks61 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=half. " \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks63(dim, ...) \ + [&] { \ + if (dim % 8 == 0) { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks63_align8(__VA_ARGS__); \ + } \ + else if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks63_align4(__VA_ARGS__); \ + } \ + else if (dim % 2 == 0) { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks63_align2(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks63 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=half. " \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_pn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks3(dim, ...) \ + [&] { \ + if (dim % 8 == 0) { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks3_align8(__VA_ARGS__); \ + } \ + else if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks3_align4(__VA_ARGS__); \ + } \ + else if (dim % 2 == 0) { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks3_align2(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na2d_pn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks3 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=bfloat16. " \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_pn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks5(dim, ...) \ + [&] { \ + if (dim % 8 == 0) { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks5_align8(__VA_ARGS__); \ + } \ + else if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks5_align4(__VA_ARGS__); \ + } \ + else if (dim % 2 == 0) { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks5_align2(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na2d_pn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks5 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=bfloat16. " \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_pn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks7(dim, ...) \ + [&] { \ + if (dim % 8 == 0) { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks7_align8(__VA_ARGS__); \ + } \ + else if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks7_align4(__VA_ARGS__); \ + } \ + else if (dim % 2 == 0) { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks7_align2(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na2d_pn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks7 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=bfloat16. " \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_pn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks9(dim, ...) \ + [&] { \ + if (dim % 8 == 0) { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks9_align8(__VA_ARGS__); \ + } \ + else if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks9_align4(__VA_ARGS__); \ + } \ + else if (dim % 2 == 0) { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks9_align2(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na2d_pn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks9 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=bfloat16. " \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_pn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks11(dim, ...) \ + [&] { \ + if (dim % 8 == 0) { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks11_align8(__VA_ARGS__); \ + } \ + else if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks11_align4(__VA_ARGS__); \ + } \ + else if (dim % 2 == 0) { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks11_align2(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na2d_pn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks11 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=bfloat16. " \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_pn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks13(dim, ...) \ + [&] { \ + if (dim % 8 == 0) { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks13_align8(__VA_ARGS__); \ + } \ + else if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks13_align4(__VA_ARGS__); \ + } \ + else if (dim % 2 == 0) { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks13_align2(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na2d_pn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks13 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=bfloat16. " \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks15(dim, ...) \ + [&] { \ + if (dim % 8 == 0) { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks15_align8(__VA_ARGS__); \ + } \ + else if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks15_align4(__VA_ARGS__); \ + } \ + else if (dim % 2 == 0) { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks15_align2(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks15 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=bfloat16. " \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks17(dim, ...) \ + [&] { \ + if (dim % 8 == 0) { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks17_align8(__VA_ARGS__); \ + } \ + else if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks17_align4(__VA_ARGS__); \ + } \ + else if (dim % 2 == 0) { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks17_align2(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks17 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=bfloat16. " \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks19(dim, ...) \ + [&] { \ + if (dim % 8 == 0) { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks19_align8(__VA_ARGS__); \ + } \ + else if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks19_align4(__VA_ARGS__); \ + } \ + else if (dim % 2 == 0) { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks19_align2(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks19 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=bfloat16. " \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks21(dim, ...) \ + [&] { \ + if (dim % 8 == 0) { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks21_align8(__VA_ARGS__); \ + } \ + else if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks21_align4(__VA_ARGS__); \ + } \ + else if (dim % 2 == 0) { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks21_align2(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks21 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=bfloat16. " \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks23(dim, ...) \ + [&] { \ + if (dim % 8 == 0) { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks23_align8(__VA_ARGS__); \ + } \ + else if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks23_align4(__VA_ARGS__); \ + } \ + else if (dim % 2 == 0) { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks23_align2(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks23 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=bfloat16. " \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks25(dim, ...) \ + [&] { \ + if (dim % 8 == 0) { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks25_align8(__VA_ARGS__); \ + } \ + else if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks25_align4(__VA_ARGS__); \ + } \ + else if (dim % 2 == 0) { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks25_align2(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks25 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=bfloat16. " \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks27(dim, ...) \ + [&] { \ + if (dim % 8 == 0) { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks27_align8(__VA_ARGS__); \ + } \ + else if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks27_align4(__VA_ARGS__); \ + } \ + else if (dim % 2 == 0) { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks27_align2(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks27 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=bfloat16. " \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks29(dim, ...) \ + [&] { \ + if (dim % 8 == 0) { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks29_align8(__VA_ARGS__); \ + } \ + else if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks29_align4(__VA_ARGS__); \ + } \ + else if (dim % 2 == 0) { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks29_align2(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks29 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=bfloat16. " \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks31(dim, ...) \ + [&] { \ + if (dim % 8 == 0) { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks31_align8(__VA_ARGS__); \ + } \ + else if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks31_align4(__VA_ARGS__); \ + } \ + else if (dim % 2 == 0) { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks31_align2(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks31 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=bfloat16. " \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks33(dim, ...) \ + [&] { \ + if (dim % 8 == 0) { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks33_align8(__VA_ARGS__); \ + } \ + else if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks33_align4(__VA_ARGS__); \ + } \ + else if (dim % 2 == 0) { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks33_align2(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks33 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=bfloat16. " \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks35(dim, ...) \ + [&] { \ + if (dim % 8 == 0) { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks35_align8(__VA_ARGS__); \ + } \ + else if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks35_align4(__VA_ARGS__); \ + } \ + else if (dim % 2 == 0) { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks35_align2(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks35 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=bfloat16. " \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks37(dim, ...) \ + [&] { \ + if (dim % 8 == 0) { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks37_align8(__VA_ARGS__); \ + } \ + else if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks37_align4(__VA_ARGS__); \ + } \ + else if (dim % 2 == 0) { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks37_align2(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks37 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=bfloat16. " \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks39(dim, ...) \ + [&] { \ + if (dim % 8 == 0) { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks39_align8(__VA_ARGS__); \ + } \ + else if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks39_align4(__VA_ARGS__); \ + } \ + else if (dim % 2 == 0) { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks39_align2(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks39 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=bfloat16. " \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks41(dim, ...) \ + [&] { \ + if (dim % 8 == 0) { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks41_align8(__VA_ARGS__); \ + } \ + else if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks41_align4(__VA_ARGS__); \ + } \ + else if (dim % 2 == 0) { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks41_align2(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks41 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=bfloat16. " \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks43(dim, ...) \ + [&] { \ + if (dim % 8 == 0) { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks43_align8(__VA_ARGS__); \ + } \ + else if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks43_align4(__VA_ARGS__); \ + } \ + else if (dim % 2 == 0) { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks43_align2(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks43 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=bfloat16. " \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks45(dim, ...) \ + [&] { \ + if (dim % 8 == 0) { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks45_align8(__VA_ARGS__); \ + } \ + else if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks45_align4(__VA_ARGS__); \ + } \ + else if (dim % 2 == 0) { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks45_align2(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks45 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=bfloat16. " \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks47(dim, ...) \ + [&] { \ + if (dim % 8 == 0) { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks47_align8(__VA_ARGS__); \ + } \ + else if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks47_align4(__VA_ARGS__); \ + } \ + else if (dim % 2 == 0) { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks47_align2(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks47 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=bfloat16. " \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks49(dim, ...) \ + [&] { \ + if (dim % 8 == 0) { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks49_align8(__VA_ARGS__); \ + } \ + else if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks49_align4(__VA_ARGS__); \ + } \ + else if (dim % 2 == 0) { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks49_align2(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks49 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=bfloat16. " \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks51(dim, ...) \ + [&] { \ + if (dim % 8 == 0) { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks51_align8(__VA_ARGS__); \ + } \ + else if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks51_align4(__VA_ARGS__); \ + } \ + else if (dim % 2 == 0) { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks51_align2(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks51 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=bfloat16. " \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks53(dim, ...) \ + [&] { \ + if (dim % 8 == 0) { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks53_align8(__VA_ARGS__); \ + } \ + else if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks53_align4(__VA_ARGS__); \ + } \ + else if (dim % 2 == 0) { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks53_align2(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks53 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=bfloat16. " \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks55(dim, ...) \ + [&] { \ + if (dim % 8 == 0) { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks55_align8(__VA_ARGS__); \ + } \ + else if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks55_align4(__VA_ARGS__); \ + } \ + else if (dim % 2 == 0) { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks55_align2(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks55 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=bfloat16. " \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks57(dim, ...) \ + [&] { \ + if (dim % 8 == 0) { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks57_align8(__VA_ARGS__); \ + } \ + else if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks57_align4(__VA_ARGS__); \ + } \ + else if (dim % 2 == 0) { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks57_align2(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks57 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=bfloat16. " \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks59(dim, ...) \ + [&] { \ + if (dim % 8 == 0) { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks59_align8(__VA_ARGS__); \ + } \ + else if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks59_align4(__VA_ARGS__); \ + } \ + else if (dim % 2 == 0) { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks59_align2(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks59 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=bfloat16. " \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks61(dim, ...) \ + [&] { \ + if (dim % 8 == 0) { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks61_align8(__VA_ARGS__); \ + } \ + else if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks61_align4(__VA_ARGS__); \ + } \ + else if (dim % 2 == 0) { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks61_align2(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks61 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=bfloat16. " \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks63(dim, ...) \ + [&] { \ + if (dim % 8 == 0) { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks63_align8(__VA_ARGS__); \ + } \ + else if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks63_align4(__VA_ARGS__); \ + } \ + else if (dim % 2 == 0) { \ + natten::cuda::gemm::na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks63_align2(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks63 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=bfloat16. " \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks3(dim, ...) \ + [&] { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks3_align1(__VA_ARGS__); \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks5(dim, ...) \ + [&] { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks5_align1(__VA_ARGS__); \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks7(dim, ...) \ + [&] { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks7_align1(__VA_ARGS__); \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks9(dim, ...) \ + [&] { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks9_align1(__VA_ARGS__); \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks11(dim, ...) \ + [&] { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks11_align1(__VA_ARGS__); \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks13(dim, ...) \ + [&] { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks13_align1(__VA_ARGS__); \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks15(dim, ...) \ + [&] { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks15_align1(__VA_ARGS__); \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks17(dim, ...) \ + [&] { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks17_align1(__VA_ARGS__); \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks19(dim, ...) \ + [&] { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks19_align1(__VA_ARGS__); \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_double_128x128x16_64x64x16_8x8x4_3_sm80_ks21(dim, ...) \ + [&] { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_double_128x128x16_64x64x16_8x8x4_3_sm80_ks21_align1(__VA_ARGS__); \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_double_128x128x16_64x64x16_8x8x4_3_sm80_ks23(dim, ...) \ + [&] { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_double_128x128x16_64x64x16_8x8x4_3_sm80_ks23_align1(__VA_ARGS__); \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_double_128x128x16_64x64x16_8x8x4_3_sm80_ks25(dim, ...) \ + [&] { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_double_128x128x16_64x64x16_8x8x4_3_sm80_ks25_align1(__VA_ARGS__); \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_double_128x128x16_64x64x16_8x8x4_3_sm80_ks27(dim, ...) \ + [&] { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_double_128x128x16_64x64x16_8x8x4_3_sm80_ks27_align1(__VA_ARGS__); \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_double_128x128x16_64x64x16_8x8x4_3_sm80_ks29(dim, ...) \ + [&] { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_double_128x128x16_64x64x16_8x8x4_3_sm80_ks29_align1(__VA_ARGS__); \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_double_128x128x16_64x64x16_8x8x4_3_sm80_ks31(dim, ...) \ + [&] { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_double_128x128x16_64x64x16_8x8x4_3_sm80_ks31_align1(__VA_ARGS__); \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_double_128x128x16_64x64x16_8x8x4_3_sm80_ks33(dim, ...) \ + [&] { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_double_128x128x16_64x64x16_8x8x4_3_sm80_ks33_align1(__VA_ARGS__); \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_double_128x128x16_64x64x16_8x8x4_3_sm80_ks35(dim, ...) \ + [&] { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_double_128x128x16_64x64x16_8x8x4_3_sm80_ks35_align1(__VA_ARGS__); \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_double_128x128x16_64x64x16_8x8x4_3_sm80_ks37(dim, ...) \ + [&] { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_double_128x128x16_64x64x16_8x8x4_3_sm80_ks37_align1(__VA_ARGS__); \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_double_128x128x16_64x64x16_8x8x4_3_sm80_ks39(dim, ...) \ + [&] { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_double_128x128x16_64x64x16_8x8x4_3_sm80_ks39_align1(__VA_ARGS__); \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_double_128x128x16_64x64x16_8x8x4_3_sm80_ks41(dim, ...) \ + [&] { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_double_128x128x16_64x64x16_8x8x4_3_sm80_ks41_align1(__VA_ARGS__); \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_double_128x128x16_64x64x16_8x8x4_3_sm80_ks43(dim, ...) \ + [&] { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_double_128x128x16_64x64x16_8x8x4_3_sm80_ks43_align1(__VA_ARGS__); \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_double_128x128x16_64x64x16_8x8x4_3_sm80_ks45(dim, ...) \ + [&] { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_double_128x128x16_64x64x16_8x8x4_3_sm80_ks45_align1(__VA_ARGS__); \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_double_128x128x16_64x64x16_8x8x4_3_sm80_ks47(dim, ...) \ + [&] { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_double_128x128x16_64x64x16_8x8x4_3_sm80_ks47_align1(__VA_ARGS__); \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_double_128x128x16_64x64x16_8x8x4_3_sm80_ks49(dim, ...) \ + [&] { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_double_128x128x16_64x64x16_8x8x4_3_sm80_ks49_align1(__VA_ARGS__); \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_double_128x128x16_64x64x16_8x8x4_3_sm80_ks51(dim, ...) \ + [&] { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_double_128x128x16_64x64x16_8x8x4_3_sm80_ks51_align1(__VA_ARGS__); \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_double_128x128x16_64x64x16_8x8x4_3_sm80_ks53(dim, ...) \ + [&] { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_double_128x128x16_64x64x16_8x8x4_3_sm80_ks53_align1(__VA_ARGS__); \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_double_128x128x16_64x64x16_8x8x4_3_sm80_ks55(dim, ...) \ + [&] { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_double_128x128x16_64x64x16_8x8x4_3_sm80_ks55_align1(__VA_ARGS__); \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_double_128x128x16_64x64x16_8x8x4_3_sm80_ks57(dim, ...) \ + [&] { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_double_128x128x16_64x64x16_8x8x4_3_sm80_ks57_align1(__VA_ARGS__); \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_double_128x128x16_64x64x16_8x8x4_3_sm80_ks59(dim, ...) \ + [&] { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_double_128x128x16_64x64x16_8x8x4_3_sm80_ks59_align1(__VA_ARGS__); \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_double_128x128x16_64x64x16_8x8x4_3_sm80_ks61(dim, ...) \ + [&] { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_double_128x128x16_64x64x16_8x8x4_3_sm80_ks61_align1(__VA_ARGS__); \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_double_128x128x16_64x64x16_8x8x4_3_sm80_ks63(dim, ...) \ + [&] { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_double_128x128x16_64x64x16_8x8x4_3_sm80_ks63_align1(__VA_ARGS__); \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks3(dim, ...) \ + [&] { \ + if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks3_align4(__VA_ARGS__); \ + } \ + else if (dim % 2 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks3_align2(__VA_ARGS__); \ + } \ + else if (dim % 1 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks3_align1(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na2d_nn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks3 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=float. " \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks5(dim, ...) \ + [&] { \ + if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks5_align4(__VA_ARGS__); \ + } \ + else if (dim % 2 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks5_align2(__VA_ARGS__); \ + } \ + else if (dim % 1 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks5_align1(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na2d_nn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks5 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=float. " \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks7(dim, ...) \ + [&] { \ + if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks7_align4(__VA_ARGS__); \ + } \ + else if (dim % 2 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks7_align2(__VA_ARGS__); \ + } \ + else if (dim % 1 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks7_align1(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na2d_nn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks7 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=float. " \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks9(dim, ...) \ + [&] { \ + if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks9_align4(__VA_ARGS__); \ + } \ + else if (dim % 2 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks9_align2(__VA_ARGS__); \ + } \ + else if (dim % 1 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks9_align1(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na2d_nn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks9 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=float. " \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks11(dim, ...) \ + [&] { \ + if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks11_align4(__VA_ARGS__); \ + } \ + else if (dim % 2 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks11_align2(__VA_ARGS__); \ + } \ + else if (dim % 1 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks11_align1(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na2d_nn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks11 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=float. " \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks13(dim, ...) \ + [&] { \ + if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks13_align4(__VA_ARGS__); \ + } \ + else if (dim % 2 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks13_align2(__VA_ARGS__); \ + } \ + else if (dim % 1 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks13_align1(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na2d_nn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks13 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=float. " \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks15(dim, ...) \ + [&] { \ + if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks15_align4(__VA_ARGS__); \ + } \ + else if (dim % 2 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks15_align2(__VA_ARGS__); \ + } \ + else if (dim % 1 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks15_align1(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na2d_nn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks15 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=float. " \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks17(dim, ...) \ + [&] { \ + if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks17_align4(__VA_ARGS__); \ + } \ + else if (dim % 2 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks17_align2(__VA_ARGS__); \ + } \ + else if (dim % 1 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks17_align1(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na2d_nn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks17 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=float. " \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks19(dim, ...) \ + [&] { \ + if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks19_align4(__VA_ARGS__); \ + } \ + else if (dim % 2 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks19_align2(__VA_ARGS__); \ + } \ + else if (dim % 1 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks19_align1(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na2d_nn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks19 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=float. " \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks21(dim, ...) \ + [&] { \ + if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks21_align4(__VA_ARGS__); \ + } \ + else if (dim % 2 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks21_align2(__VA_ARGS__); \ + } \ + else if (dim % 1 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks21_align1(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks21 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=float. " \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks23(dim, ...) \ + [&] { \ + if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks23_align4(__VA_ARGS__); \ + } \ + else if (dim % 2 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks23_align2(__VA_ARGS__); \ + } \ + else if (dim % 1 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks23_align1(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks23 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=float. " \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks25(dim, ...) \ + [&] { \ + if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks25_align4(__VA_ARGS__); \ + } \ + else if (dim % 2 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks25_align2(__VA_ARGS__); \ + } \ + else if (dim % 1 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks25_align1(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks25 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=float. " \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks27(dim, ...) \ + [&] { \ + if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks27_align4(__VA_ARGS__); \ + } \ + else if (dim % 2 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks27_align2(__VA_ARGS__); \ + } \ + else if (dim % 1 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks27_align1(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks27 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=float. " \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks29(dim, ...) \ + [&] { \ + if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks29_align4(__VA_ARGS__); \ + } \ + else if (dim % 2 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks29_align2(__VA_ARGS__); \ + } \ + else if (dim % 1 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks29_align1(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks29 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=float. " \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks31(dim, ...) \ + [&] { \ + if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks31_align4(__VA_ARGS__); \ + } \ + else if (dim % 2 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks31_align2(__VA_ARGS__); \ + } \ + else if (dim % 1 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks31_align1(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks31 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=float. " \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks33(dim, ...) \ + [&] { \ + if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks33_align4(__VA_ARGS__); \ + } \ + else if (dim % 2 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks33_align2(__VA_ARGS__); \ + } \ + else if (dim % 1 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks33_align1(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks33 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=float. " \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks35(dim, ...) \ + [&] { \ + if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks35_align4(__VA_ARGS__); \ + } \ + else if (dim % 2 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks35_align2(__VA_ARGS__); \ + } \ + else if (dim % 1 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks35_align1(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks35 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=float. " \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks37(dim, ...) \ + [&] { \ + if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks37_align4(__VA_ARGS__); \ + } \ + else if (dim % 2 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks37_align2(__VA_ARGS__); \ + } \ + else if (dim % 1 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks37_align1(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks37 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=float. " \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks39(dim, ...) \ + [&] { \ + if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks39_align4(__VA_ARGS__); \ + } \ + else if (dim % 2 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks39_align2(__VA_ARGS__); \ + } \ + else if (dim % 1 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks39_align1(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks39 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=float. " \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks41(dim, ...) \ + [&] { \ + if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks41_align4(__VA_ARGS__); \ + } \ + else if (dim % 2 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks41_align2(__VA_ARGS__); \ + } \ + else if (dim % 1 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks41_align1(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks41 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=float. " \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks43(dim, ...) \ + [&] { \ + if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks43_align4(__VA_ARGS__); \ + } \ + else if (dim % 2 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks43_align2(__VA_ARGS__); \ + } \ + else if (dim % 1 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks43_align1(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks43 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=float. " \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks45(dim, ...) \ + [&] { \ + if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks45_align4(__VA_ARGS__); \ + } \ + else if (dim % 2 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks45_align2(__VA_ARGS__); \ + } \ + else if (dim % 1 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks45_align1(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks45 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=float. " \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks47(dim, ...) \ + [&] { \ + if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks47_align4(__VA_ARGS__); \ + } \ + else if (dim % 2 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks47_align2(__VA_ARGS__); \ + } \ + else if (dim % 1 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks47_align1(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks47 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=float. " \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks49(dim, ...) \ + [&] { \ + if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks49_align4(__VA_ARGS__); \ + } \ + else if (dim % 2 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks49_align2(__VA_ARGS__); \ + } \ + else if (dim % 1 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks49_align1(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks49 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=float. " \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks51(dim, ...) \ + [&] { \ + if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks51_align4(__VA_ARGS__); \ + } \ + else if (dim % 2 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks51_align2(__VA_ARGS__); \ + } \ + else if (dim % 1 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks51_align1(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks51 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=float. " \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks53(dim, ...) \ + [&] { \ + if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks53_align4(__VA_ARGS__); \ + } \ + else if (dim % 2 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks53_align2(__VA_ARGS__); \ + } \ + else if (dim % 1 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks53_align1(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks53 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=float. " \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks55(dim, ...) \ + [&] { \ + if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks55_align4(__VA_ARGS__); \ + } \ + else if (dim % 2 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks55_align2(__VA_ARGS__); \ + } \ + else if (dim % 1 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks55_align1(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks55 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=float. " \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks57(dim, ...) \ + [&] { \ + if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks57_align4(__VA_ARGS__); \ + } \ + else if (dim % 2 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks57_align2(__VA_ARGS__); \ + } \ + else if (dim % 1 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks57_align1(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks57 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=float. " \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks59(dim, ...) \ + [&] { \ + if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks59_align4(__VA_ARGS__); \ + } \ + else if (dim % 2 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks59_align2(__VA_ARGS__); \ + } \ + else if (dim % 1 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks59_align1(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks59 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=float. " \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks61(dim, ...) \ + [&] { \ + if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks61_align4(__VA_ARGS__); \ + } \ + else if (dim % 2 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks61_align2(__VA_ARGS__); \ + } \ + else if (dim % 1 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks61_align1(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks61 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=float. " \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks63(dim, ...) \ + [&] { \ + if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks63_align4(__VA_ARGS__); \ + } \ + else if (dim % 2 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks63_align2(__VA_ARGS__); \ + } \ + else if (dim % 1 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks63_align1(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks63 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=float. " \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks3(dim, ...) \ + [&] { \ + if (dim % 8 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks3_align8(__VA_ARGS__); \ + } \ + else if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks3_align4(__VA_ARGS__); \ + } \ + else if (dim % 2 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks3_align2(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks3 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=half. " \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks5(dim, ...) \ + [&] { \ + if (dim % 8 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks5_align8(__VA_ARGS__); \ + } \ + else if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks5_align4(__VA_ARGS__); \ + } \ + else if (dim % 2 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks5_align2(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks5 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=half. " \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks7(dim, ...) \ + [&] { \ + if (dim % 8 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks7_align8(__VA_ARGS__); \ + } \ + else if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks7_align4(__VA_ARGS__); \ + } \ + else if (dim % 2 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks7_align2(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks7 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=half. " \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks9(dim, ...) \ + [&] { \ + if (dim % 8 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks9_align8(__VA_ARGS__); \ + } \ + else if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks9_align4(__VA_ARGS__); \ + } \ + else if (dim % 2 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks9_align2(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks9 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=half. " \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks11(dim, ...) \ + [&] { \ + if (dim % 8 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks11_align8(__VA_ARGS__); \ + } \ + else if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks11_align4(__VA_ARGS__); \ + } \ + else if (dim % 2 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks11_align2(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks11 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=half. " \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks13(dim, ...) \ + [&] { \ + if (dim % 8 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks13_align8(__VA_ARGS__); \ + } \ + else if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks13_align4(__VA_ARGS__); \ + } \ + else if (dim % 2 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks13_align2(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks13 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=half. " \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks15(dim, ...) \ + [&] { \ + if (dim % 8 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks15_align8(__VA_ARGS__); \ + } \ + else if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks15_align4(__VA_ARGS__); \ + } \ + else if (dim % 2 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks15_align2(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks15 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=half. " \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks17(dim, ...) \ + [&] { \ + if (dim % 8 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks17_align8(__VA_ARGS__); \ + } \ + else if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks17_align4(__VA_ARGS__); \ + } \ + else if (dim % 2 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks17_align2(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks17 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=half. " \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks19(dim, ...) \ + [&] { \ + if (dim % 8 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks19_align8(__VA_ARGS__); \ + } \ + else if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks19_align4(__VA_ARGS__); \ + } \ + else if (dim % 2 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks19_align2(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks19 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=half. " \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks21(dim, ...) \ + [&] { \ + if (dim % 8 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks21_align8(__VA_ARGS__); \ + } \ + else if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks21_align4(__VA_ARGS__); \ + } \ + else if (dim % 2 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks21_align2(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks21 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=half. " \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks23(dim, ...) \ + [&] { \ + if (dim % 8 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks23_align8(__VA_ARGS__); \ + } \ + else if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks23_align4(__VA_ARGS__); \ + } \ + else if (dim % 2 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks23_align2(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks23 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=half. " \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks25(dim, ...) \ + [&] { \ + if (dim % 8 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks25_align8(__VA_ARGS__); \ + } \ + else if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks25_align4(__VA_ARGS__); \ + } \ + else if (dim % 2 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks25_align2(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks25 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=half. " \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks27(dim, ...) \ + [&] { \ + if (dim % 8 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks27_align8(__VA_ARGS__); \ + } \ + else if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks27_align4(__VA_ARGS__); \ + } \ + else if (dim % 2 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks27_align2(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks27 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=half. " \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks29(dim, ...) \ + [&] { \ + if (dim % 8 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks29_align8(__VA_ARGS__); \ + } \ + else if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks29_align4(__VA_ARGS__); \ + } \ + else if (dim % 2 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks29_align2(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks29 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=half. " \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks31(dim, ...) \ + [&] { \ + if (dim % 8 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks31_align8(__VA_ARGS__); \ + } \ + else if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks31_align4(__VA_ARGS__); \ + } \ + else if (dim % 2 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks31_align2(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks31 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=half. " \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks33(dim, ...) \ + [&] { \ + if (dim % 8 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks33_align8(__VA_ARGS__); \ + } \ + else if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks33_align4(__VA_ARGS__); \ + } \ + else if (dim % 2 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks33_align2(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks33 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=half. " \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks35(dim, ...) \ + [&] { \ + if (dim % 8 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks35_align8(__VA_ARGS__); \ + } \ + else if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks35_align4(__VA_ARGS__); \ + } \ + else if (dim % 2 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks35_align2(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks35 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=half. " \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks37(dim, ...) \ + [&] { \ + if (dim % 8 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks37_align8(__VA_ARGS__); \ + } \ + else if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks37_align4(__VA_ARGS__); \ + } \ + else if (dim % 2 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks37_align2(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks37 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=half. " \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks39(dim, ...) \ + [&] { \ + if (dim % 8 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks39_align8(__VA_ARGS__); \ + } \ + else if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks39_align4(__VA_ARGS__); \ + } \ + else if (dim % 2 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks39_align2(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks39 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=half. " \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks41(dim, ...) \ + [&] { \ + if (dim % 8 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks41_align8(__VA_ARGS__); \ + } \ + else if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks41_align4(__VA_ARGS__); \ + } \ + else if (dim % 2 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks41_align2(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks41 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=half. " \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks43(dim, ...) \ + [&] { \ + if (dim % 8 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks43_align8(__VA_ARGS__); \ + } \ + else if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks43_align4(__VA_ARGS__); \ + } \ + else if (dim % 2 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks43_align2(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks43 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=half. " \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks45(dim, ...) \ + [&] { \ + if (dim % 8 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks45_align8(__VA_ARGS__); \ + } \ + else if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks45_align4(__VA_ARGS__); \ + } \ + else if (dim % 2 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks45_align2(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks45 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=half. " \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks47(dim, ...) \ + [&] { \ + if (dim % 8 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks47_align8(__VA_ARGS__); \ + } \ + else if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks47_align4(__VA_ARGS__); \ + } \ + else if (dim % 2 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks47_align2(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks47 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=half. " \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks49(dim, ...) \ + [&] { \ + if (dim % 8 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks49_align8(__VA_ARGS__); \ + } \ + else if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks49_align4(__VA_ARGS__); \ + } \ + else if (dim % 2 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks49_align2(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks49 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=half. " \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks51(dim, ...) \ + [&] { \ + if (dim % 8 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks51_align8(__VA_ARGS__); \ + } \ + else if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks51_align4(__VA_ARGS__); \ + } \ + else if (dim % 2 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks51_align2(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks51 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=half. " \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks53(dim, ...) \ + [&] { \ + if (dim % 8 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks53_align8(__VA_ARGS__); \ + } \ + else if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks53_align4(__VA_ARGS__); \ + } \ + else if (dim % 2 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks53_align2(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks53 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=half. " \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks55(dim, ...) \ + [&] { \ + if (dim % 8 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks55_align8(__VA_ARGS__); \ + } \ + else if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks55_align4(__VA_ARGS__); \ + } \ + else if (dim % 2 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks55_align2(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks55 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=half. " \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks57(dim, ...) \ + [&] { \ + if (dim % 8 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks57_align8(__VA_ARGS__); \ + } \ + else if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks57_align4(__VA_ARGS__); \ + } \ + else if (dim % 2 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks57_align2(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks57 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=half. " \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks59(dim, ...) \ + [&] { \ + if (dim % 8 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks59_align8(__VA_ARGS__); \ + } \ + else if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks59_align4(__VA_ARGS__); \ + } \ + else if (dim % 2 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks59_align2(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks59 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=half. " \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks61(dim, ...) \ + [&] { \ + if (dim % 8 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks61_align8(__VA_ARGS__); \ + } \ + else if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks61_align4(__VA_ARGS__); \ + } \ + else if (dim % 2 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks61_align2(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks61 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=half. " \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks63(dim, ...) \ + [&] { \ + if (dim % 8 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks63_align8(__VA_ARGS__); \ + } \ + else if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks63_align4(__VA_ARGS__); \ + } \ + else if (dim % 2 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks63_align2(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks63 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=half. " \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks3(dim, ...) \ + [&] { \ + if (dim % 8 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks3_align8(__VA_ARGS__); \ + } \ + else if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks3_align4(__VA_ARGS__); \ + } \ + else if (dim % 2 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks3_align2(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks3 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=bfloat16. " \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks5(dim, ...) \ + [&] { \ + if (dim % 8 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks5_align8(__VA_ARGS__); \ + } \ + else if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks5_align4(__VA_ARGS__); \ + } \ + else if (dim % 2 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks5_align2(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks5 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=bfloat16. " \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks7(dim, ...) \ + [&] { \ + if (dim % 8 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks7_align8(__VA_ARGS__); \ + } \ + else if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks7_align4(__VA_ARGS__); \ + } \ + else if (dim % 2 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks7_align2(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks7 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=bfloat16. " \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks9(dim, ...) \ + [&] { \ + if (dim % 8 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks9_align8(__VA_ARGS__); \ + } \ + else if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks9_align4(__VA_ARGS__); \ + } \ + else if (dim % 2 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks9_align2(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks9 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=bfloat16. " \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks11(dim, ...) \ + [&] { \ + if (dim % 8 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks11_align8(__VA_ARGS__); \ + } \ + else if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks11_align4(__VA_ARGS__); \ + } \ + else if (dim % 2 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks11_align2(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks11 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=bfloat16. " \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks13(dim, ...) \ + [&] { \ + if (dim % 8 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks13_align8(__VA_ARGS__); \ + } \ + else if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks13_align4(__VA_ARGS__); \ + } \ + else if (dim % 2 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks13_align2(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks13 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=bfloat16. " \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks15(dim, ...) \ + [&] { \ + if (dim % 8 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks15_align8(__VA_ARGS__); \ + } \ + else if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks15_align4(__VA_ARGS__); \ + } \ + else if (dim % 2 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks15_align2(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks15 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=bfloat16. " \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks17(dim, ...) \ + [&] { \ + if (dim % 8 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks17_align8(__VA_ARGS__); \ + } \ + else if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks17_align4(__VA_ARGS__); \ + } \ + else if (dim % 2 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks17_align2(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks17 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=bfloat16. " \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks19(dim, ...) \ + [&] { \ + if (dim % 8 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks19_align8(__VA_ARGS__); \ + } \ + else if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks19_align4(__VA_ARGS__); \ + } \ + else if (dim % 2 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks19_align2(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks19 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=bfloat16. " \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks21(dim, ...) \ + [&] { \ + if (dim % 8 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks21_align8(__VA_ARGS__); \ + } \ + else if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks21_align4(__VA_ARGS__); \ + } \ + else if (dim % 2 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks21_align2(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks21 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=bfloat16. " \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks23(dim, ...) \ + [&] { \ + if (dim % 8 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks23_align8(__VA_ARGS__); \ + } \ + else if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks23_align4(__VA_ARGS__); \ + } \ + else if (dim % 2 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks23_align2(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks23 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=bfloat16. " \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks25(dim, ...) \ + [&] { \ + if (dim % 8 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks25_align8(__VA_ARGS__); \ + } \ + else if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks25_align4(__VA_ARGS__); \ + } \ + else if (dim % 2 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks25_align2(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks25 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=bfloat16. " \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks27(dim, ...) \ + [&] { \ + if (dim % 8 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks27_align8(__VA_ARGS__); \ + } \ + else if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks27_align4(__VA_ARGS__); \ + } \ + else if (dim % 2 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks27_align2(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks27 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=bfloat16. " \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks29(dim, ...) \ + [&] { \ + if (dim % 8 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks29_align8(__VA_ARGS__); \ + } \ + else if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks29_align4(__VA_ARGS__); \ + } \ + else if (dim % 2 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks29_align2(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks29 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=bfloat16. " \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks31(dim, ...) \ + [&] { \ + if (dim % 8 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks31_align8(__VA_ARGS__); \ + } \ + else if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks31_align4(__VA_ARGS__); \ + } \ + else if (dim % 2 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks31_align2(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks31 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=bfloat16. " \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks33(dim, ...) \ + [&] { \ + if (dim % 8 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks33_align8(__VA_ARGS__); \ + } \ + else if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks33_align4(__VA_ARGS__); \ + } \ + else if (dim % 2 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks33_align2(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks33 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=bfloat16. " \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks35(dim, ...) \ + [&] { \ + if (dim % 8 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks35_align8(__VA_ARGS__); \ + } \ + else if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks35_align4(__VA_ARGS__); \ + } \ + else if (dim % 2 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks35_align2(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks35 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=bfloat16. " \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks37(dim, ...) \ + [&] { \ + if (dim % 8 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks37_align8(__VA_ARGS__); \ + } \ + else if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks37_align4(__VA_ARGS__); \ + } \ + else if (dim % 2 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks37_align2(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks37 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=bfloat16. " \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks39(dim, ...) \ + [&] { \ + if (dim % 8 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks39_align8(__VA_ARGS__); \ + } \ + else if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks39_align4(__VA_ARGS__); \ + } \ + else if (dim % 2 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks39_align2(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks39 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=bfloat16. " \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks41(dim, ...) \ + [&] { \ + if (dim % 8 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks41_align8(__VA_ARGS__); \ + } \ + else if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks41_align4(__VA_ARGS__); \ + } \ + else if (dim % 2 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks41_align2(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks41 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=bfloat16. " \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks43(dim, ...) \ + [&] { \ + if (dim % 8 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks43_align8(__VA_ARGS__); \ + } \ + else if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks43_align4(__VA_ARGS__); \ + } \ + else if (dim % 2 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks43_align2(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks43 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=bfloat16. " \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks45(dim, ...) \ + [&] { \ + if (dim % 8 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks45_align8(__VA_ARGS__); \ + } \ + else if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks45_align4(__VA_ARGS__); \ + } \ + else if (dim % 2 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks45_align2(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks45 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=bfloat16. " \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks47(dim, ...) \ + [&] { \ + if (dim % 8 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks47_align8(__VA_ARGS__); \ + } \ + else if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks47_align4(__VA_ARGS__); \ + } \ + else if (dim % 2 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks47_align2(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks47 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=bfloat16. " \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks49(dim, ...) \ + [&] { \ + if (dim % 8 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks49_align8(__VA_ARGS__); \ + } \ + else if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks49_align4(__VA_ARGS__); \ + } \ + else if (dim % 2 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks49_align2(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks49 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=bfloat16. " \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks51(dim, ...) \ + [&] { \ + if (dim % 8 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks51_align8(__VA_ARGS__); \ + } \ + else if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks51_align4(__VA_ARGS__); \ + } \ + else if (dim % 2 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks51_align2(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks51 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=bfloat16. " \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks53(dim, ...) \ + [&] { \ + if (dim % 8 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks53_align8(__VA_ARGS__); \ + } \ + else if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks53_align4(__VA_ARGS__); \ + } \ + else if (dim % 2 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks53_align2(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks53 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=bfloat16. " \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks55(dim, ...) \ + [&] { \ + if (dim % 8 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks55_align8(__VA_ARGS__); \ + } \ + else if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks55_align4(__VA_ARGS__); \ + } \ + else if (dim % 2 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks55_align2(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks55 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=bfloat16. " \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks57(dim, ...) \ + [&] { \ + if (dim % 8 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks57_align8(__VA_ARGS__); \ + } \ + else if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks57_align4(__VA_ARGS__); \ + } \ + else if (dim % 2 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks57_align2(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks57 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=bfloat16. " \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks59(dim, ...) \ + [&] { \ + if (dim % 8 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks59_align8(__VA_ARGS__); \ + } \ + else if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks59_align4(__VA_ARGS__); \ + } \ + else if (dim % 2 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks59_align2(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks59 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=bfloat16. " \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks61(dim, ...) \ + [&] { \ + if (dim % 8 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks61_align8(__VA_ARGS__); \ + } \ + else if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks61_align4(__VA_ARGS__); \ + } \ + else if (dim % 2 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks61_align2(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks61 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=bfloat16. " \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks63(dim, ...) \ + [&] { \ + if (dim % 8 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks63_align8(__VA_ARGS__); \ + } \ + else if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks63_align4(__VA_ARGS__); \ + } \ + else if (dim % 2 == 0) { \ + natten::cuda::gemm::na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks63_align2(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks63 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=bfloat16. " \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks3(dim, ...) \ + [&] { \ + natten::cuda::gemm::na2d_in_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks3_align1(__VA_ARGS__); \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks5(dim, ...) \ + [&] { \ + natten::cuda::gemm::na2d_in_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks5_align1(__VA_ARGS__); \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks7(dim, ...) \ + [&] { \ + natten::cuda::gemm::na2d_in_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks7_align1(__VA_ARGS__); \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks9(dim, ...) \ + [&] { \ + natten::cuda::gemm::na2d_in_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks9_align1(__VA_ARGS__); \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks11(dim, ...) \ + [&] { \ + natten::cuda::gemm::na2d_in_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks11_align1(__VA_ARGS__); \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks13(dim, ...) \ + [&] { \ + natten::cuda::gemm::na2d_in_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks13_align1(__VA_ARGS__); \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks15(dim, ...) \ [&] { \ - natten::cuda::gemm::na2d_nn_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks15_align1(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks15_align1(__VA_ARGS__); \ }(); -#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks17(dim, ...) \ +#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks17(dim, ...) \ [&] { \ - natten::cuda::gemm::na2d_nn_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks17_align1(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks17_align1(__VA_ARGS__); \ }(); -#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks19(dim, ...) \ +#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks19(dim, ...) \ [&] { \ - natten::cuda::gemm::na2d_nn_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks19_align1(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks19_align1(__VA_ARGS__); \ }(); -#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_double_128x128x16_64x64x16_8x8x4_3_sm80_ks21(dim, ...) \ +#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks21(dim, ...) \ [&] { \ - natten::cuda::gemm::na2d_nn_cuda_gemm_double_128x128x16_64x64x16_8x8x4_3_sm80_ks21_align1(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks21_align1(__VA_ARGS__); \ }(); -#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_double_128x128x16_64x64x16_8x8x4_3_sm80_ks23(dim, ...) \ +#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks23(dim, ...) \ [&] { \ - natten::cuda::gemm::na2d_nn_cuda_gemm_double_128x128x16_64x64x16_8x8x4_3_sm80_ks23_align1(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks23_align1(__VA_ARGS__); \ }(); -#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_double_128x128x16_64x64x16_8x8x4_3_sm80_ks25(dim, ...) \ +#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks25(dim, ...) \ [&] { \ - natten::cuda::gemm::na2d_nn_cuda_gemm_double_128x128x16_64x64x16_8x8x4_3_sm80_ks25_align1(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks25_align1(__VA_ARGS__); \ }(); -#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_double_128x128x16_64x64x16_8x8x4_3_sm80_ks27(dim, ...) \ +#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks27(dim, ...) \ [&] { \ - natten::cuda::gemm::na2d_nn_cuda_gemm_double_128x128x16_64x64x16_8x8x4_3_sm80_ks27_align1(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks27_align1(__VA_ARGS__); \ }(); -#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_double_128x128x16_64x64x16_8x8x4_3_sm80_ks29(dim, ...) \ +#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks29(dim, ...) \ [&] { \ - natten::cuda::gemm::na2d_nn_cuda_gemm_double_128x128x16_64x64x16_8x8x4_3_sm80_ks29_align1(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks29_align1(__VA_ARGS__); \ }(); -#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_double_128x128x16_64x64x16_8x8x4_3_sm80_ks31(dim, ...) \ +#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks31(dim, ...) \ [&] { \ - natten::cuda::gemm::na2d_nn_cuda_gemm_double_128x128x16_64x64x16_8x8x4_3_sm80_ks31_align1(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks31_align1(__VA_ARGS__); \ }(); -#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_double_128x128x16_64x64x16_8x8x4_3_sm80_ks33(dim, ...) \ +#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks33(dim, ...) \ [&] { \ - natten::cuda::gemm::na2d_nn_cuda_gemm_double_128x128x16_64x64x16_8x8x4_3_sm80_ks33_align1(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks33_align1(__VA_ARGS__); \ }(); -#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks3(dim, ...) \ +#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks35(dim, ...) \ [&] { \ - if (dim % 4 == 0) { \ - natten::cuda::gemm::na2d_nn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks3_align4(__VA_ARGS__); \ - } \ - else if (dim % 2 == 0) { \ - natten::cuda::gemm::na2d_nn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks3_align2(__VA_ARGS__); \ - } \ - else if (dim % 1 == 0) { \ - natten::cuda::gemm::na2d_nn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks3_align1(__VA_ARGS__); \ - } \ - else { \ - std::cerr << "NATTEN kernel launch failed!" \ - << "na2d_nn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks3 requires at least 32-bit alignment." \ - << "Got dim=" << dim << ", dtype=float. " \ - << std::endl; \ - exit(EXIT_FAILURE); \ - } \ + natten::cuda::gemm::na2d_in_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks35_align1(__VA_ARGS__); \ }(); -#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks5(dim, ...) \ +#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks37(dim, ...) \ [&] { \ - if (dim % 4 == 0) { \ - natten::cuda::gemm::na2d_nn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks5_align4(__VA_ARGS__); \ - } \ - else if (dim % 2 == 0) { \ - natten::cuda::gemm::na2d_nn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks5_align2(__VA_ARGS__); \ - } \ - else if (dim % 1 == 0) { \ - natten::cuda::gemm::na2d_nn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks5_align1(__VA_ARGS__); \ - } \ - else { \ - std::cerr << "NATTEN kernel launch failed!" \ - << "na2d_nn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks5 requires at least 32-bit alignment." \ - << "Got dim=" << dim << ", dtype=float. " \ - << std::endl; \ - exit(EXIT_FAILURE); \ - } \ + natten::cuda::gemm::na2d_in_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks37_align1(__VA_ARGS__); \ }(); -#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks7(dim, ...) \ +#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks39(dim, ...) \ [&] { \ - if (dim % 4 == 0) { \ - natten::cuda::gemm::na2d_nn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks7_align4(__VA_ARGS__); \ - } \ - else if (dim % 2 == 0) { \ - natten::cuda::gemm::na2d_nn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks7_align2(__VA_ARGS__); \ - } \ - else if (dim % 1 == 0) { \ - natten::cuda::gemm::na2d_nn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks7_align1(__VA_ARGS__); \ - } \ - else { \ - std::cerr << "NATTEN kernel launch failed!" \ - << "na2d_nn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks7 requires at least 32-bit alignment." \ - << "Got dim=" << dim << ", dtype=float. " \ - << std::endl; \ - exit(EXIT_FAILURE); \ - } \ + natten::cuda::gemm::na2d_in_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks39_align1(__VA_ARGS__); \ }(); -#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks9(dim, ...) \ +#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks41(dim, ...) \ [&] { \ - if (dim % 4 == 0) { \ - natten::cuda::gemm::na2d_nn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks9_align4(__VA_ARGS__); \ - } \ - else if (dim % 2 == 0) { \ - natten::cuda::gemm::na2d_nn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks9_align2(__VA_ARGS__); \ - } \ - else if (dim % 1 == 0) { \ - natten::cuda::gemm::na2d_nn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks9_align1(__VA_ARGS__); \ - } \ - else { \ - std::cerr << "NATTEN kernel launch failed!" \ - << "na2d_nn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks9 requires at least 32-bit alignment." \ - << "Got dim=" << dim << ", dtype=float. " \ - << std::endl; \ - exit(EXIT_FAILURE); \ - } \ + natten::cuda::gemm::na2d_in_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks41_align1(__VA_ARGS__); \ }(); -#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks11(dim, ...) \ +#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks43(dim, ...) \ + [&] { \ + natten::cuda::gemm::na2d_in_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks43_align1(__VA_ARGS__); \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks45(dim, ...) \ + [&] { \ + natten::cuda::gemm::na2d_in_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks45_align1(__VA_ARGS__); \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks47(dim, ...) \ + [&] { \ + natten::cuda::gemm::na2d_in_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks47_align1(__VA_ARGS__); \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks49(dim, ...) \ + [&] { \ + natten::cuda::gemm::na2d_in_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks49_align1(__VA_ARGS__); \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks51(dim, ...) \ + [&] { \ + natten::cuda::gemm::na2d_in_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks51_align1(__VA_ARGS__); \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks53(dim, ...) \ + [&] { \ + natten::cuda::gemm::na2d_in_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks53_align1(__VA_ARGS__); \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks55(dim, ...) \ + [&] { \ + natten::cuda::gemm::na2d_in_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks55_align1(__VA_ARGS__); \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks57(dim, ...) \ + [&] { \ + natten::cuda::gemm::na2d_in_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks57_align1(__VA_ARGS__); \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks59(dim, ...) \ + [&] { \ + natten::cuda::gemm::na2d_in_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks59_align1(__VA_ARGS__); \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks61(dim, ...) \ + [&] { \ + natten::cuda::gemm::na2d_in_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks61_align1(__VA_ARGS__); \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks63(dim, ...) \ + [&] { \ + natten::cuda::gemm::na2d_in_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks63_align1(__VA_ARGS__); \ +}(); + +#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks3(dim, ...) \ [&] { \ if (dim % 4 == 0) { \ - natten::cuda::gemm::na2d_nn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks11_align4(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks3_align4(__VA_ARGS__); \ } \ else if (dim % 2 == 0) { \ - natten::cuda::gemm::na2d_nn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks11_align2(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks3_align2(__VA_ARGS__); \ } \ else if (dim % 1 == 0) { \ - natten::cuda::gemm::na2d_nn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks11_align1(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks3_align1(__VA_ARGS__); \ } \ else { \ std::cerr << "NATTEN kernel launch failed!" \ - << "na2d_nn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks11 requires at least 32-bit alignment." \ + << "na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks3 requires at least 32-bit alignment." \ << "Got dim=" << dim << ", dtype=float. " \ << std::endl; \ exit(EXIT_FAILURE); \ } \ }(); -#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks13(dim, ...) \ +#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks5(dim, ...) \ [&] { \ if (dim % 4 == 0) { \ - natten::cuda::gemm::na2d_nn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks13_align4(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks5_align4(__VA_ARGS__); \ } \ else if (dim % 2 == 0) { \ - natten::cuda::gemm::na2d_nn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks13_align2(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks5_align2(__VA_ARGS__); \ } \ else if (dim % 1 == 0) { \ - natten::cuda::gemm::na2d_nn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks13_align1(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks5_align1(__VA_ARGS__); \ } \ else { \ std::cerr << "NATTEN kernel launch failed!" \ - << "na2d_nn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks13 requires at least 32-bit alignment." \ + << "na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks5 requires at least 32-bit alignment." \ << "Got dim=" << dim << ", dtype=float. " \ << std::endl; \ exit(EXIT_FAILURE); \ } \ }(); -#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks15(dim, ...) \ +#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks7(dim, ...) \ [&] { \ if (dim % 4 == 0) { \ - natten::cuda::gemm::na2d_nn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks15_align4(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks7_align4(__VA_ARGS__); \ } \ else if (dim % 2 == 0) { \ - natten::cuda::gemm::na2d_nn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks15_align2(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks7_align2(__VA_ARGS__); \ } \ else if (dim % 1 == 0) { \ - natten::cuda::gemm::na2d_nn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks15_align1(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks7_align1(__VA_ARGS__); \ } \ else { \ std::cerr << "NATTEN kernel launch failed!" \ - << "na2d_nn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks15 requires at least 32-bit alignment." \ + << "na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks7 requires at least 32-bit alignment." \ << "Got dim=" << dim << ", dtype=float. " \ << std::endl; \ exit(EXIT_FAILURE); \ } \ }(); -#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks17(dim, ...) \ +#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks9(dim, ...) \ [&] { \ if (dim % 4 == 0) { \ - natten::cuda::gemm::na2d_nn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks17_align4(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks9_align4(__VA_ARGS__); \ } \ else if (dim % 2 == 0) { \ - natten::cuda::gemm::na2d_nn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks17_align2(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks9_align2(__VA_ARGS__); \ } \ else if (dim % 1 == 0) { \ - natten::cuda::gemm::na2d_nn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks17_align1(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks9_align1(__VA_ARGS__); \ } \ else { \ std::cerr << "NATTEN kernel launch failed!" \ - << "na2d_nn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks17 requires at least 32-bit alignment." \ + << "na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks9 requires at least 32-bit alignment." \ << "Got dim=" << dim << ", dtype=float. " \ << std::endl; \ exit(EXIT_FAILURE); \ } \ }(); -#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks19(dim, ...) \ +#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks11(dim, ...) \ [&] { \ if (dim % 4 == 0) { \ - natten::cuda::gemm::na2d_nn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks19_align4(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks11_align4(__VA_ARGS__); \ } \ else if (dim % 2 == 0) { \ - natten::cuda::gemm::na2d_nn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks19_align2(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks11_align2(__VA_ARGS__); \ } \ else if (dim % 1 == 0) { \ - natten::cuda::gemm::na2d_nn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks19_align1(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks11_align1(__VA_ARGS__); \ } \ else { \ std::cerr << "NATTEN kernel launch failed!" \ - << "na2d_nn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks19 requires at least 32-bit alignment." \ + << "na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks11 requires at least 32-bit alignment." \ << "Got dim=" << dim << ", dtype=float. " \ << std::endl; \ exit(EXIT_FAILURE); \ } \ }(); -#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks21(dim, ...) \ +#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks13(dim, ...) \ [&] { \ if (dim % 4 == 0) { \ - natten::cuda::gemm::na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks21_align4(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks13_align4(__VA_ARGS__); \ } \ else if (dim % 2 == 0) { \ - natten::cuda::gemm::na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks21_align2(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks13_align2(__VA_ARGS__); \ } \ else if (dim % 1 == 0) { \ - natten::cuda::gemm::na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks21_align1(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks13_align1(__VA_ARGS__); \ } \ else { \ std::cerr << "NATTEN kernel launch failed!" \ - << "na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks21 requires at least 32-bit alignment." \ + << "na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks13 requires at least 32-bit alignment." \ << "Got dim=" << dim << ", dtype=float. " \ << std::endl; \ exit(EXIT_FAILURE); \ } \ }(); -#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks23(dim, ...) \ +#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks15(dim, ...) \ [&] { \ if (dim % 4 == 0) { \ - natten::cuda::gemm::na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks23_align4(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks15_align4(__VA_ARGS__); \ } \ else if (dim % 2 == 0) { \ - natten::cuda::gemm::na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks23_align2(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks15_align2(__VA_ARGS__); \ } \ else if (dim % 1 == 0) { \ - natten::cuda::gemm::na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks23_align1(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks15_align1(__VA_ARGS__); \ } \ else { \ std::cerr << "NATTEN kernel launch failed!" \ - << "na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks23 requires at least 32-bit alignment." \ + << "na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks15 requires at least 32-bit alignment." \ << "Got dim=" << dim << ", dtype=float. " \ << std::endl; \ exit(EXIT_FAILURE); \ } \ }(); -#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks25(dim, ...) \ +#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks17(dim, ...) \ [&] { \ if (dim % 4 == 0) { \ - natten::cuda::gemm::na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks25_align4(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks17_align4(__VA_ARGS__); \ } \ else if (dim % 2 == 0) { \ - natten::cuda::gemm::na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks25_align2(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks17_align2(__VA_ARGS__); \ } \ else if (dim % 1 == 0) { \ - natten::cuda::gemm::na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks25_align1(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks17_align1(__VA_ARGS__); \ } \ else { \ std::cerr << "NATTEN kernel launch failed!" \ - << "na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks25 requires at least 32-bit alignment." \ + << "na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks17 requires at least 32-bit alignment." \ << "Got dim=" << dim << ", dtype=float. " \ << std::endl; \ exit(EXIT_FAILURE); \ } \ }(); -#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks27(dim, ...) \ +#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks19(dim, ...) \ [&] { \ if (dim % 4 == 0) { \ - natten::cuda::gemm::na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks27_align4(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks19_align4(__VA_ARGS__); \ } \ else if (dim % 2 == 0) { \ - natten::cuda::gemm::na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks27_align2(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks19_align2(__VA_ARGS__); \ } \ else if (dim % 1 == 0) { \ - natten::cuda::gemm::na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks27_align1(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks19_align1(__VA_ARGS__); \ } \ else { \ std::cerr << "NATTEN kernel launch failed!" \ - << "na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks27 requires at least 32-bit alignment." \ + << "na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks19 requires at least 32-bit alignment." \ << "Got dim=" << dim << ", dtype=float. " \ << std::endl; \ exit(EXIT_FAILURE); \ } \ }(); -#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks29(dim, ...) \ +#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks21(dim, ...) \ [&] { \ if (dim % 4 == 0) { \ - natten::cuda::gemm::na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks29_align4(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks21_align4(__VA_ARGS__); \ } \ else if (dim % 2 == 0) { \ - natten::cuda::gemm::na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks29_align2(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks21_align2(__VA_ARGS__); \ } \ else if (dim % 1 == 0) { \ - natten::cuda::gemm::na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks29_align1(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks21_align1(__VA_ARGS__); \ } \ else { \ std::cerr << "NATTEN kernel launch failed!" \ - << "na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks29 requires at least 32-bit alignment." \ + << "na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks21 requires at least 32-bit alignment." \ << "Got dim=" << dim << ", dtype=float. " \ << std::endl; \ exit(EXIT_FAILURE); \ } \ }(); -#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks31(dim, ...) \ +#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks23(dim, ...) \ [&] { \ if (dim % 4 == 0) { \ - natten::cuda::gemm::na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks31_align4(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks23_align4(__VA_ARGS__); \ } \ else if (dim % 2 == 0) { \ - natten::cuda::gemm::na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks31_align2(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks23_align2(__VA_ARGS__); \ } \ else if (dim % 1 == 0) { \ - natten::cuda::gemm::na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks31_align1(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks23_align1(__VA_ARGS__); \ } \ else { \ std::cerr << "NATTEN kernel launch failed!" \ - << "na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks31 requires at least 32-bit alignment." \ + << "na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks23 requires at least 32-bit alignment." \ << "Got dim=" << dim << ", dtype=float. " \ << std::endl; \ exit(EXIT_FAILURE); \ } \ }(); -#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks33(dim, ...) \ +#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks25(dim, ...) \ [&] { \ if (dim % 4 == 0) { \ - natten::cuda::gemm::na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks33_align4(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks25_align4(__VA_ARGS__); \ } \ else if (dim % 2 == 0) { \ - natten::cuda::gemm::na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks33_align2(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks25_align2(__VA_ARGS__); \ } \ else if (dim % 1 == 0) { \ - natten::cuda::gemm::na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks33_align1(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks25_align1(__VA_ARGS__); \ } \ else { \ std::cerr << "NATTEN kernel launch failed!" \ - << "na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks33 requires at least 32-bit alignment." \ + << "na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks25 requires at least 32-bit alignment." \ << "Got dim=" << dim << ", dtype=float. " \ << std::endl; \ exit(EXIT_FAILURE); \ } \ }(); -#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks3(dim, ...) \ +#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks27(dim, ...) \ [&] { \ - if (dim % 8 == 0) { \ - natten::cuda::gemm::na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks3_align8(__VA_ARGS__); \ - } \ - else if (dim % 4 == 0) { \ - natten::cuda::gemm::na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks3_align4(__VA_ARGS__); \ + if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks27_align4(__VA_ARGS__); \ } \ else if (dim % 2 == 0) { \ - natten::cuda::gemm::na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks3_align2(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks27_align2(__VA_ARGS__); \ + } \ + else if (dim % 1 == 0) { \ + natten::cuda::gemm::na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks27_align1(__VA_ARGS__); \ } \ else { \ std::cerr << "NATTEN kernel launch failed!" \ - << "na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks3 requires at least 32-bit alignment." \ - << "Got dim=" << dim << ", dtype=half. " \ + << "na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks27 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=float. " \ << std::endl; \ exit(EXIT_FAILURE); \ } \ }(); -#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks5(dim, ...) \ +#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks29(dim, ...) \ [&] { \ - if (dim % 8 == 0) { \ - natten::cuda::gemm::na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks5_align8(__VA_ARGS__); \ - } \ - else if (dim % 4 == 0) { \ - natten::cuda::gemm::na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks5_align4(__VA_ARGS__); \ + if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks29_align4(__VA_ARGS__); \ } \ else if (dim % 2 == 0) { \ - natten::cuda::gemm::na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks5_align2(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks29_align2(__VA_ARGS__); \ + } \ + else if (dim % 1 == 0) { \ + natten::cuda::gemm::na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks29_align1(__VA_ARGS__); \ } \ else { \ std::cerr << "NATTEN kernel launch failed!" \ - << "na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks5 requires at least 32-bit alignment." \ - << "Got dim=" << dim << ", dtype=half. " \ + << "na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks29 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=float. " \ << std::endl; \ exit(EXIT_FAILURE); \ } \ }(); -#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks7(dim, ...) \ +#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks31(dim, ...) \ [&] { \ - if (dim % 8 == 0) { \ - natten::cuda::gemm::na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks7_align8(__VA_ARGS__); \ - } \ - else if (dim % 4 == 0) { \ - natten::cuda::gemm::na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks7_align4(__VA_ARGS__); \ + if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks31_align4(__VA_ARGS__); \ } \ else if (dim % 2 == 0) { \ - natten::cuda::gemm::na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks7_align2(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks31_align2(__VA_ARGS__); \ + } \ + else if (dim % 1 == 0) { \ + natten::cuda::gemm::na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks31_align1(__VA_ARGS__); \ } \ else { \ std::cerr << "NATTEN kernel launch failed!" \ - << "na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks7 requires at least 32-bit alignment." \ - << "Got dim=" << dim << ", dtype=half. " \ + << "na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks31 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=float. " \ << std::endl; \ exit(EXIT_FAILURE); \ } \ }(); -#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks9(dim, ...) \ +#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks33(dim, ...) \ [&] { \ - if (dim % 8 == 0) { \ - natten::cuda::gemm::na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks9_align8(__VA_ARGS__); \ - } \ - else if (dim % 4 == 0) { \ - natten::cuda::gemm::na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks9_align4(__VA_ARGS__); \ + if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks33_align4(__VA_ARGS__); \ } \ else if (dim % 2 == 0) { \ - natten::cuda::gemm::na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks9_align2(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks33_align2(__VA_ARGS__); \ + } \ + else if (dim % 1 == 0) { \ + natten::cuda::gemm::na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks33_align1(__VA_ARGS__); \ } \ else { \ std::cerr << "NATTEN kernel launch failed!" \ - << "na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks9 requires at least 32-bit alignment." \ - << "Got dim=" << dim << ", dtype=half. " \ + << "na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks33 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=float. " \ << std::endl; \ exit(EXIT_FAILURE); \ } \ }(); -#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks11(dim, ...) \ +#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks35(dim, ...) \ [&] { \ - if (dim % 8 == 0) { \ - natten::cuda::gemm::na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks11_align8(__VA_ARGS__); \ - } \ - else if (dim % 4 == 0) { \ - natten::cuda::gemm::na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks11_align4(__VA_ARGS__); \ + if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks35_align4(__VA_ARGS__); \ } \ else if (dim % 2 == 0) { \ - natten::cuda::gemm::na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks11_align2(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks35_align2(__VA_ARGS__); \ + } \ + else if (dim % 1 == 0) { \ + natten::cuda::gemm::na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks35_align1(__VA_ARGS__); \ } \ else { \ std::cerr << "NATTEN kernel launch failed!" \ - << "na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks11 requires at least 32-bit alignment." \ - << "Got dim=" << dim << ", dtype=half. " \ + << "na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks35 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=float. " \ << std::endl; \ exit(EXIT_FAILURE); \ } \ }(); -#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks13(dim, ...) \ +#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks37(dim, ...) \ [&] { \ - if (dim % 8 == 0) { \ - natten::cuda::gemm::na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks13_align8(__VA_ARGS__); \ - } \ - else if (dim % 4 == 0) { \ - natten::cuda::gemm::na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks13_align4(__VA_ARGS__); \ + if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks37_align4(__VA_ARGS__); \ } \ else if (dim % 2 == 0) { \ - natten::cuda::gemm::na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks13_align2(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks37_align2(__VA_ARGS__); \ + } \ + else if (dim % 1 == 0) { \ + natten::cuda::gemm::na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks37_align1(__VA_ARGS__); \ } \ else { \ std::cerr << "NATTEN kernel launch failed!" \ - << "na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks13 requires at least 32-bit alignment." \ - << "Got dim=" << dim << ", dtype=half. " \ + << "na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks37 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=float. " \ << std::endl; \ exit(EXIT_FAILURE); \ } \ }(); -#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks15(dim, ...) \ +#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks39(dim, ...) \ [&] { \ - if (dim % 8 == 0) { \ - natten::cuda::gemm::na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks15_align8(__VA_ARGS__); \ - } \ - else if (dim % 4 == 0) { \ - natten::cuda::gemm::na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks15_align4(__VA_ARGS__); \ + if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks39_align4(__VA_ARGS__); \ } \ else if (dim % 2 == 0) { \ - natten::cuda::gemm::na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks15_align2(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks39_align2(__VA_ARGS__); \ + } \ + else if (dim % 1 == 0) { \ + natten::cuda::gemm::na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks39_align1(__VA_ARGS__); \ } \ else { \ std::cerr << "NATTEN kernel launch failed!" \ - << "na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks15 requires at least 32-bit alignment." \ - << "Got dim=" << dim << ", dtype=half. " \ + << "na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks39 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=float. " \ << std::endl; \ exit(EXIT_FAILURE); \ } \ }(); -#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks17(dim, ...) \ +#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks41(dim, ...) \ [&] { \ - if (dim % 8 == 0) { \ - natten::cuda::gemm::na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks17_align8(__VA_ARGS__); \ - } \ - else if (dim % 4 == 0) { \ - natten::cuda::gemm::na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks17_align4(__VA_ARGS__); \ + if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks41_align4(__VA_ARGS__); \ } \ else if (dim % 2 == 0) { \ - natten::cuda::gemm::na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks17_align2(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks41_align2(__VA_ARGS__); \ + } \ + else if (dim % 1 == 0) { \ + natten::cuda::gemm::na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks41_align1(__VA_ARGS__); \ } \ else { \ std::cerr << "NATTEN kernel launch failed!" \ - << "na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks17 requires at least 32-bit alignment." \ - << "Got dim=" << dim << ", dtype=half. " \ + << "na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks41 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=float. " \ << std::endl; \ exit(EXIT_FAILURE); \ } \ }(); -#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks19(dim, ...) \ +#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks43(dim, ...) \ [&] { \ - if (dim % 8 == 0) { \ - natten::cuda::gemm::na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks19_align8(__VA_ARGS__); \ - } \ - else if (dim % 4 == 0) { \ - natten::cuda::gemm::na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks19_align4(__VA_ARGS__); \ + if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks43_align4(__VA_ARGS__); \ } \ else if (dim % 2 == 0) { \ - natten::cuda::gemm::na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks19_align2(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks43_align2(__VA_ARGS__); \ + } \ + else if (dim % 1 == 0) { \ + natten::cuda::gemm::na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks43_align1(__VA_ARGS__); \ } \ else { \ std::cerr << "NATTEN kernel launch failed!" \ - << "na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks19 requires at least 32-bit alignment." \ - << "Got dim=" << dim << ", dtype=half. " \ + << "na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks43 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=float. " \ << std::endl; \ exit(EXIT_FAILURE); \ } \ }(); -#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks21(dim, ...) \ +#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks45(dim, ...) \ [&] { \ - if (dim % 8 == 0) { \ - natten::cuda::gemm::na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks21_align8(__VA_ARGS__); \ - } \ - else if (dim % 4 == 0) { \ - natten::cuda::gemm::na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks21_align4(__VA_ARGS__); \ + if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks45_align4(__VA_ARGS__); \ } \ else if (dim % 2 == 0) { \ - natten::cuda::gemm::na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks21_align2(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks45_align2(__VA_ARGS__); \ + } \ + else if (dim % 1 == 0) { \ + natten::cuda::gemm::na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks45_align1(__VA_ARGS__); \ } \ else { \ std::cerr << "NATTEN kernel launch failed!" \ - << "na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks21 requires at least 32-bit alignment." \ - << "Got dim=" << dim << ", dtype=half. " \ + << "na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks45 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=float. " \ << std::endl; \ exit(EXIT_FAILURE); \ } \ }(); -#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks23(dim, ...) \ +#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks47(dim, ...) \ [&] { \ - if (dim % 8 == 0) { \ - natten::cuda::gemm::na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks23_align8(__VA_ARGS__); \ - } \ - else if (dim % 4 == 0) { \ - natten::cuda::gemm::na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks23_align4(__VA_ARGS__); \ + if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks47_align4(__VA_ARGS__); \ } \ else if (dim % 2 == 0) { \ - natten::cuda::gemm::na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks23_align2(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks47_align2(__VA_ARGS__); \ + } \ + else if (dim % 1 == 0) { \ + natten::cuda::gemm::na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks47_align1(__VA_ARGS__); \ } \ else { \ std::cerr << "NATTEN kernel launch failed!" \ - << "na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks23 requires at least 32-bit alignment." \ - << "Got dim=" << dim << ", dtype=half. " \ + << "na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks47 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=float. " \ << std::endl; \ exit(EXIT_FAILURE); \ } \ }(); -#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks25(dim, ...) \ +#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks49(dim, ...) \ [&] { \ - if (dim % 8 == 0) { \ - natten::cuda::gemm::na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks25_align8(__VA_ARGS__); \ - } \ - else if (dim % 4 == 0) { \ - natten::cuda::gemm::na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks25_align4(__VA_ARGS__); \ + if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks49_align4(__VA_ARGS__); \ } \ else if (dim % 2 == 0) { \ - natten::cuda::gemm::na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks25_align2(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks49_align2(__VA_ARGS__); \ + } \ + else if (dim % 1 == 0) { \ + natten::cuda::gemm::na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks49_align1(__VA_ARGS__); \ } \ else { \ std::cerr << "NATTEN kernel launch failed!" \ - << "na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks25 requires at least 32-bit alignment." \ - << "Got dim=" << dim << ", dtype=half. " \ + << "na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks49 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=float. " \ << std::endl; \ exit(EXIT_FAILURE); \ } \ }(); -#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks27(dim, ...) \ +#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks51(dim, ...) \ [&] { \ - if (dim % 8 == 0) { \ - natten::cuda::gemm::na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks27_align8(__VA_ARGS__); \ - } \ - else if (dim % 4 == 0) { \ - natten::cuda::gemm::na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks27_align4(__VA_ARGS__); \ + if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks51_align4(__VA_ARGS__); \ } \ else if (dim % 2 == 0) { \ - natten::cuda::gemm::na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks27_align2(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks51_align2(__VA_ARGS__); \ + } \ + else if (dim % 1 == 0) { \ + natten::cuda::gemm::na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks51_align1(__VA_ARGS__); \ } \ else { \ std::cerr << "NATTEN kernel launch failed!" \ - << "na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks27 requires at least 32-bit alignment." \ - << "Got dim=" << dim << ", dtype=half. " \ + << "na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks51 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=float. " \ << std::endl; \ exit(EXIT_FAILURE); \ } \ }(); -#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks29(dim, ...) \ +#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks53(dim, ...) \ [&] { \ - if (dim % 8 == 0) { \ - natten::cuda::gemm::na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks29_align8(__VA_ARGS__); \ - } \ - else if (dim % 4 == 0) { \ - natten::cuda::gemm::na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks29_align4(__VA_ARGS__); \ + if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks53_align4(__VA_ARGS__); \ } \ else if (dim % 2 == 0) { \ - natten::cuda::gemm::na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks29_align2(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks53_align2(__VA_ARGS__); \ + } \ + else if (dim % 1 == 0) { \ + natten::cuda::gemm::na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks53_align1(__VA_ARGS__); \ } \ else { \ std::cerr << "NATTEN kernel launch failed!" \ - << "na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks29 requires at least 32-bit alignment." \ - << "Got dim=" << dim << ", dtype=half. " \ + << "na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks53 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=float. " \ << std::endl; \ exit(EXIT_FAILURE); \ } \ }(); -#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks31(dim, ...) \ +#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks55(dim, ...) \ [&] { \ - if (dim % 8 == 0) { \ - natten::cuda::gemm::na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks31_align8(__VA_ARGS__); \ - } \ - else if (dim % 4 == 0) { \ - natten::cuda::gemm::na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks31_align4(__VA_ARGS__); \ + if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks55_align4(__VA_ARGS__); \ } \ else if (dim % 2 == 0) { \ - natten::cuda::gemm::na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks31_align2(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks55_align2(__VA_ARGS__); \ + } \ + else if (dim % 1 == 0) { \ + natten::cuda::gemm::na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks55_align1(__VA_ARGS__); \ } \ else { \ std::cerr << "NATTEN kernel launch failed!" \ - << "na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks31 requires at least 32-bit alignment." \ - << "Got dim=" << dim << ", dtype=half. " \ + << "na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks55 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=float. " \ << std::endl; \ exit(EXIT_FAILURE); \ } \ }(); -#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks33(dim, ...) \ +#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks57(dim, ...) \ [&] { \ - if (dim % 8 == 0) { \ - natten::cuda::gemm::na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks33_align8(__VA_ARGS__); \ - } \ - else if (dim % 4 == 0) { \ - natten::cuda::gemm::na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks33_align4(__VA_ARGS__); \ + if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks57_align4(__VA_ARGS__); \ } \ else if (dim % 2 == 0) { \ - natten::cuda::gemm::na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks33_align2(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks57_align2(__VA_ARGS__); \ + } \ + else if (dim % 1 == 0) { \ + natten::cuda::gemm::na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks57_align1(__VA_ARGS__); \ } \ else { \ std::cerr << "NATTEN kernel launch failed!" \ - << "na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks33 requires at least 32-bit alignment." \ - << "Got dim=" << dim << ", dtype=half. " \ + << "na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks57 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=float. " \ << std::endl; \ exit(EXIT_FAILURE); \ } \ }(); -#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks3(dim, ...) \ +#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks59(dim, ...) \ [&] { \ - if (dim % 8 == 0) { \ - natten::cuda::gemm::na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks3_align8(__VA_ARGS__); \ - } \ - else if (dim % 4 == 0) { \ - natten::cuda::gemm::na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks3_align4(__VA_ARGS__); \ + if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks59_align4(__VA_ARGS__); \ } \ else if (dim % 2 == 0) { \ - natten::cuda::gemm::na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks3_align2(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks59_align2(__VA_ARGS__); \ + } \ + else if (dim % 1 == 0) { \ + natten::cuda::gemm::na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks59_align1(__VA_ARGS__); \ } \ else { \ std::cerr << "NATTEN kernel launch failed!" \ - << "na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks3 requires at least 32-bit alignment." \ - << "Got dim=" << dim << ", dtype=bfloat16. " \ + << "na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks59 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=float. " \ << std::endl; \ exit(EXIT_FAILURE); \ } \ }(); - -#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks5(dim, ...) \ - [&] { \ - if (dim % 8 == 0) { \ - natten::cuda::gemm::na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks5_align8(__VA_ARGS__); \ - } \ - else if (dim % 4 == 0) { \ - natten::cuda::gemm::na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks5_align4(__VA_ARGS__); \ + +#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks61(dim, ...) \ + [&] { \ + if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks61_align4(__VA_ARGS__); \ } \ else if (dim % 2 == 0) { \ - natten::cuda::gemm::na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks5_align2(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks61_align2(__VA_ARGS__); \ + } \ + else if (dim % 1 == 0) { \ + natten::cuda::gemm::na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks61_align1(__VA_ARGS__); \ } \ else { \ std::cerr << "NATTEN kernel launch failed!" \ - << "na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks5 requires at least 32-bit alignment." \ - << "Got dim=" << dim << ", dtype=bfloat16. " \ + << "na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks61 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=float. " \ << std::endl; \ exit(EXIT_FAILURE); \ } \ }(); -#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks7(dim, ...) \ +#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks63(dim, ...) \ [&] { \ - if (dim % 8 == 0) { \ - natten::cuda::gemm::na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks7_align8(__VA_ARGS__); \ - } \ - else if (dim % 4 == 0) { \ - natten::cuda::gemm::na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks7_align4(__VA_ARGS__); \ + if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks63_align4(__VA_ARGS__); \ } \ else if (dim % 2 == 0) { \ - natten::cuda::gemm::na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks7_align2(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks63_align2(__VA_ARGS__); \ + } \ + else if (dim % 1 == 0) { \ + natten::cuda::gemm::na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks63_align1(__VA_ARGS__); \ } \ else { \ std::cerr << "NATTEN kernel launch failed!" \ - << "na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks7 requires at least 32-bit alignment." \ - << "Got dim=" << dim << ", dtype=bfloat16. " \ + << "na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks63 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=float. " \ << std::endl; \ exit(EXIT_FAILURE); \ } \ }(); -#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks9(dim, ...) \ +#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks3(dim, ...) \ [&] { \ if (dim % 8 == 0) { \ - natten::cuda::gemm::na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks9_align8(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks3_align8(__VA_ARGS__); \ } \ else if (dim % 4 == 0) { \ - natten::cuda::gemm::na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks9_align4(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks3_align4(__VA_ARGS__); \ } \ else if (dim % 2 == 0) { \ - natten::cuda::gemm::na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks9_align2(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks3_align2(__VA_ARGS__); \ } \ else { \ std::cerr << "NATTEN kernel launch failed!" \ - << "na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks9 requires at least 32-bit alignment." \ - << "Got dim=" << dim << ", dtype=bfloat16. " \ + << "na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks3 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=half. " \ << std::endl; \ exit(EXIT_FAILURE); \ } \ }(); -#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks11(dim, ...) \ +#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks5(dim, ...) \ [&] { \ if (dim % 8 == 0) { \ - natten::cuda::gemm::na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks11_align8(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks5_align8(__VA_ARGS__); \ } \ else if (dim % 4 == 0) { \ - natten::cuda::gemm::na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks11_align4(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks5_align4(__VA_ARGS__); \ } \ else if (dim % 2 == 0) { \ - natten::cuda::gemm::na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks11_align2(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks5_align2(__VA_ARGS__); \ } \ else { \ std::cerr << "NATTEN kernel launch failed!" \ - << "na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks11 requires at least 32-bit alignment." \ - << "Got dim=" << dim << ", dtype=bfloat16. " \ + << "na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks5 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=half. " \ << std::endl; \ exit(EXIT_FAILURE); \ } \ }(); -#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks13(dim, ...) \ +#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks7(dim, ...) \ [&] { \ if (dim % 8 == 0) { \ - natten::cuda::gemm::na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks13_align8(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks7_align8(__VA_ARGS__); \ } \ else if (dim % 4 == 0) { \ - natten::cuda::gemm::na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks13_align4(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks7_align4(__VA_ARGS__); \ } \ else if (dim % 2 == 0) { \ - natten::cuda::gemm::na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks13_align2(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks7_align2(__VA_ARGS__); \ } \ else { \ std::cerr << "NATTEN kernel launch failed!" \ - << "na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks13 requires at least 32-bit alignment." \ - << "Got dim=" << dim << ", dtype=bfloat16. " \ + << "na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks7 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=half. " \ << std::endl; \ exit(EXIT_FAILURE); \ } \ }(); -#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks15(dim, ...) \ +#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks9(dim, ...) \ [&] { \ if (dim % 8 == 0) { \ - natten::cuda::gemm::na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks15_align8(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks9_align8(__VA_ARGS__); \ } \ else if (dim % 4 == 0) { \ - natten::cuda::gemm::na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks15_align4(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks9_align4(__VA_ARGS__); \ } \ else if (dim % 2 == 0) { \ - natten::cuda::gemm::na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks15_align2(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks9_align2(__VA_ARGS__); \ } \ else { \ std::cerr << "NATTEN kernel launch failed!" \ - << "na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks15 requires at least 32-bit alignment." \ - << "Got dim=" << dim << ", dtype=bfloat16. " \ + << "na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks9 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=half. " \ << std::endl; \ exit(EXIT_FAILURE); \ } \ }(); -#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks17(dim, ...) \ +#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks11(dim, ...) \ [&] { \ if (dim % 8 == 0) { \ - natten::cuda::gemm::na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks17_align8(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks11_align8(__VA_ARGS__); \ } \ else if (dim % 4 == 0) { \ - natten::cuda::gemm::na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks17_align4(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks11_align4(__VA_ARGS__); \ } \ else if (dim % 2 == 0) { \ - natten::cuda::gemm::na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks17_align2(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks11_align2(__VA_ARGS__); \ } \ else { \ std::cerr << "NATTEN kernel launch failed!" \ - << "na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks17 requires at least 32-bit alignment." \ - << "Got dim=" << dim << ", dtype=bfloat16. " \ + << "na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks11 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=half. " \ << std::endl; \ exit(EXIT_FAILURE); \ } \ }(); -#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks19(dim, ...) \ +#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks13(dim, ...) \ [&] { \ if (dim % 8 == 0) { \ - natten::cuda::gemm::na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks19_align8(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks13_align8(__VA_ARGS__); \ } \ else if (dim % 4 == 0) { \ - natten::cuda::gemm::na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks19_align4(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks13_align4(__VA_ARGS__); \ } \ else if (dim % 2 == 0) { \ - natten::cuda::gemm::na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks19_align2(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks13_align2(__VA_ARGS__); \ } \ else { \ std::cerr << "NATTEN kernel launch failed!" \ - << "na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks19 requires at least 32-bit alignment." \ - << "Got dim=" << dim << ", dtype=bfloat16. " \ + << "na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks13 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=half. " \ << std::endl; \ exit(EXIT_FAILURE); \ } \ }(); -#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks21(dim, ...) \ +#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks15(dim, ...) \ [&] { \ if (dim % 8 == 0) { \ - natten::cuda::gemm::na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks21_align8(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks15_align8(__VA_ARGS__); \ } \ else if (dim % 4 == 0) { \ - natten::cuda::gemm::na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks21_align4(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks15_align4(__VA_ARGS__); \ } \ else if (dim % 2 == 0) { \ - natten::cuda::gemm::na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks21_align2(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks15_align2(__VA_ARGS__); \ } \ else { \ std::cerr << "NATTEN kernel launch failed!" \ - << "na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks21 requires at least 32-bit alignment." \ - << "Got dim=" << dim << ", dtype=bfloat16. " \ + << "na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks15 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=half. " \ << std::endl; \ exit(EXIT_FAILURE); \ } \ }(); -#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks23(dim, ...) \ +#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks17(dim, ...) \ [&] { \ if (dim % 8 == 0) { \ - natten::cuda::gemm::na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks23_align8(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks17_align8(__VA_ARGS__); \ } \ else if (dim % 4 == 0) { \ - natten::cuda::gemm::na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks23_align4(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks17_align4(__VA_ARGS__); \ } \ else if (dim % 2 == 0) { \ - natten::cuda::gemm::na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks23_align2(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks17_align2(__VA_ARGS__); \ } \ else { \ std::cerr << "NATTEN kernel launch failed!" \ - << "na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks23 requires at least 32-bit alignment." \ - << "Got dim=" << dim << ", dtype=bfloat16. " \ + << "na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks17 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=half. " \ << std::endl; \ exit(EXIT_FAILURE); \ } \ }(); -#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks25(dim, ...) \ +#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks19(dim, ...) \ [&] { \ if (dim % 8 == 0) { \ - natten::cuda::gemm::na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks25_align8(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks19_align8(__VA_ARGS__); \ } \ else if (dim % 4 == 0) { \ - natten::cuda::gemm::na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks25_align4(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks19_align4(__VA_ARGS__); \ } \ else if (dim % 2 == 0) { \ - natten::cuda::gemm::na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks25_align2(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks19_align2(__VA_ARGS__); \ } \ else { \ std::cerr << "NATTEN kernel launch failed!" \ - << "na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks25 requires at least 32-bit alignment." \ - << "Got dim=" << dim << ", dtype=bfloat16. " \ + << "na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks19 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=half. " \ << std::endl; \ exit(EXIT_FAILURE); \ } \ }(); -#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks27(dim, ...) \ +#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks21(dim, ...) \ [&] { \ if (dim % 8 == 0) { \ - natten::cuda::gemm::na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks27_align8(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks21_align8(__VA_ARGS__); \ } \ else if (dim % 4 == 0) { \ - natten::cuda::gemm::na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks27_align4(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks21_align4(__VA_ARGS__); \ } \ else if (dim % 2 == 0) { \ - natten::cuda::gemm::na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks27_align2(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks21_align2(__VA_ARGS__); \ } \ else { \ std::cerr << "NATTEN kernel launch failed!" \ - << "na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks27 requires at least 32-bit alignment." \ - << "Got dim=" << dim << ", dtype=bfloat16. " \ + << "na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks21 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=half. " \ << std::endl; \ exit(EXIT_FAILURE); \ } \ }(); -#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks29(dim, ...) \ +#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks23(dim, ...) \ [&] { \ if (dim % 8 == 0) { \ - natten::cuda::gemm::na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks29_align8(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks23_align8(__VA_ARGS__); \ } \ else if (dim % 4 == 0) { \ - natten::cuda::gemm::na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks29_align4(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks23_align4(__VA_ARGS__); \ } \ else if (dim % 2 == 0) { \ - natten::cuda::gemm::na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks29_align2(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks23_align2(__VA_ARGS__); \ } \ else { \ std::cerr << "NATTEN kernel launch failed!" \ - << "na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks29 requires at least 32-bit alignment." \ - << "Got dim=" << dim << ", dtype=bfloat16. " \ + << "na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks23 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=half. " \ << std::endl; \ exit(EXIT_FAILURE); \ } \ }(); -#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks31(dim, ...) \ +#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks25(dim, ...) \ [&] { \ if (dim % 8 == 0) { \ - natten::cuda::gemm::na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks31_align8(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks25_align8(__VA_ARGS__); \ } \ else if (dim % 4 == 0) { \ - natten::cuda::gemm::na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks31_align4(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks25_align4(__VA_ARGS__); \ } \ else if (dim % 2 == 0) { \ - natten::cuda::gemm::na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks31_align2(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks25_align2(__VA_ARGS__); \ } \ else { \ std::cerr << "NATTEN kernel launch failed!" \ - << "na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks31 requires at least 32-bit alignment." \ - << "Got dim=" << dim << ", dtype=bfloat16. " \ + << "na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks25 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=half. " \ << std::endl; \ exit(EXIT_FAILURE); \ } \ }(); -#define DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks33(dim, ...) \ +#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks27(dim, ...) \ [&] { \ if (dim % 8 == 0) { \ - natten::cuda::gemm::na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks33_align8(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks27_align8(__VA_ARGS__); \ } \ else if (dim % 4 == 0) { \ - natten::cuda::gemm::na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks33_align4(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks27_align4(__VA_ARGS__); \ } \ else if (dim % 2 == 0) { \ - natten::cuda::gemm::na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks33_align2(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks27_align2(__VA_ARGS__); \ } \ else { \ std::cerr << "NATTEN kernel launch failed!" \ - << "na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks33 requires at least 32-bit alignment." \ - << "Got dim=" << dim << ", dtype=bfloat16. " \ - << std::endl; \ - exit(EXIT_FAILURE); \ - } \ -}(); - -#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks3(dim, ...) \ - [&] { \ - natten::cuda::gemm::na2d_in_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks3_align1(__VA_ARGS__); \ -}(); - -#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks5(dim, ...) \ - [&] { \ - natten::cuda::gemm::na2d_in_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks5_align1(__VA_ARGS__); \ -}(); - -#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks7(dim, ...) \ - [&] { \ - natten::cuda::gemm::na2d_in_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks7_align1(__VA_ARGS__); \ -}(); - -#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks9(dim, ...) \ - [&] { \ - natten::cuda::gemm::na2d_in_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks9_align1(__VA_ARGS__); \ -}(); - -#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks11(dim, ...) \ - [&] { \ - natten::cuda::gemm::na2d_in_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks11_align1(__VA_ARGS__); \ -}(); - -#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks13(dim, ...) \ - [&] { \ - natten::cuda::gemm::na2d_in_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks13_align1(__VA_ARGS__); \ -}(); - -#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks15(dim, ...) \ - [&] { \ - natten::cuda::gemm::na2d_in_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks15_align1(__VA_ARGS__); \ -}(); - -#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks17(dim, ...) \ - [&] { \ - natten::cuda::gemm::na2d_in_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks17_align1(__VA_ARGS__); \ -}(); - -#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks19(dim, ...) \ - [&] { \ - natten::cuda::gemm::na2d_in_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks19_align1(__VA_ARGS__); \ -}(); - -#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks21(dim, ...) \ - [&] { \ - natten::cuda::gemm::na2d_in_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks21_align1(__VA_ARGS__); \ -}(); - -#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks23(dim, ...) \ - [&] { \ - natten::cuda::gemm::na2d_in_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks23_align1(__VA_ARGS__); \ -}(); - -#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks25(dim, ...) \ - [&] { \ - natten::cuda::gemm::na2d_in_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks25_align1(__VA_ARGS__); \ -}(); - -#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks27(dim, ...) \ - [&] { \ - natten::cuda::gemm::na2d_in_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks27_align1(__VA_ARGS__); \ -}(); - -#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks29(dim, ...) \ - [&] { \ - natten::cuda::gemm::na2d_in_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks29_align1(__VA_ARGS__); \ -}(); - -#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks31(dim, ...) \ - [&] { \ - natten::cuda::gemm::na2d_in_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks31_align1(__VA_ARGS__); \ + << "na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks27 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=half. " \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ }(); -#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks33(dim, ...) \ +#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks29(dim, ...) \ [&] { \ - natten::cuda::gemm::na2d_in_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks33_align1(__VA_ARGS__); \ + if (dim % 8 == 0) { \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks29_align8(__VA_ARGS__); \ + } \ + else if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks29_align4(__VA_ARGS__); \ + } \ + else if (dim % 2 == 0) { \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks29_align2(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks29 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=half. " \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ }(); -#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks3(dim, ...) \ +#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks31(dim, ...) \ [&] { \ - if (dim % 4 == 0) { \ - natten::cuda::gemm::na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks3_align4(__VA_ARGS__); \ + if (dim % 8 == 0) { \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks31_align8(__VA_ARGS__); \ } \ - else if (dim % 2 == 0) { \ - natten::cuda::gemm::na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks3_align2(__VA_ARGS__); \ + else if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks31_align4(__VA_ARGS__); \ } \ - else if (dim % 1 == 0) { \ - natten::cuda::gemm::na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks3_align1(__VA_ARGS__); \ + else if (dim % 2 == 0) { \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks31_align2(__VA_ARGS__); \ } \ else { \ std::cerr << "NATTEN kernel launch failed!" \ - << "na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks3 requires at least 32-bit alignment." \ - << "Got dim=" << dim << ", dtype=float. " \ + << "na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks31 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=half. " \ << std::endl; \ exit(EXIT_FAILURE); \ } \ }(); -#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks5(dim, ...) \ +#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks33(dim, ...) \ [&] { \ - if (dim % 4 == 0) { \ - natten::cuda::gemm::na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks5_align4(__VA_ARGS__); \ + if (dim % 8 == 0) { \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks33_align8(__VA_ARGS__); \ } \ - else if (dim % 2 == 0) { \ - natten::cuda::gemm::na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks5_align2(__VA_ARGS__); \ + else if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks33_align4(__VA_ARGS__); \ } \ - else if (dim % 1 == 0) { \ - natten::cuda::gemm::na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks5_align1(__VA_ARGS__); \ + else if (dim % 2 == 0) { \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks33_align2(__VA_ARGS__); \ } \ else { \ std::cerr << "NATTEN kernel launch failed!" \ - << "na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks5 requires at least 32-bit alignment." \ - << "Got dim=" << dim << ", dtype=float. " \ + << "na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks33 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=half. " \ << std::endl; \ exit(EXIT_FAILURE); \ } \ }(); -#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks7(dim, ...) \ +#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks35(dim, ...) \ [&] { \ - if (dim % 4 == 0) { \ - natten::cuda::gemm::na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks7_align4(__VA_ARGS__); \ + if (dim % 8 == 0) { \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks35_align8(__VA_ARGS__); \ } \ - else if (dim % 2 == 0) { \ - natten::cuda::gemm::na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks7_align2(__VA_ARGS__); \ + else if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks35_align4(__VA_ARGS__); \ } \ - else if (dim % 1 == 0) { \ - natten::cuda::gemm::na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks7_align1(__VA_ARGS__); \ + else if (dim % 2 == 0) { \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks35_align2(__VA_ARGS__); \ } \ else { \ std::cerr << "NATTEN kernel launch failed!" \ - << "na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks7 requires at least 32-bit alignment." \ - << "Got dim=" << dim << ", dtype=float. " \ + << "na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks35 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=half. " \ << std::endl; \ exit(EXIT_FAILURE); \ } \ }(); -#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks9(dim, ...) \ +#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks37(dim, ...) \ [&] { \ - if (dim % 4 == 0) { \ - natten::cuda::gemm::na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks9_align4(__VA_ARGS__); \ + if (dim % 8 == 0) { \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks37_align8(__VA_ARGS__); \ } \ - else if (dim % 2 == 0) { \ - natten::cuda::gemm::na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks9_align2(__VA_ARGS__); \ + else if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks37_align4(__VA_ARGS__); \ } \ - else if (dim % 1 == 0) { \ - natten::cuda::gemm::na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks9_align1(__VA_ARGS__); \ + else if (dim % 2 == 0) { \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks37_align2(__VA_ARGS__); \ } \ else { \ std::cerr << "NATTEN kernel launch failed!" \ - << "na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks9 requires at least 32-bit alignment." \ - << "Got dim=" << dim << ", dtype=float. " \ + << "na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks37 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=half. " \ << std::endl; \ exit(EXIT_FAILURE); \ } \ }(); -#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks11(dim, ...) \ +#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks39(dim, ...) \ [&] { \ - if (dim % 4 == 0) { \ - natten::cuda::gemm::na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks11_align4(__VA_ARGS__); \ + if (dim % 8 == 0) { \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks39_align8(__VA_ARGS__); \ } \ - else if (dim % 2 == 0) { \ - natten::cuda::gemm::na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks11_align2(__VA_ARGS__); \ + else if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks39_align4(__VA_ARGS__); \ } \ - else if (dim % 1 == 0) { \ - natten::cuda::gemm::na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks11_align1(__VA_ARGS__); \ + else if (dim % 2 == 0) { \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks39_align2(__VA_ARGS__); \ } \ else { \ std::cerr << "NATTEN kernel launch failed!" \ - << "na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks11 requires at least 32-bit alignment." \ - << "Got dim=" << dim << ", dtype=float. " \ + << "na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks39 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=half. " \ << std::endl; \ exit(EXIT_FAILURE); \ } \ }(); -#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks13(dim, ...) \ +#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks41(dim, ...) \ [&] { \ - if (dim % 4 == 0) { \ - natten::cuda::gemm::na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks13_align4(__VA_ARGS__); \ + if (dim % 8 == 0) { \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks41_align8(__VA_ARGS__); \ } \ - else if (dim % 2 == 0) { \ - natten::cuda::gemm::na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks13_align2(__VA_ARGS__); \ + else if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks41_align4(__VA_ARGS__); \ } \ - else if (dim % 1 == 0) { \ - natten::cuda::gemm::na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks13_align1(__VA_ARGS__); \ + else if (dim % 2 == 0) { \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks41_align2(__VA_ARGS__); \ } \ else { \ std::cerr << "NATTEN kernel launch failed!" \ - << "na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks13 requires at least 32-bit alignment." \ - << "Got dim=" << dim << ", dtype=float. " \ + << "na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks41 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=half. " \ << std::endl; \ exit(EXIT_FAILURE); \ } \ }(); -#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks15(dim, ...) \ +#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks43(dim, ...) \ [&] { \ - if (dim % 4 == 0) { \ - natten::cuda::gemm::na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks15_align4(__VA_ARGS__); \ + if (dim % 8 == 0) { \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks43_align8(__VA_ARGS__); \ } \ - else if (dim % 2 == 0) { \ - natten::cuda::gemm::na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks15_align2(__VA_ARGS__); \ + else if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks43_align4(__VA_ARGS__); \ } \ - else if (dim % 1 == 0) { \ - natten::cuda::gemm::na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks15_align1(__VA_ARGS__); \ + else if (dim % 2 == 0) { \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks43_align2(__VA_ARGS__); \ } \ else { \ std::cerr << "NATTEN kernel launch failed!" \ - << "na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks15 requires at least 32-bit alignment." \ - << "Got dim=" << dim << ", dtype=float. " \ + << "na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks43 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=half. " \ << std::endl; \ exit(EXIT_FAILURE); \ } \ }(); -#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks17(dim, ...) \ +#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks45(dim, ...) \ [&] { \ - if (dim % 4 == 0) { \ - natten::cuda::gemm::na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks17_align4(__VA_ARGS__); \ + if (dim % 8 == 0) { \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks45_align8(__VA_ARGS__); \ } \ - else if (dim % 2 == 0) { \ - natten::cuda::gemm::na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks17_align2(__VA_ARGS__); \ + else if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks45_align4(__VA_ARGS__); \ } \ - else if (dim % 1 == 0) { \ - natten::cuda::gemm::na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks17_align1(__VA_ARGS__); \ + else if (dim % 2 == 0) { \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks45_align2(__VA_ARGS__); \ } \ else { \ std::cerr << "NATTEN kernel launch failed!" \ - << "na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks17 requires at least 32-bit alignment." \ - << "Got dim=" << dim << ", dtype=float. " \ + << "na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks45 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=half. " \ << std::endl; \ exit(EXIT_FAILURE); \ } \ }(); -#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks19(dim, ...) \ +#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks47(dim, ...) \ [&] { \ - if (dim % 4 == 0) { \ - natten::cuda::gemm::na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks19_align4(__VA_ARGS__); \ + if (dim % 8 == 0) { \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks47_align8(__VA_ARGS__); \ } \ - else if (dim % 2 == 0) { \ - natten::cuda::gemm::na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks19_align2(__VA_ARGS__); \ + else if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks47_align4(__VA_ARGS__); \ } \ - else if (dim % 1 == 0) { \ - natten::cuda::gemm::na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks19_align1(__VA_ARGS__); \ + else if (dim % 2 == 0) { \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks47_align2(__VA_ARGS__); \ } \ else { \ std::cerr << "NATTEN kernel launch failed!" \ - << "na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks19 requires at least 32-bit alignment." \ - << "Got dim=" << dim << ", dtype=float. " \ + << "na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks47 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=half. " \ << std::endl; \ exit(EXIT_FAILURE); \ } \ }(); -#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks21(dim, ...) \ +#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks49(dim, ...) \ [&] { \ - if (dim % 4 == 0) { \ - natten::cuda::gemm::na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks21_align4(__VA_ARGS__); \ + if (dim % 8 == 0) { \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks49_align8(__VA_ARGS__); \ } \ - else if (dim % 2 == 0) { \ - natten::cuda::gemm::na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks21_align2(__VA_ARGS__); \ + else if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks49_align4(__VA_ARGS__); \ } \ - else if (dim % 1 == 0) { \ - natten::cuda::gemm::na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks21_align1(__VA_ARGS__); \ + else if (dim % 2 == 0) { \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks49_align2(__VA_ARGS__); \ } \ else { \ std::cerr << "NATTEN kernel launch failed!" \ - << "na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks21 requires at least 32-bit alignment." \ - << "Got dim=" << dim << ", dtype=float. " \ + << "na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks49 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=half. " \ << std::endl; \ exit(EXIT_FAILURE); \ } \ }(); -#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks23(dim, ...) \ +#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks51(dim, ...) \ [&] { \ - if (dim % 4 == 0) { \ - natten::cuda::gemm::na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks23_align4(__VA_ARGS__); \ + if (dim % 8 == 0) { \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks51_align8(__VA_ARGS__); \ } \ - else if (dim % 2 == 0) { \ - natten::cuda::gemm::na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks23_align2(__VA_ARGS__); \ + else if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks51_align4(__VA_ARGS__); \ } \ - else if (dim % 1 == 0) { \ - natten::cuda::gemm::na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks23_align1(__VA_ARGS__); \ + else if (dim % 2 == 0) { \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks51_align2(__VA_ARGS__); \ } \ else { \ std::cerr << "NATTEN kernel launch failed!" \ - << "na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks23 requires at least 32-bit alignment." \ - << "Got dim=" << dim << ", dtype=float. " \ + << "na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks51 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=half. " \ << std::endl; \ exit(EXIT_FAILURE); \ } \ }(); -#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks25(dim, ...) \ +#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks53(dim, ...) \ [&] { \ - if (dim % 4 == 0) { \ - natten::cuda::gemm::na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks25_align4(__VA_ARGS__); \ + if (dim % 8 == 0) { \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks53_align8(__VA_ARGS__); \ } \ - else if (dim % 2 == 0) { \ - natten::cuda::gemm::na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks25_align2(__VA_ARGS__); \ + else if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks53_align4(__VA_ARGS__); \ } \ - else if (dim % 1 == 0) { \ - natten::cuda::gemm::na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks25_align1(__VA_ARGS__); \ + else if (dim % 2 == 0) { \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks53_align2(__VA_ARGS__); \ } \ else { \ std::cerr << "NATTEN kernel launch failed!" \ - << "na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks25 requires at least 32-bit alignment." \ - << "Got dim=" << dim << ", dtype=float. " \ + << "na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks53 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=half. " \ << std::endl; \ exit(EXIT_FAILURE); \ } \ }(); -#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks27(dim, ...) \ +#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks55(dim, ...) \ [&] { \ - if (dim % 4 == 0) { \ - natten::cuda::gemm::na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks27_align4(__VA_ARGS__); \ + if (dim % 8 == 0) { \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks55_align8(__VA_ARGS__); \ } \ - else if (dim % 2 == 0) { \ - natten::cuda::gemm::na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks27_align2(__VA_ARGS__); \ + else if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks55_align4(__VA_ARGS__); \ } \ - else if (dim % 1 == 0) { \ - natten::cuda::gemm::na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks27_align1(__VA_ARGS__); \ + else if (dim % 2 == 0) { \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks55_align2(__VA_ARGS__); \ } \ else { \ std::cerr << "NATTEN kernel launch failed!" \ - << "na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks27 requires at least 32-bit alignment." \ - << "Got dim=" << dim << ", dtype=float. " \ + << "na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks55 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=half. " \ << std::endl; \ exit(EXIT_FAILURE); \ } \ }(); -#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks29(dim, ...) \ +#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks57(dim, ...) \ [&] { \ - if (dim % 4 == 0) { \ - natten::cuda::gemm::na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks29_align4(__VA_ARGS__); \ + if (dim % 8 == 0) { \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks57_align8(__VA_ARGS__); \ } \ - else if (dim % 2 == 0) { \ - natten::cuda::gemm::na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks29_align2(__VA_ARGS__); \ + else if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks57_align4(__VA_ARGS__); \ } \ - else if (dim % 1 == 0) { \ - natten::cuda::gemm::na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks29_align1(__VA_ARGS__); \ + else if (dim % 2 == 0) { \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks57_align2(__VA_ARGS__); \ } \ else { \ std::cerr << "NATTEN kernel launch failed!" \ - << "na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks29 requires at least 32-bit alignment." \ - << "Got dim=" << dim << ", dtype=float. " \ + << "na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks57 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=half. " \ << std::endl; \ exit(EXIT_FAILURE); \ } \ }(); -#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks31(dim, ...) \ +#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks59(dim, ...) \ [&] { \ - if (dim % 4 == 0) { \ - natten::cuda::gemm::na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks31_align4(__VA_ARGS__); \ + if (dim % 8 == 0) { \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks59_align8(__VA_ARGS__); \ } \ - else if (dim % 2 == 0) { \ - natten::cuda::gemm::na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks31_align2(__VA_ARGS__); \ + else if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks59_align4(__VA_ARGS__); \ } \ - else if (dim % 1 == 0) { \ - natten::cuda::gemm::na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks31_align1(__VA_ARGS__); \ + else if (dim % 2 == 0) { \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks59_align2(__VA_ARGS__); \ } \ else { \ std::cerr << "NATTEN kernel launch failed!" \ - << "na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks31 requires at least 32-bit alignment." \ - << "Got dim=" << dim << ", dtype=float. " \ + << "na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks59 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=half. " \ << std::endl; \ exit(EXIT_FAILURE); \ } \ }(); -#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks33(dim, ...) \ +#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks61(dim, ...) \ [&] { \ - if (dim % 4 == 0) { \ - natten::cuda::gemm::na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks33_align4(__VA_ARGS__); \ + if (dim % 8 == 0) { \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks61_align8(__VA_ARGS__); \ } \ - else if (dim % 2 == 0) { \ - natten::cuda::gemm::na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks33_align2(__VA_ARGS__); \ + else if (dim % 4 == 0) { \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks61_align4(__VA_ARGS__); \ } \ - else if (dim % 1 == 0) { \ - natten::cuda::gemm::na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks33_align1(__VA_ARGS__); \ + else if (dim % 2 == 0) { \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks61_align2(__VA_ARGS__); \ } \ else { \ std::cerr << "NATTEN kernel launch failed!" \ - << "na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks33 requires at least 32-bit alignment." \ - << "Got dim=" << dim << ", dtype=float. " \ + << "na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks61 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=half. " \ << std::endl; \ exit(EXIT_FAILURE); \ } \ }(); -#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks3(dim, ...) \ +#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks63(dim, ...) \ [&] { \ if (dim % 8 == 0) { \ - natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks3_align8(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks63_align8(__VA_ARGS__); \ } \ else if (dim % 4 == 0) { \ - natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks3_align4(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks63_align4(__VA_ARGS__); \ } \ else if (dim % 2 == 0) { \ - natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks3_align2(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks63_align2(__VA_ARGS__); \ } \ else { \ std::cerr << "NATTEN kernel launch failed!" \ - << "na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks3 requires at least 32-bit alignment." \ + << "na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks63 requires at least 32-bit alignment." \ << "Got dim=" << dim << ", dtype=half. " \ << std::endl; \ exit(EXIT_FAILURE); \ } \ }(); -#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks5(dim, ...) \ +#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks3(dim, ...) \ [&] { \ if (dim % 8 == 0) { \ - natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks5_align8(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks3_align8(__VA_ARGS__); \ } \ else if (dim % 4 == 0) { \ - natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks5_align4(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks3_align4(__VA_ARGS__); \ } \ else if (dim % 2 == 0) { \ - natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks5_align2(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks3_align2(__VA_ARGS__); \ } \ else { \ - std::cerr << "NATTEN kernel launch failed!" \ - << "na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks5 requires at least 32-bit alignment." \ - << "Got dim=" << dim << ", dtype=half. " \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks3 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=bfloat16. " \ << std::endl; \ exit(EXIT_FAILURE); \ } \ }(); -#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks7(dim, ...) \ +#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks5(dim, ...) \ [&] { \ if (dim % 8 == 0) { \ - natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks7_align8(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks5_align8(__VA_ARGS__); \ } \ else if (dim % 4 == 0) { \ - natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks7_align4(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks5_align4(__VA_ARGS__); \ } \ else if (dim % 2 == 0) { \ - natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks7_align2(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks5_align2(__VA_ARGS__); \ } \ else { \ std::cerr << "NATTEN kernel launch failed!" \ - << "na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks7 requires at least 32-bit alignment." \ - << "Got dim=" << dim << ", dtype=half. " \ + << "na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks5 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=bfloat16. " \ << std::endl; \ exit(EXIT_FAILURE); \ } \ }(); -#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks9(dim, ...) \ +#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks7(dim, ...) \ [&] { \ if (dim % 8 == 0) { \ - natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks9_align8(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks7_align8(__VA_ARGS__); \ } \ else if (dim % 4 == 0) { \ - natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks9_align4(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks7_align4(__VA_ARGS__); \ } \ else if (dim % 2 == 0) { \ - natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks9_align2(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks7_align2(__VA_ARGS__); \ } \ else { \ std::cerr << "NATTEN kernel launch failed!" \ - << "na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks9 requires at least 32-bit alignment." \ - << "Got dim=" << dim << ", dtype=half. " \ + << "na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks7 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=bfloat16. " \ << std::endl; \ exit(EXIT_FAILURE); \ } \ }(); -#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks11(dim, ...) \ +#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks9(dim, ...) \ [&] { \ if (dim % 8 == 0) { \ - natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks11_align8(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks9_align8(__VA_ARGS__); \ } \ else if (dim % 4 == 0) { \ - natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks11_align4(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks9_align4(__VA_ARGS__); \ } \ else if (dim % 2 == 0) { \ - natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks11_align2(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks9_align2(__VA_ARGS__); \ } \ else { \ std::cerr << "NATTEN kernel launch failed!" \ - << "na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks11 requires at least 32-bit alignment." \ - << "Got dim=" << dim << ", dtype=half. " \ + << "na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks9 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=bfloat16. " \ << std::endl; \ exit(EXIT_FAILURE); \ } \ }(); -#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks13(dim, ...) \ +#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks11(dim, ...) \ [&] { \ if (dim % 8 == 0) { \ - natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks13_align8(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks11_align8(__VA_ARGS__); \ } \ else if (dim % 4 == 0) { \ - natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks13_align4(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks11_align4(__VA_ARGS__); \ } \ else if (dim % 2 == 0) { \ - natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks13_align2(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks11_align2(__VA_ARGS__); \ } \ else { \ std::cerr << "NATTEN kernel launch failed!" \ - << "na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks13 requires at least 32-bit alignment." \ - << "Got dim=" << dim << ", dtype=half. " \ + << "na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks11 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=bfloat16. " \ << std::endl; \ exit(EXIT_FAILURE); \ } \ }(); -#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks15(dim, ...) \ +#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks13(dim, ...) \ [&] { \ if (dim % 8 == 0) { \ - natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks15_align8(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks13_align8(__VA_ARGS__); \ } \ else if (dim % 4 == 0) { \ - natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks15_align4(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks13_align4(__VA_ARGS__); \ } \ else if (dim % 2 == 0) { \ - natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks15_align2(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks13_align2(__VA_ARGS__); \ } \ else { \ std::cerr << "NATTEN kernel launch failed!" \ - << "na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks15 requires at least 32-bit alignment." \ - << "Got dim=" << dim << ", dtype=half. " \ + << "na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks13 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=bfloat16. " \ << std::endl; \ exit(EXIT_FAILURE); \ } \ }(); -#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks17(dim, ...) \ +#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks15(dim, ...) \ [&] { \ if (dim % 8 == 0) { \ - natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks17_align8(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks15_align8(__VA_ARGS__); \ } \ else if (dim % 4 == 0) { \ - natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks17_align4(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks15_align4(__VA_ARGS__); \ } \ else if (dim % 2 == 0) { \ - natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks17_align2(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks15_align2(__VA_ARGS__); \ } \ else { \ std::cerr << "NATTEN kernel launch failed!" \ - << "na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks17 requires at least 32-bit alignment." \ - << "Got dim=" << dim << ", dtype=half. " \ + << "na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks15 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=bfloat16. " \ << std::endl; \ exit(EXIT_FAILURE); \ } \ }(); -#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks19(dim, ...) \ +#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks17(dim, ...) \ [&] { \ if (dim % 8 == 0) { \ - natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks19_align8(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks17_align8(__VA_ARGS__); \ } \ else if (dim % 4 == 0) { \ - natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks19_align4(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks17_align4(__VA_ARGS__); \ } \ else if (dim % 2 == 0) { \ - natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks19_align2(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks17_align2(__VA_ARGS__); \ } \ else { \ std::cerr << "NATTEN kernel launch failed!" \ - << "na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks19 requires at least 32-bit alignment." \ - << "Got dim=" << dim << ", dtype=half. " \ + << "na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks17 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=bfloat16. " \ << std::endl; \ exit(EXIT_FAILURE); \ } \ }(); -#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks21(dim, ...) \ +#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks19(dim, ...) \ [&] { \ if (dim % 8 == 0) { \ - natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks21_align8(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks19_align8(__VA_ARGS__); \ } \ else if (dim % 4 == 0) { \ - natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks21_align4(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks19_align4(__VA_ARGS__); \ } \ else if (dim % 2 == 0) { \ - natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks21_align2(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks19_align2(__VA_ARGS__); \ } \ else { \ std::cerr << "NATTEN kernel launch failed!" \ - << "na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks21 requires at least 32-bit alignment." \ - << "Got dim=" << dim << ", dtype=half. " \ + << "na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks19 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=bfloat16. " \ << std::endl; \ exit(EXIT_FAILURE); \ } \ }(); -#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks23(dim, ...) \ +#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks21(dim, ...) \ [&] { \ if (dim % 8 == 0) { \ - natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks23_align8(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks21_align8(__VA_ARGS__); \ } \ else if (dim % 4 == 0) { \ - natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks23_align4(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks21_align4(__VA_ARGS__); \ } \ else if (dim % 2 == 0) { \ - natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks23_align2(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks21_align2(__VA_ARGS__); \ } \ else { \ std::cerr << "NATTEN kernel launch failed!" \ - << "na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks23 requires at least 32-bit alignment." \ - << "Got dim=" << dim << ", dtype=half. " \ + << "na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks21 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=bfloat16. " \ << std::endl; \ exit(EXIT_FAILURE); \ } \ }(); -#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks25(dim, ...) \ +#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks23(dim, ...) \ [&] { \ if (dim % 8 == 0) { \ - natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks25_align8(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks23_align8(__VA_ARGS__); \ } \ else if (dim % 4 == 0) { \ - natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks25_align4(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks23_align4(__VA_ARGS__); \ } \ else if (dim % 2 == 0) { \ - natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks25_align2(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks23_align2(__VA_ARGS__); \ } \ else { \ std::cerr << "NATTEN kernel launch failed!" \ - << "na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks25 requires at least 32-bit alignment." \ - << "Got dim=" << dim << ", dtype=half. " \ + << "na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks23 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=bfloat16. " \ << std::endl; \ exit(EXIT_FAILURE); \ } \ }(); -#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks27(dim, ...) \ +#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks25(dim, ...) \ [&] { \ if (dim % 8 == 0) { \ - natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks27_align8(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks25_align8(__VA_ARGS__); \ } \ else if (dim % 4 == 0) { \ - natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks27_align4(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks25_align4(__VA_ARGS__); \ } \ else if (dim % 2 == 0) { \ - natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks27_align2(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks25_align2(__VA_ARGS__); \ } \ else { \ std::cerr << "NATTEN kernel launch failed!" \ - << "na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks27 requires at least 32-bit alignment." \ - << "Got dim=" << dim << ", dtype=half. " \ + << "na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks25 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=bfloat16. " \ << std::endl; \ exit(EXIT_FAILURE); \ } \ }(); -#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks29(dim, ...) \ +#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks27(dim, ...) \ [&] { \ if (dim % 8 == 0) { \ - natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks29_align8(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks27_align8(__VA_ARGS__); \ } \ else if (dim % 4 == 0) { \ - natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks29_align4(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks27_align4(__VA_ARGS__); \ } \ else if (dim % 2 == 0) { \ - natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks29_align2(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks27_align2(__VA_ARGS__); \ } \ else { \ std::cerr << "NATTEN kernel launch failed!" \ - << "na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks29 requires at least 32-bit alignment." \ - << "Got dim=" << dim << ", dtype=half. " \ + << "na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks27 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=bfloat16. " \ << std::endl; \ exit(EXIT_FAILURE); \ } \ }(); -#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks31(dim, ...) \ +#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks29(dim, ...) \ [&] { \ if (dim % 8 == 0) { \ - natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks31_align8(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks29_align8(__VA_ARGS__); \ } \ else if (dim % 4 == 0) { \ - natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks31_align4(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks29_align4(__VA_ARGS__); \ } \ else if (dim % 2 == 0) { \ - natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks31_align2(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks29_align2(__VA_ARGS__); \ } \ else { \ std::cerr << "NATTEN kernel launch failed!" \ - << "na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks31 requires at least 32-bit alignment." \ - << "Got dim=" << dim << ", dtype=half. " \ + << "na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks29 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=bfloat16. " \ << std::endl; \ exit(EXIT_FAILURE); \ } \ }(); -#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks33(dim, ...) \ +#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks31(dim, ...) \ [&] { \ if (dim % 8 == 0) { \ - natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks33_align8(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks31_align8(__VA_ARGS__); \ } \ else if (dim % 4 == 0) { \ - natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks33_align4(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks31_align4(__VA_ARGS__); \ } \ else if (dim % 2 == 0) { \ - natten::cuda::gemm::na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks33_align2(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks31_align2(__VA_ARGS__); \ } \ else { \ std::cerr << "NATTEN kernel launch failed!" \ - << "na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks33 requires at least 32-bit alignment." \ - << "Got dim=" << dim << ", dtype=half. " \ + << "na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks31 requires at least 32-bit alignment." \ + << "Got dim=" << dim << ", dtype=bfloat16. " \ << std::endl; \ exit(EXIT_FAILURE); \ } \ }(); -#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks3(dim, ...) \ +#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks33(dim, ...) \ [&] { \ if (dim % 8 == 0) { \ - natten::cuda::gemm::na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks3_align8(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks33_align8(__VA_ARGS__); \ } \ else if (dim % 4 == 0) { \ - natten::cuda::gemm::na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks3_align4(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks33_align4(__VA_ARGS__); \ } \ else if (dim % 2 == 0) { \ - natten::cuda::gemm::na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks3_align2(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks33_align2(__VA_ARGS__); \ } \ else { \ std::cerr << "NATTEN kernel launch failed!" \ - << "na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks3 requires at least 32-bit alignment." \ + << "na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks33 requires at least 32-bit alignment." \ << "Got dim=" << dim << ", dtype=bfloat16. " \ << std::endl; \ exit(EXIT_FAILURE); \ } \ }(); -#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks5(dim, ...) \ +#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks35(dim, ...) \ [&] { \ if (dim % 8 == 0) { \ - natten::cuda::gemm::na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks5_align8(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks35_align8(__VA_ARGS__); \ } \ else if (dim % 4 == 0) { \ - natten::cuda::gemm::na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks5_align4(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks35_align4(__VA_ARGS__); \ } \ else if (dim % 2 == 0) { \ - natten::cuda::gemm::na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks5_align2(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks35_align2(__VA_ARGS__); \ } \ else { \ std::cerr << "NATTEN kernel launch failed!" \ - << "na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks5 requires at least 32-bit alignment." \ + << "na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks35 requires at least 32-bit alignment." \ << "Got dim=" << dim << ", dtype=bfloat16. " \ << std::endl; \ exit(EXIT_FAILURE); \ } \ }(); -#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks7(dim, ...) \ +#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks37(dim, ...) \ [&] { \ if (dim % 8 == 0) { \ - natten::cuda::gemm::na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks7_align8(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks37_align8(__VA_ARGS__); \ } \ else if (dim % 4 == 0) { \ - natten::cuda::gemm::na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks7_align4(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks37_align4(__VA_ARGS__); \ } \ else if (dim % 2 == 0) { \ - natten::cuda::gemm::na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks7_align2(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks37_align2(__VA_ARGS__); \ } \ else { \ std::cerr << "NATTEN kernel launch failed!" \ - << "na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks7 requires at least 32-bit alignment." \ + << "na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks37 requires at least 32-bit alignment." \ << "Got dim=" << dim << ", dtype=bfloat16. " \ << std::endl; \ exit(EXIT_FAILURE); \ } \ }(); -#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks9(dim, ...) \ +#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks39(dim, ...) \ [&] { \ if (dim % 8 == 0) { \ - natten::cuda::gemm::na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks9_align8(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks39_align8(__VA_ARGS__); \ } \ else if (dim % 4 == 0) { \ - natten::cuda::gemm::na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks9_align4(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks39_align4(__VA_ARGS__); \ } \ else if (dim % 2 == 0) { \ - natten::cuda::gemm::na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks9_align2(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks39_align2(__VA_ARGS__); \ } \ else { \ std::cerr << "NATTEN kernel launch failed!" \ - << "na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks9 requires at least 32-bit alignment." \ + << "na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks39 requires at least 32-bit alignment." \ << "Got dim=" << dim << ", dtype=bfloat16. " \ << std::endl; \ exit(EXIT_FAILURE); \ } \ }(); -#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks11(dim, ...) \ +#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks41(dim, ...) \ [&] { \ if (dim % 8 == 0) { \ - natten::cuda::gemm::na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks11_align8(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks41_align8(__VA_ARGS__); \ } \ else if (dim % 4 == 0) { \ - natten::cuda::gemm::na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks11_align4(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks41_align4(__VA_ARGS__); \ } \ else if (dim % 2 == 0) { \ - natten::cuda::gemm::na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks11_align2(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks41_align2(__VA_ARGS__); \ } \ else { \ std::cerr << "NATTEN kernel launch failed!" \ - << "na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks11 requires at least 32-bit alignment." \ + << "na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks41 requires at least 32-bit alignment." \ << "Got dim=" << dim << ", dtype=bfloat16. " \ << std::endl; \ exit(EXIT_FAILURE); \ } \ }(); -#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks13(dim, ...) \ +#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks43(dim, ...) \ [&] { \ if (dim % 8 == 0) { \ - natten::cuda::gemm::na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks13_align8(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks43_align8(__VA_ARGS__); \ } \ else if (dim % 4 == 0) { \ - natten::cuda::gemm::na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks13_align4(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks43_align4(__VA_ARGS__); \ } \ else if (dim % 2 == 0) { \ - natten::cuda::gemm::na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks13_align2(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks43_align2(__VA_ARGS__); \ } \ else { \ std::cerr << "NATTEN kernel launch failed!" \ - << "na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks13 requires at least 32-bit alignment." \ + << "na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks43 requires at least 32-bit alignment." \ << "Got dim=" << dim << ", dtype=bfloat16. " \ << std::endl; \ exit(EXIT_FAILURE); \ } \ }(); -#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks15(dim, ...) \ +#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks45(dim, ...) \ [&] { \ if (dim % 8 == 0) { \ - natten::cuda::gemm::na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks15_align8(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks45_align8(__VA_ARGS__); \ } \ else if (dim % 4 == 0) { \ - natten::cuda::gemm::na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks15_align4(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks45_align4(__VA_ARGS__); \ } \ else if (dim % 2 == 0) { \ - natten::cuda::gemm::na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks15_align2(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks45_align2(__VA_ARGS__); \ } \ else { \ std::cerr << "NATTEN kernel launch failed!" \ - << "na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks15 requires at least 32-bit alignment." \ + << "na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks45 requires at least 32-bit alignment." \ << "Got dim=" << dim << ", dtype=bfloat16. " \ << std::endl; \ exit(EXIT_FAILURE); \ } \ }(); -#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks17(dim, ...) \ +#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks47(dim, ...) \ [&] { \ if (dim % 8 == 0) { \ - natten::cuda::gemm::na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks17_align8(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks47_align8(__VA_ARGS__); \ } \ else if (dim % 4 == 0) { \ - natten::cuda::gemm::na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks17_align4(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks47_align4(__VA_ARGS__); \ } \ else if (dim % 2 == 0) { \ - natten::cuda::gemm::na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks17_align2(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks47_align2(__VA_ARGS__); \ } \ else { \ std::cerr << "NATTEN kernel launch failed!" \ - << "na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks17 requires at least 32-bit alignment." \ + << "na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks47 requires at least 32-bit alignment." \ << "Got dim=" << dim << ", dtype=bfloat16. " \ << std::endl; \ exit(EXIT_FAILURE); \ } \ }(); -#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks19(dim, ...) \ +#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks49(dim, ...) \ [&] { \ if (dim % 8 == 0) { \ - natten::cuda::gemm::na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks19_align8(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks49_align8(__VA_ARGS__); \ } \ else if (dim % 4 == 0) { \ - natten::cuda::gemm::na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks19_align4(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks49_align4(__VA_ARGS__); \ } \ else if (dim % 2 == 0) { \ - natten::cuda::gemm::na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks19_align2(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks49_align2(__VA_ARGS__); \ } \ else { \ std::cerr << "NATTEN kernel launch failed!" \ - << "na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks19 requires at least 32-bit alignment." \ + << "na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks49 requires at least 32-bit alignment." \ << "Got dim=" << dim << ", dtype=bfloat16. " \ << std::endl; \ exit(EXIT_FAILURE); \ } \ }(); -#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks21(dim, ...) \ +#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks51(dim, ...) \ [&] { \ if (dim % 8 == 0) { \ - natten::cuda::gemm::na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks21_align8(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks51_align8(__VA_ARGS__); \ } \ else if (dim % 4 == 0) { \ - natten::cuda::gemm::na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks21_align4(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks51_align4(__VA_ARGS__); \ } \ else if (dim % 2 == 0) { \ - natten::cuda::gemm::na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks21_align2(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks51_align2(__VA_ARGS__); \ } \ else { \ std::cerr << "NATTEN kernel launch failed!" \ - << "na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks21 requires at least 32-bit alignment." \ + << "na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks51 requires at least 32-bit alignment." \ << "Got dim=" << dim << ", dtype=bfloat16. " \ << std::endl; \ exit(EXIT_FAILURE); \ } \ }(); -#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks23(dim, ...) \ +#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks53(dim, ...) \ [&] { \ if (dim % 8 == 0) { \ - natten::cuda::gemm::na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks23_align8(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks53_align8(__VA_ARGS__); \ } \ else if (dim % 4 == 0) { \ - natten::cuda::gemm::na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks23_align4(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks53_align4(__VA_ARGS__); \ } \ else if (dim % 2 == 0) { \ - natten::cuda::gemm::na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks23_align2(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks53_align2(__VA_ARGS__); \ } \ else { \ std::cerr << "NATTEN kernel launch failed!" \ - << "na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks23 requires at least 32-bit alignment." \ + << "na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks53 requires at least 32-bit alignment." \ << "Got dim=" << dim << ", dtype=bfloat16. " \ << std::endl; \ exit(EXIT_FAILURE); \ } \ }(); -#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks25(dim, ...) \ +#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks55(dim, ...) \ [&] { \ if (dim % 8 == 0) { \ - natten::cuda::gemm::na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks25_align8(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks55_align8(__VA_ARGS__); \ } \ else if (dim % 4 == 0) { \ - natten::cuda::gemm::na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks25_align4(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks55_align4(__VA_ARGS__); \ } \ else if (dim % 2 == 0) { \ - natten::cuda::gemm::na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks25_align2(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks55_align2(__VA_ARGS__); \ } \ else { \ std::cerr << "NATTEN kernel launch failed!" \ - << "na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks25 requires at least 32-bit alignment." \ + << "na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks55 requires at least 32-bit alignment." \ << "Got dim=" << dim << ", dtype=bfloat16. " \ << std::endl; \ exit(EXIT_FAILURE); \ } \ }(); -#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks27(dim, ...) \ +#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks57(dim, ...) \ [&] { \ if (dim % 8 == 0) { \ - natten::cuda::gemm::na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks27_align8(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks57_align8(__VA_ARGS__); \ } \ else if (dim % 4 == 0) { \ - natten::cuda::gemm::na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks27_align4(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks57_align4(__VA_ARGS__); \ } \ else if (dim % 2 == 0) { \ - natten::cuda::gemm::na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks27_align2(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks57_align2(__VA_ARGS__); \ } \ else { \ std::cerr << "NATTEN kernel launch failed!" \ - << "na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks27 requires at least 32-bit alignment." \ + << "na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks57 requires at least 32-bit alignment." \ << "Got dim=" << dim << ", dtype=bfloat16. " \ << std::endl; \ exit(EXIT_FAILURE); \ } \ }(); -#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks29(dim, ...) \ +#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks59(dim, ...) \ [&] { \ if (dim % 8 == 0) { \ - natten::cuda::gemm::na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks29_align8(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks59_align8(__VA_ARGS__); \ } \ else if (dim % 4 == 0) { \ - natten::cuda::gemm::na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks29_align4(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks59_align4(__VA_ARGS__); \ } \ else if (dim % 2 == 0) { \ - natten::cuda::gemm::na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks29_align2(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks59_align2(__VA_ARGS__); \ } \ else { \ std::cerr << "NATTEN kernel launch failed!" \ - << "na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks29 requires at least 32-bit alignment." \ + << "na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks59 requires at least 32-bit alignment." \ << "Got dim=" << dim << ", dtype=bfloat16. " \ << std::endl; \ exit(EXIT_FAILURE); \ } \ }(); -#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks31(dim, ...) \ +#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks61(dim, ...) \ [&] { \ if (dim % 8 == 0) { \ - natten::cuda::gemm::na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks31_align8(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks61_align8(__VA_ARGS__); \ } \ else if (dim % 4 == 0) { \ - natten::cuda::gemm::na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks31_align4(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks61_align4(__VA_ARGS__); \ } \ else if (dim % 2 == 0) { \ - natten::cuda::gemm::na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks31_align2(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks61_align2(__VA_ARGS__); \ } \ else { \ std::cerr << "NATTEN kernel launch failed!" \ - << "na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks31 requires at least 32-bit alignment." \ + << "na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks61 requires at least 32-bit alignment." \ << "Got dim=" << dim << ", dtype=bfloat16. " \ << std::endl; \ exit(EXIT_FAILURE); \ } \ }(); -#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks33(dim, ...) \ +#define DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks63(dim, ...) \ [&] { \ if (dim % 8 == 0) { \ - natten::cuda::gemm::na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks33_align8(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks63_align8(__VA_ARGS__); \ } \ else if (dim % 4 == 0) { \ - natten::cuda::gemm::na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks33_align4(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks63_align4(__VA_ARGS__); \ } \ else if (dim % 2 == 0) { \ - natten::cuda::gemm::na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks33_align2(__VA_ARGS__); \ + natten::cuda::gemm::na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks63_align2(__VA_ARGS__); \ } \ else { \ std::cerr << "NATTEN kernel launch failed!" \ - << "na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks33 requires at least 32-bit alignment." \ + << "na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks63 requires at least 32-bit alignment." \ << "Got dim=" << dim << ", dtype=bfloat16. " \ << std::endl; \ exit(EXIT_FAILURE); \ diff --git a/csrc/autogen/include/natten_autogen/cuda/gemm/2d/sm80/dispatch_kernel_size.h b/csrc/autogen/include/natten_autogen/cuda/gemm/2d/sm80/dispatch_kernel_size.h index 8c9480c..922b316 100644 --- a/csrc/autogen/include/natten_autogen/cuda/gemm/2d/sm80/dispatch_kernel_size.h +++ b/csrc/autogen/include/natten_autogen/cuda/gemm/2d/sm80/dispatch_kernel_size.h @@ -60,6 +60,51 @@ namespace gemm { else if (kernel_size == 33) { \ DISPATCH_ALIGNMENT_na2d_pn_cuda_gemm_double_128x128x16_64x64x16_8x8x4_3_sm80_ks33(dim, __VA_ARGS__); \ } \ + else if (kernel_size == 35) { \ + DISPATCH_ALIGNMENT_na2d_pn_cuda_gemm_double_128x128x16_64x64x16_8x8x4_3_sm80_ks35(dim, __VA_ARGS__); \ + } \ + else if (kernel_size == 37) { \ + DISPATCH_ALIGNMENT_na2d_pn_cuda_gemm_double_128x128x16_64x64x16_8x8x4_3_sm80_ks37(dim, __VA_ARGS__); \ + } \ + else if (kernel_size == 39) { \ + DISPATCH_ALIGNMENT_na2d_pn_cuda_gemm_double_128x128x16_64x64x16_8x8x4_3_sm80_ks39(dim, __VA_ARGS__); \ + } \ + else if (kernel_size == 41) { \ + DISPATCH_ALIGNMENT_na2d_pn_cuda_gemm_double_128x128x16_64x64x16_8x8x4_3_sm80_ks41(dim, __VA_ARGS__); \ + } \ + else if (kernel_size == 43) { \ + DISPATCH_ALIGNMENT_na2d_pn_cuda_gemm_double_128x128x16_64x64x16_8x8x4_3_sm80_ks43(dim, __VA_ARGS__); \ + } \ + else if (kernel_size == 45) { \ + DISPATCH_ALIGNMENT_na2d_pn_cuda_gemm_double_128x128x16_64x64x16_8x8x4_3_sm80_ks45(dim, __VA_ARGS__); \ + } \ + else if (kernel_size == 47) { \ + DISPATCH_ALIGNMENT_na2d_pn_cuda_gemm_double_128x128x16_64x64x16_8x8x4_3_sm80_ks47(dim, __VA_ARGS__); \ + } \ + else if (kernel_size == 49) { \ + DISPATCH_ALIGNMENT_na2d_pn_cuda_gemm_double_128x128x16_64x64x16_8x8x4_3_sm80_ks49(dim, __VA_ARGS__); \ + } \ + else if (kernel_size == 51) { \ + DISPATCH_ALIGNMENT_na2d_pn_cuda_gemm_double_128x128x16_64x64x16_8x8x4_3_sm80_ks51(dim, __VA_ARGS__); \ + } \ + else if (kernel_size == 53) { \ + DISPATCH_ALIGNMENT_na2d_pn_cuda_gemm_double_128x128x16_64x64x16_8x8x4_3_sm80_ks53(dim, __VA_ARGS__); \ + } \ + else if (kernel_size == 55) { \ + DISPATCH_ALIGNMENT_na2d_pn_cuda_gemm_double_128x128x16_64x64x16_8x8x4_3_sm80_ks55(dim, __VA_ARGS__); \ + } \ + else if (kernel_size == 57) { \ + DISPATCH_ALIGNMENT_na2d_pn_cuda_gemm_double_128x128x16_64x64x16_8x8x4_3_sm80_ks57(dim, __VA_ARGS__); \ + } \ + else if (kernel_size == 59) { \ + DISPATCH_ALIGNMENT_na2d_pn_cuda_gemm_double_128x128x16_64x64x16_8x8x4_3_sm80_ks59(dim, __VA_ARGS__); \ + } \ + else if (kernel_size == 61) { \ + DISPATCH_ALIGNMENT_na2d_pn_cuda_gemm_double_128x128x16_64x64x16_8x8x4_3_sm80_ks61(dim, __VA_ARGS__); \ + } \ + else if (kernel_size == 63) { \ + DISPATCH_ALIGNMENT_na2d_pn_cuda_gemm_double_128x128x16_64x64x16_8x8x4_3_sm80_ks63(dim, __VA_ARGS__); \ + } \ else { \ std::cerr << "NATTEN kernel launch failed! " \ << "na2d_pn_cuda_gemm_sm80_double does not support implement " \ @@ -121,6 +166,51 @@ namespace gemm { else if (kernel_size == 33) { \ DISPATCH_ALIGNMENT_na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks33(dim, __VA_ARGS__); \ } \ + else if (kernel_size == 35) { \ + DISPATCH_ALIGNMENT_na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks35(dim, __VA_ARGS__); \ + } \ + else if (kernel_size == 37) { \ + DISPATCH_ALIGNMENT_na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks37(dim, __VA_ARGS__); \ + } \ + else if (kernel_size == 39) { \ + DISPATCH_ALIGNMENT_na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks39(dim, __VA_ARGS__); \ + } \ + else if (kernel_size == 41) { \ + DISPATCH_ALIGNMENT_na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks41(dim, __VA_ARGS__); \ + } \ + else if (kernel_size == 43) { \ + DISPATCH_ALIGNMENT_na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks43(dim, __VA_ARGS__); \ + } \ + else if (kernel_size == 45) { \ + DISPATCH_ALIGNMENT_na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks45(dim, __VA_ARGS__); \ + } \ + else if (kernel_size == 47) { \ + DISPATCH_ALIGNMENT_na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks47(dim, __VA_ARGS__); \ + } \ + else if (kernel_size == 49) { \ + DISPATCH_ALIGNMENT_na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks49(dim, __VA_ARGS__); \ + } \ + else if (kernel_size == 51) { \ + DISPATCH_ALIGNMENT_na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks51(dim, __VA_ARGS__); \ + } \ + else if (kernel_size == 53) { \ + DISPATCH_ALIGNMENT_na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks53(dim, __VA_ARGS__); \ + } \ + else if (kernel_size == 55) { \ + DISPATCH_ALIGNMENT_na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks55(dim, __VA_ARGS__); \ + } \ + else if (kernel_size == 57) { \ + DISPATCH_ALIGNMENT_na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks57(dim, __VA_ARGS__); \ + } \ + else if (kernel_size == 59) { \ + DISPATCH_ALIGNMENT_na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks59(dim, __VA_ARGS__); \ + } \ + else if (kernel_size == 61) { \ + DISPATCH_ALIGNMENT_na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks61(dim, __VA_ARGS__); \ + } \ + else if (kernel_size == 63) { \ + DISPATCH_ALIGNMENT_na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks63(dim, __VA_ARGS__); \ + } \ else { \ std::cerr << "NATTEN kernel launch failed! " \ << "na2d_pn_cuda_gemm_sm80_float does not support implement " \ @@ -182,6 +272,51 @@ namespace gemm { else if (kernel_size == 33) { \ DISPATCH_ALIGNMENT_na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks33(dim, __VA_ARGS__); \ } \ + else if (kernel_size == 35) { \ + DISPATCH_ALIGNMENT_na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks35(dim, __VA_ARGS__); \ + } \ + else if (kernel_size == 37) { \ + DISPATCH_ALIGNMENT_na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks37(dim, __VA_ARGS__); \ + } \ + else if (kernel_size == 39) { \ + DISPATCH_ALIGNMENT_na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks39(dim, __VA_ARGS__); \ + } \ + else if (kernel_size == 41) { \ + DISPATCH_ALIGNMENT_na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks41(dim, __VA_ARGS__); \ + } \ + else if (kernel_size == 43) { \ + DISPATCH_ALIGNMENT_na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks43(dim, __VA_ARGS__); \ + } \ + else if (kernel_size == 45) { \ + DISPATCH_ALIGNMENT_na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks45(dim, __VA_ARGS__); \ + } \ + else if (kernel_size == 47) { \ + DISPATCH_ALIGNMENT_na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks47(dim, __VA_ARGS__); \ + } \ + else if (kernel_size == 49) { \ + DISPATCH_ALIGNMENT_na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks49(dim, __VA_ARGS__); \ + } \ + else if (kernel_size == 51) { \ + DISPATCH_ALIGNMENT_na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks51(dim, __VA_ARGS__); \ + } \ + else if (kernel_size == 53) { \ + DISPATCH_ALIGNMENT_na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks53(dim, __VA_ARGS__); \ + } \ + else if (kernel_size == 55) { \ + DISPATCH_ALIGNMENT_na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks55(dim, __VA_ARGS__); \ + } \ + else if (kernel_size == 57) { \ + DISPATCH_ALIGNMENT_na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks57(dim, __VA_ARGS__); \ + } \ + else if (kernel_size == 59) { \ + DISPATCH_ALIGNMENT_na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks59(dim, __VA_ARGS__); \ + } \ + else if (kernel_size == 61) { \ + DISPATCH_ALIGNMENT_na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks61(dim, __VA_ARGS__); \ + } \ + else if (kernel_size == 63) { \ + DISPATCH_ALIGNMENT_na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks63(dim, __VA_ARGS__); \ + } \ else { \ std::cerr << "NATTEN kernel launch failed! " \ << "na2d_pn_cuda_gemm_sm80_half does not support implement " \ @@ -243,6 +378,51 @@ namespace gemm { else if (kernel_size == 33) { \ DISPATCH_ALIGNMENT_na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks33(dim, __VA_ARGS__); \ } \ + else if (kernel_size == 35) { \ + DISPATCH_ALIGNMENT_na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks35(dim, __VA_ARGS__); \ + } \ + else if (kernel_size == 37) { \ + DISPATCH_ALIGNMENT_na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks37(dim, __VA_ARGS__); \ + } \ + else if (kernel_size == 39) { \ + DISPATCH_ALIGNMENT_na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks39(dim, __VA_ARGS__); \ + } \ + else if (kernel_size == 41) { \ + DISPATCH_ALIGNMENT_na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks41(dim, __VA_ARGS__); \ + } \ + else if (kernel_size == 43) { \ + DISPATCH_ALIGNMENT_na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks43(dim, __VA_ARGS__); \ + } \ + else if (kernel_size == 45) { \ + DISPATCH_ALIGNMENT_na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks45(dim, __VA_ARGS__); \ + } \ + else if (kernel_size == 47) { \ + DISPATCH_ALIGNMENT_na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks47(dim, __VA_ARGS__); \ + } \ + else if (kernel_size == 49) { \ + DISPATCH_ALIGNMENT_na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks49(dim, __VA_ARGS__); \ + } \ + else if (kernel_size == 51) { \ + DISPATCH_ALIGNMENT_na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks51(dim, __VA_ARGS__); \ + } \ + else if (kernel_size == 53) { \ + DISPATCH_ALIGNMENT_na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks53(dim, __VA_ARGS__); \ + } \ + else if (kernel_size == 55) { \ + DISPATCH_ALIGNMENT_na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks55(dim, __VA_ARGS__); \ + } \ + else if (kernel_size == 57) { \ + DISPATCH_ALIGNMENT_na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks57(dim, __VA_ARGS__); \ + } \ + else if (kernel_size == 59) { \ + DISPATCH_ALIGNMENT_na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks59(dim, __VA_ARGS__); \ + } \ + else if (kernel_size == 61) { \ + DISPATCH_ALIGNMENT_na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks61(dim, __VA_ARGS__); \ + } \ + else if (kernel_size == 63) { \ + DISPATCH_ALIGNMENT_na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks63(dim, __VA_ARGS__); \ + } \ else { \ std::cerr << "NATTEN kernel launch failed! " \ << "na2d_pn_cuda_gemm_sm80_bfloat16 does not support implement " \ @@ -304,6 +484,51 @@ namespace gemm { else if (kernel_size == 33) { \ DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_double_128x128x16_64x64x16_8x8x4_3_sm80_ks33(dim, __VA_ARGS__); \ } \ + else if (kernel_size == 35) { \ + DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_double_128x128x16_64x64x16_8x8x4_3_sm80_ks35(dim, __VA_ARGS__); \ + } \ + else if (kernel_size == 37) { \ + DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_double_128x128x16_64x64x16_8x8x4_3_sm80_ks37(dim, __VA_ARGS__); \ + } \ + else if (kernel_size == 39) { \ + DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_double_128x128x16_64x64x16_8x8x4_3_sm80_ks39(dim, __VA_ARGS__); \ + } \ + else if (kernel_size == 41) { \ + DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_double_128x128x16_64x64x16_8x8x4_3_sm80_ks41(dim, __VA_ARGS__); \ + } \ + else if (kernel_size == 43) { \ + DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_double_128x128x16_64x64x16_8x8x4_3_sm80_ks43(dim, __VA_ARGS__); \ + } \ + else if (kernel_size == 45) { \ + DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_double_128x128x16_64x64x16_8x8x4_3_sm80_ks45(dim, __VA_ARGS__); \ + } \ + else if (kernel_size == 47) { \ + DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_double_128x128x16_64x64x16_8x8x4_3_sm80_ks47(dim, __VA_ARGS__); \ + } \ + else if (kernel_size == 49) { \ + DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_double_128x128x16_64x64x16_8x8x4_3_sm80_ks49(dim, __VA_ARGS__); \ + } \ + else if (kernel_size == 51) { \ + DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_double_128x128x16_64x64x16_8x8x4_3_sm80_ks51(dim, __VA_ARGS__); \ + } \ + else if (kernel_size == 53) { \ + DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_double_128x128x16_64x64x16_8x8x4_3_sm80_ks53(dim, __VA_ARGS__); \ + } \ + else if (kernel_size == 55) { \ + DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_double_128x128x16_64x64x16_8x8x4_3_sm80_ks55(dim, __VA_ARGS__); \ + } \ + else if (kernel_size == 57) { \ + DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_double_128x128x16_64x64x16_8x8x4_3_sm80_ks57(dim, __VA_ARGS__); \ + } \ + else if (kernel_size == 59) { \ + DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_double_128x128x16_64x64x16_8x8x4_3_sm80_ks59(dim, __VA_ARGS__); \ + } \ + else if (kernel_size == 61) { \ + DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_double_128x128x16_64x64x16_8x8x4_3_sm80_ks61(dim, __VA_ARGS__); \ + } \ + else if (kernel_size == 63) { \ + DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_double_128x128x16_64x64x16_8x8x4_3_sm80_ks63(dim, __VA_ARGS__); \ + } \ else { \ std::cerr << "NATTEN kernel launch failed! " \ << "na2d_nn_cuda_gemm_sm80_double does not support implement " \ @@ -365,6 +590,51 @@ namespace gemm { else if (kernel_size == 33) { \ DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks33(dim, __VA_ARGS__); \ } \ + else if (kernel_size == 35) { \ + DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks35(dim, __VA_ARGS__); \ + } \ + else if (kernel_size == 37) { \ + DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks37(dim, __VA_ARGS__); \ + } \ + else if (kernel_size == 39) { \ + DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks39(dim, __VA_ARGS__); \ + } \ + else if (kernel_size == 41) { \ + DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks41(dim, __VA_ARGS__); \ + } \ + else if (kernel_size == 43) { \ + DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks43(dim, __VA_ARGS__); \ + } \ + else if (kernel_size == 45) { \ + DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks45(dim, __VA_ARGS__); \ + } \ + else if (kernel_size == 47) { \ + DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks47(dim, __VA_ARGS__); \ + } \ + else if (kernel_size == 49) { \ + DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks49(dim, __VA_ARGS__); \ + } \ + else if (kernel_size == 51) { \ + DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks51(dim, __VA_ARGS__); \ + } \ + else if (kernel_size == 53) { \ + DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks53(dim, __VA_ARGS__); \ + } \ + else if (kernel_size == 55) { \ + DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks55(dim, __VA_ARGS__); \ + } \ + else if (kernel_size == 57) { \ + DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks57(dim, __VA_ARGS__); \ + } \ + else if (kernel_size == 59) { \ + DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks59(dim, __VA_ARGS__); \ + } \ + else if (kernel_size == 61) { \ + DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks61(dim, __VA_ARGS__); \ + } \ + else if (kernel_size == 63) { \ + DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks63(dim, __VA_ARGS__); \ + } \ else { \ std::cerr << "NATTEN kernel launch failed! " \ << "na2d_nn_cuda_gemm_sm80_float does not support implement " \ @@ -426,6 +696,51 @@ namespace gemm { else if (kernel_size == 33) { \ DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks33(dim, __VA_ARGS__); \ } \ + else if (kernel_size == 35) { \ + DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks35(dim, __VA_ARGS__); \ + } \ + else if (kernel_size == 37) { \ + DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks37(dim, __VA_ARGS__); \ + } \ + else if (kernel_size == 39) { \ + DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks39(dim, __VA_ARGS__); \ + } \ + else if (kernel_size == 41) { \ + DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks41(dim, __VA_ARGS__); \ + } \ + else if (kernel_size == 43) { \ + DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks43(dim, __VA_ARGS__); \ + } \ + else if (kernel_size == 45) { \ + DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks45(dim, __VA_ARGS__); \ + } \ + else if (kernel_size == 47) { \ + DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks47(dim, __VA_ARGS__); \ + } \ + else if (kernel_size == 49) { \ + DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks49(dim, __VA_ARGS__); \ + } \ + else if (kernel_size == 51) { \ + DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks51(dim, __VA_ARGS__); \ + } \ + else if (kernel_size == 53) { \ + DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks53(dim, __VA_ARGS__); \ + } \ + else if (kernel_size == 55) { \ + DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks55(dim, __VA_ARGS__); \ + } \ + else if (kernel_size == 57) { \ + DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks57(dim, __VA_ARGS__); \ + } \ + else if (kernel_size == 59) { \ + DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks59(dim, __VA_ARGS__); \ + } \ + else if (kernel_size == 61) { \ + DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks61(dim, __VA_ARGS__); \ + } \ + else if (kernel_size == 63) { \ + DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks63(dim, __VA_ARGS__); \ + } \ else { \ std::cerr << "NATTEN kernel launch failed! " \ << "na2d_nn_cuda_gemm_sm80_half does not support implement " \ @@ -487,6 +802,51 @@ namespace gemm { else if (kernel_size == 33) { \ DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks33(dim, __VA_ARGS__); \ } \ + else if (kernel_size == 35) { \ + DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks35(dim, __VA_ARGS__); \ + } \ + else if (kernel_size == 37) { \ + DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks37(dim, __VA_ARGS__); \ + } \ + else if (kernel_size == 39) { \ + DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks39(dim, __VA_ARGS__); \ + } \ + else if (kernel_size == 41) { \ + DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks41(dim, __VA_ARGS__); \ + } \ + else if (kernel_size == 43) { \ + DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks43(dim, __VA_ARGS__); \ + } \ + else if (kernel_size == 45) { \ + DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks45(dim, __VA_ARGS__); \ + } \ + else if (kernel_size == 47) { \ + DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks47(dim, __VA_ARGS__); \ + } \ + else if (kernel_size == 49) { \ + DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks49(dim, __VA_ARGS__); \ + } \ + else if (kernel_size == 51) { \ + DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks51(dim, __VA_ARGS__); \ + } \ + else if (kernel_size == 53) { \ + DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks53(dim, __VA_ARGS__); \ + } \ + else if (kernel_size == 55) { \ + DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks55(dim, __VA_ARGS__); \ + } \ + else if (kernel_size == 57) { \ + DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks57(dim, __VA_ARGS__); \ + } \ + else if (kernel_size == 59) { \ + DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks59(dim, __VA_ARGS__); \ + } \ + else if (kernel_size == 61) { \ + DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks61(dim, __VA_ARGS__); \ + } \ + else if (kernel_size == 63) { \ + DISPATCH_ALIGNMENT_na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks63(dim, __VA_ARGS__); \ + } \ else { \ std::cerr << "NATTEN kernel launch failed! " \ << "na2d_nn_cuda_gemm_sm80_bfloat16 does not support implement " \ @@ -548,6 +908,51 @@ namespace gemm { else if (kernel_size == 33) { \ DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks33(dim, __VA_ARGS__); \ } \ + else if (kernel_size == 35) { \ + DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks35(dim, __VA_ARGS__); \ + } \ + else if (kernel_size == 37) { \ + DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks37(dim, __VA_ARGS__); \ + } \ + else if (kernel_size == 39) { \ + DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks39(dim, __VA_ARGS__); \ + } \ + else if (kernel_size == 41) { \ + DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks41(dim, __VA_ARGS__); \ + } \ + else if (kernel_size == 43) { \ + DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks43(dim, __VA_ARGS__); \ + } \ + else if (kernel_size == 45) { \ + DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks45(dim, __VA_ARGS__); \ + } \ + else if (kernel_size == 47) { \ + DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks47(dim, __VA_ARGS__); \ + } \ + else if (kernel_size == 49) { \ + DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks49(dim, __VA_ARGS__); \ + } \ + else if (kernel_size == 51) { \ + DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks51(dim, __VA_ARGS__); \ + } \ + else if (kernel_size == 53) { \ + DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks53(dim, __VA_ARGS__); \ + } \ + else if (kernel_size == 55) { \ + DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks55(dim, __VA_ARGS__); \ + } \ + else if (kernel_size == 57) { \ + DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks57(dim, __VA_ARGS__); \ + } \ + else if (kernel_size == 59) { \ + DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks59(dim, __VA_ARGS__); \ + } \ + else if (kernel_size == 61) { \ + DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks61(dim, __VA_ARGS__); \ + } \ + else if (kernel_size == 63) { \ + DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks63(dim, __VA_ARGS__); \ + } \ else { \ std::cerr << "NATTEN kernel launch failed! " \ << "na2d_in_cuda_gemm_sm80_double does not support implement " \ @@ -609,6 +1014,51 @@ namespace gemm { else if (kernel_size == 33) { \ DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks33(dim, __VA_ARGS__); \ } \ + else if (kernel_size == 35) { \ + DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks35(dim, __VA_ARGS__); \ + } \ + else if (kernel_size == 37) { \ + DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks37(dim, __VA_ARGS__); \ + } \ + else if (kernel_size == 39) { \ + DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks39(dim, __VA_ARGS__); \ + } \ + else if (kernel_size == 41) { \ + DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks41(dim, __VA_ARGS__); \ + } \ + else if (kernel_size == 43) { \ + DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks43(dim, __VA_ARGS__); \ + } \ + else if (kernel_size == 45) { \ + DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks45(dim, __VA_ARGS__); \ + } \ + else if (kernel_size == 47) { \ + DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks47(dim, __VA_ARGS__); \ + } \ + else if (kernel_size == 49) { \ + DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks49(dim, __VA_ARGS__); \ + } \ + else if (kernel_size == 51) { \ + DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks51(dim, __VA_ARGS__); \ + } \ + else if (kernel_size == 53) { \ + DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks53(dim, __VA_ARGS__); \ + } \ + else if (kernel_size == 55) { \ + DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks55(dim, __VA_ARGS__); \ + } \ + else if (kernel_size == 57) { \ + DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks57(dim, __VA_ARGS__); \ + } \ + else if (kernel_size == 59) { \ + DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks59(dim, __VA_ARGS__); \ + } \ + else if (kernel_size == 61) { \ + DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks61(dim, __VA_ARGS__); \ + } \ + else if (kernel_size == 63) { \ + DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks63(dim, __VA_ARGS__); \ + } \ else { \ std::cerr << "NATTEN kernel launch failed! " \ << "na2d_in_cuda_gemm_sm80_float does not support implement " \ @@ -670,6 +1120,51 @@ namespace gemm { else if (kernel_size == 33) { \ DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks33(dim, __VA_ARGS__); \ } \ + else if (kernel_size == 35) { \ + DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks35(dim, __VA_ARGS__); \ + } \ + else if (kernel_size == 37) { \ + DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks37(dim, __VA_ARGS__); \ + } \ + else if (kernel_size == 39) { \ + DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks39(dim, __VA_ARGS__); \ + } \ + else if (kernel_size == 41) { \ + DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks41(dim, __VA_ARGS__); \ + } \ + else if (kernel_size == 43) { \ + DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks43(dim, __VA_ARGS__); \ + } \ + else if (kernel_size == 45) { \ + DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks45(dim, __VA_ARGS__); \ + } \ + else if (kernel_size == 47) { \ + DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks47(dim, __VA_ARGS__); \ + } \ + else if (kernel_size == 49) { \ + DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks49(dim, __VA_ARGS__); \ + } \ + else if (kernel_size == 51) { \ + DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks51(dim, __VA_ARGS__); \ + } \ + else if (kernel_size == 53) { \ + DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks53(dim, __VA_ARGS__); \ + } \ + else if (kernel_size == 55) { \ + DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks55(dim, __VA_ARGS__); \ + } \ + else if (kernel_size == 57) { \ + DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks57(dim, __VA_ARGS__); \ + } \ + else if (kernel_size == 59) { \ + DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks59(dim, __VA_ARGS__); \ + } \ + else if (kernel_size == 61) { \ + DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks61(dim, __VA_ARGS__); \ + } \ + else if (kernel_size == 63) { \ + DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks63(dim, __VA_ARGS__); \ + } \ else { \ std::cerr << "NATTEN kernel launch failed! " \ << "na2d_in_cuda_gemm_sm80_half does not support implement " \ @@ -731,6 +1226,51 @@ namespace gemm { else if (kernel_size == 33) { \ DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks33(dim, __VA_ARGS__); \ } \ + else if (kernel_size == 35) { \ + DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks35(dim, __VA_ARGS__); \ + } \ + else if (kernel_size == 37) { \ + DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks37(dim, __VA_ARGS__); \ + } \ + else if (kernel_size == 39) { \ + DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks39(dim, __VA_ARGS__); \ + } \ + else if (kernel_size == 41) { \ + DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks41(dim, __VA_ARGS__); \ + } \ + else if (kernel_size == 43) { \ + DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks43(dim, __VA_ARGS__); \ + } \ + else if (kernel_size == 45) { \ + DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks45(dim, __VA_ARGS__); \ + } \ + else if (kernel_size == 47) { \ + DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks47(dim, __VA_ARGS__); \ + } \ + else if (kernel_size == 49) { \ + DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks49(dim, __VA_ARGS__); \ + } \ + else if (kernel_size == 51) { \ + DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks51(dim, __VA_ARGS__); \ + } \ + else if (kernel_size == 53) { \ + DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks53(dim, __VA_ARGS__); \ + } \ + else if (kernel_size == 55) { \ + DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks55(dim, __VA_ARGS__); \ + } \ + else if (kernel_size == 57) { \ + DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks57(dim, __VA_ARGS__); \ + } \ + else if (kernel_size == 59) { \ + DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks59(dim, __VA_ARGS__); \ + } \ + else if (kernel_size == 61) { \ + DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks61(dim, __VA_ARGS__); \ + } \ + else if (kernel_size == 63) { \ + DISPATCH_ALIGNMENT_na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks63(dim, __VA_ARGS__); \ + } \ else { \ std::cerr << "NATTEN kernel launch failed! " \ << "na2d_in_cuda_gemm_sm80_bfloat16 does not support implement " \ diff --git a/csrc/autogen/include/natten_autogen/cuda/gemm/2d/sm80/kernels.h b/csrc/autogen/include/natten_autogen/cuda/gemm/2d/sm80/kernels.h index a4cac07..f4067ff 100644 --- a/csrc/autogen/include/natten_autogen/cuda/gemm/2d/sm80/kernels.h +++ b/csrc/autogen/include/natten_autogen/cuda/gemm/2d/sm80/kernels.h @@ -313,7 +313,7 @@ void na2d_pn_cuda_gemm_double_128x128x16_64x64x16_8x8x4_3_sm80_ks33_align1( float scale, cudaStream_t stream); -void na2d_pn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks3_align4( +void na2d_pn_cuda_gemm_double_128x128x16_64x64x16_8x8x4_3_sm80_ks35_align1( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -332,7 +332,7 @@ void na2d_pn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks3_align4( float scale, cudaStream_t stream); -void na2d_pn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks3_align2( +void na2d_pn_cuda_gemm_double_128x128x16_64x64x16_8x8x4_3_sm80_ks37_align1( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -351,7 +351,7 @@ void na2d_pn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks3_align2( float scale, cudaStream_t stream); -void na2d_pn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks3_align1( +void na2d_pn_cuda_gemm_double_128x128x16_64x64x16_8x8x4_3_sm80_ks39_align1( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -370,7 +370,7 @@ void na2d_pn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks3_align1( float scale, cudaStream_t stream); -void na2d_pn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks5_align4( +void na2d_pn_cuda_gemm_double_128x128x16_64x64x16_8x8x4_3_sm80_ks41_align1( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -389,7 +389,7 @@ void na2d_pn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks5_align4( float scale, cudaStream_t stream); -void na2d_pn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks5_align2( +void na2d_pn_cuda_gemm_double_128x128x16_64x64x16_8x8x4_3_sm80_ks43_align1( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -408,7 +408,7 @@ void na2d_pn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks5_align2( float scale, cudaStream_t stream); -void na2d_pn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks5_align1( +void na2d_pn_cuda_gemm_double_128x128x16_64x64x16_8x8x4_3_sm80_ks45_align1( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -427,7 +427,7 @@ void na2d_pn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks5_align1( float scale, cudaStream_t stream); -void na2d_pn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks7_align4( +void na2d_pn_cuda_gemm_double_128x128x16_64x64x16_8x8x4_3_sm80_ks47_align1( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -446,7 +446,7 @@ void na2d_pn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks7_align4( float scale, cudaStream_t stream); -void na2d_pn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks7_align2( +void na2d_pn_cuda_gemm_double_128x128x16_64x64x16_8x8x4_3_sm80_ks49_align1( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -465,7 +465,7 @@ void na2d_pn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks7_align2( float scale, cudaStream_t stream); -void na2d_pn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks7_align1( +void na2d_pn_cuda_gemm_double_128x128x16_64x64x16_8x8x4_3_sm80_ks51_align1( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -484,7 +484,7 @@ void na2d_pn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks7_align1( float scale, cudaStream_t stream); -void na2d_pn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks9_align4( +void na2d_pn_cuda_gemm_double_128x128x16_64x64x16_8x8x4_3_sm80_ks53_align1( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -503,7 +503,7 @@ void na2d_pn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks9_align4( float scale, cudaStream_t stream); -void na2d_pn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks9_align2( +void na2d_pn_cuda_gemm_double_128x128x16_64x64x16_8x8x4_3_sm80_ks55_align1( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -522,7 +522,7 @@ void na2d_pn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks9_align2( float scale, cudaStream_t stream); -void na2d_pn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks9_align1( +void na2d_pn_cuda_gemm_double_128x128x16_64x64x16_8x8x4_3_sm80_ks57_align1( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -541,7 +541,7 @@ void na2d_pn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks9_align1( float scale, cudaStream_t stream); -void na2d_pn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks11_align4( +void na2d_pn_cuda_gemm_double_128x128x16_64x64x16_8x8x4_3_sm80_ks59_align1( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -560,7 +560,7 @@ void na2d_pn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks11_align4( float scale, cudaStream_t stream); -void na2d_pn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks11_align2( +void na2d_pn_cuda_gemm_double_128x128x16_64x64x16_8x8x4_3_sm80_ks61_align1( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -579,7 +579,7 @@ void na2d_pn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks11_align2( float scale, cudaStream_t stream); -void na2d_pn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks11_align1( +void na2d_pn_cuda_gemm_double_128x128x16_64x64x16_8x8x4_3_sm80_ks63_align1( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -598,7 +598,7 @@ void na2d_pn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks11_align1( float scale, cudaStream_t stream); -void na2d_pn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks13_align4( +void na2d_pn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks3_align4( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -617,7 +617,7 @@ void na2d_pn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks13_align4( float scale, cudaStream_t stream); -void na2d_pn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks13_align2( +void na2d_pn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks3_align2( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -636,7 +636,7 @@ void na2d_pn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks13_align2( float scale, cudaStream_t stream); -void na2d_pn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks13_align1( +void na2d_pn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks3_align1( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -655,7 +655,7 @@ void na2d_pn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks13_align1( float scale, cudaStream_t stream); -void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks15_align4( +void na2d_pn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks5_align4( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -674,7 +674,7 @@ void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks15_align4( float scale, cudaStream_t stream); -void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks15_align2( +void na2d_pn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks5_align2( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -693,7 +693,7 @@ void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks15_align2( float scale, cudaStream_t stream); -void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks15_align1( +void na2d_pn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks5_align1( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -712,7 +712,7 @@ void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks15_align1( float scale, cudaStream_t stream); -void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks17_align4( +void na2d_pn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks7_align4( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -731,7 +731,7 @@ void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks17_align4( float scale, cudaStream_t stream); -void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks17_align2( +void na2d_pn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks7_align2( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -750,7 +750,7 @@ void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks17_align2( float scale, cudaStream_t stream); -void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks17_align1( +void na2d_pn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks7_align1( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -769,7 +769,7 @@ void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks17_align1( float scale, cudaStream_t stream); -void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks19_align4( +void na2d_pn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks9_align4( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -788,7 +788,7 @@ void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks19_align4( float scale, cudaStream_t stream); -void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks19_align2( +void na2d_pn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks9_align2( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -807,7 +807,7 @@ void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks19_align2( float scale, cudaStream_t stream); -void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks19_align1( +void na2d_pn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks9_align1( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -826,7 +826,7 @@ void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks19_align1( float scale, cudaStream_t stream); -void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks21_align4( +void na2d_pn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks11_align4( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -845,7 +845,7 @@ void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks21_align4( float scale, cudaStream_t stream); -void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks21_align2( +void na2d_pn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks11_align2( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -864,7 +864,7 @@ void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks21_align2( float scale, cudaStream_t stream); -void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks21_align1( +void na2d_pn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks11_align1( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -883,7 +883,7 @@ void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks21_align1( float scale, cudaStream_t stream); -void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks23_align4( +void na2d_pn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks13_align4( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -902,7 +902,7 @@ void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks23_align4( float scale, cudaStream_t stream); -void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks23_align2( +void na2d_pn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks13_align2( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -921,7 +921,7 @@ void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks23_align2( float scale, cudaStream_t stream); -void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks23_align1( +void na2d_pn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks13_align1( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -940,7 +940,7 @@ void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks23_align1( float scale, cudaStream_t stream); -void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks25_align4( +void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks15_align4( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -959,7 +959,7 @@ void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks25_align4( float scale, cudaStream_t stream); -void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks25_align2( +void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks15_align2( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -978,7 +978,7 @@ void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks25_align2( float scale, cudaStream_t stream); -void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks25_align1( +void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks15_align1( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -997,7 +997,7 @@ void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks25_align1( float scale, cudaStream_t stream); -void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks27_align4( +void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks17_align4( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -1016,7 +1016,7 @@ void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks27_align4( float scale, cudaStream_t stream); -void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks27_align2( +void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks17_align2( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -1035,7 +1035,7 @@ void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks27_align2( float scale, cudaStream_t stream); -void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks27_align1( +void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks17_align1( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -1054,7 +1054,7 @@ void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks27_align1( float scale, cudaStream_t stream); -void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks29_align4( +void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks19_align4( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -1073,7 +1073,7 @@ void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks29_align4( float scale, cudaStream_t stream); -void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks29_align2( +void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks19_align2( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -1092,7 +1092,7 @@ void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks29_align2( float scale, cudaStream_t stream); -void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks29_align1( +void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks19_align1( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -1111,7 +1111,7 @@ void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks29_align1( float scale, cudaStream_t stream); -void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks31_align4( +void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks21_align4( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -1130,7 +1130,7 @@ void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks31_align4( float scale, cudaStream_t stream); -void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks31_align2( +void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks21_align2( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -1149,7 +1149,7 @@ void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks31_align2( float scale, cudaStream_t stream); -void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks31_align1( +void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks21_align1( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -1168,7 +1168,7 @@ void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks31_align1( float scale, cudaStream_t stream); -void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks33_align4( +void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks23_align4( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -1187,7 +1187,7 @@ void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks33_align4( float scale, cudaStream_t stream); -void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks33_align2( +void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks23_align2( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -1206,7 +1206,7 @@ void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks33_align2( float scale, cudaStream_t stream); -void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks33_align1( +void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks23_align1( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -1225,7 +1225,7 @@ void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks33_align1( float scale, cudaStream_t stream); -void na2d_pn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks3_align8( +void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks25_align4( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -1244,7 +1244,7 @@ void na2d_pn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks3_align8( float scale, cudaStream_t stream); -void na2d_pn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks3_align4( +void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks25_align2( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -1263,7 +1263,7 @@ void na2d_pn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks3_align4( float scale, cudaStream_t stream); -void na2d_pn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks3_align2( +void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks25_align1( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -1282,7 +1282,7 @@ void na2d_pn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks3_align2( float scale, cudaStream_t stream); -void na2d_pn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks5_align8( +void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks27_align4( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -1301,7 +1301,7 @@ void na2d_pn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks5_align8( float scale, cudaStream_t stream); -void na2d_pn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks5_align4( +void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks27_align2( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -1320,7 +1320,7 @@ void na2d_pn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks5_align4( float scale, cudaStream_t stream); -void na2d_pn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks5_align2( +void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks27_align1( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -1339,7 +1339,7 @@ void na2d_pn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks5_align2( float scale, cudaStream_t stream); -void na2d_pn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks7_align8( +void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks29_align4( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -1358,7 +1358,7 @@ void na2d_pn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks7_align8( float scale, cudaStream_t stream); -void na2d_pn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks7_align4( +void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks29_align2( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -1377,7 +1377,7 @@ void na2d_pn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks7_align4( float scale, cudaStream_t stream); -void na2d_pn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks7_align2( +void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks29_align1( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -1396,7 +1396,7 @@ void na2d_pn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks7_align2( float scale, cudaStream_t stream); -void na2d_pn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks9_align8( +void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks31_align4( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -1415,7 +1415,7 @@ void na2d_pn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks9_align8( float scale, cudaStream_t stream); -void na2d_pn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks9_align4( +void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks31_align2( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -1434,7 +1434,7 @@ void na2d_pn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks9_align4( float scale, cudaStream_t stream); -void na2d_pn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks9_align2( +void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks31_align1( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -1453,7 +1453,7 @@ void na2d_pn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks9_align2( float scale, cudaStream_t stream); -void na2d_pn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks11_align8( +void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks33_align4( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -1472,7 +1472,7 @@ void na2d_pn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks11_align8( float scale, cudaStream_t stream); -void na2d_pn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks11_align4( +void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks33_align2( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -1491,7 +1491,7 @@ void na2d_pn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks11_align4( float scale, cudaStream_t stream); -void na2d_pn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks11_align2( +void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks33_align1( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -1510,7 +1510,7 @@ void na2d_pn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks11_align2( float scale, cudaStream_t stream); -void na2d_pn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks13_align8( +void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks35_align4( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -1529,7 +1529,7 @@ void na2d_pn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks13_align8( float scale, cudaStream_t stream); -void na2d_pn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks13_align4( +void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks35_align2( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -1548,7 +1548,7 @@ void na2d_pn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks13_align4( float scale, cudaStream_t stream); -void na2d_pn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks13_align2( +void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks35_align1( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -1567,7 +1567,7 @@ void na2d_pn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks13_align2( float scale, cudaStream_t stream); -void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks15_align8( +void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks37_align4( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -1586,7 +1586,7 @@ void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks15_align8( float scale, cudaStream_t stream); -void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks15_align4( +void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks37_align2( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -1605,7 +1605,7 @@ void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks15_align4( float scale, cudaStream_t stream); -void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks15_align2( +void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks37_align1( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -1624,7 +1624,7 @@ void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks15_align2( float scale, cudaStream_t stream); -void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks17_align8( +void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks39_align4( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -1643,7 +1643,7 @@ void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks17_align8( float scale, cudaStream_t stream); -void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks17_align4( +void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks39_align2( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -1662,7 +1662,7 @@ void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks17_align4( float scale, cudaStream_t stream); -void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks17_align2( +void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks39_align1( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -1681,7 +1681,7 @@ void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks17_align2( float scale, cudaStream_t stream); -void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks19_align8( +void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks41_align4( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -1700,7 +1700,7 @@ void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks19_align8( float scale, cudaStream_t stream); -void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks19_align4( +void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks41_align2( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -1719,7 +1719,7 @@ void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks19_align4( float scale, cudaStream_t stream); -void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks19_align2( +void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks41_align1( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -1738,7 +1738,7 @@ void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks19_align2( float scale, cudaStream_t stream); -void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks21_align8( +void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks43_align4( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -1757,7 +1757,7 @@ void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks21_align8( float scale, cudaStream_t stream); -void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks21_align4( +void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks43_align2( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -1776,7 +1776,7 @@ void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks21_align4( float scale, cudaStream_t stream); -void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks21_align2( +void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks43_align1( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -1795,7 +1795,7 @@ void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks21_align2( float scale, cudaStream_t stream); -void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks23_align8( +void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks45_align4( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -1814,7 +1814,7 @@ void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks23_align8( float scale, cudaStream_t stream); -void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks23_align4( +void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks45_align2( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -1833,7 +1833,7 @@ void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks23_align4( float scale, cudaStream_t stream); -void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks23_align2( +void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks45_align1( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -1852,7 +1852,7 @@ void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks23_align2( float scale, cudaStream_t stream); -void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks25_align8( +void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks47_align4( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -1871,7 +1871,7 @@ void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks25_align8( float scale, cudaStream_t stream); -void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks25_align4( +void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks47_align2( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -1890,7 +1890,7 @@ void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks25_align4( float scale, cudaStream_t stream); -void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks25_align2( +void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks47_align1( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -1909,7 +1909,7 @@ void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks25_align2( float scale, cudaStream_t stream); -void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks27_align8( +void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks49_align4( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -1928,7 +1928,7 @@ void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks27_align8( float scale, cudaStream_t stream); -void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks27_align4( +void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks49_align2( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -1947,7 +1947,7 @@ void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks27_align4( float scale, cudaStream_t stream); -void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks27_align2( +void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks49_align1( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -1966,7 +1966,7 @@ void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks27_align2( float scale, cudaStream_t stream); -void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks29_align8( +void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks51_align4( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -1985,7 +1985,7 @@ void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks29_align8( float scale, cudaStream_t stream); -void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks29_align4( +void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks51_align2( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -2004,7 +2004,7 @@ void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks29_align4( float scale, cudaStream_t stream); -void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks29_align2( +void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks51_align1( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -2023,7 +2023,7 @@ void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks29_align2( float scale, cudaStream_t stream); -void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks31_align8( +void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks53_align4( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -2042,7 +2042,7 @@ void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks31_align8( float scale, cudaStream_t stream); -void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks31_align4( +void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks53_align2( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -2061,7 +2061,7 @@ void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks31_align4( float scale, cudaStream_t stream); -void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks31_align2( +void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks53_align1( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -2080,7 +2080,7 @@ void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks31_align2( float scale, cudaStream_t stream); -void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks33_align8( +void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks55_align4( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -2099,7 +2099,7 @@ void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks33_align8( float scale, cudaStream_t stream); -void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks33_align4( +void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks55_align2( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -2118,7 +2118,7 @@ void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks33_align4( float scale, cudaStream_t stream); -void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks33_align2( +void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks55_align1( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -2137,7 +2137,7 @@ void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks33_align2( float scale, cudaStream_t stream); -void na2d_pn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks3_align8( +void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks57_align4( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -2156,7 +2156,7 @@ void na2d_pn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks3_align8( float scale, cudaStream_t stream); -void na2d_pn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks3_align4( +void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks57_align2( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -2175,7 +2175,7 @@ void na2d_pn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks3_align4( float scale, cudaStream_t stream); -void na2d_pn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks3_align2( +void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks57_align1( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -2194,7 +2194,7 @@ void na2d_pn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks3_align2( float scale, cudaStream_t stream); -void na2d_pn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks5_align8( +void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks59_align4( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -2213,7 +2213,7 @@ void na2d_pn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks5_align8( float scale, cudaStream_t stream); -void na2d_pn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks5_align4( +void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks59_align2( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -2232,7 +2232,7 @@ void na2d_pn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks5_align4( float scale, cudaStream_t stream); -void na2d_pn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks5_align2( +void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks59_align1( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -2251,7 +2251,7 @@ void na2d_pn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks5_align2( float scale, cudaStream_t stream); -void na2d_pn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks7_align8( +void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks61_align4( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -2270,7 +2270,7 @@ void na2d_pn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks7_align8( float scale, cudaStream_t stream); -void na2d_pn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks7_align4( +void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks61_align2( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -2289,7 +2289,7 @@ void na2d_pn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks7_align4( float scale, cudaStream_t stream); -void na2d_pn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks7_align2( +void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks61_align1( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -2308,7 +2308,7 @@ void na2d_pn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks7_align2( float scale, cudaStream_t stream); -void na2d_pn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks9_align8( +void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks63_align4( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -2327,7 +2327,7 @@ void na2d_pn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks9_align8( float scale, cudaStream_t stream); -void na2d_pn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks9_align4( +void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks63_align2( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -2346,7 +2346,7 @@ void na2d_pn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks9_align4( float scale, cudaStream_t stream); -void na2d_pn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks9_align2( +void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks63_align1( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -2365,7 +2365,7 @@ void na2d_pn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks9_align2( float scale, cudaStream_t stream); -void na2d_pn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks11_align8( +void na2d_pn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks3_align8( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -2384,7 +2384,7 @@ void na2d_pn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks11_align8( float scale, cudaStream_t stream); -void na2d_pn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks11_align4( +void na2d_pn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks3_align4( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -2403,7 +2403,7 @@ void na2d_pn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks11_align4( float scale, cudaStream_t stream); -void na2d_pn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks11_align2( +void na2d_pn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks3_align2( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -2422,7 +2422,7 @@ void na2d_pn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks11_align2( float scale, cudaStream_t stream); -void na2d_pn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks13_align8( +void na2d_pn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks5_align8( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -2441,7 +2441,7 @@ void na2d_pn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks13_align8( float scale, cudaStream_t stream); -void na2d_pn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks13_align4( +void na2d_pn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks5_align4( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -2460,7 +2460,7 @@ void na2d_pn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks13_align4( float scale, cudaStream_t stream); -void na2d_pn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks13_align2( +void na2d_pn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks5_align2( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -2479,7 +2479,7 @@ void na2d_pn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks13_align2( float scale, cudaStream_t stream); -void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks15_align8( +void na2d_pn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks7_align8( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -2498,7 +2498,7 @@ void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks15_align8( float scale, cudaStream_t stream); -void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks15_align4( +void na2d_pn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks7_align4( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -2517,7 +2517,7 @@ void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks15_align4( float scale, cudaStream_t stream); -void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks15_align2( +void na2d_pn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks7_align2( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -2536,7 +2536,7 @@ void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks15_align2( float scale, cudaStream_t stream); -void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks17_align8( +void na2d_pn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks9_align8( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -2555,7 +2555,7 @@ void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks17_align8( float scale, cudaStream_t stream); -void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks17_align4( +void na2d_pn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks9_align4( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -2574,7 +2574,7 @@ void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks17_align4( float scale, cudaStream_t stream); -void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks17_align2( +void na2d_pn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks9_align2( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -2593,7 +2593,7 @@ void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks17_align2( float scale, cudaStream_t stream); -void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks19_align8( +void na2d_pn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks11_align8( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -2612,7 +2612,7 @@ void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks19_align8( float scale, cudaStream_t stream); -void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks19_align4( +void na2d_pn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks11_align4( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -2631,7 +2631,7 @@ void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks19_align4( float scale, cudaStream_t stream); -void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks19_align2( +void na2d_pn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks11_align2( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -2650,7 +2650,7 @@ void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks19_align2( float scale, cudaStream_t stream); -void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks21_align8( +void na2d_pn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks13_align8( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -2669,7 +2669,7 @@ void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks21_align8( float scale, cudaStream_t stream); -void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks21_align4( +void na2d_pn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks13_align4( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -2688,7 +2688,7 @@ void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks21_align4( float scale, cudaStream_t stream); -void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks21_align2( +void na2d_pn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks13_align2( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -2707,7 +2707,7 @@ void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks21_align2( float scale, cudaStream_t stream); -void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks23_align8( +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks15_align8( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -2726,7 +2726,7 @@ void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks23_align8( float scale, cudaStream_t stream); -void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks23_align4( +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks15_align4( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -2745,7 +2745,7 @@ void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks23_align4( float scale, cudaStream_t stream); -void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks23_align2( +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks15_align2( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -2764,7 +2764,7 @@ void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks23_align2( float scale, cudaStream_t stream); -void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks25_align8( +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks17_align8( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -2783,7 +2783,7 @@ void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks25_align8( float scale, cudaStream_t stream); -void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks25_align4( +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks17_align4( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -2802,7 +2802,7 @@ void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks25_align4( float scale, cudaStream_t stream); -void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks25_align2( +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks17_align2( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -2821,7 +2821,7 @@ void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks25_align2( float scale, cudaStream_t stream); -void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks27_align8( +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks19_align8( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -2840,7 +2840,7 @@ void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks27_align8( float scale, cudaStream_t stream); -void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks27_align4( +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks19_align4( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -2859,7 +2859,7 @@ void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks27_align4( float scale, cudaStream_t stream); -void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks27_align2( +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks19_align2( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -2878,7 +2878,7 @@ void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks27_align2( float scale, cudaStream_t stream); -void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks29_align8( +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks21_align8( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -2897,7 +2897,7 @@ void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks29_align8( float scale, cudaStream_t stream); -void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks29_align4( +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks21_align4( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -2916,7 +2916,7 @@ void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks29_align4( float scale, cudaStream_t stream); -void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks29_align2( +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks21_align2( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -2935,7 +2935,7 @@ void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks29_align2( float scale, cudaStream_t stream); -void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks31_align8( +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks23_align8( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -2954,7 +2954,7 @@ void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks31_align8( float scale, cudaStream_t stream); -void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks31_align4( +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks23_align4( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -2973,7 +2973,7 @@ void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks31_align4( float scale, cudaStream_t stream); -void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks31_align2( +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks23_align2( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -2992,7 +2992,7 @@ void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks31_align2( float scale, cudaStream_t stream); -void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks33_align8( +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks25_align8( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -3011,7 +3011,7 @@ void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks33_align8( float scale, cudaStream_t stream); -void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks33_align4( +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks25_align4( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -3030,7 +3030,7 @@ void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks33_align4( float scale, cudaStream_t stream); -void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks33_align2( +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks25_align2( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -3049,10 +3049,11 @@ void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks33_align2( float scale, cudaStream_t stream); -void na2d_nn_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks3_align1( +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks27_align8( + void * query_ptr, + void * key_ptr, void * attn_ptr, - void * value_ptr, - void * output_ptr, + void * bias_ptr, int batch_size, int heads, int height, @@ -3067,10 +3068,11 @@ void na2d_nn_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks3_align1( float scale, cudaStream_t stream); -void na2d_nn_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks5_align1( +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks27_align4( + void * query_ptr, + void * key_ptr, void * attn_ptr, - void * value_ptr, - void * output_ptr, + void * bias_ptr, int batch_size, int heads, int height, @@ -3085,10 +3087,11 @@ void na2d_nn_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks5_align1( float scale, cudaStream_t stream); -void na2d_nn_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks7_align1( +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks27_align2( + void * query_ptr, + void * key_ptr, void * attn_ptr, - void * value_ptr, - void * output_ptr, + void * bias_ptr, int batch_size, int heads, int height, @@ -3103,10 +3106,2857 @@ void na2d_nn_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks7_align1( float scale, cudaStream_t stream); -void na2d_nn_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks9_align1( +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks29_align8( + void * query_ptr, + void * key_ptr, void * attn_ptr, - void * value_ptr, - void * output_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks29_align4( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks29_align2( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks31_align8( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks31_align4( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks31_align2( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks33_align8( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks33_align4( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks33_align2( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks35_align8( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks35_align4( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks35_align2( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks37_align8( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks37_align4( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks37_align2( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks39_align8( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks39_align4( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks39_align2( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks41_align8( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks41_align4( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks41_align2( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks43_align8( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks43_align4( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks43_align2( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks45_align8( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks45_align4( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks45_align2( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks47_align8( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks47_align4( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks47_align2( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks49_align8( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks49_align4( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks49_align2( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks51_align8( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks51_align4( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks51_align2( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks53_align8( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks53_align4( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks53_align2( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks55_align8( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks55_align4( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks55_align2( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks57_align8( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks57_align4( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks57_align2( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks59_align8( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks59_align4( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks59_align2( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks61_align8( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks61_align4( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks61_align2( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks63_align8( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks63_align4( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks63_align2( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_pn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks3_align8( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_pn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks3_align4( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_pn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks3_align2( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_pn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks5_align8( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_pn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks5_align4( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_pn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks5_align2( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_pn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks7_align8( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_pn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks7_align4( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_pn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks7_align2( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_pn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks9_align8( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_pn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks9_align4( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_pn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks9_align2( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_pn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks11_align8( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_pn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks11_align4( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_pn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks11_align2( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_pn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks13_align8( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_pn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks13_align4( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_pn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks13_align2( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks15_align8( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks15_align4( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks15_align2( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks17_align8( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks17_align4( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks17_align2( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks19_align8( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks19_align4( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks19_align2( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks21_align8( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks21_align4( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks21_align2( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks23_align8( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks23_align4( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks23_align2( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks25_align8( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks25_align4( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks25_align2( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks27_align8( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks27_align4( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks27_align2( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks29_align8( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks29_align4( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks29_align2( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks31_align8( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks31_align4( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks31_align2( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks33_align8( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks33_align4( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks33_align2( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks35_align8( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks35_align4( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks35_align2( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks37_align8( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks37_align4( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks37_align2( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks39_align8( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks39_align4( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks39_align2( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks41_align8( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks41_align4( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks41_align2( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks43_align8( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks43_align4( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks43_align2( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks45_align8( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks45_align4( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks45_align2( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks47_align8( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks47_align4( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks47_align2( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks49_align8( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks49_align4( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks49_align2( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks51_align8( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks51_align4( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks51_align2( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks53_align8( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks53_align4( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks53_align2( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks55_align8( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks55_align4( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks55_align2( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks57_align8( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks57_align4( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks57_align2( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks59_align8( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks59_align4( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks59_align2( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks61_align8( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks61_align4( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks61_align2( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks63_align8( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks63_align4( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks63_align2( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks3_align1( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks5_align1( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks7_align1( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks9_align1( + void * attn_ptr, + void * value_ptr, + void * output_ptr, int batch_size, int heads, int height, @@ -3139,7 +5989,5173 @@ void na2d_nn_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks11_align1( float scale, cudaStream_t stream); -void na2d_nn_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks13_align1( +void na2d_nn_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks13_align1( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks15_align1( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks17_align1( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks19_align1( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_double_128x128x16_64x64x16_8x8x4_3_sm80_ks21_align1( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_double_128x128x16_64x64x16_8x8x4_3_sm80_ks23_align1( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_double_128x128x16_64x64x16_8x8x4_3_sm80_ks25_align1( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_double_128x128x16_64x64x16_8x8x4_3_sm80_ks27_align1( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_double_128x128x16_64x64x16_8x8x4_3_sm80_ks29_align1( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_double_128x128x16_64x64x16_8x8x4_3_sm80_ks31_align1( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_double_128x128x16_64x64x16_8x8x4_3_sm80_ks33_align1( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_double_128x128x16_64x64x16_8x8x4_3_sm80_ks35_align1( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_double_128x128x16_64x64x16_8x8x4_3_sm80_ks37_align1( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_double_128x128x16_64x64x16_8x8x4_3_sm80_ks39_align1( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_double_128x128x16_64x64x16_8x8x4_3_sm80_ks41_align1( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_double_128x128x16_64x64x16_8x8x4_3_sm80_ks43_align1( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_double_128x128x16_64x64x16_8x8x4_3_sm80_ks45_align1( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_double_128x128x16_64x64x16_8x8x4_3_sm80_ks47_align1( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_double_128x128x16_64x64x16_8x8x4_3_sm80_ks49_align1( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_double_128x128x16_64x64x16_8x8x4_3_sm80_ks51_align1( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_double_128x128x16_64x64x16_8x8x4_3_sm80_ks53_align1( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_double_128x128x16_64x64x16_8x8x4_3_sm80_ks55_align1( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_double_128x128x16_64x64x16_8x8x4_3_sm80_ks57_align1( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_double_128x128x16_64x64x16_8x8x4_3_sm80_ks59_align1( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_double_128x128x16_64x64x16_8x8x4_3_sm80_ks61_align1( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_double_128x128x16_64x64x16_8x8x4_3_sm80_ks63_align1( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks3_align4( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks3_align2( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks3_align1( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks5_align4( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks5_align2( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks5_align1( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks7_align4( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks7_align2( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks7_align1( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks9_align4( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks9_align2( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks9_align1( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks11_align4( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks11_align2( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks11_align1( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks13_align4( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks13_align2( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks13_align1( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks15_align4( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks15_align2( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks15_align1( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks17_align4( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks17_align2( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks17_align1( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks19_align4( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks19_align2( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks19_align1( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks21_align4( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks21_align2( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks21_align1( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks23_align4( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks23_align2( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks23_align1( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks25_align4( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks25_align2( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks25_align1( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks27_align4( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks27_align2( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks27_align1( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks29_align4( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks29_align2( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks29_align1( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks31_align4( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks31_align2( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks31_align1( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks33_align4( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks33_align2( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks33_align1( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks35_align4( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks35_align2( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks35_align1( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks37_align4( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks37_align2( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks37_align1( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks39_align4( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks39_align2( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks39_align1( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks41_align4( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks41_align2( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks41_align1( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks43_align4( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks43_align2( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks43_align1( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks45_align4( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks45_align2( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks45_align1( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks47_align4( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks47_align2( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks47_align1( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks49_align4( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks49_align2( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks49_align1( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks51_align4( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks51_align2( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks51_align1( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks53_align4( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks53_align2( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks53_align1( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks55_align4( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks55_align2( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks55_align1( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks57_align4( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks57_align2( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks57_align1( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks59_align4( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks59_align2( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks59_align1( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks61_align4( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks61_align2( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks61_align1( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks63_align4( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks63_align2( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks63_align1( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks3_align8( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks3_align4( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks3_align2( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks5_align8( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks5_align4( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks5_align2( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks7_align8( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks7_align4( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks7_align2( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks9_align8( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks9_align4( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks9_align2( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks11_align8( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks11_align4( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks11_align2( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks13_align8( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks13_align4( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks13_align2( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks15_align8( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks15_align4( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks15_align2( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks17_align8( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks17_align4( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks17_align2( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks19_align8( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks19_align4( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks19_align2( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks21_align8( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks21_align4( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks21_align2( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks23_align8( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks23_align4( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks23_align2( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks25_align8( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks25_align4( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks25_align2( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks27_align8( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks27_align4( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks27_align2( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks29_align8( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks29_align4( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks29_align2( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks31_align8( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks31_align4( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks31_align2( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks33_align8( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks33_align4( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks33_align2( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks35_align8( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks35_align4( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks35_align2( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks37_align8( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks37_align4( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks37_align2( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks39_align8( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks39_align4( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks39_align2( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks41_align8( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks41_align4( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks41_align2( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks43_align8( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks43_align4( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks43_align2( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks45_align8( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks45_align4( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks45_align2( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks47_align8( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks47_align4( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks47_align2( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks49_align8( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks49_align4( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks49_align2( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks51_align8( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks51_align4( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks51_align2( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks53_align8( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks53_align4( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks53_align2( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks55_align8( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks55_align4( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks55_align2( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks57_align8( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks57_align4( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks57_align2( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks59_align8( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks59_align4( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks59_align2( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks61_align8( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks61_align4( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks61_align2( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks63_align8( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks63_align4( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks63_align2( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks3_align8( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks3_align4( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks3_align2( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks5_align8( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks5_align4( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks5_align2( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks7_align8( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks7_align4( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks7_align2( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks9_align8( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks9_align4( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks9_align2( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks11_align8( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks11_align4( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks11_align2( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks13_align8( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks13_align4( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks13_align2( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks15_align8( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks15_align4( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks15_align2( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks17_align8( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks17_align4( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks17_align2( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks19_align8( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks19_align4( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks19_align2( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks21_align8( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks21_align4( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks21_align2( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks23_align8( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks23_align4( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks23_align2( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks25_align8( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks25_align4( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks25_align2( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks27_align8( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks27_align4( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks27_align2( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks29_align8( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks29_align4( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks29_align2( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks31_align8( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks31_align4( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks31_align2( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks33_align8( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks33_align4( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks33_align2( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks35_align8( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks35_align4( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks35_align2( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks37_align8( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks37_align4( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks37_align2( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks39_align8( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks39_align4( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks39_align2( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks41_align8( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks41_align4( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks41_align2( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks43_align8( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks43_align4( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks43_align2( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks45_align8( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks45_align4( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks45_align2( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks47_align8( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks47_align4( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks47_align2( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks49_align8( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks49_align4( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks49_align2( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks51_align8( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks51_align4( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks51_align2( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks53_align8( void * attn_ptr, void * value_ptr, void * output_ptr, @@ -3157,7 +11173,7 @@ void na2d_nn_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks13_align1( float scale, cudaStream_t stream); -void na2d_nn_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks15_align1( +void na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks53_align4( void * attn_ptr, void * value_ptr, void * output_ptr, @@ -3175,7 +11191,7 @@ void na2d_nn_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks15_align1( float scale, cudaStream_t stream); -void na2d_nn_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks17_align1( +void na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks53_align2( void * attn_ptr, void * value_ptr, void * output_ptr, @@ -3193,7 +11209,7 @@ void na2d_nn_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks17_align1( float scale, cudaStream_t stream); -void na2d_nn_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks19_align1( +void na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks55_align8( void * attn_ptr, void * value_ptr, void * output_ptr, @@ -3211,7 +11227,7 @@ void na2d_nn_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks19_align1( float scale, cudaStream_t stream); -void na2d_nn_cuda_gemm_double_128x128x16_64x64x16_8x8x4_3_sm80_ks21_align1( +void na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks55_align4( void * attn_ptr, void * value_ptr, void * output_ptr, @@ -3229,7 +11245,7 @@ void na2d_nn_cuda_gemm_double_128x128x16_64x64x16_8x8x4_3_sm80_ks21_align1( float scale, cudaStream_t stream); -void na2d_nn_cuda_gemm_double_128x128x16_64x64x16_8x8x4_3_sm80_ks23_align1( +void na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks55_align2( void * attn_ptr, void * value_ptr, void * output_ptr, @@ -3247,7 +11263,7 @@ void na2d_nn_cuda_gemm_double_128x128x16_64x64x16_8x8x4_3_sm80_ks23_align1( float scale, cudaStream_t stream); -void na2d_nn_cuda_gemm_double_128x128x16_64x64x16_8x8x4_3_sm80_ks25_align1( +void na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks57_align8( void * attn_ptr, void * value_ptr, void * output_ptr, @@ -3265,7 +11281,7 @@ void na2d_nn_cuda_gemm_double_128x128x16_64x64x16_8x8x4_3_sm80_ks25_align1( float scale, cudaStream_t stream); -void na2d_nn_cuda_gemm_double_128x128x16_64x64x16_8x8x4_3_sm80_ks27_align1( +void na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks57_align4( void * attn_ptr, void * value_ptr, void * output_ptr, @@ -3283,7 +11299,7 @@ void na2d_nn_cuda_gemm_double_128x128x16_64x64x16_8x8x4_3_sm80_ks27_align1( float scale, cudaStream_t stream); -void na2d_nn_cuda_gemm_double_128x128x16_64x64x16_8x8x4_3_sm80_ks29_align1( +void na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks57_align2( void * attn_ptr, void * value_ptr, void * output_ptr, @@ -3301,7 +11317,7 @@ void na2d_nn_cuda_gemm_double_128x128x16_64x64x16_8x8x4_3_sm80_ks29_align1( float scale, cudaStream_t stream); -void na2d_nn_cuda_gemm_double_128x128x16_64x64x16_8x8x4_3_sm80_ks31_align1( +void na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks59_align8( void * attn_ptr, void * value_ptr, void * output_ptr, @@ -3319,7 +11335,7 @@ void na2d_nn_cuda_gemm_double_128x128x16_64x64x16_8x8x4_3_sm80_ks31_align1( float scale, cudaStream_t stream); -void na2d_nn_cuda_gemm_double_128x128x16_64x64x16_8x8x4_3_sm80_ks33_align1( +void na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks59_align4( void * attn_ptr, void * value_ptr, void * output_ptr, @@ -3337,7 +11353,7 @@ void na2d_nn_cuda_gemm_double_128x128x16_64x64x16_8x8x4_3_sm80_ks33_align1( float scale, cudaStream_t stream); -void na2d_nn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks3_align4( +void na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks59_align2( void * attn_ptr, void * value_ptr, void * output_ptr, @@ -3355,7 +11371,97 @@ void na2d_nn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks3_align4( float scale, cudaStream_t stream); -void na2d_nn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks3_align2( +void na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks61_align8( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks61_align4( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks61_align2( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks63_align8( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks63_align4( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks63_align2( void * attn_ptr, void * value_ptr, void * output_ptr, @@ -3373,10 +11479,154 @@ void na2d_nn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks3_align2( float scale, cudaStream_t stream); -void na2d_nn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks3_align1( +void na2d_in_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks3_align1( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_in_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks5_align1( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_in_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks7_align1( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_in_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks9_align1( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_in_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks11_align1( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_in_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks13_align1( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_in_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks15_align1( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_in_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks17_align1( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream); + +void na2d_in_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks19_align1( void * attn_ptr, - void * value_ptr, - void * output_ptr, + void * d_output_ptr, + void * d_value_ptr, int batch_size, int heads, int height, @@ -3391,10 +11641,10 @@ void na2d_nn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks3_align1( float scale, cudaStream_t stream); -void na2d_nn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks5_align4( +void na2d_in_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks21_align1( void * attn_ptr, - void * value_ptr, - void * output_ptr, + void * d_output_ptr, + void * d_value_ptr, int batch_size, int heads, int height, @@ -3409,10 +11659,10 @@ void na2d_nn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks5_align4( float scale, cudaStream_t stream); -void na2d_nn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks5_align2( +void na2d_in_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks23_align1( void * attn_ptr, - void * value_ptr, - void * output_ptr, + void * d_output_ptr, + void * d_value_ptr, int batch_size, int heads, int height, @@ -3427,10 +11677,10 @@ void na2d_nn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks5_align2( float scale, cudaStream_t stream); -void na2d_nn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks5_align1( +void na2d_in_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks25_align1( void * attn_ptr, - void * value_ptr, - void * output_ptr, + void * d_output_ptr, + void * d_value_ptr, int batch_size, int heads, int height, @@ -3445,10 +11695,10 @@ void na2d_nn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks5_align1( float scale, cudaStream_t stream); -void na2d_nn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks7_align4( +void na2d_in_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks27_align1( void * attn_ptr, - void * value_ptr, - void * output_ptr, + void * d_output_ptr, + void * d_value_ptr, int batch_size, int heads, int height, @@ -3463,10 +11713,10 @@ void na2d_nn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks7_align4( float scale, cudaStream_t stream); -void na2d_nn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks7_align2( +void na2d_in_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks29_align1( void * attn_ptr, - void * value_ptr, - void * output_ptr, + void * d_output_ptr, + void * d_value_ptr, int batch_size, int heads, int height, @@ -3481,10 +11731,10 @@ void na2d_nn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks7_align2( float scale, cudaStream_t stream); -void na2d_nn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks7_align1( +void na2d_in_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks31_align1( void * attn_ptr, - void * value_ptr, - void * output_ptr, + void * d_output_ptr, + void * d_value_ptr, int batch_size, int heads, int height, @@ -3499,10 +11749,10 @@ void na2d_nn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks7_align1( float scale, cudaStream_t stream); -void na2d_nn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks9_align4( +void na2d_in_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks33_align1( void * attn_ptr, - void * value_ptr, - void * output_ptr, + void * d_output_ptr, + void * d_value_ptr, int batch_size, int heads, int height, @@ -3517,10 +11767,10 @@ void na2d_nn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks9_align4( float scale, cudaStream_t stream); -void na2d_nn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks9_align2( +void na2d_in_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks35_align1( void * attn_ptr, - void * value_ptr, - void * output_ptr, + void * d_output_ptr, + void * d_value_ptr, int batch_size, int heads, int height, @@ -3535,10 +11785,10 @@ void na2d_nn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks9_align2( float scale, cudaStream_t stream); -void na2d_nn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks9_align1( +void na2d_in_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks37_align1( void * attn_ptr, - void * value_ptr, - void * output_ptr, + void * d_output_ptr, + void * d_value_ptr, int batch_size, int heads, int height, @@ -3553,10 +11803,10 @@ void na2d_nn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks9_align1( float scale, cudaStream_t stream); -void na2d_nn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks11_align4( +void na2d_in_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks39_align1( void * attn_ptr, - void * value_ptr, - void * output_ptr, + void * d_output_ptr, + void * d_value_ptr, int batch_size, int heads, int height, @@ -3571,10 +11821,10 @@ void na2d_nn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks11_align4( float scale, cudaStream_t stream); -void na2d_nn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks11_align2( +void na2d_in_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks41_align1( void * attn_ptr, - void * value_ptr, - void * output_ptr, + void * d_output_ptr, + void * d_value_ptr, int batch_size, int heads, int height, @@ -3589,10 +11839,10 @@ void na2d_nn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks11_align2( float scale, cudaStream_t stream); -void na2d_nn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks11_align1( +void na2d_in_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks43_align1( void * attn_ptr, - void * value_ptr, - void * output_ptr, + void * d_output_ptr, + void * d_value_ptr, int batch_size, int heads, int height, @@ -3607,10 +11857,10 @@ void na2d_nn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks11_align1( float scale, cudaStream_t stream); -void na2d_nn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks13_align4( +void na2d_in_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks45_align1( void * attn_ptr, - void * value_ptr, - void * output_ptr, + void * d_output_ptr, + void * d_value_ptr, int batch_size, int heads, int height, @@ -3625,10 +11875,10 @@ void na2d_nn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks13_align4( float scale, cudaStream_t stream); -void na2d_nn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks13_align2( +void na2d_in_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks47_align1( void * attn_ptr, - void * value_ptr, - void * output_ptr, + void * d_output_ptr, + void * d_value_ptr, int batch_size, int heads, int height, @@ -3643,10 +11893,10 @@ void na2d_nn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks13_align2( float scale, cudaStream_t stream); -void na2d_nn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks13_align1( +void na2d_in_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks49_align1( void * attn_ptr, - void * value_ptr, - void * output_ptr, + void * d_output_ptr, + void * d_value_ptr, int batch_size, int heads, int height, @@ -3661,10 +11911,10 @@ void na2d_nn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks13_align1( float scale, cudaStream_t stream); -void na2d_nn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks15_align4( +void na2d_in_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks51_align1( void * attn_ptr, - void * value_ptr, - void * output_ptr, + void * d_output_ptr, + void * d_value_ptr, int batch_size, int heads, int height, @@ -3679,10 +11929,10 @@ void na2d_nn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks15_align4( float scale, cudaStream_t stream); -void na2d_nn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks15_align2( +void na2d_in_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks53_align1( void * attn_ptr, - void * value_ptr, - void * output_ptr, + void * d_output_ptr, + void * d_value_ptr, int batch_size, int heads, int height, @@ -3697,10 +11947,10 @@ void na2d_nn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks15_align2( float scale, cudaStream_t stream); -void na2d_nn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks15_align1( +void na2d_in_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks55_align1( void * attn_ptr, - void * value_ptr, - void * output_ptr, + void * d_output_ptr, + void * d_value_ptr, int batch_size, int heads, int height, @@ -3715,10 +11965,10 @@ void na2d_nn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks15_align1( float scale, cudaStream_t stream); -void na2d_nn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks17_align4( +void na2d_in_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks57_align1( void * attn_ptr, - void * value_ptr, - void * output_ptr, + void * d_output_ptr, + void * d_value_ptr, int batch_size, int heads, int height, @@ -3733,10 +11983,10 @@ void na2d_nn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks17_align4( float scale, cudaStream_t stream); -void na2d_nn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks17_align2( +void na2d_in_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks59_align1( void * attn_ptr, - void * value_ptr, - void * output_ptr, + void * d_output_ptr, + void * d_value_ptr, int batch_size, int heads, int height, @@ -3751,10 +12001,10 @@ void na2d_nn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks17_align2( float scale, cudaStream_t stream); -void na2d_nn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks17_align1( +void na2d_in_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks61_align1( void * attn_ptr, - void * value_ptr, - void * output_ptr, + void * d_output_ptr, + void * d_value_ptr, int batch_size, int heads, int height, @@ -3769,10 +12019,10 @@ void na2d_nn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks17_align1( float scale, cudaStream_t stream); -void na2d_nn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks19_align4( +void na2d_in_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks63_align1( void * attn_ptr, - void * value_ptr, - void * output_ptr, + void * d_output_ptr, + void * d_value_ptr, int batch_size, int heads, int height, @@ -3787,10 +12037,10 @@ void na2d_nn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks19_align4( float scale, cudaStream_t stream); -void na2d_nn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks19_align2( +void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks3_align4( void * attn_ptr, - void * value_ptr, - void * output_ptr, + void * d_output_ptr, + void * d_value_ptr, int batch_size, int heads, int height, @@ -3805,10 +12055,10 @@ void na2d_nn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks19_align2( float scale, cudaStream_t stream); -void na2d_nn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks19_align1( +void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks3_align2( void * attn_ptr, - void * value_ptr, - void * output_ptr, + void * d_output_ptr, + void * d_value_ptr, int batch_size, int heads, int height, @@ -3823,10 +12073,10 @@ void na2d_nn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks19_align1( float scale, cudaStream_t stream); -void na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks21_align4( +void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks3_align1( void * attn_ptr, - void * value_ptr, - void * output_ptr, + void * d_output_ptr, + void * d_value_ptr, int batch_size, int heads, int height, @@ -3841,10 +12091,10 @@ void na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks21_align4( float scale, cudaStream_t stream); -void na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks21_align2( +void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks5_align4( void * attn_ptr, - void * value_ptr, - void * output_ptr, + void * d_output_ptr, + void * d_value_ptr, int batch_size, int heads, int height, @@ -3859,10 +12109,10 @@ void na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks21_align2( float scale, cudaStream_t stream); -void na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks21_align1( +void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks5_align2( void * attn_ptr, - void * value_ptr, - void * output_ptr, + void * d_output_ptr, + void * d_value_ptr, int batch_size, int heads, int height, @@ -3877,10 +12127,10 @@ void na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks21_align1( float scale, cudaStream_t stream); -void na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks23_align4( +void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks5_align1( void * attn_ptr, - void * value_ptr, - void * output_ptr, + void * d_output_ptr, + void * d_value_ptr, int batch_size, int heads, int height, @@ -3895,10 +12145,10 @@ void na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks23_align4( float scale, cudaStream_t stream); -void na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks23_align2( +void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks7_align4( void * attn_ptr, - void * value_ptr, - void * output_ptr, + void * d_output_ptr, + void * d_value_ptr, int batch_size, int heads, int height, @@ -3913,10 +12163,10 @@ void na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks23_align2( float scale, cudaStream_t stream); -void na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks23_align1( +void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks7_align2( void * attn_ptr, - void * value_ptr, - void * output_ptr, + void * d_output_ptr, + void * d_value_ptr, int batch_size, int heads, int height, @@ -3931,10 +12181,10 @@ void na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks23_align1( float scale, cudaStream_t stream); -void na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks25_align4( +void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks7_align1( void * attn_ptr, - void * value_ptr, - void * output_ptr, + void * d_output_ptr, + void * d_value_ptr, int batch_size, int heads, int height, @@ -3949,10 +12199,10 @@ void na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks25_align4( float scale, cudaStream_t stream); -void na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks25_align2( +void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks9_align4( void * attn_ptr, - void * value_ptr, - void * output_ptr, + void * d_output_ptr, + void * d_value_ptr, int batch_size, int heads, int height, @@ -3967,10 +12217,10 @@ void na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks25_align2( float scale, cudaStream_t stream); -void na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks25_align1( +void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks9_align2( void * attn_ptr, - void * value_ptr, - void * output_ptr, + void * d_output_ptr, + void * d_value_ptr, int batch_size, int heads, int height, @@ -3985,10 +12235,10 @@ void na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks25_align1( float scale, cudaStream_t stream); -void na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks27_align4( +void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks9_align1( void * attn_ptr, - void * value_ptr, - void * output_ptr, + void * d_output_ptr, + void * d_value_ptr, int batch_size, int heads, int height, @@ -4003,10 +12253,10 @@ void na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks27_align4( float scale, cudaStream_t stream); -void na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks27_align2( +void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks11_align4( void * attn_ptr, - void * value_ptr, - void * output_ptr, + void * d_output_ptr, + void * d_value_ptr, int batch_size, int heads, int height, @@ -4021,10 +12271,10 @@ void na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks27_align2( float scale, cudaStream_t stream); -void na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks27_align1( +void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks11_align2( void * attn_ptr, - void * value_ptr, - void * output_ptr, + void * d_output_ptr, + void * d_value_ptr, int batch_size, int heads, int height, @@ -4039,10 +12289,10 @@ void na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks27_align1( float scale, cudaStream_t stream); -void na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks29_align4( +void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks11_align1( void * attn_ptr, - void * value_ptr, - void * output_ptr, + void * d_output_ptr, + void * d_value_ptr, int batch_size, int heads, int height, @@ -4057,10 +12307,10 @@ void na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks29_align4( float scale, cudaStream_t stream); -void na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks29_align2( +void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks13_align4( void * attn_ptr, - void * value_ptr, - void * output_ptr, + void * d_output_ptr, + void * d_value_ptr, int batch_size, int heads, int height, @@ -4075,10 +12325,10 @@ void na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks29_align2( float scale, cudaStream_t stream); -void na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks29_align1( +void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks13_align2( void * attn_ptr, - void * value_ptr, - void * output_ptr, + void * d_output_ptr, + void * d_value_ptr, int batch_size, int heads, int height, @@ -4093,10 +12343,10 @@ void na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks29_align1( float scale, cudaStream_t stream); -void na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks31_align4( +void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks13_align1( void * attn_ptr, - void * value_ptr, - void * output_ptr, + void * d_output_ptr, + void * d_value_ptr, int batch_size, int heads, int height, @@ -4111,10 +12361,10 @@ void na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks31_align4( float scale, cudaStream_t stream); -void na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks31_align2( +void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks15_align4( void * attn_ptr, - void * value_ptr, - void * output_ptr, + void * d_output_ptr, + void * d_value_ptr, int batch_size, int heads, int height, @@ -4129,10 +12379,10 @@ void na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks31_align2( float scale, cudaStream_t stream); -void na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks31_align1( +void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks15_align2( void * attn_ptr, - void * value_ptr, - void * output_ptr, + void * d_output_ptr, + void * d_value_ptr, int batch_size, int heads, int height, @@ -4147,10 +12397,10 @@ void na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks31_align1( float scale, cudaStream_t stream); -void na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks33_align4( +void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks15_align1( void * attn_ptr, - void * value_ptr, - void * output_ptr, + void * d_output_ptr, + void * d_value_ptr, int batch_size, int heads, int height, @@ -4165,10 +12415,10 @@ void na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks33_align4( float scale, cudaStream_t stream); -void na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks33_align2( +void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks17_align4( void * attn_ptr, - void * value_ptr, - void * output_ptr, + void * d_output_ptr, + void * d_value_ptr, int batch_size, int heads, int height, @@ -4183,10 +12433,10 @@ void na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks33_align2( float scale, cudaStream_t stream); -void na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks33_align1( +void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks17_align2( void * attn_ptr, - void * value_ptr, - void * output_ptr, + void * d_output_ptr, + void * d_value_ptr, int batch_size, int heads, int height, @@ -4201,10 +12451,10 @@ void na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks33_align1( float scale, cudaStream_t stream); -void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks3_align8( +void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks17_align1( void * attn_ptr, - void * value_ptr, - void * output_ptr, + void * d_output_ptr, + void * d_value_ptr, int batch_size, int heads, int height, @@ -4219,10 +12469,10 @@ void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks3_align8( float scale, cudaStream_t stream); -void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks3_align4( +void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks19_align4( void * attn_ptr, - void * value_ptr, - void * output_ptr, + void * d_output_ptr, + void * d_value_ptr, int batch_size, int heads, int height, @@ -4237,10 +12487,10 @@ void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks3_align4( float scale, cudaStream_t stream); -void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks3_align2( +void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks19_align2( void * attn_ptr, - void * value_ptr, - void * output_ptr, + void * d_output_ptr, + void * d_value_ptr, int batch_size, int heads, int height, @@ -4255,10 +12505,10 @@ void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks3_align2( float scale, cudaStream_t stream); -void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks5_align8( +void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks19_align1( void * attn_ptr, - void * value_ptr, - void * output_ptr, + void * d_output_ptr, + void * d_value_ptr, int batch_size, int heads, int height, @@ -4273,10 +12523,10 @@ void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks5_align8( float scale, cudaStream_t stream); -void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks5_align4( +void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks21_align4( void * attn_ptr, - void * value_ptr, - void * output_ptr, + void * d_output_ptr, + void * d_value_ptr, int batch_size, int heads, int height, @@ -4291,10 +12541,10 @@ void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks5_align4( float scale, cudaStream_t stream); -void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks5_align2( +void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks21_align2( void * attn_ptr, - void * value_ptr, - void * output_ptr, + void * d_output_ptr, + void * d_value_ptr, int batch_size, int heads, int height, @@ -4309,10 +12559,10 @@ void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks5_align2( float scale, cudaStream_t stream); -void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks7_align8( +void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks21_align1( void * attn_ptr, - void * value_ptr, - void * output_ptr, + void * d_output_ptr, + void * d_value_ptr, int batch_size, int heads, int height, @@ -4327,10 +12577,10 @@ void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks7_align8( float scale, cudaStream_t stream); -void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks7_align4( +void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks23_align4( void * attn_ptr, - void * value_ptr, - void * output_ptr, + void * d_output_ptr, + void * d_value_ptr, int batch_size, int heads, int height, @@ -4345,10 +12595,10 @@ void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks7_align4( float scale, cudaStream_t stream); -void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks7_align2( +void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks23_align2( void * attn_ptr, - void * value_ptr, - void * output_ptr, + void * d_output_ptr, + void * d_value_ptr, int batch_size, int heads, int height, @@ -4363,10 +12613,10 @@ void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks7_align2( float scale, cudaStream_t stream); -void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks9_align8( +void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks23_align1( void * attn_ptr, - void * value_ptr, - void * output_ptr, + void * d_output_ptr, + void * d_value_ptr, int batch_size, int heads, int height, @@ -4381,10 +12631,10 @@ void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks9_align8( float scale, cudaStream_t stream); -void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks9_align4( +void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks25_align4( void * attn_ptr, - void * value_ptr, - void * output_ptr, + void * d_output_ptr, + void * d_value_ptr, int batch_size, int heads, int height, @@ -4399,10 +12649,10 @@ void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks9_align4( float scale, cudaStream_t stream); -void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks9_align2( +void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks25_align2( void * attn_ptr, - void * value_ptr, - void * output_ptr, + void * d_output_ptr, + void * d_value_ptr, int batch_size, int heads, int height, @@ -4417,10 +12667,10 @@ void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks9_align2( float scale, cudaStream_t stream); -void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks11_align8( +void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks25_align1( void * attn_ptr, - void * value_ptr, - void * output_ptr, + void * d_output_ptr, + void * d_value_ptr, int batch_size, int heads, int height, @@ -4435,10 +12685,10 @@ void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks11_align8( float scale, cudaStream_t stream); -void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks11_align4( +void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks27_align4( void * attn_ptr, - void * value_ptr, - void * output_ptr, + void * d_output_ptr, + void * d_value_ptr, int batch_size, int heads, int height, @@ -4453,10 +12703,10 @@ void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks11_align4( float scale, cudaStream_t stream); -void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks11_align2( +void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks27_align2( void * attn_ptr, - void * value_ptr, - void * output_ptr, + void * d_output_ptr, + void * d_value_ptr, int batch_size, int heads, int height, @@ -4471,10 +12721,10 @@ void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks11_align2( float scale, cudaStream_t stream); -void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks13_align8( +void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks27_align1( void * attn_ptr, - void * value_ptr, - void * output_ptr, + void * d_output_ptr, + void * d_value_ptr, int batch_size, int heads, int height, @@ -4489,10 +12739,10 @@ void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks13_align8( float scale, cudaStream_t stream); -void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks13_align4( +void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks29_align4( void * attn_ptr, - void * value_ptr, - void * output_ptr, + void * d_output_ptr, + void * d_value_ptr, int batch_size, int heads, int height, @@ -4507,10 +12757,10 @@ void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks13_align4( float scale, cudaStream_t stream); -void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks13_align2( +void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks29_align2( void * attn_ptr, - void * value_ptr, - void * output_ptr, + void * d_output_ptr, + void * d_value_ptr, int batch_size, int heads, int height, @@ -4525,10 +12775,10 @@ void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks13_align2( float scale, cudaStream_t stream); -void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks15_align8( +void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks29_align1( void * attn_ptr, - void * value_ptr, - void * output_ptr, + void * d_output_ptr, + void * d_value_ptr, int batch_size, int heads, int height, @@ -4543,10 +12793,10 @@ void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks15_align8( float scale, cudaStream_t stream); -void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks15_align4( +void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks31_align4( void * attn_ptr, - void * value_ptr, - void * output_ptr, + void * d_output_ptr, + void * d_value_ptr, int batch_size, int heads, int height, @@ -4561,10 +12811,10 @@ void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks15_align4( float scale, cudaStream_t stream); -void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks15_align2( +void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks31_align2( void * attn_ptr, - void * value_ptr, - void * output_ptr, + void * d_output_ptr, + void * d_value_ptr, int batch_size, int heads, int height, @@ -4579,10 +12829,10 @@ void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks15_align2( float scale, cudaStream_t stream); -void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks17_align8( +void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks31_align1( void * attn_ptr, - void * value_ptr, - void * output_ptr, + void * d_output_ptr, + void * d_value_ptr, int batch_size, int heads, int height, @@ -4597,10 +12847,10 @@ void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks17_align8( float scale, cudaStream_t stream); -void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks17_align4( +void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks33_align4( void * attn_ptr, - void * value_ptr, - void * output_ptr, + void * d_output_ptr, + void * d_value_ptr, int batch_size, int heads, int height, @@ -4615,10 +12865,10 @@ void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks17_align4( float scale, cudaStream_t stream); -void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks17_align2( +void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks33_align2( void * attn_ptr, - void * value_ptr, - void * output_ptr, + void * d_output_ptr, + void * d_value_ptr, int batch_size, int heads, int height, @@ -4633,10 +12883,10 @@ void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks17_align2( float scale, cudaStream_t stream); -void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks19_align8( +void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks33_align1( void * attn_ptr, - void * value_ptr, - void * output_ptr, + void * d_output_ptr, + void * d_value_ptr, int batch_size, int heads, int height, @@ -4651,10 +12901,10 @@ void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks19_align8( float scale, cudaStream_t stream); -void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks19_align4( +void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks35_align4( void * attn_ptr, - void * value_ptr, - void * output_ptr, + void * d_output_ptr, + void * d_value_ptr, int batch_size, int heads, int height, @@ -4669,10 +12919,10 @@ void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks19_align4( float scale, cudaStream_t stream); -void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks19_align2( +void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks35_align2( void * attn_ptr, - void * value_ptr, - void * output_ptr, + void * d_output_ptr, + void * d_value_ptr, int batch_size, int heads, int height, @@ -4687,10 +12937,10 @@ void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks19_align2( float scale, cudaStream_t stream); -void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks21_align8( +void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks35_align1( void * attn_ptr, - void * value_ptr, - void * output_ptr, + void * d_output_ptr, + void * d_value_ptr, int batch_size, int heads, int height, @@ -4705,10 +12955,10 @@ void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks21_align8( float scale, cudaStream_t stream); -void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks21_align4( +void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks37_align4( void * attn_ptr, - void * value_ptr, - void * output_ptr, + void * d_output_ptr, + void * d_value_ptr, int batch_size, int heads, int height, @@ -4723,10 +12973,10 @@ void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks21_align4( float scale, cudaStream_t stream); -void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks21_align2( +void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks37_align2( void * attn_ptr, - void * value_ptr, - void * output_ptr, + void * d_output_ptr, + void * d_value_ptr, int batch_size, int heads, int height, @@ -4741,10 +12991,10 @@ void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks21_align2( float scale, cudaStream_t stream); -void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks23_align8( +void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks37_align1( void * attn_ptr, - void * value_ptr, - void * output_ptr, + void * d_output_ptr, + void * d_value_ptr, int batch_size, int heads, int height, @@ -4759,10 +13009,10 @@ void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks23_align8( float scale, cudaStream_t stream); -void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks23_align4( +void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks39_align4( void * attn_ptr, - void * value_ptr, - void * output_ptr, + void * d_output_ptr, + void * d_value_ptr, int batch_size, int heads, int height, @@ -4777,10 +13027,10 @@ void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks23_align4( float scale, cudaStream_t stream); -void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks23_align2( +void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks39_align2( void * attn_ptr, - void * value_ptr, - void * output_ptr, + void * d_output_ptr, + void * d_value_ptr, int batch_size, int heads, int height, @@ -4795,10 +13045,10 @@ void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks23_align2( float scale, cudaStream_t stream); -void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks25_align8( +void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks39_align1( void * attn_ptr, - void * value_ptr, - void * output_ptr, + void * d_output_ptr, + void * d_value_ptr, int batch_size, int heads, int height, @@ -4813,10 +13063,10 @@ void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks25_align8( float scale, cudaStream_t stream); -void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks25_align4( +void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks41_align4( void * attn_ptr, - void * value_ptr, - void * output_ptr, + void * d_output_ptr, + void * d_value_ptr, int batch_size, int heads, int height, @@ -4831,10 +13081,10 @@ void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks25_align4( float scale, cudaStream_t stream); -void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks25_align2( +void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks41_align2( void * attn_ptr, - void * value_ptr, - void * output_ptr, + void * d_output_ptr, + void * d_value_ptr, int batch_size, int heads, int height, @@ -4849,10 +13099,10 @@ void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks25_align2( float scale, cudaStream_t stream); -void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks27_align8( +void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks41_align1( void * attn_ptr, - void * value_ptr, - void * output_ptr, + void * d_output_ptr, + void * d_value_ptr, int batch_size, int heads, int height, @@ -4867,10 +13117,10 @@ void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks27_align8( float scale, cudaStream_t stream); -void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks27_align4( +void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks43_align4( void * attn_ptr, - void * value_ptr, - void * output_ptr, + void * d_output_ptr, + void * d_value_ptr, int batch_size, int heads, int height, @@ -4885,10 +13135,10 @@ void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks27_align4( float scale, cudaStream_t stream); -void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks27_align2( +void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks43_align2( void * attn_ptr, - void * value_ptr, - void * output_ptr, + void * d_output_ptr, + void * d_value_ptr, int batch_size, int heads, int height, @@ -4903,10 +13153,10 @@ void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks27_align2( float scale, cudaStream_t stream); -void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks29_align8( +void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks43_align1( void * attn_ptr, - void * value_ptr, - void * output_ptr, + void * d_output_ptr, + void * d_value_ptr, int batch_size, int heads, int height, @@ -4921,10 +13171,10 @@ void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks29_align8( float scale, cudaStream_t stream); -void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks29_align4( +void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks45_align4( void * attn_ptr, - void * value_ptr, - void * output_ptr, + void * d_output_ptr, + void * d_value_ptr, int batch_size, int heads, int height, @@ -4939,10 +13189,10 @@ void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks29_align4( float scale, cudaStream_t stream); -void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks29_align2( +void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks45_align2( void * attn_ptr, - void * value_ptr, - void * output_ptr, + void * d_output_ptr, + void * d_value_ptr, int batch_size, int heads, int height, @@ -4957,10 +13207,10 @@ void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks29_align2( float scale, cudaStream_t stream); -void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks31_align8( +void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks45_align1( void * attn_ptr, - void * value_ptr, - void * output_ptr, + void * d_output_ptr, + void * d_value_ptr, int batch_size, int heads, int height, @@ -4975,10 +13225,10 @@ void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks31_align8( float scale, cudaStream_t stream); -void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks31_align4( +void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks47_align4( void * attn_ptr, - void * value_ptr, - void * output_ptr, + void * d_output_ptr, + void * d_value_ptr, int batch_size, int heads, int height, @@ -4993,10 +13243,10 @@ void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks31_align4( float scale, cudaStream_t stream); -void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks31_align2( +void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks47_align2( void * attn_ptr, - void * value_ptr, - void * output_ptr, + void * d_output_ptr, + void * d_value_ptr, int batch_size, int heads, int height, @@ -5011,10 +13261,10 @@ void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks31_align2( float scale, cudaStream_t stream); -void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks33_align8( +void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks47_align1( void * attn_ptr, - void * value_ptr, - void * output_ptr, + void * d_output_ptr, + void * d_value_ptr, int batch_size, int heads, int height, @@ -5029,10 +13279,10 @@ void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks33_align8( float scale, cudaStream_t stream); -void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks33_align4( +void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks49_align4( void * attn_ptr, - void * value_ptr, - void * output_ptr, + void * d_output_ptr, + void * d_value_ptr, int batch_size, int heads, int height, @@ -5047,10 +13297,10 @@ void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks33_align4( float scale, cudaStream_t stream); -void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks33_align2( +void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks49_align2( void * attn_ptr, - void * value_ptr, - void * output_ptr, + void * d_output_ptr, + void * d_value_ptr, int batch_size, int heads, int height, @@ -5065,10 +13315,10 @@ void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks33_align2( float scale, cudaStream_t stream); -void na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks3_align8( +void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks49_align1( void * attn_ptr, - void * value_ptr, - void * output_ptr, + void * d_output_ptr, + void * d_value_ptr, int batch_size, int heads, int height, @@ -5083,10 +13333,10 @@ void na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks3_align8( float scale, cudaStream_t stream); -void na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks3_align4( +void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks51_align4( void * attn_ptr, - void * value_ptr, - void * output_ptr, + void * d_output_ptr, + void * d_value_ptr, int batch_size, int heads, int height, @@ -5101,10 +13351,10 @@ void na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks3_align4( float scale, cudaStream_t stream); -void na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks3_align2( +void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks51_align2( void * attn_ptr, - void * value_ptr, - void * output_ptr, + void * d_output_ptr, + void * d_value_ptr, int batch_size, int heads, int height, @@ -5119,10 +13369,10 @@ void na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks3_align2( float scale, cudaStream_t stream); -void na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks5_align8( +void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks51_align1( void * attn_ptr, - void * value_ptr, - void * output_ptr, + void * d_output_ptr, + void * d_value_ptr, int batch_size, int heads, int height, @@ -5137,10 +13387,10 @@ void na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks5_align8( float scale, cudaStream_t stream); -void na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks5_align4( +void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks53_align4( void * attn_ptr, - void * value_ptr, - void * output_ptr, + void * d_output_ptr, + void * d_value_ptr, int batch_size, int heads, int height, @@ -5155,10 +13405,10 @@ void na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks5_align4( float scale, cudaStream_t stream); -void na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks5_align2( +void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks53_align2( void * attn_ptr, - void * value_ptr, - void * output_ptr, + void * d_output_ptr, + void * d_value_ptr, int batch_size, int heads, int height, @@ -5173,10 +13423,10 @@ void na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks5_align2( float scale, cudaStream_t stream); -void na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks7_align8( +void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks53_align1( void * attn_ptr, - void * value_ptr, - void * output_ptr, + void * d_output_ptr, + void * d_value_ptr, int batch_size, int heads, int height, @@ -5191,10 +13441,10 @@ void na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks7_align8( float scale, cudaStream_t stream); -void na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks7_align4( +void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks55_align4( void * attn_ptr, - void * value_ptr, - void * output_ptr, + void * d_output_ptr, + void * d_value_ptr, int batch_size, int heads, int height, @@ -5209,10 +13459,10 @@ void na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks7_align4( float scale, cudaStream_t stream); -void na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks7_align2( +void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks55_align2( void * attn_ptr, - void * value_ptr, - void * output_ptr, + void * d_output_ptr, + void * d_value_ptr, int batch_size, int heads, int height, @@ -5227,10 +13477,10 @@ void na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks7_align2( float scale, cudaStream_t stream); -void na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks9_align8( +void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks55_align1( void * attn_ptr, - void * value_ptr, - void * output_ptr, + void * d_output_ptr, + void * d_value_ptr, int batch_size, int heads, int height, @@ -5245,10 +13495,10 @@ void na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks9_align8( float scale, cudaStream_t stream); -void na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks9_align4( +void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks57_align4( void * attn_ptr, - void * value_ptr, - void * output_ptr, + void * d_output_ptr, + void * d_value_ptr, int batch_size, int heads, int height, @@ -5263,10 +13513,10 @@ void na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks9_align4( float scale, cudaStream_t stream); -void na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks9_align2( +void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks57_align2( void * attn_ptr, - void * value_ptr, - void * output_ptr, + void * d_output_ptr, + void * d_value_ptr, int batch_size, int heads, int height, @@ -5281,10 +13531,10 @@ void na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks9_align2( float scale, cudaStream_t stream); -void na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks11_align8( +void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks57_align1( void * attn_ptr, - void * value_ptr, - void * output_ptr, + void * d_output_ptr, + void * d_value_ptr, int batch_size, int heads, int height, @@ -5299,10 +13549,10 @@ void na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks11_align8( float scale, cudaStream_t stream); -void na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks11_align4( +void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks59_align4( void * attn_ptr, - void * value_ptr, - void * output_ptr, + void * d_output_ptr, + void * d_value_ptr, int batch_size, int heads, int height, @@ -5317,10 +13567,10 @@ void na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks11_align4( float scale, cudaStream_t stream); -void na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks11_align2( +void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks59_align2( void * attn_ptr, - void * value_ptr, - void * output_ptr, + void * d_output_ptr, + void * d_value_ptr, int batch_size, int heads, int height, @@ -5335,10 +13585,10 @@ void na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks11_align2( float scale, cudaStream_t stream); -void na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks13_align8( +void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks59_align1( void * attn_ptr, - void * value_ptr, - void * output_ptr, + void * d_output_ptr, + void * d_value_ptr, int batch_size, int heads, int height, @@ -5353,10 +13603,10 @@ void na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks13_align8( float scale, cudaStream_t stream); -void na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks13_align4( +void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks61_align4( void * attn_ptr, - void * value_ptr, - void * output_ptr, + void * d_output_ptr, + void * d_value_ptr, int batch_size, int heads, int height, @@ -5371,10 +13621,10 @@ void na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks13_align4( float scale, cudaStream_t stream); -void na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks13_align2( +void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks61_align2( void * attn_ptr, - void * value_ptr, - void * output_ptr, + void * d_output_ptr, + void * d_value_ptr, int batch_size, int heads, int height, @@ -5389,10 +13639,10 @@ void na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks13_align2( float scale, cudaStream_t stream); -void na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks15_align8( +void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks61_align1( void * attn_ptr, - void * value_ptr, - void * output_ptr, + void * d_output_ptr, + void * d_value_ptr, int batch_size, int heads, int height, @@ -5407,10 +13657,10 @@ void na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks15_align8( float scale, cudaStream_t stream); -void na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks15_align4( +void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks63_align4( void * attn_ptr, - void * value_ptr, - void * output_ptr, + void * d_output_ptr, + void * d_value_ptr, int batch_size, int heads, int height, @@ -5425,10 +13675,10 @@ void na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks15_align4( float scale, cudaStream_t stream); -void na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks15_align2( +void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks63_align2( void * attn_ptr, - void * value_ptr, - void * output_ptr, + void * d_output_ptr, + void * d_value_ptr, int batch_size, int heads, int height, @@ -5443,10 +13693,10 @@ void na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks15_align2( float scale, cudaStream_t stream); -void na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks17_align8( +void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks63_align1( void * attn_ptr, - void * value_ptr, - void * output_ptr, + void * d_output_ptr, + void * d_value_ptr, int batch_size, int heads, int height, @@ -5461,10 +13711,10 @@ void na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks17_align8( float scale, cudaStream_t stream); -void na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks17_align4( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks3_align8( void * attn_ptr, - void * value_ptr, - void * output_ptr, + void * d_output_ptr, + void * d_value_ptr, int batch_size, int heads, int height, @@ -5479,10 +13729,10 @@ void na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks17_align4( float scale, cudaStream_t stream); -void na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks17_align2( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks3_align4( void * attn_ptr, - void * value_ptr, - void * output_ptr, + void * d_output_ptr, + void * d_value_ptr, int batch_size, int heads, int height, @@ -5497,10 +13747,10 @@ void na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks17_align2( float scale, cudaStream_t stream); -void na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks19_align8( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks3_align2( void * attn_ptr, - void * value_ptr, - void * output_ptr, + void * d_output_ptr, + void * d_value_ptr, int batch_size, int heads, int height, @@ -5515,10 +13765,10 @@ void na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks19_align8( float scale, cudaStream_t stream); -void na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks19_align4( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks5_align8( void * attn_ptr, - void * value_ptr, - void * output_ptr, + void * d_output_ptr, + void * d_value_ptr, int batch_size, int heads, int height, @@ -5533,10 +13783,10 @@ void na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks19_align4( float scale, cudaStream_t stream); -void na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks19_align2( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks5_align4( void * attn_ptr, - void * value_ptr, - void * output_ptr, + void * d_output_ptr, + void * d_value_ptr, int batch_size, int heads, int height, @@ -5551,10 +13801,10 @@ void na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks19_align2( float scale, cudaStream_t stream); -void na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks21_align8( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks5_align2( void * attn_ptr, - void * value_ptr, - void * output_ptr, + void * d_output_ptr, + void * d_value_ptr, int batch_size, int heads, int height, @@ -5569,10 +13819,10 @@ void na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks21_align8( float scale, cudaStream_t stream); -void na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks21_align4( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks7_align8( void * attn_ptr, - void * value_ptr, - void * output_ptr, + void * d_output_ptr, + void * d_value_ptr, int batch_size, int heads, int height, @@ -5587,10 +13837,10 @@ void na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks21_align4( float scale, cudaStream_t stream); -void na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks21_align2( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks7_align4( void * attn_ptr, - void * value_ptr, - void * output_ptr, + void * d_output_ptr, + void * d_value_ptr, int batch_size, int heads, int height, @@ -5605,10 +13855,10 @@ void na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks21_align2( float scale, cudaStream_t stream); -void na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks23_align8( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks7_align2( void * attn_ptr, - void * value_ptr, - void * output_ptr, + void * d_output_ptr, + void * d_value_ptr, int batch_size, int heads, int height, @@ -5623,10 +13873,10 @@ void na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks23_align8( float scale, cudaStream_t stream); -void na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks23_align4( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks9_align8( void * attn_ptr, - void * value_ptr, - void * output_ptr, + void * d_output_ptr, + void * d_value_ptr, int batch_size, int heads, int height, @@ -5641,10 +13891,10 @@ void na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks23_align4( float scale, cudaStream_t stream); -void na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks23_align2( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks9_align4( void * attn_ptr, - void * value_ptr, - void * output_ptr, + void * d_output_ptr, + void * d_value_ptr, int batch_size, int heads, int height, @@ -5659,10 +13909,10 @@ void na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks23_align2( float scale, cudaStream_t stream); -void na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks25_align8( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks9_align2( void * attn_ptr, - void * value_ptr, - void * output_ptr, + void * d_output_ptr, + void * d_value_ptr, int batch_size, int heads, int height, @@ -5677,10 +13927,10 @@ void na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks25_align8( float scale, cudaStream_t stream); -void na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks25_align4( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks11_align8( void * attn_ptr, - void * value_ptr, - void * output_ptr, + void * d_output_ptr, + void * d_value_ptr, int batch_size, int heads, int height, @@ -5695,10 +13945,10 @@ void na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks25_align4( float scale, cudaStream_t stream); -void na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks25_align2( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks11_align4( void * attn_ptr, - void * value_ptr, - void * output_ptr, + void * d_output_ptr, + void * d_value_ptr, int batch_size, int heads, int height, @@ -5713,10 +13963,10 @@ void na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks25_align2( float scale, cudaStream_t stream); -void na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks27_align8( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks11_align2( void * attn_ptr, - void * value_ptr, - void * output_ptr, + void * d_output_ptr, + void * d_value_ptr, int batch_size, int heads, int height, @@ -5731,10 +13981,10 @@ void na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks27_align8( float scale, cudaStream_t stream); -void na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks27_align4( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks13_align8( void * attn_ptr, - void * value_ptr, - void * output_ptr, + void * d_output_ptr, + void * d_value_ptr, int batch_size, int heads, int height, @@ -5749,10 +13999,10 @@ void na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks27_align4( float scale, cudaStream_t stream); -void na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks27_align2( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks13_align4( void * attn_ptr, - void * value_ptr, - void * output_ptr, + void * d_output_ptr, + void * d_value_ptr, int batch_size, int heads, int height, @@ -5767,10 +14017,10 @@ void na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks27_align2( float scale, cudaStream_t stream); -void na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks29_align8( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks13_align2( void * attn_ptr, - void * value_ptr, - void * output_ptr, + void * d_output_ptr, + void * d_value_ptr, int batch_size, int heads, int height, @@ -5785,10 +14035,10 @@ void na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks29_align8( float scale, cudaStream_t stream); -void na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks29_align4( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks15_align8( void * attn_ptr, - void * value_ptr, - void * output_ptr, + void * d_output_ptr, + void * d_value_ptr, int batch_size, int heads, int height, @@ -5803,10 +14053,10 @@ void na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks29_align4( float scale, cudaStream_t stream); -void na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks29_align2( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks15_align4( void * attn_ptr, - void * value_ptr, - void * output_ptr, + void * d_output_ptr, + void * d_value_ptr, int batch_size, int heads, int height, @@ -5821,10 +14071,10 @@ void na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks29_align2( float scale, cudaStream_t stream); -void na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks31_align8( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks15_align2( void * attn_ptr, - void * value_ptr, - void * output_ptr, + void * d_output_ptr, + void * d_value_ptr, int batch_size, int heads, int height, @@ -5839,10 +14089,10 @@ void na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks31_align8( float scale, cudaStream_t stream); -void na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks31_align4( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks17_align8( void * attn_ptr, - void * value_ptr, - void * output_ptr, + void * d_output_ptr, + void * d_value_ptr, int batch_size, int heads, int height, @@ -5857,10 +14107,10 @@ void na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks31_align4( float scale, cudaStream_t stream); -void na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks31_align2( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks17_align4( void * attn_ptr, - void * value_ptr, - void * output_ptr, + void * d_output_ptr, + void * d_value_ptr, int batch_size, int heads, int height, @@ -5875,10 +14125,10 @@ void na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks31_align2( float scale, cudaStream_t stream); -void na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks33_align8( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks17_align2( void * attn_ptr, - void * value_ptr, - void * output_ptr, + void * d_output_ptr, + void * d_value_ptr, int batch_size, int heads, int height, @@ -5893,10 +14143,10 @@ void na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks33_align8( float scale, cudaStream_t stream); -void na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks33_align4( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks19_align8( void * attn_ptr, - void * value_ptr, - void * output_ptr, + void * d_output_ptr, + void * d_value_ptr, int batch_size, int heads, int height, @@ -5911,10 +14161,10 @@ void na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks33_align4( float scale, cudaStream_t stream); -void na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks33_align2( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks19_align4( void * attn_ptr, - void * value_ptr, - void * output_ptr, + void * d_output_ptr, + void * d_value_ptr, int batch_size, int heads, int height, @@ -5929,7 +14179,7 @@ void na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks33_align2( float scale, cudaStream_t stream); -void na2d_in_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks3_align1( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks19_align2( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -5947,7 +14197,7 @@ void na2d_in_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks3_align1( float scale, cudaStream_t stream); -void na2d_in_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks5_align1( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks21_align8( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -5965,7 +14215,7 @@ void na2d_in_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks5_align1( float scale, cudaStream_t stream); -void na2d_in_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks7_align1( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks21_align4( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -5983,7 +14233,7 @@ void na2d_in_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks7_align1( float scale, cudaStream_t stream); -void na2d_in_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks9_align1( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks21_align2( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -6001,7 +14251,7 @@ void na2d_in_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks9_align1( float scale, cudaStream_t stream); -void na2d_in_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks11_align1( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks23_align8( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -6019,7 +14269,7 @@ void na2d_in_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks11_align1( float scale, cudaStream_t stream); -void na2d_in_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks13_align1( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks23_align4( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -6037,7 +14287,7 @@ void na2d_in_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks13_align1( float scale, cudaStream_t stream); -void na2d_in_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks15_align1( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks23_align2( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -6055,7 +14305,7 @@ void na2d_in_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks15_align1( float scale, cudaStream_t stream); -void na2d_in_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks17_align1( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks25_align8( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -6073,7 +14323,7 @@ void na2d_in_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks17_align1( float scale, cudaStream_t stream); -void na2d_in_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks19_align1( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks25_align4( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -6091,7 +14341,7 @@ void na2d_in_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks19_align1( float scale, cudaStream_t stream); -void na2d_in_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks21_align1( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks25_align2( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -6109,7 +14359,7 @@ void na2d_in_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks21_align1( float scale, cudaStream_t stream); -void na2d_in_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks23_align1( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks27_align8( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -6127,7 +14377,7 @@ void na2d_in_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks23_align1( float scale, cudaStream_t stream); -void na2d_in_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks25_align1( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks27_align4( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -6145,7 +14395,7 @@ void na2d_in_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks25_align1( float scale, cudaStream_t stream); -void na2d_in_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks27_align1( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks27_align2( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -6163,7 +14413,7 @@ void na2d_in_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks27_align1( float scale, cudaStream_t stream); -void na2d_in_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks29_align1( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks29_align8( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -6181,7 +14431,7 @@ void na2d_in_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks29_align1( float scale, cudaStream_t stream); -void na2d_in_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks31_align1( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks29_align4( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -6199,7 +14449,7 @@ void na2d_in_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks31_align1( float scale, cudaStream_t stream); -void na2d_in_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks33_align1( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks29_align2( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -6217,7 +14467,7 @@ void na2d_in_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks33_align1( float scale, cudaStream_t stream); -void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks3_align4( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks31_align8( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -6235,7 +14485,7 @@ void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks3_align4( float scale, cudaStream_t stream); -void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks3_align2( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks31_align4( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -6253,7 +14503,7 @@ void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks3_align2( float scale, cudaStream_t stream); -void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks3_align1( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks31_align2( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -6271,7 +14521,7 @@ void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks3_align1( float scale, cudaStream_t stream); -void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks5_align4( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks33_align8( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -6289,7 +14539,7 @@ void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks5_align4( float scale, cudaStream_t stream); -void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks5_align2( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks33_align4( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -6307,7 +14557,7 @@ void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks5_align2( float scale, cudaStream_t stream); -void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks5_align1( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks33_align2( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -6325,7 +14575,7 @@ void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks5_align1( float scale, cudaStream_t stream); -void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks7_align4( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks35_align8( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -6343,7 +14593,7 @@ void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks7_align4( float scale, cudaStream_t stream); -void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks7_align2( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks35_align4( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -6361,7 +14611,7 @@ void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks7_align2( float scale, cudaStream_t stream); -void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks7_align1( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks35_align2( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -6379,7 +14629,7 @@ void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks7_align1( float scale, cudaStream_t stream); -void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks9_align4( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks37_align8( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -6397,7 +14647,7 @@ void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks9_align4( float scale, cudaStream_t stream); -void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks9_align2( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks37_align4( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -6415,7 +14665,7 @@ void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks9_align2( float scale, cudaStream_t stream); -void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks9_align1( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks37_align2( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -6433,7 +14683,7 @@ void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks9_align1( float scale, cudaStream_t stream); -void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks11_align4( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks39_align8( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -6451,7 +14701,7 @@ void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks11_align4( float scale, cudaStream_t stream); -void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks11_align2( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks39_align4( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -6469,7 +14719,7 @@ void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks11_align2( float scale, cudaStream_t stream); -void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks11_align1( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks39_align2( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -6487,7 +14737,7 @@ void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks11_align1( float scale, cudaStream_t stream); -void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks13_align4( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks41_align8( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -6505,7 +14755,7 @@ void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks13_align4( float scale, cudaStream_t stream); -void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks13_align2( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks41_align4( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -6523,7 +14773,7 @@ void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks13_align2( float scale, cudaStream_t stream); -void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks13_align1( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks41_align2( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -6541,7 +14791,7 @@ void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks13_align1( float scale, cudaStream_t stream); -void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks15_align4( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks43_align8( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -6559,7 +14809,7 @@ void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks15_align4( float scale, cudaStream_t stream); -void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks15_align2( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks43_align4( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -6577,7 +14827,7 @@ void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks15_align2( float scale, cudaStream_t stream); -void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks15_align1( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks43_align2( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -6595,7 +14845,7 @@ void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks15_align1( float scale, cudaStream_t stream); -void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks17_align4( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks45_align8( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -6613,7 +14863,7 @@ void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks17_align4( float scale, cudaStream_t stream); -void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks17_align2( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks45_align4( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -6631,7 +14881,7 @@ void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks17_align2( float scale, cudaStream_t stream); -void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks17_align1( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks45_align2( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -6649,7 +14899,7 @@ void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks17_align1( float scale, cudaStream_t stream); -void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks19_align4( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks47_align8( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -6667,7 +14917,7 @@ void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks19_align4( float scale, cudaStream_t stream); -void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks19_align2( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks47_align4( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -6685,7 +14935,7 @@ void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks19_align2( float scale, cudaStream_t stream); -void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks19_align1( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks47_align2( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -6703,7 +14953,7 @@ void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks19_align1( float scale, cudaStream_t stream); -void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks21_align4( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks49_align8( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -6721,7 +14971,7 @@ void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks21_align4( float scale, cudaStream_t stream); -void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks21_align2( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks49_align4( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -6739,7 +14989,7 @@ void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks21_align2( float scale, cudaStream_t stream); -void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks21_align1( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks49_align2( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -6757,7 +15007,7 @@ void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks21_align1( float scale, cudaStream_t stream); -void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks23_align4( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks51_align8( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -6775,7 +15025,7 @@ void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks23_align4( float scale, cudaStream_t stream); -void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks23_align2( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks51_align4( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -6793,7 +15043,7 @@ void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks23_align2( float scale, cudaStream_t stream); -void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks23_align1( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks51_align2( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -6811,7 +15061,7 @@ void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks23_align1( float scale, cudaStream_t stream); -void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks25_align4( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks53_align8( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -6829,7 +15079,7 @@ void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks25_align4( float scale, cudaStream_t stream); -void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks25_align2( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks53_align4( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -6847,7 +15097,7 @@ void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks25_align2( float scale, cudaStream_t stream); -void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks25_align1( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks53_align2( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -6865,7 +15115,7 @@ void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks25_align1( float scale, cudaStream_t stream); -void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks27_align4( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks55_align8( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -6883,7 +15133,7 @@ void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks27_align4( float scale, cudaStream_t stream); -void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks27_align2( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks55_align4( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -6901,7 +15151,7 @@ void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks27_align2( float scale, cudaStream_t stream); -void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks27_align1( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks55_align2( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -6919,7 +15169,7 @@ void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks27_align1( float scale, cudaStream_t stream); -void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks29_align4( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks57_align8( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -6937,7 +15187,7 @@ void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks29_align4( float scale, cudaStream_t stream); -void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks29_align2( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks57_align4( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -6955,7 +15205,7 @@ void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks29_align2( float scale, cudaStream_t stream); -void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks29_align1( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks57_align2( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -6973,7 +15223,7 @@ void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks29_align1( float scale, cudaStream_t stream); -void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks31_align4( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks59_align8( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -6991,7 +15241,7 @@ void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks31_align4( float scale, cudaStream_t stream); -void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks31_align2( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks59_align4( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -7009,7 +15259,7 @@ void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks31_align2( float scale, cudaStream_t stream); -void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks31_align1( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks59_align2( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -7027,7 +15277,7 @@ void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks31_align1( float scale, cudaStream_t stream); -void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks33_align4( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks61_align8( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -7045,7 +15295,7 @@ void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks33_align4( float scale, cudaStream_t stream); -void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks33_align2( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks61_align4( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -7063,7 +15313,7 @@ void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks33_align2( float scale, cudaStream_t stream); -void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks33_align1( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks61_align2( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -7081,7 +15331,7 @@ void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks33_align1( float scale, cudaStream_t stream); -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks3_align8( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks63_align8( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -7099,7 +15349,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks3_align8( float scale, cudaStream_t stream); -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks3_align4( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks63_align4( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -7117,7 +15367,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks3_align4( float scale, cudaStream_t stream); -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks3_align2( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks63_align2( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -7135,7 +15385,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks3_align2( float scale, cudaStream_t stream); -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks5_align8( +void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks3_align8( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -7153,7 +15403,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks5_align8( float scale, cudaStream_t stream); -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks5_align4( +void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks3_align4( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -7171,7 +15421,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks5_align4( float scale, cudaStream_t stream); -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks5_align2( +void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks3_align2( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -7189,7 +15439,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks5_align2( float scale, cudaStream_t stream); -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks7_align8( +void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks5_align8( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -7207,7 +15457,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks7_align8( float scale, cudaStream_t stream); -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks7_align4( +void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks5_align4( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -7225,7 +15475,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks7_align4( float scale, cudaStream_t stream); -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks7_align2( +void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks5_align2( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -7243,7 +15493,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks7_align2( float scale, cudaStream_t stream); -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks9_align8( +void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks7_align8( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -7261,7 +15511,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks9_align8( float scale, cudaStream_t stream); -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks9_align4( +void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks7_align4( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -7279,7 +15529,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks9_align4( float scale, cudaStream_t stream); -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks9_align2( +void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks7_align2( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -7297,7 +15547,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks9_align2( float scale, cudaStream_t stream); -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks11_align8( +void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks9_align8( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -7315,7 +15565,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks11_align8( float scale, cudaStream_t stream); -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks11_align4( +void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks9_align4( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -7333,7 +15583,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks11_align4( float scale, cudaStream_t stream); -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks11_align2( +void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks9_align2( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -7351,7 +15601,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks11_align2( float scale, cudaStream_t stream); -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks13_align8( +void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks11_align8( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -7369,7 +15619,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks13_align8( float scale, cudaStream_t stream); -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks13_align4( +void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks11_align4( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -7387,7 +15637,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks13_align4( float scale, cudaStream_t stream); -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks13_align2( +void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks11_align2( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -7405,7 +15655,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks13_align2( float scale, cudaStream_t stream); -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks15_align8( +void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks13_align8( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -7423,7 +15673,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks15_align8( float scale, cudaStream_t stream); -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks15_align4( +void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks13_align4( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -7441,7 +15691,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks15_align4( float scale, cudaStream_t stream); -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks15_align2( +void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks13_align2( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -7459,7 +15709,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks15_align2( float scale, cudaStream_t stream); -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks17_align8( +void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks15_align8( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -7477,7 +15727,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks17_align8( float scale, cudaStream_t stream); -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks17_align4( +void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks15_align4( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -7495,7 +15745,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks17_align4( float scale, cudaStream_t stream); -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks17_align2( +void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks15_align2( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -7513,7 +15763,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks17_align2( float scale, cudaStream_t stream); -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks19_align8( +void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks17_align8( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -7531,7 +15781,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks19_align8( float scale, cudaStream_t stream); -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks19_align4( +void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks17_align4( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -7549,7 +15799,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks19_align4( float scale, cudaStream_t stream); -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks19_align2( +void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks17_align2( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -7567,7 +15817,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks19_align2( float scale, cudaStream_t stream); -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks21_align8( +void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks19_align8( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -7585,7 +15835,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks21_align8( float scale, cudaStream_t stream); -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks21_align4( +void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks19_align4( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -7603,7 +15853,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks21_align4( float scale, cudaStream_t stream); -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks21_align2( +void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks19_align2( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -7621,7 +15871,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks21_align2( float scale, cudaStream_t stream); -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks23_align8( +void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks21_align8( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -7639,7 +15889,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks23_align8( float scale, cudaStream_t stream); -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks23_align4( +void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks21_align4( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -7657,7 +15907,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks23_align4( float scale, cudaStream_t stream); -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks23_align2( +void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks21_align2( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -7675,7 +15925,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks23_align2( float scale, cudaStream_t stream); -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks25_align8( +void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks23_align8( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -7693,7 +15943,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks25_align8( float scale, cudaStream_t stream); -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks25_align4( +void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks23_align4( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -7711,7 +15961,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks25_align4( float scale, cudaStream_t stream); -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks25_align2( +void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks23_align2( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -7729,7 +15979,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks25_align2( float scale, cudaStream_t stream); -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks27_align8( +void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks25_align8( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -7747,7 +15997,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks27_align8( float scale, cudaStream_t stream); -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks27_align4( +void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks25_align4( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -7765,7 +16015,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks27_align4( float scale, cudaStream_t stream); -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks27_align2( +void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks25_align2( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -7783,7 +16033,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks27_align2( float scale, cudaStream_t stream); -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks29_align8( +void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks27_align8( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -7801,7 +16051,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks29_align8( float scale, cudaStream_t stream); -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks29_align4( +void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks27_align4( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -7819,7 +16069,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks29_align4( float scale, cudaStream_t stream); -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks29_align2( +void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks27_align2( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -7837,7 +16087,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks29_align2( float scale, cudaStream_t stream); -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks31_align8( +void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks29_align8( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -7855,7 +16105,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks31_align8( float scale, cudaStream_t stream); -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks31_align4( +void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks29_align4( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -7873,7 +16123,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks31_align4( float scale, cudaStream_t stream); -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks31_align2( +void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks29_align2( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -7891,7 +16141,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks31_align2( float scale, cudaStream_t stream); -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks33_align8( +void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks31_align8( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -7909,7 +16159,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks33_align8( float scale, cudaStream_t stream); -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks33_align4( +void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks31_align4( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -7927,7 +16177,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks33_align4( float scale, cudaStream_t stream); -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks33_align2( +void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks31_align2( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -7945,7 +16195,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks33_align2( float scale, cudaStream_t stream); -void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks3_align8( +void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks33_align8( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -7963,7 +16213,7 @@ void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks3_align8( float scale, cudaStream_t stream); -void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks3_align4( +void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks33_align4( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -7981,7 +16231,7 @@ void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks3_align4( float scale, cudaStream_t stream); -void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks3_align2( +void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks33_align2( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -7999,7 +16249,7 @@ void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks3_align2( float scale, cudaStream_t stream); -void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks5_align8( +void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks35_align8( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -8017,7 +16267,7 @@ void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks5_align8( float scale, cudaStream_t stream); -void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks5_align4( +void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks35_align4( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -8035,7 +16285,7 @@ void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks5_align4( float scale, cudaStream_t stream); -void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks5_align2( +void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks35_align2( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -8053,7 +16303,7 @@ void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks5_align2( float scale, cudaStream_t stream); -void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks7_align8( +void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks37_align8( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -8071,7 +16321,7 @@ void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks7_align8( float scale, cudaStream_t stream); -void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks7_align4( +void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks37_align4( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -8089,7 +16339,7 @@ void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks7_align4( float scale, cudaStream_t stream); -void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks7_align2( +void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks37_align2( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -8107,7 +16357,7 @@ void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks7_align2( float scale, cudaStream_t stream); -void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks9_align8( +void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks39_align8( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -8125,7 +16375,7 @@ void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks9_align8( float scale, cudaStream_t stream); -void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks9_align4( +void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks39_align4( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -8143,7 +16393,7 @@ void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks9_align4( float scale, cudaStream_t stream); -void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks9_align2( +void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks39_align2( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -8161,7 +16411,7 @@ void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks9_align2( float scale, cudaStream_t stream); -void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks11_align8( +void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks41_align8( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -8179,7 +16429,7 @@ void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks11_align8( float scale, cudaStream_t stream); -void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks11_align4( +void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks41_align4( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -8197,7 +16447,7 @@ void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks11_align4( float scale, cudaStream_t stream); -void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks11_align2( +void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks41_align2( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -8215,7 +16465,7 @@ void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks11_align2( float scale, cudaStream_t stream); -void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks13_align8( +void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks43_align8( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -8233,7 +16483,7 @@ void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks13_align8( float scale, cudaStream_t stream); -void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks13_align4( +void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks43_align4( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -8251,7 +16501,7 @@ void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks13_align4( float scale, cudaStream_t stream); -void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks13_align2( +void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks43_align2( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -8269,7 +16519,7 @@ void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks13_align2( float scale, cudaStream_t stream); -void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks15_align8( +void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks45_align8( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -8287,7 +16537,7 @@ void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks15_align8( float scale, cudaStream_t stream); -void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks15_align4( +void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks45_align4( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -8305,7 +16555,7 @@ void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks15_align4( float scale, cudaStream_t stream); -void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks15_align2( +void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks45_align2( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -8323,7 +16573,7 @@ void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks15_align2( float scale, cudaStream_t stream); -void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks17_align8( +void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks47_align8( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -8341,7 +16591,7 @@ void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks17_align8( float scale, cudaStream_t stream); -void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks17_align4( +void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks47_align4( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -8359,7 +16609,7 @@ void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks17_align4( float scale, cudaStream_t stream); -void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks17_align2( +void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks47_align2( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -8377,7 +16627,7 @@ void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks17_align2( float scale, cudaStream_t stream); -void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks19_align8( +void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks49_align8( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -8395,7 +16645,7 @@ void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks19_align8( float scale, cudaStream_t stream); -void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks19_align4( +void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks49_align4( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -8413,7 +16663,7 @@ void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks19_align4( float scale, cudaStream_t stream); -void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks19_align2( +void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks49_align2( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -8431,7 +16681,7 @@ void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks19_align2( float scale, cudaStream_t stream); -void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks21_align8( +void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks51_align8( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -8449,7 +16699,7 @@ void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks21_align8( float scale, cudaStream_t stream); -void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks21_align4( +void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks51_align4( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -8467,7 +16717,7 @@ void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks21_align4( float scale, cudaStream_t stream); -void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks21_align2( +void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks51_align2( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -8485,7 +16735,7 @@ void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks21_align2( float scale, cudaStream_t stream); -void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks23_align8( +void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks53_align8( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -8503,7 +16753,7 @@ void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks23_align8( float scale, cudaStream_t stream); -void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks23_align4( +void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks53_align4( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -8521,7 +16771,7 @@ void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks23_align4( float scale, cudaStream_t stream); -void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks23_align2( +void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks53_align2( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -8539,7 +16789,7 @@ void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks23_align2( float scale, cudaStream_t stream); -void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks25_align8( +void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks55_align8( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -8557,7 +16807,7 @@ void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks25_align8( float scale, cudaStream_t stream); -void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks25_align4( +void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks55_align4( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -8575,7 +16825,7 @@ void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks25_align4( float scale, cudaStream_t stream); -void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks25_align2( +void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks55_align2( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -8593,7 +16843,7 @@ void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks25_align2( float scale, cudaStream_t stream); -void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks27_align8( +void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks57_align8( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -8611,7 +16861,7 @@ void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks27_align8( float scale, cudaStream_t stream); -void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks27_align4( +void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks57_align4( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -8629,7 +16879,7 @@ void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks27_align4( float scale, cudaStream_t stream); -void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks27_align2( +void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks57_align2( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -8647,7 +16897,7 @@ void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks27_align2( float scale, cudaStream_t stream); -void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks29_align8( +void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks59_align8( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -8665,7 +16915,7 @@ void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks29_align8( float scale, cudaStream_t stream); -void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks29_align4( +void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks59_align4( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -8683,7 +16933,7 @@ void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks29_align4( float scale, cudaStream_t stream); -void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks29_align2( +void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks59_align2( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -8701,7 +16951,7 @@ void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks29_align2( float scale, cudaStream_t stream); -void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks31_align8( +void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks61_align8( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -8719,7 +16969,7 @@ void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks31_align8( float scale, cudaStream_t stream); -void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks31_align4( +void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks61_align4( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -8737,7 +16987,7 @@ void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks31_align4( float scale, cudaStream_t stream); -void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks31_align2( +void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks61_align2( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -8755,7 +17005,7 @@ void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks31_align2( float scale, cudaStream_t stream); -void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks33_align8( +void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks63_align8( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -8773,7 +17023,7 @@ void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks33_align8( float scale, cudaStream_t stream); -void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks33_align4( +void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks63_align4( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -8791,7 +17041,7 @@ void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks33_align4( float scale, cudaStream_t stream); -void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks33_align2( +void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks63_align2( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, diff --git a/csrc/autogen/include/natten_autogen/cuda/naive/dispatch_cm.h b/csrc/autogen/include/natten_autogen/cuda/naive/dispatch_cm.h new file mode 100644 index 0000000..b271850 --- /dev/null +++ b/csrc/autogen/include/natten_autogen/cuda/naive/dispatch_cm.h @@ -0,0 +1,1192 @@ +#pragma once + + +#include +#include +#include +#include +namespace natten { +namespace cuda { +namespace naive { +#define DISPATCH_CM_na1d_pn_cuda_naive_double(is_causal, ...) \ + [&] { \ + if (!std::get<0>(is_causal)) { \ + naive::na1d_pn_cuda_naive_double_cm_0(__VA_ARGS__); \ + } \ + else if (std::get<0>(is_causal)) { \ + naive::na1d_pn_cuda_naive_double_cm_1(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na1d_pn_cuda_naive_double got invalid causal mask!" \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_CM_na1d_pn_cuda_naive_float(is_causal, ...) \ + [&] { \ + if (!std::get<0>(is_causal)) { \ + naive::na1d_pn_cuda_naive_float_cm_0(__VA_ARGS__); \ + } \ + else if (std::get<0>(is_causal)) { \ + naive::na1d_pn_cuda_naive_float_cm_1(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na1d_pn_cuda_naive_float got invalid causal mask!" \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_CM_na1d_pn_cuda_naive_half(is_causal, ...) \ + [&] { \ + if (!std::get<0>(is_causal)) { \ + naive::na1d_pn_cuda_naive_half_cm_0(__VA_ARGS__); \ + } \ + else if (std::get<0>(is_causal)) { \ + naive::na1d_pn_cuda_naive_half_cm_1(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na1d_pn_cuda_naive_half got invalid causal mask!" \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_CM_na1d_pn_cuda_naive_bfloat16(is_causal, ...) \ + [&] { \ + if (!std::get<0>(is_causal)) { \ + naive::na1d_pn_cuda_naive_bfloat16_cm_0(__VA_ARGS__); \ + } \ + else if (std::get<0>(is_causal)) { \ + naive::na1d_pn_cuda_naive_bfloat16_cm_1(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na1d_pn_cuda_naive_bfloat16 got invalid causal mask!" \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_CM_na2d_pn_cuda_naive_double(is_causal, ...) \ + [&] { \ + if (!std::get<0>(is_causal) && !std::get<1>(is_causal)) { \ + naive::na2d_pn_cuda_naive_double_cm_0_0(__VA_ARGS__); \ + } \ + else if (!std::get<0>(is_causal) && std::get<1>(is_causal)) { \ + naive::na2d_pn_cuda_naive_double_cm_0_1(__VA_ARGS__); \ + } \ + else if (std::get<0>(is_causal) && !std::get<1>(is_causal)) { \ + naive::na2d_pn_cuda_naive_double_cm_1_0(__VA_ARGS__); \ + } \ + else if (std::get<0>(is_causal) && std::get<1>(is_causal)) { \ + naive::na2d_pn_cuda_naive_double_cm_1_1(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na2d_pn_cuda_naive_double got invalid causal mask!" \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_CM_na2d_pn_cuda_naive_float(is_causal, ...) \ + [&] { \ + if (!std::get<0>(is_causal) && !std::get<1>(is_causal)) { \ + naive::na2d_pn_cuda_naive_float_cm_0_0(__VA_ARGS__); \ + } \ + else if (!std::get<0>(is_causal) && std::get<1>(is_causal)) { \ + naive::na2d_pn_cuda_naive_float_cm_0_1(__VA_ARGS__); \ + } \ + else if (std::get<0>(is_causal) && !std::get<1>(is_causal)) { \ + naive::na2d_pn_cuda_naive_float_cm_1_0(__VA_ARGS__); \ + } \ + else if (std::get<0>(is_causal) && std::get<1>(is_causal)) { \ + naive::na2d_pn_cuda_naive_float_cm_1_1(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na2d_pn_cuda_naive_float got invalid causal mask!" \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_CM_na2d_pn_cuda_naive_half(is_causal, ...) \ + [&] { \ + if (!std::get<0>(is_causal) && !std::get<1>(is_causal)) { \ + naive::na2d_pn_cuda_naive_half_cm_0_0(__VA_ARGS__); \ + } \ + else if (!std::get<0>(is_causal) && std::get<1>(is_causal)) { \ + naive::na2d_pn_cuda_naive_half_cm_0_1(__VA_ARGS__); \ + } \ + else if (std::get<0>(is_causal) && !std::get<1>(is_causal)) { \ + naive::na2d_pn_cuda_naive_half_cm_1_0(__VA_ARGS__); \ + } \ + else if (std::get<0>(is_causal) && std::get<1>(is_causal)) { \ + naive::na2d_pn_cuda_naive_half_cm_1_1(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na2d_pn_cuda_naive_half got invalid causal mask!" \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_CM_na2d_pn_cuda_naive_bfloat16(is_causal, ...) \ + [&] { \ + if (!std::get<0>(is_causal) && !std::get<1>(is_causal)) { \ + naive::na2d_pn_cuda_naive_bfloat16_cm_0_0(__VA_ARGS__); \ + } \ + else if (!std::get<0>(is_causal) && std::get<1>(is_causal)) { \ + naive::na2d_pn_cuda_naive_bfloat16_cm_0_1(__VA_ARGS__); \ + } \ + else if (std::get<0>(is_causal) && !std::get<1>(is_causal)) { \ + naive::na2d_pn_cuda_naive_bfloat16_cm_1_0(__VA_ARGS__); \ + } \ + else if (std::get<0>(is_causal) && std::get<1>(is_causal)) { \ + naive::na2d_pn_cuda_naive_bfloat16_cm_1_1(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na2d_pn_cuda_naive_bfloat16 got invalid causal mask!" \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_CM_na3d_pn_cuda_naive_double(is_causal, ...) \ + [&] { \ + if (!std::get<0>(is_causal) && !std::get<1>(is_causal) && !std::get<2>(is_causal)) { \ + naive::na3d_pn_cuda_naive_double_cm_0_0_0(__VA_ARGS__); \ + } \ + else if (!std::get<0>(is_causal) && !std::get<1>(is_causal) && std::get<2>(is_causal)) { \ + naive::na3d_pn_cuda_naive_double_cm_0_0_1(__VA_ARGS__); \ + } \ + else if (!std::get<0>(is_causal) && std::get<1>(is_causal) && !std::get<2>(is_causal)) { \ + naive::na3d_pn_cuda_naive_double_cm_0_1_0(__VA_ARGS__); \ + } \ + else if (!std::get<0>(is_causal) && std::get<1>(is_causal) && std::get<2>(is_causal)) { \ + naive::na3d_pn_cuda_naive_double_cm_0_1_1(__VA_ARGS__); \ + } \ + else if (std::get<0>(is_causal) && !std::get<1>(is_causal) && !std::get<2>(is_causal)) { \ + naive::na3d_pn_cuda_naive_double_cm_1_0_0(__VA_ARGS__); \ + } \ + else if (std::get<0>(is_causal) && !std::get<1>(is_causal) && std::get<2>(is_causal)) { \ + naive::na3d_pn_cuda_naive_double_cm_1_0_1(__VA_ARGS__); \ + } \ + else if (std::get<0>(is_causal) && std::get<1>(is_causal) && !std::get<2>(is_causal)) { \ + naive::na3d_pn_cuda_naive_double_cm_1_1_0(__VA_ARGS__); \ + } \ + else if (std::get<0>(is_causal) && std::get<1>(is_causal) && std::get<2>(is_causal)) { \ + naive::na3d_pn_cuda_naive_double_cm_1_1_1(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na3d_pn_cuda_naive_double got invalid causal mask!" \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_CM_na3d_pn_cuda_naive_float(is_causal, ...) \ + [&] { \ + if (!std::get<0>(is_causal) && !std::get<1>(is_causal) && !std::get<2>(is_causal)) { \ + naive::na3d_pn_cuda_naive_float_cm_0_0_0(__VA_ARGS__); \ + } \ + else if (!std::get<0>(is_causal) && !std::get<1>(is_causal) && std::get<2>(is_causal)) { \ + naive::na3d_pn_cuda_naive_float_cm_0_0_1(__VA_ARGS__); \ + } \ + else if (!std::get<0>(is_causal) && std::get<1>(is_causal) && !std::get<2>(is_causal)) { \ + naive::na3d_pn_cuda_naive_float_cm_0_1_0(__VA_ARGS__); \ + } \ + else if (!std::get<0>(is_causal) && std::get<1>(is_causal) && std::get<2>(is_causal)) { \ + naive::na3d_pn_cuda_naive_float_cm_0_1_1(__VA_ARGS__); \ + } \ + else if (std::get<0>(is_causal) && !std::get<1>(is_causal) && !std::get<2>(is_causal)) { \ + naive::na3d_pn_cuda_naive_float_cm_1_0_0(__VA_ARGS__); \ + } \ + else if (std::get<0>(is_causal) && !std::get<1>(is_causal) && std::get<2>(is_causal)) { \ + naive::na3d_pn_cuda_naive_float_cm_1_0_1(__VA_ARGS__); \ + } \ + else if (std::get<0>(is_causal) && std::get<1>(is_causal) && !std::get<2>(is_causal)) { \ + naive::na3d_pn_cuda_naive_float_cm_1_1_0(__VA_ARGS__); \ + } \ + else if (std::get<0>(is_causal) && std::get<1>(is_causal) && std::get<2>(is_causal)) { \ + naive::na3d_pn_cuda_naive_float_cm_1_1_1(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na3d_pn_cuda_naive_float got invalid causal mask!" \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_CM_na3d_pn_cuda_naive_half(is_causal, ...) \ + [&] { \ + if (!std::get<0>(is_causal) && !std::get<1>(is_causal) && !std::get<2>(is_causal)) { \ + naive::na3d_pn_cuda_naive_half_cm_0_0_0(__VA_ARGS__); \ + } \ + else if (!std::get<0>(is_causal) && !std::get<1>(is_causal) && std::get<2>(is_causal)) { \ + naive::na3d_pn_cuda_naive_half_cm_0_0_1(__VA_ARGS__); \ + } \ + else if (!std::get<0>(is_causal) && std::get<1>(is_causal) && !std::get<2>(is_causal)) { \ + naive::na3d_pn_cuda_naive_half_cm_0_1_0(__VA_ARGS__); \ + } \ + else if (!std::get<0>(is_causal) && std::get<1>(is_causal) && std::get<2>(is_causal)) { \ + naive::na3d_pn_cuda_naive_half_cm_0_1_1(__VA_ARGS__); \ + } \ + else if (std::get<0>(is_causal) && !std::get<1>(is_causal) && !std::get<2>(is_causal)) { \ + naive::na3d_pn_cuda_naive_half_cm_1_0_0(__VA_ARGS__); \ + } \ + else if (std::get<0>(is_causal) && !std::get<1>(is_causal) && std::get<2>(is_causal)) { \ + naive::na3d_pn_cuda_naive_half_cm_1_0_1(__VA_ARGS__); \ + } \ + else if (std::get<0>(is_causal) && std::get<1>(is_causal) && !std::get<2>(is_causal)) { \ + naive::na3d_pn_cuda_naive_half_cm_1_1_0(__VA_ARGS__); \ + } \ + else if (std::get<0>(is_causal) && std::get<1>(is_causal) && std::get<2>(is_causal)) { \ + naive::na3d_pn_cuda_naive_half_cm_1_1_1(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na3d_pn_cuda_naive_half got invalid causal mask!" \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_CM_na3d_pn_cuda_naive_bfloat16(is_causal, ...) \ + [&] { \ + if (!std::get<0>(is_causal) && !std::get<1>(is_causal) && !std::get<2>(is_causal)) { \ + naive::na3d_pn_cuda_naive_bfloat16_cm_0_0_0(__VA_ARGS__); \ + } \ + else if (!std::get<0>(is_causal) && !std::get<1>(is_causal) && std::get<2>(is_causal)) { \ + naive::na3d_pn_cuda_naive_bfloat16_cm_0_0_1(__VA_ARGS__); \ + } \ + else if (!std::get<0>(is_causal) && std::get<1>(is_causal) && !std::get<2>(is_causal)) { \ + naive::na3d_pn_cuda_naive_bfloat16_cm_0_1_0(__VA_ARGS__); \ + } \ + else if (!std::get<0>(is_causal) && std::get<1>(is_causal) && std::get<2>(is_causal)) { \ + naive::na3d_pn_cuda_naive_bfloat16_cm_0_1_1(__VA_ARGS__); \ + } \ + else if (std::get<0>(is_causal) && !std::get<1>(is_causal) && !std::get<2>(is_causal)) { \ + naive::na3d_pn_cuda_naive_bfloat16_cm_1_0_0(__VA_ARGS__); \ + } \ + else if (std::get<0>(is_causal) && !std::get<1>(is_causal) && std::get<2>(is_causal)) { \ + naive::na3d_pn_cuda_naive_bfloat16_cm_1_0_1(__VA_ARGS__); \ + } \ + else if (std::get<0>(is_causal) && std::get<1>(is_causal) && !std::get<2>(is_causal)) { \ + naive::na3d_pn_cuda_naive_bfloat16_cm_1_1_0(__VA_ARGS__); \ + } \ + else if (std::get<0>(is_causal) && std::get<1>(is_causal) && std::get<2>(is_causal)) { \ + naive::na3d_pn_cuda_naive_bfloat16_cm_1_1_1(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na3d_pn_cuda_naive_bfloat16 got invalid causal mask!" \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_CM_na1d_pn_bias_cuda_naive_double(is_causal, ...) \ + [&] { \ + if (!std::get<0>(is_causal)) { \ + naive::na1d_pn_bias_cuda_naive_double_cm_0(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na1d_pn_bias_cuda_naive_double got invalid causal mask!" \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_CM_na1d_pn_bias_cuda_naive_float(is_causal, ...) \ + [&] { \ + if (!std::get<0>(is_causal)) { \ + naive::na1d_pn_bias_cuda_naive_float_cm_0(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na1d_pn_bias_cuda_naive_float got invalid causal mask!" \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_CM_na1d_pn_bias_cuda_naive_half(is_causal, ...) \ + [&] { \ + if (!std::get<0>(is_causal)) { \ + naive::na1d_pn_bias_cuda_naive_half_cm_0(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na1d_pn_bias_cuda_naive_half got invalid causal mask!" \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_CM_na1d_pn_bias_cuda_naive_bfloat16(is_causal, ...) \ + [&] { \ + if (!std::get<0>(is_causal)) { \ + naive::na1d_pn_bias_cuda_naive_bfloat16_cm_0(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na1d_pn_bias_cuda_naive_bfloat16 got invalid causal mask!" \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_CM_na2d_pn_bias_cuda_naive_double(is_causal, ...) \ + [&] { \ + if (!std::get<0>(is_causal) && !std::get<1>(is_causal)) { \ + naive::na2d_pn_bias_cuda_naive_double_cm_0_0(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na2d_pn_bias_cuda_naive_double got invalid causal mask!" \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_CM_na2d_pn_bias_cuda_naive_float(is_causal, ...) \ + [&] { \ + if (!std::get<0>(is_causal) && !std::get<1>(is_causal)) { \ + naive::na2d_pn_bias_cuda_naive_float_cm_0_0(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na2d_pn_bias_cuda_naive_float got invalid causal mask!" \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_CM_na2d_pn_bias_cuda_naive_half(is_causal, ...) \ + [&] { \ + if (!std::get<0>(is_causal) && !std::get<1>(is_causal)) { \ + naive::na2d_pn_bias_cuda_naive_half_cm_0_0(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na2d_pn_bias_cuda_naive_half got invalid causal mask!" \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_CM_na2d_pn_bias_cuda_naive_bfloat16(is_causal, ...) \ + [&] { \ + if (!std::get<0>(is_causal) && !std::get<1>(is_causal)) { \ + naive::na2d_pn_bias_cuda_naive_bfloat16_cm_0_0(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na2d_pn_bias_cuda_naive_bfloat16 got invalid causal mask!" \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_CM_na3d_pn_bias_cuda_naive_double(is_causal, ...) \ + [&] { \ + if (!std::get<0>(is_causal) && !std::get<1>(is_causal) && !std::get<2>(is_causal)) { \ + naive::na3d_pn_bias_cuda_naive_double_cm_0_0_0(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na3d_pn_bias_cuda_naive_double got invalid causal mask!" \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_CM_na3d_pn_bias_cuda_naive_float(is_causal, ...) \ + [&] { \ + if (!std::get<0>(is_causal) && !std::get<1>(is_causal) && !std::get<2>(is_causal)) { \ + naive::na3d_pn_bias_cuda_naive_float_cm_0_0_0(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na3d_pn_bias_cuda_naive_float got invalid causal mask!" \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_CM_na3d_pn_bias_cuda_naive_half(is_causal, ...) \ + [&] { \ + if (!std::get<0>(is_causal) && !std::get<1>(is_causal) && !std::get<2>(is_causal)) { \ + naive::na3d_pn_bias_cuda_naive_half_cm_0_0_0(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na3d_pn_bias_cuda_naive_half got invalid causal mask!" \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_CM_na3d_pn_bias_cuda_naive_bfloat16(is_causal, ...) \ + [&] { \ + if (!std::get<0>(is_causal) && !std::get<1>(is_causal) && !std::get<2>(is_causal)) { \ + naive::na3d_pn_bias_cuda_naive_bfloat16_cm_0_0_0(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na3d_pn_bias_cuda_naive_bfloat16 got invalid causal mask!" \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_CM_na1d_nn_cuda_naive_double(is_causal, ...) \ + [&] { \ + if (!std::get<0>(is_causal)) { \ + naive::na1d_nn_cuda_naive_double_cm_0(__VA_ARGS__); \ + } \ + else if (std::get<0>(is_causal)) { \ + naive::na1d_nn_cuda_naive_double_cm_1(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na1d_nn_cuda_naive_double got invalid causal mask!" \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_CM_na1d_nn_cuda_naive_float(is_causal, ...) \ + [&] { \ + if (!std::get<0>(is_causal)) { \ + naive::na1d_nn_cuda_naive_float_cm_0(__VA_ARGS__); \ + } \ + else if (std::get<0>(is_causal)) { \ + naive::na1d_nn_cuda_naive_float_cm_1(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na1d_nn_cuda_naive_float got invalid causal mask!" \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_CM_na1d_nn_cuda_naive_half(is_causal, ...) \ + [&] { \ + if (!std::get<0>(is_causal)) { \ + naive::na1d_nn_cuda_naive_half_cm_0(__VA_ARGS__); \ + } \ + else if (std::get<0>(is_causal)) { \ + naive::na1d_nn_cuda_naive_half_cm_1(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na1d_nn_cuda_naive_half got invalid causal mask!" \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_CM_na1d_nn_cuda_naive_bfloat16(is_causal, ...) \ + [&] { \ + if (!std::get<0>(is_causal)) { \ + naive::na1d_nn_cuda_naive_bfloat16_cm_0(__VA_ARGS__); \ + } \ + else if (std::get<0>(is_causal)) { \ + naive::na1d_nn_cuda_naive_bfloat16_cm_1(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na1d_nn_cuda_naive_bfloat16 got invalid causal mask!" \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_CM_na2d_nn_cuda_naive_double(is_causal, ...) \ + [&] { \ + if (!std::get<0>(is_causal) && !std::get<1>(is_causal)) { \ + naive::na2d_nn_cuda_naive_double_cm_0_0(__VA_ARGS__); \ + } \ + else if (!std::get<0>(is_causal) && std::get<1>(is_causal)) { \ + naive::na2d_nn_cuda_naive_double_cm_0_1(__VA_ARGS__); \ + } \ + else if (std::get<0>(is_causal) && !std::get<1>(is_causal)) { \ + naive::na2d_nn_cuda_naive_double_cm_1_0(__VA_ARGS__); \ + } \ + else if (std::get<0>(is_causal) && std::get<1>(is_causal)) { \ + naive::na2d_nn_cuda_naive_double_cm_1_1(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na2d_nn_cuda_naive_double got invalid causal mask!" \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_CM_na2d_nn_cuda_naive_float(is_causal, ...) \ + [&] { \ + if (!std::get<0>(is_causal) && !std::get<1>(is_causal)) { \ + naive::na2d_nn_cuda_naive_float_cm_0_0(__VA_ARGS__); \ + } \ + else if (!std::get<0>(is_causal) && std::get<1>(is_causal)) { \ + naive::na2d_nn_cuda_naive_float_cm_0_1(__VA_ARGS__); \ + } \ + else if (std::get<0>(is_causal) && !std::get<1>(is_causal)) { \ + naive::na2d_nn_cuda_naive_float_cm_1_0(__VA_ARGS__); \ + } \ + else if (std::get<0>(is_causal) && std::get<1>(is_causal)) { \ + naive::na2d_nn_cuda_naive_float_cm_1_1(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na2d_nn_cuda_naive_float got invalid causal mask!" \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_CM_na2d_nn_cuda_naive_half(is_causal, ...) \ + [&] { \ + if (!std::get<0>(is_causal) && !std::get<1>(is_causal)) { \ + naive::na2d_nn_cuda_naive_half_cm_0_0(__VA_ARGS__); \ + } \ + else if (!std::get<0>(is_causal) && std::get<1>(is_causal)) { \ + naive::na2d_nn_cuda_naive_half_cm_0_1(__VA_ARGS__); \ + } \ + else if (std::get<0>(is_causal) && !std::get<1>(is_causal)) { \ + naive::na2d_nn_cuda_naive_half_cm_1_0(__VA_ARGS__); \ + } \ + else if (std::get<0>(is_causal) && std::get<1>(is_causal)) { \ + naive::na2d_nn_cuda_naive_half_cm_1_1(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na2d_nn_cuda_naive_half got invalid causal mask!" \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_CM_na2d_nn_cuda_naive_bfloat16(is_causal, ...) \ + [&] { \ + if (!std::get<0>(is_causal) && !std::get<1>(is_causal)) { \ + naive::na2d_nn_cuda_naive_bfloat16_cm_0_0(__VA_ARGS__); \ + } \ + else if (!std::get<0>(is_causal) && std::get<1>(is_causal)) { \ + naive::na2d_nn_cuda_naive_bfloat16_cm_0_1(__VA_ARGS__); \ + } \ + else if (std::get<0>(is_causal) && !std::get<1>(is_causal)) { \ + naive::na2d_nn_cuda_naive_bfloat16_cm_1_0(__VA_ARGS__); \ + } \ + else if (std::get<0>(is_causal) && std::get<1>(is_causal)) { \ + naive::na2d_nn_cuda_naive_bfloat16_cm_1_1(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na2d_nn_cuda_naive_bfloat16 got invalid causal mask!" \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_CM_na3d_nn_cuda_naive_double(is_causal, ...) \ + [&] { \ + if (!std::get<0>(is_causal) && !std::get<1>(is_causal) && !std::get<2>(is_causal)) { \ + naive::na3d_nn_cuda_naive_double_cm_0_0_0(__VA_ARGS__); \ + } \ + else if (!std::get<0>(is_causal) && !std::get<1>(is_causal) && std::get<2>(is_causal)) { \ + naive::na3d_nn_cuda_naive_double_cm_0_0_1(__VA_ARGS__); \ + } \ + else if (!std::get<0>(is_causal) && std::get<1>(is_causal) && !std::get<2>(is_causal)) { \ + naive::na3d_nn_cuda_naive_double_cm_0_1_0(__VA_ARGS__); \ + } \ + else if (!std::get<0>(is_causal) && std::get<1>(is_causal) && std::get<2>(is_causal)) { \ + naive::na3d_nn_cuda_naive_double_cm_0_1_1(__VA_ARGS__); \ + } \ + else if (std::get<0>(is_causal) && !std::get<1>(is_causal) && !std::get<2>(is_causal)) { \ + naive::na3d_nn_cuda_naive_double_cm_1_0_0(__VA_ARGS__); \ + } \ + else if (std::get<0>(is_causal) && !std::get<1>(is_causal) && std::get<2>(is_causal)) { \ + naive::na3d_nn_cuda_naive_double_cm_1_0_1(__VA_ARGS__); \ + } \ + else if (std::get<0>(is_causal) && std::get<1>(is_causal) && !std::get<2>(is_causal)) { \ + naive::na3d_nn_cuda_naive_double_cm_1_1_0(__VA_ARGS__); \ + } \ + else if (std::get<0>(is_causal) && std::get<1>(is_causal) && std::get<2>(is_causal)) { \ + naive::na3d_nn_cuda_naive_double_cm_1_1_1(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na3d_nn_cuda_naive_double got invalid causal mask!" \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_CM_na3d_nn_cuda_naive_float(is_causal, ...) \ + [&] { \ + if (!std::get<0>(is_causal) && !std::get<1>(is_causal) && !std::get<2>(is_causal)) { \ + naive::na3d_nn_cuda_naive_float_cm_0_0_0(__VA_ARGS__); \ + } \ + else if (!std::get<0>(is_causal) && !std::get<1>(is_causal) && std::get<2>(is_causal)) { \ + naive::na3d_nn_cuda_naive_float_cm_0_0_1(__VA_ARGS__); \ + } \ + else if (!std::get<0>(is_causal) && std::get<1>(is_causal) && !std::get<2>(is_causal)) { \ + naive::na3d_nn_cuda_naive_float_cm_0_1_0(__VA_ARGS__); \ + } \ + else if (!std::get<0>(is_causal) && std::get<1>(is_causal) && std::get<2>(is_causal)) { \ + naive::na3d_nn_cuda_naive_float_cm_0_1_1(__VA_ARGS__); \ + } \ + else if (std::get<0>(is_causal) && !std::get<1>(is_causal) && !std::get<2>(is_causal)) { \ + naive::na3d_nn_cuda_naive_float_cm_1_0_0(__VA_ARGS__); \ + } \ + else if (std::get<0>(is_causal) && !std::get<1>(is_causal) && std::get<2>(is_causal)) { \ + naive::na3d_nn_cuda_naive_float_cm_1_0_1(__VA_ARGS__); \ + } \ + else if (std::get<0>(is_causal) && std::get<1>(is_causal) && !std::get<2>(is_causal)) { \ + naive::na3d_nn_cuda_naive_float_cm_1_1_0(__VA_ARGS__); \ + } \ + else if (std::get<0>(is_causal) && std::get<1>(is_causal) && std::get<2>(is_causal)) { \ + naive::na3d_nn_cuda_naive_float_cm_1_1_1(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na3d_nn_cuda_naive_float got invalid causal mask!" \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_CM_na3d_nn_cuda_naive_half(is_causal, ...) \ + [&] { \ + if (!std::get<0>(is_causal) && !std::get<1>(is_causal) && !std::get<2>(is_causal)) { \ + naive::na3d_nn_cuda_naive_half_cm_0_0_0(__VA_ARGS__); \ + } \ + else if (!std::get<0>(is_causal) && !std::get<1>(is_causal) && std::get<2>(is_causal)) { \ + naive::na3d_nn_cuda_naive_half_cm_0_0_1(__VA_ARGS__); \ + } \ + else if (!std::get<0>(is_causal) && std::get<1>(is_causal) && !std::get<2>(is_causal)) { \ + naive::na3d_nn_cuda_naive_half_cm_0_1_0(__VA_ARGS__); \ + } \ + else if (!std::get<0>(is_causal) && std::get<1>(is_causal) && std::get<2>(is_causal)) { \ + naive::na3d_nn_cuda_naive_half_cm_0_1_1(__VA_ARGS__); \ + } \ + else if (std::get<0>(is_causal) && !std::get<1>(is_causal) && !std::get<2>(is_causal)) { \ + naive::na3d_nn_cuda_naive_half_cm_1_0_0(__VA_ARGS__); \ + } \ + else if (std::get<0>(is_causal) && !std::get<1>(is_causal) && std::get<2>(is_causal)) { \ + naive::na3d_nn_cuda_naive_half_cm_1_0_1(__VA_ARGS__); \ + } \ + else if (std::get<0>(is_causal) && std::get<1>(is_causal) && !std::get<2>(is_causal)) { \ + naive::na3d_nn_cuda_naive_half_cm_1_1_0(__VA_ARGS__); \ + } \ + else if (std::get<0>(is_causal) && std::get<1>(is_causal) && std::get<2>(is_causal)) { \ + naive::na3d_nn_cuda_naive_half_cm_1_1_1(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na3d_nn_cuda_naive_half got invalid causal mask!" \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_CM_na3d_nn_cuda_naive_bfloat16(is_causal, ...) \ + [&] { \ + if (!std::get<0>(is_causal) && !std::get<1>(is_causal) && !std::get<2>(is_causal)) { \ + naive::na3d_nn_cuda_naive_bfloat16_cm_0_0_0(__VA_ARGS__); \ + } \ + else if (!std::get<0>(is_causal) && !std::get<1>(is_causal) && std::get<2>(is_causal)) { \ + naive::na3d_nn_cuda_naive_bfloat16_cm_0_0_1(__VA_ARGS__); \ + } \ + else if (!std::get<0>(is_causal) && std::get<1>(is_causal) && !std::get<2>(is_causal)) { \ + naive::na3d_nn_cuda_naive_bfloat16_cm_0_1_0(__VA_ARGS__); \ + } \ + else if (!std::get<0>(is_causal) && std::get<1>(is_causal) && std::get<2>(is_causal)) { \ + naive::na3d_nn_cuda_naive_bfloat16_cm_0_1_1(__VA_ARGS__); \ + } \ + else if (std::get<0>(is_causal) && !std::get<1>(is_causal) && !std::get<2>(is_causal)) { \ + naive::na3d_nn_cuda_naive_bfloat16_cm_1_0_0(__VA_ARGS__); \ + } \ + else if (std::get<0>(is_causal) && !std::get<1>(is_causal) && std::get<2>(is_causal)) { \ + naive::na3d_nn_cuda_naive_bfloat16_cm_1_0_1(__VA_ARGS__); \ + } \ + else if (std::get<0>(is_causal) && std::get<1>(is_causal) && !std::get<2>(is_causal)) { \ + naive::na3d_nn_cuda_naive_bfloat16_cm_1_1_0(__VA_ARGS__); \ + } \ + else if (std::get<0>(is_causal) && std::get<1>(is_causal) && std::get<2>(is_causal)) { \ + naive::na3d_nn_cuda_naive_bfloat16_cm_1_1_1(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na3d_nn_cuda_naive_bfloat16 got invalid causal mask!" \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_CM_na1d_in_cuda_naive_double(is_causal, ...) \ + [&] { \ + if (!std::get<0>(is_causal)) { \ + naive::na1d_in_cuda_naive_double_cm_0(__VA_ARGS__); \ + } \ + else if (std::get<0>(is_causal)) { \ + naive::na1d_in_cuda_naive_double_cm_1(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na1d_in_cuda_naive_double got invalid causal mask!" \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_CM_na1d_in_cuda_naive_float(is_causal, ...) \ + [&] { \ + if (!std::get<0>(is_causal)) { \ + naive::na1d_in_cuda_naive_float_cm_0(__VA_ARGS__); \ + } \ + else if (std::get<0>(is_causal)) { \ + naive::na1d_in_cuda_naive_float_cm_1(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na1d_in_cuda_naive_float got invalid causal mask!" \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_CM_na1d_in_cuda_naive_half(is_causal, ...) \ + [&] { \ + if (!std::get<0>(is_causal)) { \ + naive::na1d_in_cuda_naive_half_cm_0(__VA_ARGS__); \ + } \ + else if (std::get<0>(is_causal)) { \ + naive::na1d_in_cuda_naive_half_cm_1(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na1d_in_cuda_naive_half got invalid causal mask!" \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_CM_na1d_in_cuda_naive_bfloat16(is_causal, ...) \ + [&] { \ + if (!std::get<0>(is_causal)) { \ + naive::na1d_in_cuda_naive_bfloat16_cm_0(__VA_ARGS__); \ + } \ + else if (std::get<0>(is_causal)) { \ + naive::na1d_in_cuda_naive_bfloat16_cm_1(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na1d_in_cuda_naive_bfloat16 got invalid causal mask!" \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_CM_na2d_in_cuda_naive_double(is_causal, ...) \ + [&] { \ + if (!std::get<0>(is_causal) && !std::get<1>(is_causal)) { \ + naive::na2d_in_cuda_naive_double_cm_0_0(__VA_ARGS__); \ + } \ + else if (!std::get<0>(is_causal) && std::get<1>(is_causal)) { \ + naive::na2d_in_cuda_naive_double_cm_0_1(__VA_ARGS__); \ + } \ + else if (std::get<0>(is_causal) && !std::get<1>(is_causal)) { \ + naive::na2d_in_cuda_naive_double_cm_1_0(__VA_ARGS__); \ + } \ + else if (std::get<0>(is_causal) && std::get<1>(is_causal)) { \ + naive::na2d_in_cuda_naive_double_cm_1_1(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na2d_in_cuda_naive_double got invalid causal mask!" \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_CM_na2d_in_cuda_naive_float(is_causal, ...) \ + [&] { \ + if (!std::get<0>(is_causal) && !std::get<1>(is_causal)) { \ + naive::na2d_in_cuda_naive_float_cm_0_0(__VA_ARGS__); \ + } \ + else if (!std::get<0>(is_causal) && std::get<1>(is_causal)) { \ + naive::na2d_in_cuda_naive_float_cm_0_1(__VA_ARGS__); \ + } \ + else if (std::get<0>(is_causal) && !std::get<1>(is_causal)) { \ + naive::na2d_in_cuda_naive_float_cm_1_0(__VA_ARGS__); \ + } \ + else if (std::get<0>(is_causal) && std::get<1>(is_causal)) { \ + naive::na2d_in_cuda_naive_float_cm_1_1(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na2d_in_cuda_naive_float got invalid causal mask!" \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_CM_na2d_in_cuda_naive_half(is_causal, ...) \ + [&] { \ + if (!std::get<0>(is_causal) && !std::get<1>(is_causal)) { \ + naive::na2d_in_cuda_naive_half_cm_0_0(__VA_ARGS__); \ + } \ + else if (!std::get<0>(is_causal) && std::get<1>(is_causal)) { \ + naive::na2d_in_cuda_naive_half_cm_0_1(__VA_ARGS__); \ + } \ + else if (std::get<0>(is_causal) && !std::get<1>(is_causal)) { \ + naive::na2d_in_cuda_naive_half_cm_1_0(__VA_ARGS__); \ + } \ + else if (std::get<0>(is_causal) && std::get<1>(is_causal)) { \ + naive::na2d_in_cuda_naive_half_cm_1_1(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na2d_in_cuda_naive_half got invalid causal mask!" \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_CM_na2d_in_cuda_naive_bfloat16(is_causal, ...) \ + [&] { \ + if (!std::get<0>(is_causal) && !std::get<1>(is_causal)) { \ + naive::na2d_in_cuda_naive_bfloat16_cm_0_0(__VA_ARGS__); \ + } \ + else if (!std::get<0>(is_causal) && std::get<1>(is_causal)) { \ + naive::na2d_in_cuda_naive_bfloat16_cm_0_1(__VA_ARGS__); \ + } \ + else if (std::get<0>(is_causal) && !std::get<1>(is_causal)) { \ + naive::na2d_in_cuda_naive_bfloat16_cm_1_0(__VA_ARGS__); \ + } \ + else if (std::get<0>(is_causal) && std::get<1>(is_causal)) { \ + naive::na2d_in_cuda_naive_bfloat16_cm_1_1(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na2d_in_cuda_naive_bfloat16 got invalid causal mask!" \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_CM_na3d_in_cuda_naive_double(is_causal, ...) \ + [&] { \ + if (!std::get<0>(is_causal) && !std::get<1>(is_causal) && !std::get<2>(is_causal)) { \ + naive::na3d_in_cuda_naive_double_cm_0_0_0(__VA_ARGS__); \ + } \ + else if (!std::get<0>(is_causal) && !std::get<1>(is_causal) && std::get<2>(is_causal)) { \ + naive::na3d_in_cuda_naive_double_cm_0_0_1(__VA_ARGS__); \ + } \ + else if (!std::get<0>(is_causal) && std::get<1>(is_causal) && !std::get<2>(is_causal)) { \ + naive::na3d_in_cuda_naive_double_cm_0_1_0(__VA_ARGS__); \ + } \ + else if (!std::get<0>(is_causal) && std::get<1>(is_causal) && std::get<2>(is_causal)) { \ + naive::na3d_in_cuda_naive_double_cm_0_1_1(__VA_ARGS__); \ + } \ + else if (std::get<0>(is_causal) && !std::get<1>(is_causal) && !std::get<2>(is_causal)) { \ + naive::na3d_in_cuda_naive_double_cm_1_0_0(__VA_ARGS__); \ + } \ + else if (std::get<0>(is_causal) && !std::get<1>(is_causal) && std::get<2>(is_causal)) { \ + naive::na3d_in_cuda_naive_double_cm_1_0_1(__VA_ARGS__); \ + } \ + else if (std::get<0>(is_causal) && std::get<1>(is_causal) && !std::get<2>(is_causal)) { \ + naive::na3d_in_cuda_naive_double_cm_1_1_0(__VA_ARGS__); \ + } \ + else if (std::get<0>(is_causal) && std::get<1>(is_causal) && std::get<2>(is_causal)) { \ + naive::na3d_in_cuda_naive_double_cm_1_1_1(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na3d_in_cuda_naive_double got invalid causal mask!" \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_CM_na3d_in_cuda_naive_float(is_causal, ...) \ + [&] { \ + if (!std::get<0>(is_causal) && !std::get<1>(is_causal) && !std::get<2>(is_causal)) { \ + naive::na3d_in_cuda_naive_float_cm_0_0_0(__VA_ARGS__); \ + } \ + else if (!std::get<0>(is_causal) && !std::get<1>(is_causal) && std::get<2>(is_causal)) { \ + naive::na3d_in_cuda_naive_float_cm_0_0_1(__VA_ARGS__); \ + } \ + else if (!std::get<0>(is_causal) && std::get<1>(is_causal) && !std::get<2>(is_causal)) { \ + naive::na3d_in_cuda_naive_float_cm_0_1_0(__VA_ARGS__); \ + } \ + else if (!std::get<0>(is_causal) && std::get<1>(is_causal) && std::get<2>(is_causal)) { \ + naive::na3d_in_cuda_naive_float_cm_0_1_1(__VA_ARGS__); \ + } \ + else if (std::get<0>(is_causal) && !std::get<1>(is_causal) && !std::get<2>(is_causal)) { \ + naive::na3d_in_cuda_naive_float_cm_1_0_0(__VA_ARGS__); \ + } \ + else if (std::get<0>(is_causal) && !std::get<1>(is_causal) && std::get<2>(is_causal)) { \ + naive::na3d_in_cuda_naive_float_cm_1_0_1(__VA_ARGS__); \ + } \ + else if (std::get<0>(is_causal) && std::get<1>(is_causal) && !std::get<2>(is_causal)) { \ + naive::na3d_in_cuda_naive_float_cm_1_1_0(__VA_ARGS__); \ + } \ + else if (std::get<0>(is_causal) && std::get<1>(is_causal) && std::get<2>(is_causal)) { \ + naive::na3d_in_cuda_naive_float_cm_1_1_1(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na3d_in_cuda_naive_float got invalid causal mask!" \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_CM_na3d_in_cuda_naive_half(is_causal, ...) \ + [&] { \ + if (!std::get<0>(is_causal) && !std::get<1>(is_causal) && !std::get<2>(is_causal)) { \ + naive::na3d_in_cuda_naive_half_cm_0_0_0(__VA_ARGS__); \ + } \ + else if (!std::get<0>(is_causal) && !std::get<1>(is_causal) && std::get<2>(is_causal)) { \ + naive::na3d_in_cuda_naive_half_cm_0_0_1(__VA_ARGS__); \ + } \ + else if (!std::get<0>(is_causal) && std::get<1>(is_causal) && !std::get<2>(is_causal)) { \ + naive::na3d_in_cuda_naive_half_cm_0_1_0(__VA_ARGS__); \ + } \ + else if (!std::get<0>(is_causal) && std::get<1>(is_causal) && std::get<2>(is_causal)) { \ + naive::na3d_in_cuda_naive_half_cm_0_1_1(__VA_ARGS__); \ + } \ + else if (std::get<0>(is_causal) && !std::get<1>(is_causal) && !std::get<2>(is_causal)) { \ + naive::na3d_in_cuda_naive_half_cm_1_0_0(__VA_ARGS__); \ + } \ + else if (std::get<0>(is_causal) && !std::get<1>(is_causal) && std::get<2>(is_causal)) { \ + naive::na3d_in_cuda_naive_half_cm_1_0_1(__VA_ARGS__); \ + } \ + else if (std::get<0>(is_causal) && std::get<1>(is_causal) && !std::get<2>(is_causal)) { \ + naive::na3d_in_cuda_naive_half_cm_1_1_0(__VA_ARGS__); \ + } \ + else if (std::get<0>(is_causal) && std::get<1>(is_causal) && std::get<2>(is_causal)) { \ + naive::na3d_in_cuda_naive_half_cm_1_1_1(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na3d_in_cuda_naive_half got invalid causal mask!" \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_CM_na3d_in_cuda_naive_bfloat16(is_causal, ...) \ + [&] { \ + if (!std::get<0>(is_causal) && !std::get<1>(is_causal) && !std::get<2>(is_causal)) { \ + naive::na3d_in_cuda_naive_bfloat16_cm_0_0_0(__VA_ARGS__); \ + } \ + else if (!std::get<0>(is_causal) && !std::get<1>(is_causal) && std::get<2>(is_causal)) { \ + naive::na3d_in_cuda_naive_bfloat16_cm_0_0_1(__VA_ARGS__); \ + } \ + else if (!std::get<0>(is_causal) && std::get<1>(is_causal) && !std::get<2>(is_causal)) { \ + naive::na3d_in_cuda_naive_bfloat16_cm_0_1_0(__VA_ARGS__); \ + } \ + else if (!std::get<0>(is_causal) && std::get<1>(is_causal) && std::get<2>(is_causal)) { \ + naive::na3d_in_cuda_naive_bfloat16_cm_0_1_1(__VA_ARGS__); \ + } \ + else if (std::get<0>(is_causal) && !std::get<1>(is_causal) && !std::get<2>(is_causal)) { \ + naive::na3d_in_cuda_naive_bfloat16_cm_1_0_0(__VA_ARGS__); \ + } \ + else if (std::get<0>(is_causal) && !std::get<1>(is_causal) && std::get<2>(is_causal)) { \ + naive::na3d_in_cuda_naive_bfloat16_cm_1_0_1(__VA_ARGS__); \ + } \ + else if (std::get<0>(is_causal) && std::get<1>(is_causal) && !std::get<2>(is_causal)) { \ + naive::na3d_in_cuda_naive_bfloat16_cm_1_1_0(__VA_ARGS__); \ + } \ + else if (std::get<0>(is_causal) && std::get<1>(is_causal) && std::get<2>(is_causal)) { \ + naive::na3d_in_cuda_naive_bfloat16_cm_1_1_1(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na3d_in_cuda_naive_bfloat16 got invalid causal mask!" \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_CM_na1d_rpbgrad_cuda_naive_double(is_causal, ...) \ + [&] { \ + if (!std::get<0>(is_causal)) { \ + naive::na1d_rpbgrad_cuda_naive_double_cm_0(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na1d_rpbgrad_cuda_naive_double got invalid causal mask!" \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_CM_na1d_rpbgrad_cuda_naive_float(is_causal, ...) \ + [&] { \ + if (!std::get<0>(is_causal)) { \ + naive::na1d_rpbgrad_cuda_naive_float_cm_0(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na1d_rpbgrad_cuda_naive_float got invalid causal mask!" \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_CM_na1d_rpbgrad_cuda_naive_half(is_causal, ...) \ + [&] { \ + if (!std::get<0>(is_causal)) { \ + naive::na1d_rpbgrad_cuda_naive_half_cm_0(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na1d_rpbgrad_cuda_naive_half got invalid causal mask!" \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_CM_na1d_rpbgrad_cuda_naive_bfloat16(is_causal, ...) \ + [&] { \ + if (!std::get<0>(is_causal)) { \ + naive::na1d_rpbgrad_cuda_naive_bfloat16_cm_0(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na1d_rpbgrad_cuda_naive_bfloat16 got invalid causal mask!" \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_CM_na2d_rpbgrad_cuda_naive_double(is_causal, ...) \ + [&] { \ + if (!std::get<0>(is_causal) && !std::get<1>(is_causal)) { \ + naive::na2d_rpbgrad_cuda_naive_double_cm_0_0(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na2d_rpbgrad_cuda_naive_double got invalid causal mask!" \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_CM_na2d_rpbgrad_cuda_naive_float(is_causal, ...) \ + [&] { \ + if (!std::get<0>(is_causal) && !std::get<1>(is_causal)) { \ + naive::na2d_rpbgrad_cuda_naive_float_cm_0_0(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na2d_rpbgrad_cuda_naive_float got invalid causal mask!" \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_CM_na2d_rpbgrad_cuda_naive_half(is_causal, ...) \ + [&] { \ + if (!std::get<0>(is_causal) && !std::get<1>(is_causal)) { \ + naive::na2d_rpbgrad_cuda_naive_half_cm_0_0(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na2d_rpbgrad_cuda_naive_half got invalid causal mask!" \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_CM_na2d_rpbgrad_cuda_naive_bfloat16(is_causal, ...) \ + [&] { \ + if (!std::get<0>(is_causal) && !std::get<1>(is_causal)) { \ + naive::na2d_rpbgrad_cuda_naive_bfloat16_cm_0_0(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na2d_rpbgrad_cuda_naive_bfloat16 got invalid causal mask!" \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_CM_na3d_rpbgrad_cuda_naive_double(is_causal, ...) \ + [&] { \ + if (!std::get<0>(is_causal) && !std::get<1>(is_causal) && !std::get<2>(is_causal)) { \ + naive::na3d_rpbgrad_cuda_naive_double_cm_0_0_0(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na3d_rpbgrad_cuda_naive_double got invalid causal mask!" \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_CM_na3d_rpbgrad_cuda_naive_float(is_causal, ...) \ + [&] { \ + if (!std::get<0>(is_causal) && !std::get<1>(is_causal) && !std::get<2>(is_causal)) { \ + naive::na3d_rpbgrad_cuda_naive_float_cm_0_0_0(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na3d_rpbgrad_cuda_naive_float got invalid causal mask!" \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_CM_na3d_rpbgrad_cuda_naive_half(is_causal, ...) \ + [&] { \ + if (!std::get<0>(is_causal) && !std::get<1>(is_causal) && !std::get<2>(is_causal)) { \ + naive::na3d_rpbgrad_cuda_naive_half_cm_0_0_0(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na3d_rpbgrad_cuda_naive_half got invalid causal mask!" \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + +#define DISPATCH_CM_na3d_rpbgrad_cuda_naive_bfloat16(is_causal, ...) \ + [&] { \ + if (!std::get<0>(is_causal) && !std::get<1>(is_causal) && !std::get<2>(is_causal)) { \ + naive::na3d_rpbgrad_cuda_naive_bfloat16_cm_0_0_0(__VA_ARGS__); \ + } \ + else { \ + std::cerr << "NATTEN kernel launch failed!" \ + << "na3d_rpbgrad_cuda_naive_bfloat16 got invalid causal mask!" \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ +}(); + + + +} // namespace natten +} // namespace cuda +} // namespace naive + diff --git a/csrc/autogen/include/natten_autogen/cuda/naive/dispatch_di.h b/csrc/autogen/include/natten_autogen/cuda/naive/dispatch_di.h deleted file mode 100644 index afefcf7..0000000 --- a/csrc/autogen/include/natten_autogen/cuda/naive/dispatch_di.h +++ /dev/null @@ -1,4216 +0,0 @@ -#pragma once - - -#include -#include -#include -#include -namespace natten { -namespace cuda { -namespace naive { -#define DISPATCH_DILATION_na1d_pn_cuda_naive_double_ks_any(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na1d_pn_cuda_naive_double_ks_any_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na1d_pn_cuda_naive_double_ks_any_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na1d_pn_cuda_naive_double_ks_3(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na1d_pn_cuda_naive_double_ks_3_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na1d_pn_cuda_naive_double_ks_3_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na1d_pn_cuda_naive_double_ks_5(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na1d_pn_cuda_naive_double_ks_5_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na1d_pn_cuda_naive_double_ks_5_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na1d_pn_cuda_naive_double_ks_7(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na1d_pn_cuda_naive_double_ks_7_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na1d_pn_cuda_naive_double_ks_7_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na1d_pn_cuda_naive_double_ks_9(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na1d_pn_cuda_naive_double_ks_9_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na1d_pn_cuda_naive_double_ks_9_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na1d_pn_cuda_naive_double_ks_11(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na1d_pn_cuda_naive_double_ks_11_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na1d_pn_cuda_naive_double_ks_11_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na1d_pn_cuda_naive_double_ks_13(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na1d_pn_cuda_naive_double_ks_13_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na1d_pn_cuda_naive_double_ks_13_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na1d_pn_cuda_naive_float_ks_any(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na1d_pn_cuda_naive_float_ks_any_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na1d_pn_cuda_naive_float_ks_any_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na1d_pn_cuda_naive_float_ks_3(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na1d_pn_cuda_naive_float_ks_3_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na1d_pn_cuda_naive_float_ks_3_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na1d_pn_cuda_naive_float_ks_5(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na1d_pn_cuda_naive_float_ks_5_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na1d_pn_cuda_naive_float_ks_5_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na1d_pn_cuda_naive_float_ks_7(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na1d_pn_cuda_naive_float_ks_7_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na1d_pn_cuda_naive_float_ks_7_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na1d_pn_cuda_naive_float_ks_9(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na1d_pn_cuda_naive_float_ks_9_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na1d_pn_cuda_naive_float_ks_9_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na1d_pn_cuda_naive_float_ks_11(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na1d_pn_cuda_naive_float_ks_11_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na1d_pn_cuda_naive_float_ks_11_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na1d_pn_cuda_naive_float_ks_13(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na1d_pn_cuda_naive_float_ks_13_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na1d_pn_cuda_naive_float_ks_13_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na1d_pn_cuda_naive_half_ks_any(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na1d_pn_cuda_naive_half_ks_any_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na1d_pn_cuda_naive_half_ks_any_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na1d_pn_cuda_naive_half_ks_3(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na1d_pn_cuda_naive_half_ks_3_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na1d_pn_cuda_naive_half_ks_3_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na1d_pn_cuda_naive_half_ks_5(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na1d_pn_cuda_naive_half_ks_5_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na1d_pn_cuda_naive_half_ks_5_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na1d_pn_cuda_naive_half_ks_7(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na1d_pn_cuda_naive_half_ks_7_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na1d_pn_cuda_naive_half_ks_7_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na1d_pn_cuda_naive_half_ks_9(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na1d_pn_cuda_naive_half_ks_9_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na1d_pn_cuda_naive_half_ks_9_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na1d_pn_cuda_naive_half_ks_11(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na1d_pn_cuda_naive_half_ks_11_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na1d_pn_cuda_naive_half_ks_11_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na1d_pn_cuda_naive_half_ks_13(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na1d_pn_cuda_naive_half_ks_13_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na1d_pn_cuda_naive_half_ks_13_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na1d_pn_cuda_naive_bfloat16_ks_any(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na1d_pn_cuda_naive_bfloat16_ks_any_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na1d_pn_cuda_naive_bfloat16_ks_any_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na1d_pn_cuda_naive_bfloat16_ks_3(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na1d_pn_cuda_naive_bfloat16_ks_3_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na1d_pn_cuda_naive_bfloat16_ks_3_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na1d_pn_cuda_naive_bfloat16_ks_5(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na1d_pn_cuda_naive_bfloat16_ks_5_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na1d_pn_cuda_naive_bfloat16_ks_5_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na1d_pn_cuda_naive_bfloat16_ks_7(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na1d_pn_cuda_naive_bfloat16_ks_7_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na1d_pn_cuda_naive_bfloat16_ks_7_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na1d_pn_cuda_naive_bfloat16_ks_9(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na1d_pn_cuda_naive_bfloat16_ks_9_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na1d_pn_cuda_naive_bfloat16_ks_9_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na1d_pn_cuda_naive_bfloat16_ks_11(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na1d_pn_cuda_naive_bfloat16_ks_11_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na1d_pn_cuda_naive_bfloat16_ks_11_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na1d_pn_cuda_naive_bfloat16_ks_13(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na1d_pn_cuda_naive_bfloat16_ks_13_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na1d_pn_cuda_naive_bfloat16_ks_13_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na2d_pn_cuda_naive_double_ks_any(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na2d_pn_cuda_naive_double_ks_any_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na2d_pn_cuda_naive_double_ks_any_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na2d_pn_cuda_naive_double_ks_3(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na2d_pn_cuda_naive_double_ks_3_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na2d_pn_cuda_naive_double_ks_3_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na2d_pn_cuda_naive_double_ks_5(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na2d_pn_cuda_naive_double_ks_5_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na2d_pn_cuda_naive_double_ks_5_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na2d_pn_cuda_naive_double_ks_7(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na2d_pn_cuda_naive_double_ks_7_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na2d_pn_cuda_naive_double_ks_7_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na2d_pn_cuda_naive_double_ks_9(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na2d_pn_cuda_naive_double_ks_9_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na2d_pn_cuda_naive_double_ks_9_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na2d_pn_cuda_naive_double_ks_11(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na2d_pn_cuda_naive_double_ks_11_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na2d_pn_cuda_naive_double_ks_11_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na2d_pn_cuda_naive_double_ks_13(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na2d_pn_cuda_naive_double_ks_13_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na2d_pn_cuda_naive_double_ks_13_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na2d_pn_cuda_naive_float_ks_any(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na2d_pn_cuda_naive_float_ks_any_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na2d_pn_cuda_naive_float_ks_any_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na2d_pn_cuda_naive_float_ks_3(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na2d_pn_cuda_naive_float_ks_3_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na2d_pn_cuda_naive_float_ks_3_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na2d_pn_cuda_naive_float_ks_5(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na2d_pn_cuda_naive_float_ks_5_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na2d_pn_cuda_naive_float_ks_5_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na2d_pn_cuda_naive_float_ks_7(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na2d_pn_cuda_naive_float_ks_7_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na2d_pn_cuda_naive_float_ks_7_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na2d_pn_cuda_naive_float_ks_9(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na2d_pn_cuda_naive_float_ks_9_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na2d_pn_cuda_naive_float_ks_9_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na2d_pn_cuda_naive_float_ks_11(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na2d_pn_cuda_naive_float_ks_11_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na2d_pn_cuda_naive_float_ks_11_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na2d_pn_cuda_naive_float_ks_13(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na2d_pn_cuda_naive_float_ks_13_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na2d_pn_cuda_naive_float_ks_13_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na2d_pn_cuda_naive_half_ks_any(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na2d_pn_cuda_naive_half_ks_any_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na2d_pn_cuda_naive_half_ks_any_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na2d_pn_cuda_naive_half_ks_3(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na2d_pn_cuda_naive_half_ks_3_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na2d_pn_cuda_naive_half_ks_3_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na2d_pn_cuda_naive_half_ks_5(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na2d_pn_cuda_naive_half_ks_5_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na2d_pn_cuda_naive_half_ks_5_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na2d_pn_cuda_naive_half_ks_7(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na2d_pn_cuda_naive_half_ks_7_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na2d_pn_cuda_naive_half_ks_7_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na2d_pn_cuda_naive_half_ks_9(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na2d_pn_cuda_naive_half_ks_9_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na2d_pn_cuda_naive_half_ks_9_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na2d_pn_cuda_naive_half_ks_11(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na2d_pn_cuda_naive_half_ks_11_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na2d_pn_cuda_naive_half_ks_11_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na2d_pn_cuda_naive_half_ks_13(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na2d_pn_cuda_naive_half_ks_13_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na2d_pn_cuda_naive_half_ks_13_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na2d_pn_cuda_naive_bfloat16_ks_any(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na2d_pn_cuda_naive_bfloat16_ks_any_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na2d_pn_cuda_naive_bfloat16_ks_any_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na2d_pn_cuda_naive_bfloat16_ks_3(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na2d_pn_cuda_naive_bfloat16_ks_3_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na2d_pn_cuda_naive_bfloat16_ks_3_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na2d_pn_cuda_naive_bfloat16_ks_5(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na2d_pn_cuda_naive_bfloat16_ks_5_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na2d_pn_cuda_naive_bfloat16_ks_5_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na2d_pn_cuda_naive_bfloat16_ks_7(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na2d_pn_cuda_naive_bfloat16_ks_7_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na2d_pn_cuda_naive_bfloat16_ks_7_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na2d_pn_cuda_naive_bfloat16_ks_9(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na2d_pn_cuda_naive_bfloat16_ks_9_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na2d_pn_cuda_naive_bfloat16_ks_9_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na2d_pn_cuda_naive_bfloat16_ks_11(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na2d_pn_cuda_naive_bfloat16_ks_11_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na2d_pn_cuda_naive_bfloat16_ks_11_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na2d_pn_cuda_naive_bfloat16_ks_13(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na2d_pn_cuda_naive_bfloat16_ks_13_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na2d_pn_cuda_naive_bfloat16_ks_13_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na3d_pn_cuda_naive_double_ks_any(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na3d_pn_cuda_naive_double_ks_any_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na3d_pn_cuda_naive_double_ks_any_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na3d_pn_cuda_naive_double_ks_3(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na3d_pn_cuda_naive_double_ks_3_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na3d_pn_cuda_naive_double_ks_3_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na3d_pn_cuda_naive_double_ks_5(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na3d_pn_cuda_naive_double_ks_5_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na3d_pn_cuda_naive_double_ks_5_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na3d_pn_cuda_naive_double_ks_7(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na3d_pn_cuda_naive_double_ks_7_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na3d_pn_cuda_naive_double_ks_7_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na3d_pn_cuda_naive_double_ks_9(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na3d_pn_cuda_naive_double_ks_9_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na3d_pn_cuda_naive_double_ks_9_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na3d_pn_cuda_naive_double_ks_11(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na3d_pn_cuda_naive_double_ks_11_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na3d_pn_cuda_naive_double_ks_11_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na3d_pn_cuda_naive_double_ks_13(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na3d_pn_cuda_naive_double_ks_13_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na3d_pn_cuda_naive_double_ks_13_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na3d_pn_cuda_naive_float_ks_any(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na3d_pn_cuda_naive_float_ks_any_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na3d_pn_cuda_naive_float_ks_any_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na3d_pn_cuda_naive_float_ks_3(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na3d_pn_cuda_naive_float_ks_3_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na3d_pn_cuda_naive_float_ks_3_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na3d_pn_cuda_naive_float_ks_5(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na3d_pn_cuda_naive_float_ks_5_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na3d_pn_cuda_naive_float_ks_5_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na3d_pn_cuda_naive_float_ks_7(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na3d_pn_cuda_naive_float_ks_7_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na3d_pn_cuda_naive_float_ks_7_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na3d_pn_cuda_naive_float_ks_9(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na3d_pn_cuda_naive_float_ks_9_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na3d_pn_cuda_naive_float_ks_9_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na3d_pn_cuda_naive_float_ks_11(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na3d_pn_cuda_naive_float_ks_11_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na3d_pn_cuda_naive_float_ks_11_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na3d_pn_cuda_naive_float_ks_13(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na3d_pn_cuda_naive_float_ks_13_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na3d_pn_cuda_naive_float_ks_13_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na3d_pn_cuda_naive_half_ks_any(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na3d_pn_cuda_naive_half_ks_any_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na3d_pn_cuda_naive_half_ks_any_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na3d_pn_cuda_naive_half_ks_3(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na3d_pn_cuda_naive_half_ks_3_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na3d_pn_cuda_naive_half_ks_3_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na3d_pn_cuda_naive_half_ks_5(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na3d_pn_cuda_naive_half_ks_5_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na3d_pn_cuda_naive_half_ks_5_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na3d_pn_cuda_naive_half_ks_7(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na3d_pn_cuda_naive_half_ks_7_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na3d_pn_cuda_naive_half_ks_7_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na3d_pn_cuda_naive_half_ks_9(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na3d_pn_cuda_naive_half_ks_9_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na3d_pn_cuda_naive_half_ks_9_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na3d_pn_cuda_naive_half_ks_11(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na3d_pn_cuda_naive_half_ks_11_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na3d_pn_cuda_naive_half_ks_11_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na3d_pn_cuda_naive_half_ks_13(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na3d_pn_cuda_naive_half_ks_13_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na3d_pn_cuda_naive_half_ks_13_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na3d_pn_cuda_naive_bfloat16_ks_any(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na3d_pn_cuda_naive_bfloat16_ks_any_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na3d_pn_cuda_naive_bfloat16_ks_any_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na3d_pn_cuda_naive_bfloat16_ks_3(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na3d_pn_cuda_naive_bfloat16_ks_3_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na3d_pn_cuda_naive_bfloat16_ks_3_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na3d_pn_cuda_naive_bfloat16_ks_5(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na3d_pn_cuda_naive_bfloat16_ks_5_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na3d_pn_cuda_naive_bfloat16_ks_5_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na3d_pn_cuda_naive_bfloat16_ks_7(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na3d_pn_cuda_naive_bfloat16_ks_7_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na3d_pn_cuda_naive_bfloat16_ks_7_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na3d_pn_cuda_naive_bfloat16_ks_9(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na3d_pn_cuda_naive_bfloat16_ks_9_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na3d_pn_cuda_naive_bfloat16_ks_9_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na3d_pn_cuda_naive_bfloat16_ks_11(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na3d_pn_cuda_naive_bfloat16_ks_11_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na3d_pn_cuda_naive_bfloat16_ks_11_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na3d_pn_cuda_naive_bfloat16_ks_13(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na3d_pn_cuda_naive_bfloat16_ks_13_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na3d_pn_cuda_naive_bfloat16_ks_13_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na1d_pn_bias_cuda_naive_double_ks_any(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na1d_pn_bias_cuda_naive_double_ks_any_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na1d_pn_bias_cuda_naive_double_ks_any_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na1d_pn_bias_cuda_naive_double_ks_3(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na1d_pn_bias_cuda_naive_double_ks_3_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na1d_pn_bias_cuda_naive_double_ks_3_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na1d_pn_bias_cuda_naive_double_ks_5(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na1d_pn_bias_cuda_naive_double_ks_5_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na1d_pn_bias_cuda_naive_double_ks_5_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na1d_pn_bias_cuda_naive_double_ks_7(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na1d_pn_bias_cuda_naive_double_ks_7_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na1d_pn_bias_cuda_naive_double_ks_7_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na1d_pn_bias_cuda_naive_double_ks_9(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na1d_pn_bias_cuda_naive_double_ks_9_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na1d_pn_bias_cuda_naive_double_ks_9_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na1d_pn_bias_cuda_naive_double_ks_11(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na1d_pn_bias_cuda_naive_double_ks_11_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na1d_pn_bias_cuda_naive_double_ks_11_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na1d_pn_bias_cuda_naive_double_ks_13(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na1d_pn_bias_cuda_naive_double_ks_13_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na1d_pn_bias_cuda_naive_double_ks_13_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na1d_pn_bias_cuda_naive_float_ks_any(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na1d_pn_bias_cuda_naive_float_ks_any_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na1d_pn_bias_cuda_naive_float_ks_any_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na1d_pn_bias_cuda_naive_float_ks_3(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na1d_pn_bias_cuda_naive_float_ks_3_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na1d_pn_bias_cuda_naive_float_ks_3_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na1d_pn_bias_cuda_naive_float_ks_5(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na1d_pn_bias_cuda_naive_float_ks_5_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na1d_pn_bias_cuda_naive_float_ks_5_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na1d_pn_bias_cuda_naive_float_ks_7(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na1d_pn_bias_cuda_naive_float_ks_7_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na1d_pn_bias_cuda_naive_float_ks_7_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na1d_pn_bias_cuda_naive_float_ks_9(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na1d_pn_bias_cuda_naive_float_ks_9_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na1d_pn_bias_cuda_naive_float_ks_9_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na1d_pn_bias_cuda_naive_float_ks_11(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na1d_pn_bias_cuda_naive_float_ks_11_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na1d_pn_bias_cuda_naive_float_ks_11_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na1d_pn_bias_cuda_naive_float_ks_13(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na1d_pn_bias_cuda_naive_float_ks_13_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na1d_pn_bias_cuda_naive_float_ks_13_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na1d_pn_bias_cuda_naive_half_ks_any(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na1d_pn_bias_cuda_naive_half_ks_any_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na1d_pn_bias_cuda_naive_half_ks_any_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na1d_pn_bias_cuda_naive_half_ks_3(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na1d_pn_bias_cuda_naive_half_ks_3_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na1d_pn_bias_cuda_naive_half_ks_3_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na1d_pn_bias_cuda_naive_half_ks_5(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na1d_pn_bias_cuda_naive_half_ks_5_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na1d_pn_bias_cuda_naive_half_ks_5_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na1d_pn_bias_cuda_naive_half_ks_7(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na1d_pn_bias_cuda_naive_half_ks_7_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na1d_pn_bias_cuda_naive_half_ks_7_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na1d_pn_bias_cuda_naive_half_ks_9(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na1d_pn_bias_cuda_naive_half_ks_9_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na1d_pn_bias_cuda_naive_half_ks_9_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na1d_pn_bias_cuda_naive_half_ks_11(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na1d_pn_bias_cuda_naive_half_ks_11_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na1d_pn_bias_cuda_naive_half_ks_11_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na1d_pn_bias_cuda_naive_half_ks_13(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na1d_pn_bias_cuda_naive_half_ks_13_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na1d_pn_bias_cuda_naive_half_ks_13_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na1d_pn_bias_cuda_naive_bfloat16_ks_any(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na1d_pn_bias_cuda_naive_bfloat16_ks_any_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na1d_pn_bias_cuda_naive_bfloat16_ks_any_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na1d_pn_bias_cuda_naive_bfloat16_ks_3(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na1d_pn_bias_cuda_naive_bfloat16_ks_3_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na1d_pn_bias_cuda_naive_bfloat16_ks_3_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na1d_pn_bias_cuda_naive_bfloat16_ks_5(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na1d_pn_bias_cuda_naive_bfloat16_ks_5_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na1d_pn_bias_cuda_naive_bfloat16_ks_5_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na1d_pn_bias_cuda_naive_bfloat16_ks_7(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na1d_pn_bias_cuda_naive_bfloat16_ks_7_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na1d_pn_bias_cuda_naive_bfloat16_ks_7_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na1d_pn_bias_cuda_naive_bfloat16_ks_9(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na1d_pn_bias_cuda_naive_bfloat16_ks_9_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na1d_pn_bias_cuda_naive_bfloat16_ks_9_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na1d_pn_bias_cuda_naive_bfloat16_ks_11(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na1d_pn_bias_cuda_naive_bfloat16_ks_11_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na1d_pn_bias_cuda_naive_bfloat16_ks_11_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na1d_pn_bias_cuda_naive_bfloat16_ks_13(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na1d_pn_bias_cuda_naive_bfloat16_ks_13_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na1d_pn_bias_cuda_naive_bfloat16_ks_13_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na2d_pn_bias_cuda_naive_double_ks_any(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na2d_pn_bias_cuda_naive_double_ks_any_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na2d_pn_bias_cuda_naive_double_ks_any_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na2d_pn_bias_cuda_naive_double_ks_3(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na2d_pn_bias_cuda_naive_double_ks_3_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na2d_pn_bias_cuda_naive_double_ks_3_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na2d_pn_bias_cuda_naive_double_ks_5(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na2d_pn_bias_cuda_naive_double_ks_5_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na2d_pn_bias_cuda_naive_double_ks_5_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na2d_pn_bias_cuda_naive_double_ks_7(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na2d_pn_bias_cuda_naive_double_ks_7_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na2d_pn_bias_cuda_naive_double_ks_7_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na2d_pn_bias_cuda_naive_double_ks_9(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na2d_pn_bias_cuda_naive_double_ks_9_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na2d_pn_bias_cuda_naive_double_ks_9_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na2d_pn_bias_cuda_naive_double_ks_11(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na2d_pn_bias_cuda_naive_double_ks_11_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na2d_pn_bias_cuda_naive_double_ks_11_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na2d_pn_bias_cuda_naive_double_ks_13(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na2d_pn_bias_cuda_naive_double_ks_13_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na2d_pn_bias_cuda_naive_double_ks_13_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na2d_pn_bias_cuda_naive_float_ks_any(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na2d_pn_bias_cuda_naive_float_ks_any_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na2d_pn_bias_cuda_naive_float_ks_any_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na2d_pn_bias_cuda_naive_float_ks_3(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na2d_pn_bias_cuda_naive_float_ks_3_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na2d_pn_bias_cuda_naive_float_ks_3_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na2d_pn_bias_cuda_naive_float_ks_5(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na2d_pn_bias_cuda_naive_float_ks_5_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na2d_pn_bias_cuda_naive_float_ks_5_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na2d_pn_bias_cuda_naive_float_ks_7(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na2d_pn_bias_cuda_naive_float_ks_7_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na2d_pn_bias_cuda_naive_float_ks_7_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na2d_pn_bias_cuda_naive_float_ks_9(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na2d_pn_bias_cuda_naive_float_ks_9_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na2d_pn_bias_cuda_naive_float_ks_9_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na2d_pn_bias_cuda_naive_float_ks_11(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na2d_pn_bias_cuda_naive_float_ks_11_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na2d_pn_bias_cuda_naive_float_ks_11_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na2d_pn_bias_cuda_naive_float_ks_13(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na2d_pn_bias_cuda_naive_float_ks_13_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na2d_pn_bias_cuda_naive_float_ks_13_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na2d_pn_bias_cuda_naive_half_ks_any(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na2d_pn_bias_cuda_naive_half_ks_any_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na2d_pn_bias_cuda_naive_half_ks_any_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na2d_pn_bias_cuda_naive_half_ks_3(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na2d_pn_bias_cuda_naive_half_ks_3_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na2d_pn_bias_cuda_naive_half_ks_3_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na2d_pn_bias_cuda_naive_half_ks_5(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na2d_pn_bias_cuda_naive_half_ks_5_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na2d_pn_bias_cuda_naive_half_ks_5_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na2d_pn_bias_cuda_naive_half_ks_7(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na2d_pn_bias_cuda_naive_half_ks_7_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na2d_pn_bias_cuda_naive_half_ks_7_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na2d_pn_bias_cuda_naive_half_ks_9(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na2d_pn_bias_cuda_naive_half_ks_9_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na2d_pn_bias_cuda_naive_half_ks_9_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na2d_pn_bias_cuda_naive_half_ks_11(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na2d_pn_bias_cuda_naive_half_ks_11_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na2d_pn_bias_cuda_naive_half_ks_11_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na2d_pn_bias_cuda_naive_half_ks_13(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na2d_pn_bias_cuda_naive_half_ks_13_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na2d_pn_bias_cuda_naive_half_ks_13_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na2d_pn_bias_cuda_naive_bfloat16_ks_any(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na2d_pn_bias_cuda_naive_bfloat16_ks_any_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na2d_pn_bias_cuda_naive_bfloat16_ks_any_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na2d_pn_bias_cuda_naive_bfloat16_ks_3(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na2d_pn_bias_cuda_naive_bfloat16_ks_3_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na2d_pn_bias_cuda_naive_bfloat16_ks_3_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na2d_pn_bias_cuda_naive_bfloat16_ks_5(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na2d_pn_bias_cuda_naive_bfloat16_ks_5_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na2d_pn_bias_cuda_naive_bfloat16_ks_5_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na2d_pn_bias_cuda_naive_bfloat16_ks_7(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na2d_pn_bias_cuda_naive_bfloat16_ks_7_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na2d_pn_bias_cuda_naive_bfloat16_ks_7_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na2d_pn_bias_cuda_naive_bfloat16_ks_9(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na2d_pn_bias_cuda_naive_bfloat16_ks_9_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na2d_pn_bias_cuda_naive_bfloat16_ks_9_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na2d_pn_bias_cuda_naive_bfloat16_ks_11(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na2d_pn_bias_cuda_naive_bfloat16_ks_11_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na2d_pn_bias_cuda_naive_bfloat16_ks_11_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na2d_pn_bias_cuda_naive_bfloat16_ks_13(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na2d_pn_bias_cuda_naive_bfloat16_ks_13_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na2d_pn_bias_cuda_naive_bfloat16_ks_13_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na3d_pn_bias_cuda_naive_double_ks_any(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na3d_pn_bias_cuda_naive_double_ks_any_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na3d_pn_bias_cuda_naive_double_ks_any_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na3d_pn_bias_cuda_naive_double_ks_3(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na3d_pn_bias_cuda_naive_double_ks_3_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na3d_pn_bias_cuda_naive_double_ks_3_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na3d_pn_bias_cuda_naive_double_ks_5(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na3d_pn_bias_cuda_naive_double_ks_5_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na3d_pn_bias_cuda_naive_double_ks_5_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na3d_pn_bias_cuda_naive_double_ks_7(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na3d_pn_bias_cuda_naive_double_ks_7_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na3d_pn_bias_cuda_naive_double_ks_7_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na3d_pn_bias_cuda_naive_double_ks_9(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na3d_pn_bias_cuda_naive_double_ks_9_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na3d_pn_bias_cuda_naive_double_ks_9_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na3d_pn_bias_cuda_naive_double_ks_11(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na3d_pn_bias_cuda_naive_double_ks_11_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na3d_pn_bias_cuda_naive_double_ks_11_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na3d_pn_bias_cuda_naive_double_ks_13(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na3d_pn_bias_cuda_naive_double_ks_13_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na3d_pn_bias_cuda_naive_double_ks_13_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na3d_pn_bias_cuda_naive_float_ks_any(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na3d_pn_bias_cuda_naive_float_ks_any_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na3d_pn_bias_cuda_naive_float_ks_any_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na3d_pn_bias_cuda_naive_float_ks_3(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na3d_pn_bias_cuda_naive_float_ks_3_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na3d_pn_bias_cuda_naive_float_ks_3_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na3d_pn_bias_cuda_naive_float_ks_5(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na3d_pn_bias_cuda_naive_float_ks_5_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na3d_pn_bias_cuda_naive_float_ks_5_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na3d_pn_bias_cuda_naive_float_ks_7(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na3d_pn_bias_cuda_naive_float_ks_7_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na3d_pn_bias_cuda_naive_float_ks_7_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na3d_pn_bias_cuda_naive_float_ks_9(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na3d_pn_bias_cuda_naive_float_ks_9_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na3d_pn_bias_cuda_naive_float_ks_9_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na3d_pn_bias_cuda_naive_float_ks_11(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na3d_pn_bias_cuda_naive_float_ks_11_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na3d_pn_bias_cuda_naive_float_ks_11_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na3d_pn_bias_cuda_naive_float_ks_13(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na3d_pn_bias_cuda_naive_float_ks_13_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na3d_pn_bias_cuda_naive_float_ks_13_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na3d_pn_bias_cuda_naive_half_ks_any(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na3d_pn_bias_cuda_naive_half_ks_any_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na3d_pn_bias_cuda_naive_half_ks_any_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na3d_pn_bias_cuda_naive_half_ks_3(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na3d_pn_bias_cuda_naive_half_ks_3_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na3d_pn_bias_cuda_naive_half_ks_3_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na3d_pn_bias_cuda_naive_half_ks_5(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na3d_pn_bias_cuda_naive_half_ks_5_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na3d_pn_bias_cuda_naive_half_ks_5_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na3d_pn_bias_cuda_naive_half_ks_7(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na3d_pn_bias_cuda_naive_half_ks_7_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na3d_pn_bias_cuda_naive_half_ks_7_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na3d_pn_bias_cuda_naive_half_ks_9(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na3d_pn_bias_cuda_naive_half_ks_9_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na3d_pn_bias_cuda_naive_half_ks_9_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na3d_pn_bias_cuda_naive_half_ks_11(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na3d_pn_bias_cuda_naive_half_ks_11_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na3d_pn_bias_cuda_naive_half_ks_11_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na3d_pn_bias_cuda_naive_half_ks_13(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na3d_pn_bias_cuda_naive_half_ks_13_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na3d_pn_bias_cuda_naive_half_ks_13_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na3d_pn_bias_cuda_naive_bfloat16_ks_any(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na3d_pn_bias_cuda_naive_bfloat16_ks_any_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na3d_pn_bias_cuda_naive_bfloat16_ks_any_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na3d_pn_bias_cuda_naive_bfloat16_ks_3(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na3d_pn_bias_cuda_naive_bfloat16_ks_3_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na3d_pn_bias_cuda_naive_bfloat16_ks_3_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na3d_pn_bias_cuda_naive_bfloat16_ks_5(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na3d_pn_bias_cuda_naive_bfloat16_ks_5_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na3d_pn_bias_cuda_naive_bfloat16_ks_5_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na3d_pn_bias_cuda_naive_bfloat16_ks_7(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na3d_pn_bias_cuda_naive_bfloat16_ks_7_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na3d_pn_bias_cuda_naive_bfloat16_ks_7_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na3d_pn_bias_cuda_naive_bfloat16_ks_9(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na3d_pn_bias_cuda_naive_bfloat16_ks_9_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na3d_pn_bias_cuda_naive_bfloat16_ks_9_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na3d_pn_bias_cuda_naive_bfloat16_ks_11(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na3d_pn_bias_cuda_naive_bfloat16_ks_11_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na3d_pn_bias_cuda_naive_bfloat16_ks_11_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na3d_pn_bias_cuda_naive_bfloat16_ks_13(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na3d_pn_bias_cuda_naive_bfloat16_ks_13_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na3d_pn_bias_cuda_naive_bfloat16_ks_13_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na1d_nn_cuda_naive_double_ks_any(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na1d_nn_cuda_naive_double_ks_any_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na1d_nn_cuda_naive_double_ks_any_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na1d_nn_cuda_naive_double_ks_3(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na1d_nn_cuda_naive_double_ks_3_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na1d_nn_cuda_naive_double_ks_3_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na1d_nn_cuda_naive_double_ks_5(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na1d_nn_cuda_naive_double_ks_5_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na1d_nn_cuda_naive_double_ks_5_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na1d_nn_cuda_naive_double_ks_7(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na1d_nn_cuda_naive_double_ks_7_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na1d_nn_cuda_naive_double_ks_7_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na1d_nn_cuda_naive_double_ks_9(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na1d_nn_cuda_naive_double_ks_9_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na1d_nn_cuda_naive_double_ks_9_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na1d_nn_cuda_naive_double_ks_11(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na1d_nn_cuda_naive_double_ks_11_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na1d_nn_cuda_naive_double_ks_11_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na1d_nn_cuda_naive_double_ks_13(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na1d_nn_cuda_naive_double_ks_13_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na1d_nn_cuda_naive_double_ks_13_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na1d_nn_cuda_naive_float_ks_any(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na1d_nn_cuda_naive_float_ks_any_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na1d_nn_cuda_naive_float_ks_any_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na1d_nn_cuda_naive_float_ks_3(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na1d_nn_cuda_naive_float_ks_3_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na1d_nn_cuda_naive_float_ks_3_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na1d_nn_cuda_naive_float_ks_5(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na1d_nn_cuda_naive_float_ks_5_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na1d_nn_cuda_naive_float_ks_5_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na1d_nn_cuda_naive_float_ks_7(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na1d_nn_cuda_naive_float_ks_7_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na1d_nn_cuda_naive_float_ks_7_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na1d_nn_cuda_naive_float_ks_9(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na1d_nn_cuda_naive_float_ks_9_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na1d_nn_cuda_naive_float_ks_9_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na1d_nn_cuda_naive_float_ks_11(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na1d_nn_cuda_naive_float_ks_11_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na1d_nn_cuda_naive_float_ks_11_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na1d_nn_cuda_naive_float_ks_13(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na1d_nn_cuda_naive_float_ks_13_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na1d_nn_cuda_naive_float_ks_13_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na1d_nn_cuda_naive_half_ks_any(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na1d_nn_cuda_naive_half_ks_any_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na1d_nn_cuda_naive_half_ks_any_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na1d_nn_cuda_naive_half_ks_3(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na1d_nn_cuda_naive_half_ks_3_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na1d_nn_cuda_naive_half_ks_3_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na1d_nn_cuda_naive_half_ks_5(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na1d_nn_cuda_naive_half_ks_5_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na1d_nn_cuda_naive_half_ks_5_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na1d_nn_cuda_naive_half_ks_7(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na1d_nn_cuda_naive_half_ks_7_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na1d_nn_cuda_naive_half_ks_7_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na1d_nn_cuda_naive_half_ks_9(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na1d_nn_cuda_naive_half_ks_9_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na1d_nn_cuda_naive_half_ks_9_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na1d_nn_cuda_naive_half_ks_11(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na1d_nn_cuda_naive_half_ks_11_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na1d_nn_cuda_naive_half_ks_11_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na1d_nn_cuda_naive_half_ks_13(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na1d_nn_cuda_naive_half_ks_13_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na1d_nn_cuda_naive_half_ks_13_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na1d_nn_cuda_naive_bfloat16_ks_any(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na1d_nn_cuda_naive_bfloat16_ks_any_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na1d_nn_cuda_naive_bfloat16_ks_any_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na1d_nn_cuda_naive_bfloat16_ks_3(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na1d_nn_cuda_naive_bfloat16_ks_3_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na1d_nn_cuda_naive_bfloat16_ks_3_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na1d_nn_cuda_naive_bfloat16_ks_5(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na1d_nn_cuda_naive_bfloat16_ks_5_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na1d_nn_cuda_naive_bfloat16_ks_5_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na1d_nn_cuda_naive_bfloat16_ks_7(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na1d_nn_cuda_naive_bfloat16_ks_7_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na1d_nn_cuda_naive_bfloat16_ks_7_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na1d_nn_cuda_naive_bfloat16_ks_9(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na1d_nn_cuda_naive_bfloat16_ks_9_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na1d_nn_cuda_naive_bfloat16_ks_9_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na1d_nn_cuda_naive_bfloat16_ks_11(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na1d_nn_cuda_naive_bfloat16_ks_11_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na1d_nn_cuda_naive_bfloat16_ks_11_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na1d_nn_cuda_naive_bfloat16_ks_13(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na1d_nn_cuda_naive_bfloat16_ks_13_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na1d_nn_cuda_naive_bfloat16_ks_13_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na2d_nn_cuda_naive_double_ks_any(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na2d_nn_cuda_naive_double_ks_any_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na2d_nn_cuda_naive_double_ks_any_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na2d_nn_cuda_naive_double_ks_3(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na2d_nn_cuda_naive_double_ks_3_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na2d_nn_cuda_naive_double_ks_3_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na2d_nn_cuda_naive_double_ks_5(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na2d_nn_cuda_naive_double_ks_5_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na2d_nn_cuda_naive_double_ks_5_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na2d_nn_cuda_naive_double_ks_7(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na2d_nn_cuda_naive_double_ks_7_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na2d_nn_cuda_naive_double_ks_7_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na2d_nn_cuda_naive_double_ks_9(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na2d_nn_cuda_naive_double_ks_9_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na2d_nn_cuda_naive_double_ks_9_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na2d_nn_cuda_naive_double_ks_11(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na2d_nn_cuda_naive_double_ks_11_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na2d_nn_cuda_naive_double_ks_11_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na2d_nn_cuda_naive_double_ks_13(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na2d_nn_cuda_naive_double_ks_13_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na2d_nn_cuda_naive_double_ks_13_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na2d_nn_cuda_naive_float_ks_any(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na2d_nn_cuda_naive_float_ks_any_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na2d_nn_cuda_naive_float_ks_any_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na2d_nn_cuda_naive_float_ks_3(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na2d_nn_cuda_naive_float_ks_3_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na2d_nn_cuda_naive_float_ks_3_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na2d_nn_cuda_naive_float_ks_5(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na2d_nn_cuda_naive_float_ks_5_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na2d_nn_cuda_naive_float_ks_5_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na2d_nn_cuda_naive_float_ks_7(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na2d_nn_cuda_naive_float_ks_7_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na2d_nn_cuda_naive_float_ks_7_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na2d_nn_cuda_naive_float_ks_9(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na2d_nn_cuda_naive_float_ks_9_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na2d_nn_cuda_naive_float_ks_9_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na2d_nn_cuda_naive_float_ks_11(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na2d_nn_cuda_naive_float_ks_11_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na2d_nn_cuda_naive_float_ks_11_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na2d_nn_cuda_naive_float_ks_13(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na2d_nn_cuda_naive_float_ks_13_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na2d_nn_cuda_naive_float_ks_13_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na2d_nn_cuda_naive_half_ks_any(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na2d_nn_cuda_naive_half_ks_any_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na2d_nn_cuda_naive_half_ks_any_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na2d_nn_cuda_naive_half_ks_3(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na2d_nn_cuda_naive_half_ks_3_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na2d_nn_cuda_naive_half_ks_3_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na2d_nn_cuda_naive_half_ks_5(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na2d_nn_cuda_naive_half_ks_5_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na2d_nn_cuda_naive_half_ks_5_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na2d_nn_cuda_naive_half_ks_7(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na2d_nn_cuda_naive_half_ks_7_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na2d_nn_cuda_naive_half_ks_7_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na2d_nn_cuda_naive_half_ks_9(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na2d_nn_cuda_naive_half_ks_9_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na2d_nn_cuda_naive_half_ks_9_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na2d_nn_cuda_naive_half_ks_11(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na2d_nn_cuda_naive_half_ks_11_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na2d_nn_cuda_naive_half_ks_11_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na2d_nn_cuda_naive_half_ks_13(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na2d_nn_cuda_naive_half_ks_13_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na2d_nn_cuda_naive_half_ks_13_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na2d_nn_cuda_naive_bfloat16_ks_any(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na2d_nn_cuda_naive_bfloat16_ks_any_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na2d_nn_cuda_naive_bfloat16_ks_any_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na2d_nn_cuda_naive_bfloat16_ks_3(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na2d_nn_cuda_naive_bfloat16_ks_3_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na2d_nn_cuda_naive_bfloat16_ks_3_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na2d_nn_cuda_naive_bfloat16_ks_5(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na2d_nn_cuda_naive_bfloat16_ks_5_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na2d_nn_cuda_naive_bfloat16_ks_5_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na2d_nn_cuda_naive_bfloat16_ks_7(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na2d_nn_cuda_naive_bfloat16_ks_7_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na2d_nn_cuda_naive_bfloat16_ks_7_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na2d_nn_cuda_naive_bfloat16_ks_9(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na2d_nn_cuda_naive_bfloat16_ks_9_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na2d_nn_cuda_naive_bfloat16_ks_9_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na2d_nn_cuda_naive_bfloat16_ks_11(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na2d_nn_cuda_naive_bfloat16_ks_11_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na2d_nn_cuda_naive_bfloat16_ks_11_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na2d_nn_cuda_naive_bfloat16_ks_13(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na2d_nn_cuda_naive_bfloat16_ks_13_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na2d_nn_cuda_naive_bfloat16_ks_13_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na3d_nn_cuda_naive_double_ks_any(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na3d_nn_cuda_naive_double_ks_any_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na3d_nn_cuda_naive_double_ks_any_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na3d_nn_cuda_naive_double_ks_3(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na3d_nn_cuda_naive_double_ks_3_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na3d_nn_cuda_naive_double_ks_3_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na3d_nn_cuda_naive_double_ks_5(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na3d_nn_cuda_naive_double_ks_5_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na3d_nn_cuda_naive_double_ks_5_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na3d_nn_cuda_naive_double_ks_7(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na3d_nn_cuda_naive_double_ks_7_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na3d_nn_cuda_naive_double_ks_7_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na3d_nn_cuda_naive_double_ks_9(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na3d_nn_cuda_naive_double_ks_9_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na3d_nn_cuda_naive_double_ks_9_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na3d_nn_cuda_naive_double_ks_11(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na3d_nn_cuda_naive_double_ks_11_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na3d_nn_cuda_naive_double_ks_11_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na3d_nn_cuda_naive_double_ks_13(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na3d_nn_cuda_naive_double_ks_13_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na3d_nn_cuda_naive_double_ks_13_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na3d_nn_cuda_naive_float_ks_any(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na3d_nn_cuda_naive_float_ks_any_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na3d_nn_cuda_naive_float_ks_any_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na3d_nn_cuda_naive_float_ks_3(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na3d_nn_cuda_naive_float_ks_3_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na3d_nn_cuda_naive_float_ks_3_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na3d_nn_cuda_naive_float_ks_5(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na3d_nn_cuda_naive_float_ks_5_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na3d_nn_cuda_naive_float_ks_5_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na3d_nn_cuda_naive_float_ks_7(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na3d_nn_cuda_naive_float_ks_7_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na3d_nn_cuda_naive_float_ks_7_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na3d_nn_cuda_naive_float_ks_9(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na3d_nn_cuda_naive_float_ks_9_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na3d_nn_cuda_naive_float_ks_9_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na3d_nn_cuda_naive_float_ks_11(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na3d_nn_cuda_naive_float_ks_11_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na3d_nn_cuda_naive_float_ks_11_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na3d_nn_cuda_naive_float_ks_13(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na3d_nn_cuda_naive_float_ks_13_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na3d_nn_cuda_naive_float_ks_13_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na3d_nn_cuda_naive_half_ks_any(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na3d_nn_cuda_naive_half_ks_any_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na3d_nn_cuda_naive_half_ks_any_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na3d_nn_cuda_naive_half_ks_3(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na3d_nn_cuda_naive_half_ks_3_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na3d_nn_cuda_naive_half_ks_3_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na3d_nn_cuda_naive_half_ks_5(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na3d_nn_cuda_naive_half_ks_5_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na3d_nn_cuda_naive_half_ks_5_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na3d_nn_cuda_naive_half_ks_7(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na3d_nn_cuda_naive_half_ks_7_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na3d_nn_cuda_naive_half_ks_7_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na3d_nn_cuda_naive_half_ks_9(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na3d_nn_cuda_naive_half_ks_9_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na3d_nn_cuda_naive_half_ks_9_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na3d_nn_cuda_naive_half_ks_11(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na3d_nn_cuda_naive_half_ks_11_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na3d_nn_cuda_naive_half_ks_11_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na3d_nn_cuda_naive_half_ks_13(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na3d_nn_cuda_naive_half_ks_13_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na3d_nn_cuda_naive_half_ks_13_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na3d_nn_cuda_naive_bfloat16_ks_any(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na3d_nn_cuda_naive_bfloat16_ks_any_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na3d_nn_cuda_naive_bfloat16_ks_any_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na3d_nn_cuda_naive_bfloat16_ks_3(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na3d_nn_cuda_naive_bfloat16_ks_3_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na3d_nn_cuda_naive_bfloat16_ks_3_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na3d_nn_cuda_naive_bfloat16_ks_5(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na3d_nn_cuda_naive_bfloat16_ks_5_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na3d_nn_cuda_naive_bfloat16_ks_5_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na3d_nn_cuda_naive_bfloat16_ks_7(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na3d_nn_cuda_naive_bfloat16_ks_7_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na3d_nn_cuda_naive_bfloat16_ks_7_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na3d_nn_cuda_naive_bfloat16_ks_9(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na3d_nn_cuda_naive_bfloat16_ks_9_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na3d_nn_cuda_naive_bfloat16_ks_9_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na3d_nn_cuda_naive_bfloat16_ks_11(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na3d_nn_cuda_naive_bfloat16_ks_11_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na3d_nn_cuda_naive_bfloat16_ks_11_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na3d_nn_cuda_naive_bfloat16_ks_13(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na3d_nn_cuda_naive_bfloat16_ks_13_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na3d_nn_cuda_naive_bfloat16_ks_13_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na1d_in_cuda_naive_double_ks_any(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na1d_in_cuda_naive_double_ks_any_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na1d_in_cuda_naive_double_ks_any_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na1d_in_cuda_naive_double_ks_3(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na1d_in_cuda_naive_double_ks_3_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na1d_in_cuda_naive_double_ks_3_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na1d_in_cuda_naive_double_ks_5(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na1d_in_cuda_naive_double_ks_5_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na1d_in_cuda_naive_double_ks_5_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na1d_in_cuda_naive_double_ks_7(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na1d_in_cuda_naive_double_ks_7_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na1d_in_cuda_naive_double_ks_7_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na1d_in_cuda_naive_double_ks_9(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na1d_in_cuda_naive_double_ks_9_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na1d_in_cuda_naive_double_ks_9_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na1d_in_cuda_naive_double_ks_11(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na1d_in_cuda_naive_double_ks_11_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na1d_in_cuda_naive_double_ks_11_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na1d_in_cuda_naive_double_ks_13(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na1d_in_cuda_naive_double_ks_13_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na1d_in_cuda_naive_double_ks_13_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na1d_in_cuda_naive_float_ks_any(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na1d_in_cuda_naive_float_ks_any_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na1d_in_cuda_naive_float_ks_any_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na1d_in_cuda_naive_float_ks_3(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na1d_in_cuda_naive_float_ks_3_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na1d_in_cuda_naive_float_ks_3_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na1d_in_cuda_naive_float_ks_5(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na1d_in_cuda_naive_float_ks_5_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na1d_in_cuda_naive_float_ks_5_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na1d_in_cuda_naive_float_ks_7(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na1d_in_cuda_naive_float_ks_7_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na1d_in_cuda_naive_float_ks_7_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na1d_in_cuda_naive_float_ks_9(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na1d_in_cuda_naive_float_ks_9_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na1d_in_cuda_naive_float_ks_9_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na1d_in_cuda_naive_float_ks_11(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na1d_in_cuda_naive_float_ks_11_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na1d_in_cuda_naive_float_ks_11_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na1d_in_cuda_naive_float_ks_13(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na1d_in_cuda_naive_float_ks_13_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na1d_in_cuda_naive_float_ks_13_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na1d_in_cuda_naive_half_ks_any(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na1d_in_cuda_naive_half_ks_any_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na1d_in_cuda_naive_half_ks_any_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na1d_in_cuda_naive_half_ks_3(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na1d_in_cuda_naive_half_ks_3_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na1d_in_cuda_naive_half_ks_3_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na1d_in_cuda_naive_half_ks_5(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na1d_in_cuda_naive_half_ks_5_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na1d_in_cuda_naive_half_ks_5_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na1d_in_cuda_naive_half_ks_7(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na1d_in_cuda_naive_half_ks_7_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na1d_in_cuda_naive_half_ks_7_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na1d_in_cuda_naive_half_ks_9(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na1d_in_cuda_naive_half_ks_9_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na1d_in_cuda_naive_half_ks_9_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na1d_in_cuda_naive_half_ks_11(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na1d_in_cuda_naive_half_ks_11_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na1d_in_cuda_naive_half_ks_11_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na1d_in_cuda_naive_half_ks_13(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na1d_in_cuda_naive_half_ks_13_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na1d_in_cuda_naive_half_ks_13_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na1d_in_cuda_naive_bfloat16_ks_any(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na1d_in_cuda_naive_bfloat16_ks_any_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na1d_in_cuda_naive_bfloat16_ks_any_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na1d_in_cuda_naive_bfloat16_ks_3(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na1d_in_cuda_naive_bfloat16_ks_3_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na1d_in_cuda_naive_bfloat16_ks_3_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na1d_in_cuda_naive_bfloat16_ks_5(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na1d_in_cuda_naive_bfloat16_ks_5_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na1d_in_cuda_naive_bfloat16_ks_5_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na1d_in_cuda_naive_bfloat16_ks_7(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na1d_in_cuda_naive_bfloat16_ks_7_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na1d_in_cuda_naive_bfloat16_ks_7_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na1d_in_cuda_naive_bfloat16_ks_9(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na1d_in_cuda_naive_bfloat16_ks_9_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na1d_in_cuda_naive_bfloat16_ks_9_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na1d_in_cuda_naive_bfloat16_ks_11(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na1d_in_cuda_naive_bfloat16_ks_11_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na1d_in_cuda_naive_bfloat16_ks_11_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na1d_in_cuda_naive_bfloat16_ks_13(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na1d_in_cuda_naive_bfloat16_ks_13_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na1d_in_cuda_naive_bfloat16_ks_13_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na2d_in_cuda_naive_double_ks_any(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na2d_in_cuda_naive_double_ks_any_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na2d_in_cuda_naive_double_ks_any_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na2d_in_cuda_naive_double_ks_3(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na2d_in_cuda_naive_double_ks_3_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na2d_in_cuda_naive_double_ks_3_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na2d_in_cuda_naive_double_ks_5(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na2d_in_cuda_naive_double_ks_5_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na2d_in_cuda_naive_double_ks_5_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na2d_in_cuda_naive_double_ks_7(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na2d_in_cuda_naive_double_ks_7_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na2d_in_cuda_naive_double_ks_7_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na2d_in_cuda_naive_double_ks_9(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na2d_in_cuda_naive_double_ks_9_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na2d_in_cuda_naive_double_ks_9_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na2d_in_cuda_naive_double_ks_11(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na2d_in_cuda_naive_double_ks_11_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na2d_in_cuda_naive_double_ks_11_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na2d_in_cuda_naive_double_ks_13(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na2d_in_cuda_naive_double_ks_13_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na2d_in_cuda_naive_double_ks_13_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na2d_in_cuda_naive_float_ks_any(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na2d_in_cuda_naive_float_ks_any_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na2d_in_cuda_naive_float_ks_any_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na2d_in_cuda_naive_float_ks_3(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na2d_in_cuda_naive_float_ks_3_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na2d_in_cuda_naive_float_ks_3_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na2d_in_cuda_naive_float_ks_5(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na2d_in_cuda_naive_float_ks_5_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na2d_in_cuda_naive_float_ks_5_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na2d_in_cuda_naive_float_ks_7(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na2d_in_cuda_naive_float_ks_7_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na2d_in_cuda_naive_float_ks_7_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na2d_in_cuda_naive_float_ks_9(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na2d_in_cuda_naive_float_ks_9_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na2d_in_cuda_naive_float_ks_9_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na2d_in_cuda_naive_float_ks_11(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na2d_in_cuda_naive_float_ks_11_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na2d_in_cuda_naive_float_ks_11_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na2d_in_cuda_naive_float_ks_13(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na2d_in_cuda_naive_float_ks_13_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na2d_in_cuda_naive_float_ks_13_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na2d_in_cuda_naive_half_ks_any(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na2d_in_cuda_naive_half_ks_any_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na2d_in_cuda_naive_half_ks_any_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na2d_in_cuda_naive_half_ks_3(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na2d_in_cuda_naive_half_ks_3_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na2d_in_cuda_naive_half_ks_3_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na2d_in_cuda_naive_half_ks_5(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na2d_in_cuda_naive_half_ks_5_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na2d_in_cuda_naive_half_ks_5_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na2d_in_cuda_naive_half_ks_7(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na2d_in_cuda_naive_half_ks_7_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na2d_in_cuda_naive_half_ks_7_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na2d_in_cuda_naive_half_ks_9(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na2d_in_cuda_naive_half_ks_9_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na2d_in_cuda_naive_half_ks_9_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na2d_in_cuda_naive_half_ks_11(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na2d_in_cuda_naive_half_ks_11_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na2d_in_cuda_naive_half_ks_11_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na2d_in_cuda_naive_half_ks_13(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na2d_in_cuda_naive_half_ks_13_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na2d_in_cuda_naive_half_ks_13_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na2d_in_cuda_naive_bfloat16_ks_any(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na2d_in_cuda_naive_bfloat16_ks_any_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na2d_in_cuda_naive_bfloat16_ks_any_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na2d_in_cuda_naive_bfloat16_ks_3(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na2d_in_cuda_naive_bfloat16_ks_3_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na2d_in_cuda_naive_bfloat16_ks_3_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na2d_in_cuda_naive_bfloat16_ks_5(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na2d_in_cuda_naive_bfloat16_ks_5_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na2d_in_cuda_naive_bfloat16_ks_5_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na2d_in_cuda_naive_bfloat16_ks_7(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na2d_in_cuda_naive_bfloat16_ks_7_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na2d_in_cuda_naive_bfloat16_ks_7_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na2d_in_cuda_naive_bfloat16_ks_9(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na2d_in_cuda_naive_bfloat16_ks_9_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na2d_in_cuda_naive_bfloat16_ks_9_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na2d_in_cuda_naive_bfloat16_ks_11(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na2d_in_cuda_naive_bfloat16_ks_11_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na2d_in_cuda_naive_bfloat16_ks_11_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na2d_in_cuda_naive_bfloat16_ks_13(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na2d_in_cuda_naive_bfloat16_ks_13_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na2d_in_cuda_naive_bfloat16_ks_13_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na3d_in_cuda_naive_double_ks_any(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na3d_in_cuda_naive_double_ks_any_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na3d_in_cuda_naive_double_ks_any_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na3d_in_cuda_naive_double_ks_3(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na3d_in_cuda_naive_double_ks_3_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na3d_in_cuda_naive_double_ks_3_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na3d_in_cuda_naive_double_ks_5(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na3d_in_cuda_naive_double_ks_5_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na3d_in_cuda_naive_double_ks_5_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na3d_in_cuda_naive_double_ks_7(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na3d_in_cuda_naive_double_ks_7_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na3d_in_cuda_naive_double_ks_7_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na3d_in_cuda_naive_double_ks_9(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na3d_in_cuda_naive_double_ks_9_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na3d_in_cuda_naive_double_ks_9_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na3d_in_cuda_naive_double_ks_11(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na3d_in_cuda_naive_double_ks_11_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na3d_in_cuda_naive_double_ks_11_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na3d_in_cuda_naive_double_ks_13(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na3d_in_cuda_naive_double_ks_13_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na3d_in_cuda_naive_double_ks_13_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na3d_in_cuda_naive_float_ks_any(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na3d_in_cuda_naive_float_ks_any_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na3d_in_cuda_naive_float_ks_any_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na3d_in_cuda_naive_float_ks_3(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na3d_in_cuda_naive_float_ks_3_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na3d_in_cuda_naive_float_ks_3_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na3d_in_cuda_naive_float_ks_5(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na3d_in_cuda_naive_float_ks_5_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na3d_in_cuda_naive_float_ks_5_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na3d_in_cuda_naive_float_ks_7(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na3d_in_cuda_naive_float_ks_7_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na3d_in_cuda_naive_float_ks_7_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na3d_in_cuda_naive_float_ks_9(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na3d_in_cuda_naive_float_ks_9_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na3d_in_cuda_naive_float_ks_9_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na3d_in_cuda_naive_float_ks_11(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na3d_in_cuda_naive_float_ks_11_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na3d_in_cuda_naive_float_ks_11_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na3d_in_cuda_naive_float_ks_13(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na3d_in_cuda_naive_float_ks_13_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na3d_in_cuda_naive_float_ks_13_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na3d_in_cuda_naive_half_ks_any(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na3d_in_cuda_naive_half_ks_any_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na3d_in_cuda_naive_half_ks_any_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na3d_in_cuda_naive_half_ks_3(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na3d_in_cuda_naive_half_ks_3_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na3d_in_cuda_naive_half_ks_3_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na3d_in_cuda_naive_half_ks_5(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na3d_in_cuda_naive_half_ks_5_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na3d_in_cuda_naive_half_ks_5_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na3d_in_cuda_naive_half_ks_7(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na3d_in_cuda_naive_half_ks_7_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na3d_in_cuda_naive_half_ks_7_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na3d_in_cuda_naive_half_ks_9(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na3d_in_cuda_naive_half_ks_9_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na3d_in_cuda_naive_half_ks_9_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na3d_in_cuda_naive_half_ks_11(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na3d_in_cuda_naive_half_ks_11_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na3d_in_cuda_naive_half_ks_11_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na3d_in_cuda_naive_half_ks_13(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na3d_in_cuda_naive_half_ks_13_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na3d_in_cuda_naive_half_ks_13_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na3d_in_cuda_naive_bfloat16_ks_any(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na3d_in_cuda_naive_bfloat16_ks_any_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na3d_in_cuda_naive_bfloat16_ks_any_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na3d_in_cuda_naive_bfloat16_ks_3(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na3d_in_cuda_naive_bfloat16_ks_3_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na3d_in_cuda_naive_bfloat16_ks_3_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na3d_in_cuda_naive_bfloat16_ks_5(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na3d_in_cuda_naive_bfloat16_ks_5_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na3d_in_cuda_naive_bfloat16_ks_5_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na3d_in_cuda_naive_bfloat16_ks_7(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na3d_in_cuda_naive_bfloat16_ks_7_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na3d_in_cuda_naive_bfloat16_ks_7_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na3d_in_cuda_naive_bfloat16_ks_9(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na3d_in_cuda_naive_bfloat16_ks_9_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na3d_in_cuda_naive_bfloat16_ks_9_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na3d_in_cuda_naive_bfloat16_ks_11(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na3d_in_cuda_naive_bfloat16_ks_11_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na3d_in_cuda_naive_bfloat16_ks_11_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na3d_in_cuda_naive_bfloat16_ks_13(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na3d_in_cuda_naive_bfloat16_ks_13_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na3d_in_cuda_naive_bfloat16_ks_13_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na1d_rpbgrad_cuda_naive_double_ks_any(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na1d_rpbgrad_cuda_naive_double_ks_any_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na1d_rpbgrad_cuda_naive_double_ks_any_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na1d_rpbgrad_cuda_naive_double_ks_3(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na1d_rpbgrad_cuda_naive_double_ks_3_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na1d_rpbgrad_cuda_naive_double_ks_3_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na1d_rpbgrad_cuda_naive_double_ks_5(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na1d_rpbgrad_cuda_naive_double_ks_5_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na1d_rpbgrad_cuda_naive_double_ks_5_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na1d_rpbgrad_cuda_naive_double_ks_7(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na1d_rpbgrad_cuda_naive_double_ks_7_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na1d_rpbgrad_cuda_naive_double_ks_7_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na1d_rpbgrad_cuda_naive_double_ks_9(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na1d_rpbgrad_cuda_naive_double_ks_9_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na1d_rpbgrad_cuda_naive_double_ks_9_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na1d_rpbgrad_cuda_naive_double_ks_11(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na1d_rpbgrad_cuda_naive_double_ks_11_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na1d_rpbgrad_cuda_naive_double_ks_11_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na1d_rpbgrad_cuda_naive_double_ks_13(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na1d_rpbgrad_cuda_naive_double_ks_13_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na1d_rpbgrad_cuda_naive_double_ks_13_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na1d_rpbgrad_cuda_naive_float_ks_any(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na1d_rpbgrad_cuda_naive_float_ks_any_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na1d_rpbgrad_cuda_naive_float_ks_any_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na1d_rpbgrad_cuda_naive_float_ks_3(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na1d_rpbgrad_cuda_naive_float_ks_3_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na1d_rpbgrad_cuda_naive_float_ks_3_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na1d_rpbgrad_cuda_naive_float_ks_5(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na1d_rpbgrad_cuda_naive_float_ks_5_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na1d_rpbgrad_cuda_naive_float_ks_5_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na1d_rpbgrad_cuda_naive_float_ks_7(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na1d_rpbgrad_cuda_naive_float_ks_7_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na1d_rpbgrad_cuda_naive_float_ks_7_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na1d_rpbgrad_cuda_naive_float_ks_9(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na1d_rpbgrad_cuda_naive_float_ks_9_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na1d_rpbgrad_cuda_naive_float_ks_9_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na1d_rpbgrad_cuda_naive_float_ks_11(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na1d_rpbgrad_cuda_naive_float_ks_11_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na1d_rpbgrad_cuda_naive_float_ks_11_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na1d_rpbgrad_cuda_naive_float_ks_13(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na1d_rpbgrad_cuda_naive_float_ks_13_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na1d_rpbgrad_cuda_naive_float_ks_13_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na1d_rpbgrad_cuda_naive_half_ks_any(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na1d_rpbgrad_cuda_naive_half_ks_any_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na1d_rpbgrad_cuda_naive_half_ks_any_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na1d_rpbgrad_cuda_naive_half_ks_3(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na1d_rpbgrad_cuda_naive_half_ks_3_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na1d_rpbgrad_cuda_naive_half_ks_3_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na1d_rpbgrad_cuda_naive_half_ks_5(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na1d_rpbgrad_cuda_naive_half_ks_5_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na1d_rpbgrad_cuda_naive_half_ks_5_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na1d_rpbgrad_cuda_naive_half_ks_7(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na1d_rpbgrad_cuda_naive_half_ks_7_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na1d_rpbgrad_cuda_naive_half_ks_7_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na1d_rpbgrad_cuda_naive_half_ks_9(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na1d_rpbgrad_cuda_naive_half_ks_9_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na1d_rpbgrad_cuda_naive_half_ks_9_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na1d_rpbgrad_cuda_naive_half_ks_11(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na1d_rpbgrad_cuda_naive_half_ks_11_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na1d_rpbgrad_cuda_naive_half_ks_11_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na1d_rpbgrad_cuda_naive_half_ks_13(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na1d_rpbgrad_cuda_naive_half_ks_13_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na1d_rpbgrad_cuda_naive_half_ks_13_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na1d_rpbgrad_cuda_naive_bfloat16_ks_any(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na1d_rpbgrad_cuda_naive_bfloat16_ks_any_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na1d_rpbgrad_cuda_naive_bfloat16_ks_any_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na1d_rpbgrad_cuda_naive_bfloat16_ks_3(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na1d_rpbgrad_cuda_naive_bfloat16_ks_3_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na1d_rpbgrad_cuda_naive_bfloat16_ks_3_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na1d_rpbgrad_cuda_naive_bfloat16_ks_5(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na1d_rpbgrad_cuda_naive_bfloat16_ks_5_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na1d_rpbgrad_cuda_naive_bfloat16_ks_5_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na1d_rpbgrad_cuda_naive_bfloat16_ks_7(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na1d_rpbgrad_cuda_naive_bfloat16_ks_7_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na1d_rpbgrad_cuda_naive_bfloat16_ks_7_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na1d_rpbgrad_cuda_naive_bfloat16_ks_9(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na1d_rpbgrad_cuda_naive_bfloat16_ks_9_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na1d_rpbgrad_cuda_naive_bfloat16_ks_9_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na1d_rpbgrad_cuda_naive_bfloat16_ks_11(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na1d_rpbgrad_cuda_naive_bfloat16_ks_11_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na1d_rpbgrad_cuda_naive_bfloat16_ks_11_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na1d_rpbgrad_cuda_naive_bfloat16_ks_13(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na1d_rpbgrad_cuda_naive_bfloat16_ks_13_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na1d_rpbgrad_cuda_naive_bfloat16_ks_13_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na2d_rpbgrad_cuda_naive_double_ks_any(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na2d_rpbgrad_cuda_naive_double_ks_any_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na2d_rpbgrad_cuda_naive_double_ks_any_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na2d_rpbgrad_cuda_naive_double_ks_3(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na2d_rpbgrad_cuda_naive_double_ks_3_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na2d_rpbgrad_cuda_naive_double_ks_3_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na2d_rpbgrad_cuda_naive_double_ks_5(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na2d_rpbgrad_cuda_naive_double_ks_5_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na2d_rpbgrad_cuda_naive_double_ks_5_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na2d_rpbgrad_cuda_naive_double_ks_7(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na2d_rpbgrad_cuda_naive_double_ks_7_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na2d_rpbgrad_cuda_naive_double_ks_7_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na2d_rpbgrad_cuda_naive_double_ks_9(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na2d_rpbgrad_cuda_naive_double_ks_9_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na2d_rpbgrad_cuda_naive_double_ks_9_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na2d_rpbgrad_cuda_naive_double_ks_11(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na2d_rpbgrad_cuda_naive_double_ks_11_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na2d_rpbgrad_cuda_naive_double_ks_11_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na2d_rpbgrad_cuda_naive_double_ks_13(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na2d_rpbgrad_cuda_naive_double_ks_13_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na2d_rpbgrad_cuda_naive_double_ks_13_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na2d_rpbgrad_cuda_naive_float_ks_any(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na2d_rpbgrad_cuda_naive_float_ks_any_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na2d_rpbgrad_cuda_naive_float_ks_any_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na2d_rpbgrad_cuda_naive_float_ks_3(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na2d_rpbgrad_cuda_naive_float_ks_3_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na2d_rpbgrad_cuda_naive_float_ks_3_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na2d_rpbgrad_cuda_naive_float_ks_5(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na2d_rpbgrad_cuda_naive_float_ks_5_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na2d_rpbgrad_cuda_naive_float_ks_5_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na2d_rpbgrad_cuda_naive_float_ks_7(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na2d_rpbgrad_cuda_naive_float_ks_7_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na2d_rpbgrad_cuda_naive_float_ks_7_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na2d_rpbgrad_cuda_naive_float_ks_9(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na2d_rpbgrad_cuda_naive_float_ks_9_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na2d_rpbgrad_cuda_naive_float_ks_9_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na2d_rpbgrad_cuda_naive_float_ks_11(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na2d_rpbgrad_cuda_naive_float_ks_11_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na2d_rpbgrad_cuda_naive_float_ks_11_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na2d_rpbgrad_cuda_naive_float_ks_13(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na2d_rpbgrad_cuda_naive_float_ks_13_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na2d_rpbgrad_cuda_naive_float_ks_13_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na2d_rpbgrad_cuda_naive_half_ks_any(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na2d_rpbgrad_cuda_naive_half_ks_any_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na2d_rpbgrad_cuda_naive_half_ks_any_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na2d_rpbgrad_cuda_naive_half_ks_3(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na2d_rpbgrad_cuda_naive_half_ks_3_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na2d_rpbgrad_cuda_naive_half_ks_3_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na2d_rpbgrad_cuda_naive_half_ks_5(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na2d_rpbgrad_cuda_naive_half_ks_5_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na2d_rpbgrad_cuda_naive_half_ks_5_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na2d_rpbgrad_cuda_naive_half_ks_7(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na2d_rpbgrad_cuda_naive_half_ks_7_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na2d_rpbgrad_cuda_naive_half_ks_7_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na2d_rpbgrad_cuda_naive_half_ks_9(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na2d_rpbgrad_cuda_naive_half_ks_9_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na2d_rpbgrad_cuda_naive_half_ks_9_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na2d_rpbgrad_cuda_naive_half_ks_11(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na2d_rpbgrad_cuda_naive_half_ks_11_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na2d_rpbgrad_cuda_naive_half_ks_11_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na2d_rpbgrad_cuda_naive_half_ks_13(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na2d_rpbgrad_cuda_naive_half_ks_13_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na2d_rpbgrad_cuda_naive_half_ks_13_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na2d_rpbgrad_cuda_naive_bfloat16_ks_any(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na2d_rpbgrad_cuda_naive_bfloat16_ks_any_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na2d_rpbgrad_cuda_naive_bfloat16_ks_any_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na2d_rpbgrad_cuda_naive_bfloat16_ks_3(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na2d_rpbgrad_cuda_naive_bfloat16_ks_3_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na2d_rpbgrad_cuda_naive_bfloat16_ks_3_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na2d_rpbgrad_cuda_naive_bfloat16_ks_5(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na2d_rpbgrad_cuda_naive_bfloat16_ks_5_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na2d_rpbgrad_cuda_naive_bfloat16_ks_5_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na2d_rpbgrad_cuda_naive_bfloat16_ks_7(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na2d_rpbgrad_cuda_naive_bfloat16_ks_7_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na2d_rpbgrad_cuda_naive_bfloat16_ks_7_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na2d_rpbgrad_cuda_naive_bfloat16_ks_9(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na2d_rpbgrad_cuda_naive_bfloat16_ks_9_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na2d_rpbgrad_cuda_naive_bfloat16_ks_9_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na2d_rpbgrad_cuda_naive_bfloat16_ks_11(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na2d_rpbgrad_cuda_naive_bfloat16_ks_11_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na2d_rpbgrad_cuda_naive_bfloat16_ks_11_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na2d_rpbgrad_cuda_naive_bfloat16_ks_13(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na2d_rpbgrad_cuda_naive_bfloat16_ks_13_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na2d_rpbgrad_cuda_naive_bfloat16_ks_13_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na3d_rpbgrad_cuda_naive_double_ks_any(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na3d_rpbgrad_cuda_naive_double_ks_any_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na3d_rpbgrad_cuda_naive_double_ks_any_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na3d_rpbgrad_cuda_naive_double_ks_3(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na3d_rpbgrad_cuda_naive_double_ks_3_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na3d_rpbgrad_cuda_naive_double_ks_3_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na3d_rpbgrad_cuda_naive_double_ks_5(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na3d_rpbgrad_cuda_naive_double_ks_5_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na3d_rpbgrad_cuda_naive_double_ks_5_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na3d_rpbgrad_cuda_naive_double_ks_7(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na3d_rpbgrad_cuda_naive_double_ks_7_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na3d_rpbgrad_cuda_naive_double_ks_7_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na3d_rpbgrad_cuda_naive_double_ks_9(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na3d_rpbgrad_cuda_naive_double_ks_9_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na3d_rpbgrad_cuda_naive_double_ks_9_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na3d_rpbgrad_cuda_naive_double_ks_11(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na3d_rpbgrad_cuda_naive_double_ks_11_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na3d_rpbgrad_cuda_naive_double_ks_11_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na3d_rpbgrad_cuda_naive_double_ks_13(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na3d_rpbgrad_cuda_naive_double_ks_13_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na3d_rpbgrad_cuda_naive_double_ks_13_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na3d_rpbgrad_cuda_naive_float_ks_any(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na3d_rpbgrad_cuda_naive_float_ks_any_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na3d_rpbgrad_cuda_naive_float_ks_any_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na3d_rpbgrad_cuda_naive_float_ks_3(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na3d_rpbgrad_cuda_naive_float_ks_3_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na3d_rpbgrad_cuda_naive_float_ks_3_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na3d_rpbgrad_cuda_naive_float_ks_5(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na3d_rpbgrad_cuda_naive_float_ks_5_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na3d_rpbgrad_cuda_naive_float_ks_5_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na3d_rpbgrad_cuda_naive_float_ks_7(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na3d_rpbgrad_cuda_naive_float_ks_7_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na3d_rpbgrad_cuda_naive_float_ks_7_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na3d_rpbgrad_cuda_naive_float_ks_9(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na3d_rpbgrad_cuda_naive_float_ks_9_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na3d_rpbgrad_cuda_naive_float_ks_9_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na3d_rpbgrad_cuda_naive_float_ks_11(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na3d_rpbgrad_cuda_naive_float_ks_11_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na3d_rpbgrad_cuda_naive_float_ks_11_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na3d_rpbgrad_cuda_naive_float_ks_13(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na3d_rpbgrad_cuda_naive_float_ks_13_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na3d_rpbgrad_cuda_naive_float_ks_13_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na3d_rpbgrad_cuda_naive_half_ks_any(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na3d_rpbgrad_cuda_naive_half_ks_any_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na3d_rpbgrad_cuda_naive_half_ks_any_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na3d_rpbgrad_cuda_naive_half_ks_3(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na3d_rpbgrad_cuda_naive_half_ks_3_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na3d_rpbgrad_cuda_naive_half_ks_3_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na3d_rpbgrad_cuda_naive_half_ks_5(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na3d_rpbgrad_cuda_naive_half_ks_5_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na3d_rpbgrad_cuda_naive_half_ks_5_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na3d_rpbgrad_cuda_naive_half_ks_7(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na3d_rpbgrad_cuda_naive_half_ks_7_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na3d_rpbgrad_cuda_naive_half_ks_7_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na3d_rpbgrad_cuda_naive_half_ks_9(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na3d_rpbgrad_cuda_naive_half_ks_9_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na3d_rpbgrad_cuda_naive_half_ks_9_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na3d_rpbgrad_cuda_naive_half_ks_11(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na3d_rpbgrad_cuda_naive_half_ks_11_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na3d_rpbgrad_cuda_naive_half_ks_11_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na3d_rpbgrad_cuda_naive_half_ks_13(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na3d_rpbgrad_cuda_naive_half_ks_13_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na3d_rpbgrad_cuda_naive_half_ks_13_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na3d_rpbgrad_cuda_naive_bfloat16_ks_any(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na3d_rpbgrad_cuda_naive_bfloat16_ks_any_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na3d_rpbgrad_cuda_naive_bfloat16_ks_any_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na3d_rpbgrad_cuda_naive_bfloat16_ks_3(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na3d_rpbgrad_cuda_naive_bfloat16_ks_3_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na3d_rpbgrad_cuda_naive_bfloat16_ks_3_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na3d_rpbgrad_cuda_naive_bfloat16_ks_5(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na3d_rpbgrad_cuda_naive_bfloat16_ks_5_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na3d_rpbgrad_cuda_naive_bfloat16_ks_5_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na3d_rpbgrad_cuda_naive_bfloat16_ks_7(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na3d_rpbgrad_cuda_naive_bfloat16_ks_7_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na3d_rpbgrad_cuda_naive_bfloat16_ks_7_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na3d_rpbgrad_cuda_naive_bfloat16_ks_9(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na3d_rpbgrad_cuda_naive_bfloat16_ks_9_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na3d_rpbgrad_cuda_naive_bfloat16_ks_9_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na3d_rpbgrad_cuda_naive_bfloat16_ks_11(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na3d_rpbgrad_cuda_naive_bfloat16_ks_11_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na3d_rpbgrad_cuda_naive_bfloat16_ks_11_di_any(__VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_DILATION_na3d_rpbgrad_cuda_naive_bfloat16_ks_13(dilation, ...) \ - [&] { \ - if (dilation == 1) { \ - naive::na3d_rpbgrad_cuda_naive_bfloat16_ks_13_di_1(__VA_ARGS__); \ - } \ - else { \ - naive::na3d_rpbgrad_cuda_naive_bfloat16_ks_13_di_any(__VA_ARGS__); \ - } \ -}(); - - - -} // namespace {namespace} -} // namespace {namespace} -} // namespace {namespace} - diff --git a/csrc/autogen/include/natten_autogen/cuda/naive/dispatch_ks.h b/csrc/autogen/include/natten_autogen/cuda/naive/dispatch_ks.h deleted file mode 100644 index e362ef4..0000000 --- a/csrc/autogen/include/natten_autogen/cuda/naive/dispatch_ks.h +++ /dev/null @@ -1,1516 +0,0 @@ -#pragma once - - -#include -#include -#include -#include -namespace natten { -namespace cuda { -namespace naive { -#define DISPATCH_KERNEL_na1d_pn_cuda_naive_double(kernel_size, dilation, ...) \ - [&] { \ - if (kernel_size == 3) { \ - DISPATCH_DILATION_na1d_pn_cuda_naive_double_ks_3(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 5) { \ - DISPATCH_DILATION_na1d_pn_cuda_naive_double_ks_5(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 7) { \ - DISPATCH_DILATION_na1d_pn_cuda_naive_double_ks_7(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 9) { \ - DISPATCH_DILATION_na1d_pn_cuda_naive_double_ks_9(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 11) { \ - DISPATCH_DILATION_na1d_pn_cuda_naive_double_ks_11(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 13) { \ - DISPATCH_DILATION_na1d_pn_cuda_naive_double_ks_13(dilation, __VA_ARGS__); \ - } \ - else { \ - DISPATCH_DILATION_na1d_pn_cuda_naive_double_ks_any(dilation, __VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_KERNEL_na1d_pn_cuda_naive_float(kernel_size, dilation, ...) \ - [&] { \ - if (kernel_size == 3) { \ - DISPATCH_DILATION_na1d_pn_cuda_naive_float_ks_3(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 5) { \ - DISPATCH_DILATION_na1d_pn_cuda_naive_float_ks_5(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 7) { \ - DISPATCH_DILATION_na1d_pn_cuda_naive_float_ks_7(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 9) { \ - DISPATCH_DILATION_na1d_pn_cuda_naive_float_ks_9(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 11) { \ - DISPATCH_DILATION_na1d_pn_cuda_naive_float_ks_11(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 13) { \ - DISPATCH_DILATION_na1d_pn_cuda_naive_float_ks_13(dilation, __VA_ARGS__); \ - } \ - else { \ - DISPATCH_DILATION_na1d_pn_cuda_naive_float_ks_any(dilation, __VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_KERNEL_na1d_pn_cuda_naive_half(kernel_size, dilation, ...) \ - [&] { \ - if (kernel_size == 3) { \ - DISPATCH_DILATION_na1d_pn_cuda_naive_half_ks_3(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 5) { \ - DISPATCH_DILATION_na1d_pn_cuda_naive_half_ks_5(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 7) { \ - DISPATCH_DILATION_na1d_pn_cuda_naive_half_ks_7(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 9) { \ - DISPATCH_DILATION_na1d_pn_cuda_naive_half_ks_9(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 11) { \ - DISPATCH_DILATION_na1d_pn_cuda_naive_half_ks_11(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 13) { \ - DISPATCH_DILATION_na1d_pn_cuda_naive_half_ks_13(dilation, __VA_ARGS__); \ - } \ - else { \ - DISPATCH_DILATION_na1d_pn_cuda_naive_half_ks_any(dilation, __VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_KERNEL_na1d_pn_cuda_naive_bfloat16(kernel_size, dilation, ...) \ - [&] { \ - if (kernel_size == 3) { \ - DISPATCH_DILATION_na1d_pn_cuda_naive_bfloat16_ks_3(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 5) { \ - DISPATCH_DILATION_na1d_pn_cuda_naive_bfloat16_ks_5(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 7) { \ - DISPATCH_DILATION_na1d_pn_cuda_naive_bfloat16_ks_7(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 9) { \ - DISPATCH_DILATION_na1d_pn_cuda_naive_bfloat16_ks_9(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 11) { \ - DISPATCH_DILATION_na1d_pn_cuda_naive_bfloat16_ks_11(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 13) { \ - DISPATCH_DILATION_na1d_pn_cuda_naive_bfloat16_ks_13(dilation, __VA_ARGS__); \ - } \ - else { \ - DISPATCH_DILATION_na1d_pn_cuda_naive_bfloat16_ks_any(dilation, __VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_KERNEL_na2d_pn_cuda_naive_double(kernel_size, dilation, ...) \ - [&] { \ - if (kernel_size == 3) { \ - DISPATCH_DILATION_na2d_pn_cuda_naive_double_ks_3(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 5) { \ - DISPATCH_DILATION_na2d_pn_cuda_naive_double_ks_5(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 7) { \ - DISPATCH_DILATION_na2d_pn_cuda_naive_double_ks_7(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 9) { \ - DISPATCH_DILATION_na2d_pn_cuda_naive_double_ks_9(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 11) { \ - DISPATCH_DILATION_na2d_pn_cuda_naive_double_ks_11(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 13) { \ - DISPATCH_DILATION_na2d_pn_cuda_naive_double_ks_13(dilation, __VA_ARGS__); \ - } \ - else { \ - DISPATCH_DILATION_na2d_pn_cuda_naive_double_ks_any(dilation, __VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_KERNEL_na2d_pn_cuda_naive_float(kernel_size, dilation, ...) \ - [&] { \ - if (kernel_size == 3) { \ - DISPATCH_DILATION_na2d_pn_cuda_naive_float_ks_3(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 5) { \ - DISPATCH_DILATION_na2d_pn_cuda_naive_float_ks_5(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 7) { \ - DISPATCH_DILATION_na2d_pn_cuda_naive_float_ks_7(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 9) { \ - DISPATCH_DILATION_na2d_pn_cuda_naive_float_ks_9(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 11) { \ - DISPATCH_DILATION_na2d_pn_cuda_naive_float_ks_11(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 13) { \ - DISPATCH_DILATION_na2d_pn_cuda_naive_float_ks_13(dilation, __VA_ARGS__); \ - } \ - else { \ - DISPATCH_DILATION_na2d_pn_cuda_naive_float_ks_any(dilation, __VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_KERNEL_na2d_pn_cuda_naive_half(kernel_size, dilation, ...) \ - [&] { \ - if (kernel_size == 3) { \ - DISPATCH_DILATION_na2d_pn_cuda_naive_half_ks_3(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 5) { \ - DISPATCH_DILATION_na2d_pn_cuda_naive_half_ks_5(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 7) { \ - DISPATCH_DILATION_na2d_pn_cuda_naive_half_ks_7(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 9) { \ - DISPATCH_DILATION_na2d_pn_cuda_naive_half_ks_9(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 11) { \ - DISPATCH_DILATION_na2d_pn_cuda_naive_half_ks_11(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 13) { \ - DISPATCH_DILATION_na2d_pn_cuda_naive_half_ks_13(dilation, __VA_ARGS__); \ - } \ - else { \ - DISPATCH_DILATION_na2d_pn_cuda_naive_half_ks_any(dilation, __VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_KERNEL_na2d_pn_cuda_naive_bfloat16(kernel_size, dilation, ...) \ - [&] { \ - if (kernel_size == 3) { \ - DISPATCH_DILATION_na2d_pn_cuda_naive_bfloat16_ks_3(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 5) { \ - DISPATCH_DILATION_na2d_pn_cuda_naive_bfloat16_ks_5(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 7) { \ - DISPATCH_DILATION_na2d_pn_cuda_naive_bfloat16_ks_7(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 9) { \ - DISPATCH_DILATION_na2d_pn_cuda_naive_bfloat16_ks_9(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 11) { \ - DISPATCH_DILATION_na2d_pn_cuda_naive_bfloat16_ks_11(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 13) { \ - DISPATCH_DILATION_na2d_pn_cuda_naive_bfloat16_ks_13(dilation, __VA_ARGS__); \ - } \ - else { \ - DISPATCH_DILATION_na2d_pn_cuda_naive_bfloat16_ks_any(dilation, __VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_KERNEL_na3d_pn_cuda_naive_double(kernel_size, dilation, ...) \ - [&] { \ - if (kernel_size == 3) { \ - DISPATCH_DILATION_na3d_pn_cuda_naive_double_ks_3(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 5) { \ - DISPATCH_DILATION_na3d_pn_cuda_naive_double_ks_5(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 7) { \ - DISPATCH_DILATION_na3d_pn_cuda_naive_double_ks_7(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 9) { \ - DISPATCH_DILATION_na3d_pn_cuda_naive_double_ks_9(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 11) { \ - DISPATCH_DILATION_na3d_pn_cuda_naive_double_ks_11(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 13) { \ - DISPATCH_DILATION_na3d_pn_cuda_naive_double_ks_13(dilation, __VA_ARGS__); \ - } \ - else { \ - DISPATCH_DILATION_na3d_pn_cuda_naive_double_ks_any(dilation, __VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_KERNEL_na3d_pn_cuda_naive_float(kernel_size, dilation, ...) \ - [&] { \ - if (kernel_size == 3) { \ - DISPATCH_DILATION_na3d_pn_cuda_naive_float_ks_3(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 5) { \ - DISPATCH_DILATION_na3d_pn_cuda_naive_float_ks_5(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 7) { \ - DISPATCH_DILATION_na3d_pn_cuda_naive_float_ks_7(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 9) { \ - DISPATCH_DILATION_na3d_pn_cuda_naive_float_ks_9(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 11) { \ - DISPATCH_DILATION_na3d_pn_cuda_naive_float_ks_11(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 13) { \ - DISPATCH_DILATION_na3d_pn_cuda_naive_float_ks_13(dilation, __VA_ARGS__); \ - } \ - else { \ - DISPATCH_DILATION_na3d_pn_cuda_naive_float_ks_any(dilation, __VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_KERNEL_na3d_pn_cuda_naive_half(kernel_size, dilation, ...) \ - [&] { \ - if (kernel_size == 3) { \ - DISPATCH_DILATION_na3d_pn_cuda_naive_half_ks_3(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 5) { \ - DISPATCH_DILATION_na3d_pn_cuda_naive_half_ks_5(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 7) { \ - DISPATCH_DILATION_na3d_pn_cuda_naive_half_ks_7(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 9) { \ - DISPATCH_DILATION_na3d_pn_cuda_naive_half_ks_9(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 11) { \ - DISPATCH_DILATION_na3d_pn_cuda_naive_half_ks_11(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 13) { \ - DISPATCH_DILATION_na3d_pn_cuda_naive_half_ks_13(dilation, __VA_ARGS__); \ - } \ - else { \ - DISPATCH_DILATION_na3d_pn_cuda_naive_half_ks_any(dilation, __VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_KERNEL_na3d_pn_cuda_naive_bfloat16(kernel_size, dilation, ...) \ - [&] { \ - if (kernel_size == 3) { \ - DISPATCH_DILATION_na3d_pn_cuda_naive_bfloat16_ks_3(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 5) { \ - DISPATCH_DILATION_na3d_pn_cuda_naive_bfloat16_ks_5(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 7) { \ - DISPATCH_DILATION_na3d_pn_cuda_naive_bfloat16_ks_7(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 9) { \ - DISPATCH_DILATION_na3d_pn_cuda_naive_bfloat16_ks_9(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 11) { \ - DISPATCH_DILATION_na3d_pn_cuda_naive_bfloat16_ks_11(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 13) { \ - DISPATCH_DILATION_na3d_pn_cuda_naive_bfloat16_ks_13(dilation, __VA_ARGS__); \ - } \ - else { \ - DISPATCH_DILATION_na3d_pn_cuda_naive_bfloat16_ks_any(dilation, __VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_KERNEL_na1d_pn_bias_cuda_naive_double(kernel_size, dilation, ...) \ - [&] { \ - if (kernel_size == 3) { \ - DISPATCH_DILATION_na1d_pn_bias_cuda_naive_double_ks_3(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 5) { \ - DISPATCH_DILATION_na1d_pn_bias_cuda_naive_double_ks_5(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 7) { \ - DISPATCH_DILATION_na1d_pn_bias_cuda_naive_double_ks_7(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 9) { \ - DISPATCH_DILATION_na1d_pn_bias_cuda_naive_double_ks_9(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 11) { \ - DISPATCH_DILATION_na1d_pn_bias_cuda_naive_double_ks_11(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 13) { \ - DISPATCH_DILATION_na1d_pn_bias_cuda_naive_double_ks_13(dilation, __VA_ARGS__); \ - } \ - else { \ - DISPATCH_DILATION_na1d_pn_bias_cuda_naive_double_ks_any(dilation, __VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_KERNEL_na1d_pn_bias_cuda_naive_float(kernel_size, dilation, ...) \ - [&] { \ - if (kernel_size == 3) { \ - DISPATCH_DILATION_na1d_pn_bias_cuda_naive_float_ks_3(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 5) { \ - DISPATCH_DILATION_na1d_pn_bias_cuda_naive_float_ks_5(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 7) { \ - DISPATCH_DILATION_na1d_pn_bias_cuda_naive_float_ks_7(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 9) { \ - DISPATCH_DILATION_na1d_pn_bias_cuda_naive_float_ks_9(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 11) { \ - DISPATCH_DILATION_na1d_pn_bias_cuda_naive_float_ks_11(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 13) { \ - DISPATCH_DILATION_na1d_pn_bias_cuda_naive_float_ks_13(dilation, __VA_ARGS__); \ - } \ - else { \ - DISPATCH_DILATION_na1d_pn_bias_cuda_naive_float_ks_any(dilation, __VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_KERNEL_na1d_pn_bias_cuda_naive_half(kernel_size, dilation, ...) \ - [&] { \ - if (kernel_size == 3) { \ - DISPATCH_DILATION_na1d_pn_bias_cuda_naive_half_ks_3(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 5) { \ - DISPATCH_DILATION_na1d_pn_bias_cuda_naive_half_ks_5(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 7) { \ - DISPATCH_DILATION_na1d_pn_bias_cuda_naive_half_ks_7(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 9) { \ - DISPATCH_DILATION_na1d_pn_bias_cuda_naive_half_ks_9(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 11) { \ - DISPATCH_DILATION_na1d_pn_bias_cuda_naive_half_ks_11(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 13) { \ - DISPATCH_DILATION_na1d_pn_bias_cuda_naive_half_ks_13(dilation, __VA_ARGS__); \ - } \ - else { \ - DISPATCH_DILATION_na1d_pn_bias_cuda_naive_half_ks_any(dilation, __VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_KERNEL_na1d_pn_bias_cuda_naive_bfloat16(kernel_size, dilation, ...) \ - [&] { \ - if (kernel_size == 3) { \ - DISPATCH_DILATION_na1d_pn_bias_cuda_naive_bfloat16_ks_3(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 5) { \ - DISPATCH_DILATION_na1d_pn_bias_cuda_naive_bfloat16_ks_5(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 7) { \ - DISPATCH_DILATION_na1d_pn_bias_cuda_naive_bfloat16_ks_7(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 9) { \ - DISPATCH_DILATION_na1d_pn_bias_cuda_naive_bfloat16_ks_9(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 11) { \ - DISPATCH_DILATION_na1d_pn_bias_cuda_naive_bfloat16_ks_11(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 13) { \ - DISPATCH_DILATION_na1d_pn_bias_cuda_naive_bfloat16_ks_13(dilation, __VA_ARGS__); \ - } \ - else { \ - DISPATCH_DILATION_na1d_pn_bias_cuda_naive_bfloat16_ks_any(dilation, __VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_KERNEL_na2d_pn_bias_cuda_naive_double(kernel_size, dilation, ...) \ - [&] { \ - if (kernel_size == 3) { \ - DISPATCH_DILATION_na2d_pn_bias_cuda_naive_double_ks_3(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 5) { \ - DISPATCH_DILATION_na2d_pn_bias_cuda_naive_double_ks_5(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 7) { \ - DISPATCH_DILATION_na2d_pn_bias_cuda_naive_double_ks_7(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 9) { \ - DISPATCH_DILATION_na2d_pn_bias_cuda_naive_double_ks_9(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 11) { \ - DISPATCH_DILATION_na2d_pn_bias_cuda_naive_double_ks_11(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 13) { \ - DISPATCH_DILATION_na2d_pn_bias_cuda_naive_double_ks_13(dilation, __VA_ARGS__); \ - } \ - else { \ - DISPATCH_DILATION_na2d_pn_bias_cuda_naive_double_ks_any(dilation, __VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_KERNEL_na2d_pn_bias_cuda_naive_float(kernel_size, dilation, ...) \ - [&] { \ - if (kernel_size == 3) { \ - DISPATCH_DILATION_na2d_pn_bias_cuda_naive_float_ks_3(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 5) { \ - DISPATCH_DILATION_na2d_pn_bias_cuda_naive_float_ks_5(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 7) { \ - DISPATCH_DILATION_na2d_pn_bias_cuda_naive_float_ks_7(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 9) { \ - DISPATCH_DILATION_na2d_pn_bias_cuda_naive_float_ks_9(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 11) { \ - DISPATCH_DILATION_na2d_pn_bias_cuda_naive_float_ks_11(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 13) { \ - DISPATCH_DILATION_na2d_pn_bias_cuda_naive_float_ks_13(dilation, __VA_ARGS__); \ - } \ - else { \ - DISPATCH_DILATION_na2d_pn_bias_cuda_naive_float_ks_any(dilation, __VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_KERNEL_na2d_pn_bias_cuda_naive_half(kernel_size, dilation, ...) \ - [&] { \ - if (kernel_size == 3) { \ - DISPATCH_DILATION_na2d_pn_bias_cuda_naive_half_ks_3(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 5) { \ - DISPATCH_DILATION_na2d_pn_bias_cuda_naive_half_ks_5(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 7) { \ - DISPATCH_DILATION_na2d_pn_bias_cuda_naive_half_ks_7(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 9) { \ - DISPATCH_DILATION_na2d_pn_bias_cuda_naive_half_ks_9(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 11) { \ - DISPATCH_DILATION_na2d_pn_bias_cuda_naive_half_ks_11(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 13) { \ - DISPATCH_DILATION_na2d_pn_bias_cuda_naive_half_ks_13(dilation, __VA_ARGS__); \ - } \ - else { \ - DISPATCH_DILATION_na2d_pn_bias_cuda_naive_half_ks_any(dilation, __VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_KERNEL_na2d_pn_bias_cuda_naive_bfloat16(kernel_size, dilation, ...) \ - [&] { \ - if (kernel_size == 3) { \ - DISPATCH_DILATION_na2d_pn_bias_cuda_naive_bfloat16_ks_3(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 5) { \ - DISPATCH_DILATION_na2d_pn_bias_cuda_naive_bfloat16_ks_5(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 7) { \ - DISPATCH_DILATION_na2d_pn_bias_cuda_naive_bfloat16_ks_7(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 9) { \ - DISPATCH_DILATION_na2d_pn_bias_cuda_naive_bfloat16_ks_9(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 11) { \ - DISPATCH_DILATION_na2d_pn_bias_cuda_naive_bfloat16_ks_11(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 13) { \ - DISPATCH_DILATION_na2d_pn_bias_cuda_naive_bfloat16_ks_13(dilation, __VA_ARGS__); \ - } \ - else { \ - DISPATCH_DILATION_na2d_pn_bias_cuda_naive_bfloat16_ks_any(dilation, __VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_KERNEL_na3d_pn_bias_cuda_naive_double(kernel_size, dilation, ...) \ - [&] { \ - if (kernel_size == 3) { \ - DISPATCH_DILATION_na3d_pn_bias_cuda_naive_double_ks_3(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 5) { \ - DISPATCH_DILATION_na3d_pn_bias_cuda_naive_double_ks_5(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 7) { \ - DISPATCH_DILATION_na3d_pn_bias_cuda_naive_double_ks_7(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 9) { \ - DISPATCH_DILATION_na3d_pn_bias_cuda_naive_double_ks_9(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 11) { \ - DISPATCH_DILATION_na3d_pn_bias_cuda_naive_double_ks_11(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 13) { \ - DISPATCH_DILATION_na3d_pn_bias_cuda_naive_double_ks_13(dilation, __VA_ARGS__); \ - } \ - else { \ - DISPATCH_DILATION_na3d_pn_bias_cuda_naive_double_ks_any(dilation, __VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_KERNEL_na3d_pn_bias_cuda_naive_float(kernel_size, dilation, ...) \ - [&] { \ - if (kernel_size == 3) { \ - DISPATCH_DILATION_na3d_pn_bias_cuda_naive_float_ks_3(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 5) { \ - DISPATCH_DILATION_na3d_pn_bias_cuda_naive_float_ks_5(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 7) { \ - DISPATCH_DILATION_na3d_pn_bias_cuda_naive_float_ks_7(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 9) { \ - DISPATCH_DILATION_na3d_pn_bias_cuda_naive_float_ks_9(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 11) { \ - DISPATCH_DILATION_na3d_pn_bias_cuda_naive_float_ks_11(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 13) { \ - DISPATCH_DILATION_na3d_pn_bias_cuda_naive_float_ks_13(dilation, __VA_ARGS__); \ - } \ - else { \ - DISPATCH_DILATION_na3d_pn_bias_cuda_naive_float_ks_any(dilation, __VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_KERNEL_na3d_pn_bias_cuda_naive_half(kernel_size, dilation, ...) \ - [&] { \ - if (kernel_size == 3) { \ - DISPATCH_DILATION_na3d_pn_bias_cuda_naive_half_ks_3(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 5) { \ - DISPATCH_DILATION_na3d_pn_bias_cuda_naive_half_ks_5(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 7) { \ - DISPATCH_DILATION_na3d_pn_bias_cuda_naive_half_ks_7(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 9) { \ - DISPATCH_DILATION_na3d_pn_bias_cuda_naive_half_ks_9(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 11) { \ - DISPATCH_DILATION_na3d_pn_bias_cuda_naive_half_ks_11(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 13) { \ - DISPATCH_DILATION_na3d_pn_bias_cuda_naive_half_ks_13(dilation, __VA_ARGS__); \ - } \ - else { \ - DISPATCH_DILATION_na3d_pn_bias_cuda_naive_half_ks_any(dilation, __VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_KERNEL_na3d_pn_bias_cuda_naive_bfloat16(kernel_size, dilation, ...) \ - [&] { \ - if (kernel_size == 3) { \ - DISPATCH_DILATION_na3d_pn_bias_cuda_naive_bfloat16_ks_3(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 5) { \ - DISPATCH_DILATION_na3d_pn_bias_cuda_naive_bfloat16_ks_5(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 7) { \ - DISPATCH_DILATION_na3d_pn_bias_cuda_naive_bfloat16_ks_7(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 9) { \ - DISPATCH_DILATION_na3d_pn_bias_cuda_naive_bfloat16_ks_9(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 11) { \ - DISPATCH_DILATION_na3d_pn_bias_cuda_naive_bfloat16_ks_11(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 13) { \ - DISPATCH_DILATION_na3d_pn_bias_cuda_naive_bfloat16_ks_13(dilation, __VA_ARGS__); \ - } \ - else { \ - DISPATCH_DILATION_na3d_pn_bias_cuda_naive_bfloat16_ks_any(dilation, __VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_KERNEL_na1d_nn_cuda_naive_double(kernel_size, dilation, ...) \ - [&] { \ - if (kernel_size == 3) { \ - DISPATCH_DILATION_na1d_nn_cuda_naive_double_ks_3(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 5) { \ - DISPATCH_DILATION_na1d_nn_cuda_naive_double_ks_5(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 7) { \ - DISPATCH_DILATION_na1d_nn_cuda_naive_double_ks_7(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 9) { \ - DISPATCH_DILATION_na1d_nn_cuda_naive_double_ks_9(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 11) { \ - DISPATCH_DILATION_na1d_nn_cuda_naive_double_ks_11(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 13) { \ - DISPATCH_DILATION_na1d_nn_cuda_naive_double_ks_13(dilation, __VA_ARGS__); \ - } \ - else { \ - DISPATCH_DILATION_na1d_nn_cuda_naive_double_ks_any(dilation, __VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_KERNEL_na1d_nn_cuda_naive_float(kernel_size, dilation, ...) \ - [&] { \ - if (kernel_size == 3) { \ - DISPATCH_DILATION_na1d_nn_cuda_naive_float_ks_3(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 5) { \ - DISPATCH_DILATION_na1d_nn_cuda_naive_float_ks_5(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 7) { \ - DISPATCH_DILATION_na1d_nn_cuda_naive_float_ks_7(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 9) { \ - DISPATCH_DILATION_na1d_nn_cuda_naive_float_ks_9(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 11) { \ - DISPATCH_DILATION_na1d_nn_cuda_naive_float_ks_11(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 13) { \ - DISPATCH_DILATION_na1d_nn_cuda_naive_float_ks_13(dilation, __VA_ARGS__); \ - } \ - else { \ - DISPATCH_DILATION_na1d_nn_cuda_naive_float_ks_any(dilation, __VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_KERNEL_na1d_nn_cuda_naive_half(kernel_size, dilation, ...) \ - [&] { \ - if (kernel_size == 3) { \ - DISPATCH_DILATION_na1d_nn_cuda_naive_half_ks_3(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 5) { \ - DISPATCH_DILATION_na1d_nn_cuda_naive_half_ks_5(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 7) { \ - DISPATCH_DILATION_na1d_nn_cuda_naive_half_ks_7(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 9) { \ - DISPATCH_DILATION_na1d_nn_cuda_naive_half_ks_9(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 11) { \ - DISPATCH_DILATION_na1d_nn_cuda_naive_half_ks_11(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 13) { \ - DISPATCH_DILATION_na1d_nn_cuda_naive_half_ks_13(dilation, __VA_ARGS__); \ - } \ - else { \ - DISPATCH_DILATION_na1d_nn_cuda_naive_half_ks_any(dilation, __VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_KERNEL_na1d_nn_cuda_naive_bfloat16(kernel_size, dilation, ...) \ - [&] { \ - if (kernel_size == 3) { \ - DISPATCH_DILATION_na1d_nn_cuda_naive_bfloat16_ks_3(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 5) { \ - DISPATCH_DILATION_na1d_nn_cuda_naive_bfloat16_ks_5(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 7) { \ - DISPATCH_DILATION_na1d_nn_cuda_naive_bfloat16_ks_7(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 9) { \ - DISPATCH_DILATION_na1d_nn_cuda_naive_bfloat16_ks_9(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 11) { \ - DISPATCH_DILATION_na1d_nn_cuda_naive_bfloat16_ks_11(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 13) { \ - DISPATCH_DILATION_na1d_nn_cuda_naive_bfloat16_ks_13(dilation, __VA_ARGS__); \ - } \ - else { \ - DISPATCH_DILATION_na1d_nn_cuda_naive_bfloat16_ks_any(dilation, __VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_KERNEL_na2d_nn_cuda_naive_double(kernel_size, dilation, ...) \ - [&] { \ - if (kernel_size == 3) { \ - DISPATCH_DILATION_na2d_nn_cuda_naive_double_ks_3(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 5) { \ - DISPATCH_DILATION_na2d_nn_cuda_naive_double_ks_5(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 7) { \ - DISPATCH_DILATION_na2d_nn_cuda_naive_double_ks_7(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 9) { \ - DISPATCH_DILATION_na2d_nn_cuda_naive_double_ks_9(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 11) { \ - DISPATCH_DILATION_na2d_nn_cuda_naive_double_ks_11(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 13) { \ - DISPATCH_DILATION_na2d_nn_cuda_naive_double_ks_13(dilation, __VA_ARGS__); \ - } \ - else { \ - DISPATCH_DILATION_na2d_nn_cuda_naive_double_ks_any(dilation, __VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_KERNEL_na2d_nn_cuda_naive_float(kernel_size, dilation, ...) \ - [&] { \ - if (kernel_size == 3) { \ - DISPATCH_DILATION_na2d_nn_cuda_naive_float_ks_3(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 5) { \ - DISPATCH_DILATION_na2d_nn_cuda_naive_float_ks_5(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 7) { \ - DISPATCH_DILATION_na2d_nn_cuda_naive_float_ks_7(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 9) { \ - DISPATCH_DILATION_na2d_nn_cuda_naive_float_ks_9(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 11) { \ - DISPATCH_DILATION_na2d_nn_cuda_naive_float_ks_11(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 13) { \ - DISPATCH_DILATION_na2d_nn_cuda_naive_float_ks_13(dilation, __VA_ARGS__); \ - } \ - else { \ - DISPATCH_DILATION_na2d_nn_cuda_naive_float_ks_any(dilation, __VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_KERNEL_na2d_nn_cuda_naive_half(kernel_size, dilation, ...) \ - [&] { \ - if (kernel_size == 3) { \ - DISPATCH_DILATION_na2d_nn_cuda_naive_half_ks_3(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 5) { \ - DISPATCH_DILATION_na2d_nn_cuda_naive_half_ks_5(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 7) { \ - DISPATCH_DILATION_na2d_nn_cuda_naive_half_ks_7(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 9) { \ - DISPATCH_DILATION_na2d_nn_cuda_naive_half_ks_9(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 11) { \ - DISPATCH_DILATION_na2d_nn_cuda_naive_half_ks_11(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 13) { \ - DISPATCH_DILATION_na2d_nn_cuda_naive_half_ks_13(dilation, __VA_ARGS__); \ - } \ - else { \ - DISPATCH_DILATION_na2d_nn_cuda_naive_half_ks_any(dilation, __VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_KERNEL_na2d_nn_cuda_naive_bfloat16(kernel_size, dilation, ...) \ - [&] { \ - if (kernel_size == 3) { \ - DISPATCH_DILATION_na2d_nn_cuda_naive_bfloat16_ks_3(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 5) { \ - DISPATCH_DILATION_na2d_nn_cuda_naive_bfloat16_ks_5(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 7) { \ - DISPATCH_DILATION_na2d_nn_cuda_naive_bfloat16_ks_7(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 9) { \ - DISPATCH_DILATION_na2d_nn_cuda_naive_bfloat16_ks_9(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 11) { \ - DISPATCH_DILATION_na2d_nn_cuda_naive_bfloat16_ks_11(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 13) { \ - DISPATCH_DILATION_na2d_nn_cuda_naive_bfloat16_ks_13(dilation, __VA_ARGS__); \ - } \ - else { \ - DISPATCH_DILATION_na2d_nn_cuda_naive_bfloat16_ks_any(dilation, __VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_KERNEL_na3d_nn_cuda_naive_double(kernel_size, dilation, ...) \ - [&] { \ - if (kernel_size == 3) { \ - DISPATCH_DILATION_na3d_nn_cuda_naive_double_ks_3(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 5) { \ - DISPATCH_DILATION_na3d_nn_cuda_naive_double_ks_5(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 7) { \ - DISPATCH_DILATION_na3d_nn_cuda_naive_double_ks_7(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 9) { \ - DISPATCH_DILATION_na3d_nn_cuda_naive_double_ks_9(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 11) { \ - DISPATCH_DILATION_na3d_nn_cuda_naive_double_ks_11(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 13) { \ - DISPATCH_DILATION_na3d_nn_cuda_naive_double_ks_13(dilation, __VA_ARGS__); \ - } \ - else { \ - DISPATCH_DILATION_na3d_nn_cuda_naive_double_ks_any(dilation, __VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_KERNEL_na3d_nn_cuda_naive_float(kernel_size, dilation, ...) \ - [&] { \ - if (kernel_size == 3) { \ - DISPATCH_DILATION_na3d_nn_cuda_naive_float_ks_3(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 5) { \ - DISPATCH_DILATION_na3d_nn_cuda_naive_float_ks_5(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 7) { \ - DISPATCH_DILATION_na3d_nn_cuda_naive_float_ks_7(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 9) { \ - DISPATCH_DILATION_na3d_nn_cuda_naive_float_ks_9(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 11) { \ - DISPATCH_DILATION_na3d_nn_cuda_naive_float_ks_11(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 13) { \ - DISPATCH_DILATION_na3d_nn_cuda_naive_float_ks_13(dilation, __VA_ARGS__); \ - } \ - else { \ - DISPATCH_DILATION_na3d_nn_cuda_naive_float_ks_any(dilation, __VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_KERNEL_na3d_nn_cuda_naive_half(kernel_size, dilation, ...) \ - [&] { \ - if (kernel_size == 3) { \ - DISPATCH_DILATION_na3d_nn_cuda_naive_half_ks_3(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 5) { \ - DISPATCH_DILATION_na3d_nn_cuda_naive_half_ks_5(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 7) { \ - DISPATCH_DILATION_na3d_nn_cuda_naive_half_ks_7(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 9) { \ - DISPATCH_DILATION_na3d_nn_cuda_naive_half_ks_9(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 11) { \ - DISPATCH_DILATION_na3d_nn_cuda_naive_half_ks_11(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 13) { \ - DISPATCH_DILATION_na3d_nn_cuda_naive_half_ks_13(dilation, __VA_ARGS__); \ - } \ - else { \ - DISPATCH_DILATION_na3d_nn_cuda_naive_half_ks_any(dilation, __VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_KERNEL_na3d_nn_cuda_naive_bfloat16(kernel_size, dilation, ...) \ - [&] { \ - if (kernel_size == 3) { \ - DISPATCH_DILATION_na3d_nn_cuda_naive_bfloat16_ks_3(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 5) { \ - DISPATCH_DILATION_na3d_nn_cuda_naive_bfloat16_ks_5(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 7) { \ - DISPATCH_DILATION_na3d_nn_cuda_naive_bfloat16_ks_7(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 9) { \ - DISPATCH_DILATION_na3d_nn_cuda_naive_bfloat16_ks_9(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 11) { \ - DISPATCH_DILATION_na3d_nn_cuda_naive_bfloat16_ks_11(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 13) { \ - DISPATCH_DILATION_na3d_nn_cuda_naive_bfloat16_ks_13(dilation, __VA_ARGS__); \ - } \ - else { \ - DISPATCH_DILATION_na3d_nn_cuda_naive_bfloat16_ks_any(dilation, __VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_KERNEL_na1d_in_cuda_naive_double(kernel_size, dilation, ...) \ - [&] { \ - if (kernel_size == 3) { \ - DISPATCH_DILATION_na1d_in_cuda_naive_double_ks_3(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 5) { \ - DISPATCH_DILATION_na1d_in_cuda_naive_double_ks_5(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 7) { \ - DISPATCH_DILATION_na1d_in_cuda_naive_double_ks_7(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 9) { \ - DISPATCH_DILATION_na1d_in_cuda_naive_double_ks_9(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 11) { \ - DISPATCH_DILATION_na1d_in_cuda_naive_double_ks_11(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 13) { \ - DISPATCH_DILATION_na1d_in_cuda_naive_double_ks_13(dilation, __VA_ARGS__); \ - } \ - else { \ - DISPATCH_DILATION_na1d_in_cuda_naive_double_ks_any(dilation, __VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_KERNEL_na1d_in_cuda_naive_float(kernel_size, dilation, ...) \ - [&] { \ - if (kernel_size == 3) { \ - DISPATCH_DILATION_na1d_in_cuda_naive_float_ks_3(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 5) { \ - DISPATCH_DILATION_na1d_in_cuda_naive_float_ks_5(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 7) { \ - DISPATCH_DILATION_na1d_in_cuda_naive_float_ks_7(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 9) { \ - DISPATCH_DILATION_na1d_in_cuda_naive_float_ks_9(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 11) { \ - DISPATCH_DILATION_na1d_in_cuda_naive_float_ks_11(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 13) { \ - DISPATCH_DILATION_na1d_in_cuda_naive_float_ks_13(dilation, __VA_ARGS__); \ - } \ - else { \ - DISPATCH_DILATION_na1d_in_cuda_naive_float_ks_any(dilation, __VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_KERNEL_na1d_in_cuda_naive_half(kernel_size, dilation, ...) \ - [&] { \ - if (kernel_size == 3) { \ - DISPATCH_DILATION_na1d_in_cuda_naive_half_ks_3(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 5) { \ - DISPATCH_DILATION_na1d_in_cuda_naive_half_ks_5(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 7) { \ - DISPATCH_DILATION_na1d_in_cuda_naive_half_ks_7(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 9) { \ - DISPATCH_DILATION_na1d_in_cuda_naive_half_ks_9(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 11) { \ - DISPATCH_DILATION_na1d_in_cuda_naive_half_ks_11(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 13) { \ - DISPATCH_DILATION_na1d_in_cuda_naive_half_ks_13(dilation, __VA_ARGS__); \ - } \ - else { \ - DISPATCH_DILATION_na1d_in_cuda_naive_half_ks_any(dilation, __VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_KERNEL_na1d_in_cuda_naive_bfloat16(kernel_size, dilation, ...) \ - [&] { \ - if (kernel_size == 3) { \ - DISPATCH_DILATION_na1d_in_cuda_naive_bfloat16_ks_3(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 5) { \ - DISPATCH_DILATION_na1d_in_cuda_naive_bfloat16_ks_5(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 7) { \ - DISPATCH_DILATION_na1d_in_cuda_naive_bfloat16_ks_7(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 9) { \ - DISPATCH_DILATION_na1d_in_cuda_naive_bfloat16_ks_9(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 11) { \ - DISPATCH_DILATION_na1d_in_cuda_naive_bfloat16_ks_11(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 13) { \ - DISPATCH_DILATION_na1d_in_cuda_naive_bfloat16_ks_13(dilation, __VA_ARGS__); \ - } \ - else { \ - DISPATCH_DILATION_na1d_in_cuda_naive_bfloat16_ks_any(dilation, __VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_KERNEL_na2d_in_cuda_naive_double(kernel_size, dilation, ...) \ - [&] { \ - if (kernel_size == 3) { \ - DISPATCH_DILATION_na2d_in_cuda_naive_double_ks_3(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 5) { \ - DISPATCH_DILATION_na2d_in_cuda_naive_double_ks_5(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 7) { \ - DISPATCH_DILATION_na2d_in_cuda_naive_double_ks_7(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 9) { \ - DISPATCH_DILATION_na2d_in_cuda_naive_double_ks_9(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 11) { \ - DISPATCH_DILATION_na2d_in_cuda_naive_double_ks_11(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 13) { \ - DISPATCH_DILATION_na2d_in_cuda_naive_double_ks_13(dilation, __VA_ARGS__); \ - } \ - else { \ - DISPATCH_DILATION_na2d_in_cuda_naive_double_ks_any(dilation, __VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_KERNEL_na2d_in_cuda_naive_float(kernel_size, dilation, ...) \ - [&] { \ - if (kernel_size == 3) { \ - DISPATCH_DILATION_na2d_in_cuda_naive_float_ks_3(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 5) { \ - DISPATCH_DILATION_na2d_in_cuda_naive_float_ks_5(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 7) { \ - DISPATCH_DILATION_na2d_in_cuda_naive_float_ks_7(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 9) { \ - DISPATCH_DILATION_na2d_in_cuda_naive_float_ks_9(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 11) { \ - DISPATCH_DILATION_na2d_in_cuda_naive_float_ks_11(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 13) { \ - DISPATCH_DILATION_na2d_in_cuda_naive_float_ks_13(dilation, __VA_ARGS__); \ - } \ - else { \ - DISPATCH_DILATION_na2d_in_cuda_naive_float_ks_any(dilation, __VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_KERNEL_na2d_in_cuda_naive_half(kernel_size, dilation, ...) \ - [&] { \ - if (kernel_size == 3) { \ - DISPATCH_DILATION_na2d_in_cuda_naive_half_ks_3(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 5) { \ - DISPATCH_DILATION_na2d_in_cuda_naive_half_ks_5(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 7) { \ - DISPATCH_DILATION_na2d_in_cuda_naive_half_ks_7(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 9) { \ - DISPATCH_DILATION_na2d_in_cuda_naive_half_ks_9(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 11) { \ - DISPATCH_DILATION_na2d_in_cuda_naive_half_ks_11(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 13) { \ - DISPATCH_DILATION_na2d_in_cuda_naive_half_ks_13(dilation, __VA_ARGS__); \ - } \ - else { \ - DISPATCH_DILATION_na2d_in_cuda_naive_half_ks_any(dilation, __VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_KERNEL_na2d_in_cuda_naive_bfloat16(kernel_size, dilation, ...) \ - [&] { \ - if (kernel_size == 3) { \ - DISPATCH_DILATION_na2d_in_cuda_naive_bfloat16_ks_3(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 5) { \ - DISPATCH_DILATION_na2d_in_cuda_naive_bfloat16_ks_5(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 7) { \ - DISPATCH_DILATION_na2d_in_cuda_naive_bfloat16_ks_7(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 9) { \ - DISPATCH_DILATION_na2d_in_cuda_naive_bfloat16_ks_9(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 11) { \ - DISPATCH_DILATION_na2d_in_cuda_naive_bfloat16_ks_11(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 13) { \ - DISPATCH_DILATION_na2d_in_cuda_naive_bfloat16_ks_13(dilation, __VA_ARGS__); \ - } \ - else { \ - DISPATCH_DILATION_na2d_in_cuda_naive_bfloat16_ks_any(dilation, __VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_KERNEL_na3d_in_cuda_naive_double(kernel_size, dilation, ...) \ - [&] { \ - if (kernel_size == 3) { \ - DISPATCH_DILATION_na3d_in_cuda_naive_double_ks_3(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 5) { \ - DISPATCH_DILATION_na3d_in_cuda_naive_double_ks_5(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 7) { \ - DISPATCH_DILATION_na3d_in_cuda_naive_double_ks_7(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 9) { \ - DISPATCH_DILATION_na3d_in_cuda_naive_double_ks_9(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 11) { \ - DISPATCH_DILATION_na3d_in_cuda_naive_double_ks_11(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 13) { \ - DISPATCH_DILATION_na3d_in_cuda_naive_double_ks_13(dilation, __VA_ARGS__); \ - } \ - else { \ - DISPATCH_DILATION_na3d_in_cuda_naive_double_ks_any(dilation, __VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_KERNEL_na3d_in_cuda_naive_float(kernel_size, dilation, ...) \ - [&] { \ - if (kernel_size == 3) { \ - DISPATCH_DILATION_na3d_in_cuda_naive_float_ks_3(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 5) { \ - DISPATCH_DILATION_na3d_in_cuda_naive_float_ks_5(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 7) { \ - DISPATCH_DILATION_na3d_in_cuda_naive_float_ks_7(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 9) { \ - DISPATCH_DILATION_na3d_in_cuda_naive_float_ks_9(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 11) { \ - DISPATCH_DILATION_na3d_in_cuda_naive_float_ks_11(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 13) { \ - DISPATCH_DILATION_na3d_in_cuda_naive_float_ks_13(dilation, __VA_ARGS__); \ - } \ - else { \ - DISPATCH_DILATION_na3d_in_cuda_naive_float_ks_any(dilation, __VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_KERNEL_na3d_in_cuda_naive_half(kernel_size, dilation, ...) \ - [&] { \ - if (kernel_size == 3) { \ - DISPATCH_DILATION_na3d_in_cuda_naive_half_ks_3(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 5) { \ - DISPATCH_DILATION_na3d_in_cuda_naive_half_ks_5(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 7) { \ - DISPATCH_DILATION_na3d_in_cuda_naive_half_ks_7(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 9) { \ - DISPATCH_DILATION_na3d_in_cuda_naive_half_ks_9(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 11) { \ - DISPATCH_DILATION_na3d_in_cuda_naive_half_ks_11(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 13) { \ - DISPATCH_DILATION_na3d_in_cuda_naive_half_ks_13(dilation, __VA_ARGS__); \ - } \ - else { \ - DISPATCH_DILATION_na3d_in_cuda_naive_half_ks_any(dilation, __VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_KERNEL_na3d_in_cuda_naive_bfloat16(kernel_size, dilation, ...) \ - [&] { \ - if (kernel_size == 3) { \ - DISPATCH_DILATION_na3d_in_cuda_naive_bfloat16_ks_3(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 5) { \ - DISPATCH_DILATION_na3d_in_cuda_naive_bfloat16_ks_5(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 7) { \ - DISPATCH_DILATION_na3d_in_cuda_naive_bfloat16_ks_7(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 9) { \ - DISPATCH_DILATION_na3d_in_cuda_naive_bfloat16_ks_9(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 11) { \ - DISPATCH_DILATION_na3d_in_cuda_naive_bfloat16_ks_11(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 13) { \ - DISPATCH_DILATION_na3d_in_cuda_naive_bfloat16_ks_13(dilation, __VA_ARGS__); \ - } \ - else { \ - DISPATCH_DILATION_na3d_in_cuda_naive_bfloat16_ks_any(dilation, __VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_KERNEL_na1d_rpbgrad_cuda_naive_double(kernel_size, dilation, ...) \ - [&] { \ - if (kernel_size == 3) { \ - DISPATCH_DILATION_na1d_rpbgrad_cuda_naive_double_ks_3(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 5) { \ - DISPATCH_DILATION_na1d_rpbgrad_cuda_naive_double_ks_5(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 7) { \ - DISPATCH_DILATION_na1d_rpbgrad_cuda_naive_double_ks_7(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 9) { \ - DISPATCH_DILATION_na1d_rpbgrad_cuda_naive_double_ks_9(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 11) { \ - DISPATCH_DILATION_na1d_rpbgrad_cuda_naive_double_ks_11(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 13) { \ - DISPATCH_DILATION_na1d_rpbgrad_cuda_naive_double_ks_13(dilation, __VA_ARGS__); \ - } \ - else { \ - DISPATCH_DILATION_na1d_rpbgrad_cuda_naive_double_ks_any(dilation, __VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_KERNEL_na1d_rpbgrad_cuda_naive_float(kernel_size, dilation, ...) \ - [&] { \ - if (kernel_size == 3) { \ - DISPATCH_DILATION_na1d_rpbgrad_cuda_naive_float_ks_3(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 5) { \ - DISPATCH_DILATION_na1d_rpbgrad_cuda_naive_float_ks_5(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 7) { \ - DISPATCH_DILATION_na1d_rpbgrad_cuda_naive_float_ks_7(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 9) { \ - DISPATCH_DILATION_na1d_rpbgrad_cuda_naive_float_ks_9(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 11) { \ - DISPATCH_DILATION_na1d_rpbgrad_cuda_naive_float_ks_11(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 13) { \ - DISPATCH_DILATION_na1d_rpbgrad_cuda_naive_float_ks_13(dilation, __VA_ARGS__); \ - } \ - else { \ - DISPATCH_DILATION_na1d_rpbgrad_cuda_naive_float_ks_any(dilation, __VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_KERNEL_na1d_rpbgrad_cuda_naive_half(kernel_size, dilation, ...) \ - [&] { \ - if (kernel_size == 3) { \ - DISPATCH_DILATION_na1d_rpbgrad_cuda_naive_half_ks_3(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 5) { \ - DISPATCH_DILATION_na1d_rpbgrad_cuda_naive_half_ks_5(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 7) { \ - DISPATCH_DILATION_na1d_rpbgrad_cuda_naive_half_ks_7(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 9) { \ - DISPATCH_DILATION_na1d_rpbgrad_cuda_naive_half_ks_9(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 11) { \ - DISPATCH_DILATION_na1d_rpbgrad_cuda_naive_half_ks_11(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 13) { \ - DISPATCH_DILATION_na1d_rpbgrad_cuda_naive_half_ks_13(dilation, __VA_ARGS__); \ - } \ - else { \ - DISPATCH_DILATION_na1d_rpbgrad_cuda_naive_half_ks_any(dilation, __VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_KERNEL_na1d_rpbgrad_cuda_naive_bfloat16(kernel_size, dilation, ...) \ - [&] { \ - if (kernel_size == 3) { \ - DISPATCH_DILATION_na1d_rpbgrad_cuda_naive_bfloat16_ks_3(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 5) { \ - DISPATCH_DILATION_na1d_rpbgrad_cuda_naive_bfloat16_ks_5(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 7) { \ - DISPATCH_DILATION_na1d_rpbgrad_cuda_naive_bfloat16_ks_7(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 9) { \ - DISPATCH_DILATION_na1d_rpbgrad_cuda_naive_bfloat16_ks_9(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 11) { \ - DISPATCH_DILATION_na1d_rpbgrad_cuda_naive_bfloat16_ks_11(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 13) { \ - DISPATCH_DILATION_na1d_rpbgrad_cuda_naive_bfloat16_ks_13(dilation, __VA_ARGS__); \ - } \ - else { \ - DISPATCH_DILATION_na1d_rpbgrad_cuda_naive_bfloat16_ks_any(dilation, __VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_KERNEL_na2d_rpbgrad_cuda_naive_double(kernel_size, dilation, ...) \ - [&] { \ - if (kernel_size == 3) { \ - DISPATCH_DILATION_na2d_rpbgrad_cuda_naive_double_ks_3(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 5) { \ - DISPATCH_DILATION_na2d_rpbgrad_cuda_naive_double_ks_5(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 7) { \ - DISPATCH_DILATION_na2d_rpbgrad_cuda_naive_double_ks_7(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 9) { \ - DISPATCH_DILATION_na2d_rpbgrad_cuda_naive_double_ks_9(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 11) { \ - DISPATCH_DILATION_na2d_rpbgrad_cuda_naive_double_ks_11(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 13) { \ - DISPATCH_DILATION_na2d_rpbgrad_cuda_naive_double_ks_13(dilation, __VA_ARGS__); \ - } \ - else { \ - DISPATCH_DILATION_na2d_rpbgrad_cuda_naive_double_ks_any(dilation, __VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_KERNEL_na2d_rpbgrad_cuda_naive_float(kernel_size, dilation, ...) \ - [&] { \ - if (kernel_size == 3) { \ - DISPATCH_DILATION_na2d_rpbgrad_cuda_naive_float_ks_3(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 5) { \ - DISPATCH_DILATION_na2d_rpbgrad_cuda_naive_float_ks_5(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 7) { \ - DISPATCH_DILATION_na2d_rpbgrad_cuda_naive_float_ks_7(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 9) { \ - DISPATCH_DILATION_na2d_rpbgrad_cuda_naive_float_ks_9(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 11) { \ - DISPATCH_DILATION_na2d_rpbgrad_cuda_naive_float_ks_11(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 13) { \ - DISPATCH_DILATION_na2d_rpbgrad_cuda_naive_float_ks_13(dilation, __VA_ARGS__); \ - } \ - else { \ - DISPATCH_DILATION_na2d_rpbgrad_cuda_naive_float_ks_any(dilation, __VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_KERNEL_na2d_rpbgrad_cuda_naive_half(kernel_size, dilation, ...) \ - [&] { \ - if (kernel_size == 3) { \ - DISPATCH_DILATION_na2d_rpbgrad_cuda_naive_half_ks_3(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 5) { \ - DISPATCH_DILATION_na2d_rpbgrad_cuda_naive_half_ks_5(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 7) { \ - DISPATCH_DILATION_na2d_rpbgrad_cuda_naive_half_ks_7(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 9) { \ - DISPATCH_DILATION_na2d_rpbgrad_cuda_naive_half_ks_9(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 11) { \ - DISPATCH_DILATION_na2d_rpbgrad_cuda_naive_half_ks_11(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 13) { \ - DISPATCH_DILATION_na2d_rpbgrad_cuda_naive_half_ks_13(dilation, __VA_ARGS__); \ - } \ - else { \ - DISPATCH_DILATION_na2d_rpbgrad_cuda_naive_half_ks_any(dilation, __VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_KERNEL_na2d_rpbgrad_cuda_naive_bfloat16(kernel_size, dilation, ...) \ - [&] { \ - if (kernel_size == 3) { \ - DISPATCH_DILATION_na2d_rpbgrad_cuda_naive_bfloat16_ks_3(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 5) { \ - DISPATCH_DILATION_na2d_rpbgrad_cuda_naive_bfloat16_ks_5(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 7) { \ - DISPATCH_DILATION_na2d_rpbgrad_cuda_naive_bfloat16_ks_7(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 9) { \ - DISPATCH_DILATION_na2d_rpbgrad_cuda_naive_bfloat16_ks_9(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 11) { \ - DISPATCH_DILATION_na2d_rpbgrad_cuda_naive_bfloat16_ks_11(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 13) { \ - DISPATCH_DILATION_na2d_rpbgrad_cuda_naive_bfloat16_ks_13(dilation, __VA_ARGS__); \ - } \ - else { \ - DISPATCH_DILATION_na2d_rpbgrad_cuda_naive_bfloat16_ks_any(dilation, __VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_KERNEL_na3d_rpbgrad_cuda_naive_double(kernel_size, dilation, ...) \ - [&] { \ - if (kernel_size == 3) { \ - DISPATCH_DILATION_na3d_rpbgrad_cuda_naive_double_ks_3(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 5) { \ - DISPATCH_DILATION_na3d_rpbgrad_cuda_naive_double_ks_5(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 7) { \ - DISPATCH_DILATION_na3d_rpbgrad_cuda_naive_double_ks_7(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 9) { \ - DISPATCH_DILATION_na3d_rpbgrad_cuda_naive_double_ks_9(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 11) { \ - DISPATCH_DILATION_na3d_rpbgrad_cuda_naive_double_ks_11(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 13) { \ - DISPATCH_DILATION_na3d_rpbgrad_cuda_naive_double_ks_13(dilation, __VA_ARGS__); \ - } \ - else { \ - DISPATCH_DILATION_na3d_rpbgrad_cuda_naive_double_ks_any(dilation, __VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_KERNEL_na3d_rpbgrad_cuda_naive_float(kernel_size, dilation, ...) \ - [&] { \ - if (kernel_size == 3) { \ - DISPATCH_DILATION_na3d_rpbgrad_cuda_naive_float_ks_3(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 5) { \ - DISPATCH_DILATION_na3d_rpbgrad_cuda_naive_float_ks_5(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 7) { \ - DISPATCH_DILATION_na3d_rpbgrad_cuda_naive_float_ks_7(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 9) { \ - DISPATCH_DILATION_na3d_rpbgrad_cuda_naive_float_ks_9(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 11) { \ - DISPATCH_DILATION_na3d_rpbgrad_cuda_naive_float_ks_11(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 13) { \ - DISPATCH_DILATION_na3d_rpbgrad_cuda_naive_float_ks_13(dilation, __VA_ARGS__); \ - } \ - else { \ - DISPATCH_DILATION_na3d_rpbgrad_cuda_naive_float_ks_any(dilation, __VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_KERNEL_na3d_rpbgrad_cuda_naive_half(kernel_size, dilation, ...) \ - [&] { \ - if (kernel_size == 3) { \ - DISPATCH_DILATION_na3d_rpbgrad_cuda_naive_half_ks_3(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 5) { \ - DISPATCH_DILATION_na3d_rpbgrad_cuda_naive_half_ks_5(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 7) { \ - DISPATCH_DILATION_na3d_rpbgrad_cuda_naive_half_ks_7(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 9) { \ - DISPATCH_DILATION_na3d_rpbgrad_cuda_naive_half_ks_9(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 11) { \ - DISPATCH_DILATION_na3d_rpbgrad_cuda_naive_half_ks_11(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 13) { \ - DISPATCH_DILATION_na3d_rpbgrad_cuda_naive_half_ks_13(dilation, __VA_ARGS__); \ - } \ - else { \ - DISPATCH_DILATION_na3d_rpbgrad_cuda_naive_half_ks_any(dilation, __VA_ARGS__); \ - } \ -}(); - -#define DISPATCH_KERNEL_na3d_rpbgrad_cuda_naive_bfloat16(kernel_size, dilation, ...) \ - [&] { \ - if (kernel_size == 3) { \ - DISPATCH_DILATION_na3d_rpbgrad_cuda_naive_bfloat16_ks_3(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 5) { \ - DISPATCH_DILATION_na3d_rpbgrad_cuda_naive_bfloat16_ks_5(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 7) { \ - DISPATCH_DILATION_na3d_rpbgrad_cuda_naive_bfloat16_ks_7(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 9) { \ - DISPATCH_DILATION_na3d_rpbgrad_cuda_naive_bfloat16_ks_9(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 11) { \ - DISPATCH_DILATION_na3d_rpbgrad_cuda_naive_bfloat16_ks_11(dilation, __VA_ARGS__); \ - } \ - else if (kernel_size == 13) { \ - DISPATCH_DILATION_na3d_rpbgrad_cuda_naive_bfloat16_ks_13(dilation, __VA_ARGS__); \ - } \ - else { \ - DISPATCH_DILATION_na3d_rpbgrad_cuda_naive_bfloat16_ks_any(dilation, __VA_ARGS__); \ - } \ -}(); - - - -} // namespace {namespace} -} // namespace {namespace} -} // namespace {namespace} - diff --git a/csrc/autogen/include/natten_autogen/cuda/naive/interface.h b/csrc/autogen/include/natten_autogen/cuda/naive/interface.h index f85241c..8624daf 100644 --- a/csrc/autogen/include/natten_autogen/cuda/naive/interface.h +++ b/csrc/autogen/include/natten_autogen/cuda/naive/interface.h @@ -4,23 +4,23 @@ #include #include #include -#include +#include namespace natten { namespace cuda { namespace naive { -#define DISPATCH_DTYPE_na1d_pn_cuda_naive(dtype, kernel_size, dilation, ...) \ +#define DISPATCH_DTYPE_na1d_pn_cuda_naive(dtype, is_causal, ...) \ [&] { \ if (std::is_same::value) { \ - DISPATCH_KERNEL_na1d_pn_cuda_naive_double(kernel_size, dilation, __VA_ARGS__); \ + DISPATCH_CM_na1d_pn_cuda_naive_double(is_causal, __VA_ARGS__); \ } \ else if (std::is_same::value) { \ - DISPATCH_KERNEL_na1d_pn_cuda_naive_float(kernel_size, dilation, __VA_ARGS__); \ + DISPATCH_CM_na1d_pn_cuda_naive_float(is_causal, __VA_ARGS__); \ } \ else if (std::is_same::value) { \ - DISPATCH_KERNEL_na1d_pn_cuda_naive_half(kernel_size, dilation, __VA_ARGS__); \ + DISPATCH_CM_na1d_pn_cuda_naive_half(is_causal, __VA_ARGS__); \ } \ else if (std::is_same::value) { \ - DISPATCH_KERNEL_na1d_pn_cuda_naive_bfloat16(kernel_size, dilation, __VA_ARGS__); \ + DISPATCH_CM_na1d_pn_cuda_naive_bfloat16(is_causal, __VA_ARGS__); \ } \ else { \ std::cerr << "NATTEN kernel launch failed!" \ @@ -30,19 +30,19 @@ namespace naive { } \ }(); -#define DISPATCH_DTYPE_na2d_pn_cuda_naive(dtype, kernel_size, dilation, ...) \ +#define DISPATCH_DTYPE_na2d_pn_cuda_naive(dtype, is_causal, ...) \ [&] { \ if (std::is_same::value) { \ - DISPATCH_KERNEL_na2d_pn_cuda_naive_double(kernel_size, dilation, __VA_ARGS__); \ + DISPATCH_CM_na2d_pn_cuda_naive_double(is_causal, __VA_ARGS__); \ } \ else if (std::is_same::value) { \ - DISPATCH_KERNEL_na2d_pn_cuda_naive_float(kernel_size, dilation, __VA_ARGS__); \ + DISPATCH_CM_na2d_pn_cuda_naive_float(is_causal, __VA_ARGS__); \ } \ else if (std::is_same::value) { \ - DISPATCH_KERNEL_na2d_pn_cuda_naive_half(kernel_size, dilation, __VA_ARGS__); \ + DISPATCH_CM_na2d_pn_cuda_naive_half(is_causal, __VA_ARGS__); \ } \ else if (std::is_same::value) { \ - DISPATCH_KERNEL_na2d_pn_cuda_naive_bfloat16(kernel_size, dilation, __VA_ARGS__); \ + DISPATCH_CM_na2d_pn_cuda_naive_bfloat16(is_causal, __VA_ARGS__); \ } \ else { \ std::cerr << "NATTEN kernel launch failed!" \ @@ -52,19 +52,19 @@ namespace naive { } \ }(); -#define DISPATCH_DTYPE_na3d_pn_cuda_naive(dtype, kernel_size, dilation, ...) \ +#define DISPATCH_DTYPE_na3d_pn_cuda_naive(dtype, is_causal, ...) \ [&] { \ if (std::is_same::value) { \ - DISPATCH_KERNEL_na3d_pn_cuda_naive_double(kernel_size, dilation, __VA_ARGS__); \ + DISPATCH_CM_na3d_pn_cuda_naive_double(is_causal, __VA_ARGS__); \ } \ else if (std::is_same::value) { \ - DISPATCH_KERNEL_na3d_pn_cuda_naive_float(kernel_size, dilation, __VA_ARGS__); \ + DISPATCH_CM_na3d_pn_cuda_naive_float(is_causal, __VA_ARGS__); \ } \ else if (std::is_same::value) { \ - DISPATCH_KERNEL_na3d_pn_cuda_naive_half(kernel_size, dilation, __VA_ARGS__); \ + DISPATCH_CM_na3d_pn_cuda_naive_half(is_causal, __VA_ARGS__); \ } \ else if (std::is_same::value) { \ - DISPATCH_KERNEL_na3d_pn_cuda_naive_bfloat16(kernel_size, dilation, __VA_ARGS__); \ + DISPATCH_CM_na3d_pn_cuda_naive_bfloat16(is_causal, __VA_ARGS__); \ } \ else { \ std::cerr << "NATTEN kernel launch failed!" \ @@ -74,19 +74,19 @@ namespace naive { } \ }(); -#define DISPATCH_DTYPE_na1d_pn_bias_cuda_naive(dtype, kernel_size, dilation, ...) \ +#define DISPATCH_DTYPE_na1d_pn_bias_cuda_naive(dtype, is_causal, ...) \ [&] { \ if (std::is_same::value) { \ - DISPATCH_KERNEL_na1d_pn_bias_cuda_naive_double(kernel_size, dilation, __VA_ARGS__); \ + DISPATCH_CM_na1d_pn_bias_cuda_naive_double(is_causal, __VA_ARGS__); \ } \ else if (std::is_same::value) { \ - DISPATCH_KERNEL_na1d_pn_bias_cuda_naive_float(kernel_size, dilation, __VA_ARGS__); \ + DISPATCH_CM_na1d_pn_bias_cuda_naive_float(is_causal, __VA_ARGS__); \ } \ else if (std::is_same::value) { \ - DISPATCH_KERNEL_na1d_pn_bias_cuda_naive_half(kernel_size, dilation, __VA_ARGS__); \ + DISPATCH_CM_na1d_pn_bias_cuda_naive_half(is_causal, __VA_ARGS__); \ } \ else if (std::is_same::value) { \ - DISPATCH_KERNEL_na1d_pn_bias_cuda_naive_bfloat16(kernel_size, dilation, __VA_ARGS__); \ + DISPATCH_CM_na1d_pn_bias_cuda_naive_bfloat16(is_causal, __VA_ARGS__); \ } \ else { \ std::cerr << "NATTEN kernel launch failed!" \ @@ -96,19 +96,19 @@ namespace naive { } \ }(); -#define DISPATCH_DTYPE_na2d_pn_bias_cuda_naive(dtype, kernel_size, dilation, ...) \ +#define DISPATCH_DTYPE_na2d_pn_bias_cuda_naive(dtype, is_causal, ...) \ [&] { \ if (std::is_same::value) { \ - DISPATCH_KERNEL_na2d_pn_bias_cuda_naive_double(kernel_size, dilation, __VA_ARGS__); \ + DISPATCH_CM_na2d_pn_bias_cuda_naive_double(is_causal, __VA_ARGS__); \ } \ else if (std::is_same::value) { \ - DISPATCH_KERNEL_na2d_pn_bias_cuda_naive_float(kernel_size, dilation, __VA_ARGS__); \ + DISPATCH_CM_na2d_pn_bias_cuda_naive_float(is_causal, __VA_ARGS__); \ } \ else if (std::is_same::value) { \ - DISPATCH_KERNEL_na2d_pn_bias_cuda_naive_half(kernel_size, dilation, __VA_ARGS__); \ + DISPATCH_CM_na2d_pn_bias_cuda_naive_half(is_causal, __VA_ARGS__); \ } \ else if (std::is_same::value) { \ - DISPATCH_KERNEL_na2d_pn_bias_cuda_naive_bfloat16(kernel_size, dilation, __VA_ARGS__); \ + DISPATCH_CM_na2d_pn_bias_cuda_naive_bfloat16(is_causal, __VA_ARGS__); \ } \ else { \ std::cerr << "NATTEN kernel launch failed!" \ @@ -118,19 +118,19 @@ namespace naive { } \ }(); -#define DISPATCH_DTYPE_na3d_pn_bias_cuda_naive(dtype, kernel_size, dilation, ...) \ +#define DISPATCH_DTYPE_na3d_pn_bias_cuda_naive(dtype, is_causal, ...) \ [&] { \ if (std::is_same::value) { \ - DISPATCH_KERNEL_na3d_pn_bias_cuda_naive_double(kernel_size, dilation, __VA_ARGS__); \ + DISPATCH_CM_na3d_pn_bias_cuda_naive_double(is_causal, __VA_ARGS__); \ } \ else if (std::is_same::value) { \ - DISPATCH_KERNEL_na3d_pn_bias_cuda_naive_float(kernel_size, dilation, __VA_ARGS__); \ + DISPATCH_CM_na3d_pn_bias_cuda_naive_float(is_causal, __VA_ARGS__); \ } \ else if (std::is_same::value) { \ - DISPATCH_KERNEL_na3d_pn_bias_cuda_naive_half(kernel_size, dilation, __VA_ARGS__); \ + DISPATCH_CM_na3d_pn_bias_cuda_naive_half(is_causal, __VA_ARGS__); \ } \ else if (std::is_same::value) { \ - DISPATCH_KERNEL_na3d_pn_bias_cuda_naive_bfloat16(kernel_size, dilation, __VA_ARGS__); \ + DISPATCH_CM_na3d_pn_bias_cuda_naive_bfloat16(is_causal, __VA_ARGS__); \ } \ else { \ std::cerr << "NATTEN kernel launch failed!" \ @@ -140,19 +140,19 @@ namespace naive { } \ }(); -#define DISPATCH_DTYPE_na1d_nn_cuda_naive(dtype, kernel_size, dilation, ...) \ +#define DISPATCH_DTYPE_na1d_nn_cuda_naive(dtype, is_causal, ...) \ [&] { \ if (std::is_same::value) { \ - DISPATCH_KERNEL_na1d_nn_cuda_naive_double(kernel_size, dilation, __VA_ARGS__); \ + DISPATCH_CM_na1d_nn_cuda_naive_double(is_causal, __VA_ARGS__); \ } \ else if (std::is_same::value) { \ - DISPATCH_KERNEL_na1d_nn_cuda_naive_float(kernel_size, dilation, __VA_ARGS__); \ + DISPATCH_CM_na1d_nn_cuda_naive_float(is_causal, __VA_ARGS__); \ } \ else if (std::is_same::value) { \ - DISPATCH_KERNEL_na1d_nn_cuda_naive_half(kernel_size, dilation, __VA_ARGS__); \ + DISPATCH_CM_na1d_nn_cuda_naive_half(is_causal, __VA_ARGS__); \ } \ else if (std::is_same::value) { \ - DISPATCH_KERNEL_na1d_nn_cuda_naive_bfloat16(kernel_size, dilation, __VA_ARGS__); \ + DISPATCH_CM_na1d_nn_cuda_naive_bfloat16(is_causal, __VA_ARGS__); \ } \ else { \ std::cerr << "NATTEN kernel launch failed!" \ @@ -162,19 +162,19 @@ namespace naive { } \ }(); -#define DISPATCH_DTYPE_na2d_nn_cuda_naive(dtype, kernel_size, dilation, ...) \ +#define DISPATCH_DTYPE_na2d_nn_cuda_naive(dtype, is_causal, ...) \ [&] { \ if (std::is_same::value) { \ - DISPATCH_KERNEL_na2d_nn_cuda_naive_double(kernel_size, dilation, __VA_ARGS__); \ + DISPATCH_CM_na2d_nn_cuda_naive_double(is_causal, __VA_ARGS__); \ } \ else if (std::is_same::value) { \ - DISPATCH_KERNEL_na2d_nn_cuda_naive_float(kernel_size, dilation, __VA_ARGS__); \ + DISPATCH_CM_na2d_nn_cuda_naive_float(is_causal, __VA_ARGS__); \ } \ else if (std::is_same::value) { \ - DISPATCH_KERNEL_na2d_nn_cuda_naive_half(kernel_size, dilation, __VA_ARGS__); \ + DISPATCH_CM_na2d_nn_cuda_naive_half(is_causal, __VA_ARGS__); \ } \ else if (std::is_same::value) { \ - DISPATCH_KERNEL_na2d_nn_cuda_naive_bfloat16(kernel_size, dilation, __VA_ARGS__); \ + DISPATCH_CM_na2d_nn_cuda_naive_bfloat16(is_causal, __VA_ARGS__); \ } \ else { \ std::cerr << "NATTEN kernel launch failed!" \ @@ -184,19 +184,19 @@ namespace naive { } \ }(); -#define DISPATCH_DTYPE_na3d_nn_cuda_naive(dtype, kernel_size, dilation, ...) \ +#define DISPATCH_DTYPE_na3d_nn_cuda_naive(dtype, is_causal, ...) \ [&] { \ if (std::is_same::value) { \ - DISPATCH_KERNEL_na3d_nn_cuda_naive_double(kernel_size, dilation, __VA_ARGS__); \ + DISPATCH_CM_na3d_nn_cuda_naive_double(is_causal, __VA_ARGS__); \ } \ else if (std::is_same::value) { \ - DISPATCH_KERNEL_na3d_nn_cuda_naive_float(kernel_size, dilation, __VA_ARGS__); \ + DISPATCH_CM_na3d_nn_cuda_naive_float(is_causal, __VA_ARGS__); \ } \ else if (std::is_same::value) { \ - DISPATCH_KERNEL_na3d_nn_cuda_naive_half(kernel_size, dilation, __VA_ARGS__); \ + DISPATCH_CM_na3d_nn_cuda_naive_half(is_causal, __VA_ARGS__); \ } \ else if (std::is_same::value) { \ - DISPATCH_KERNEL_na3d_nn_cuda_naive_bfloat16(kernel_size, dilation, __VA_ARGS__); \ + DISPATCH_CM_na3d_nn_cuda_naive_bfloat16(is_causal, __VA_ARGS__); \ } \ else { \ std::cerr << "NATTEN kernel launch failed!" \ @@ -206,19 +206,19 @@ namespace naive { } \ }(); -#define DISPATCH_DTYPE_na1d_in_cuda_naive(dtype, kernel_size, dilation, ...) \ +#define DISPATCH_DTYPE_na1d_in_cuda_naive(dtype, is_causal, ...) \ [&] { \ if (std::is_same::value) { \ - DISPATCH_KERNEL_na1d_in_cuda_naive_double(kernel_size, dilation, __VA_ARGS__); \ + DISPATCH_CM_na1d_in_cuda_naive_double(is_causal, __VA_ARGS__); \ } \ else if (std::is_same::value) { \ - DISPATCH_KERNEL_na1d_in_cuda_naive_float(kernel_size, dilation, __VA_ARGS__); \ + DISPATCH_CM_na1d_in_cuda_naive_float(is_causal, __VA_ARGS__); \ } \ else if (std::is_same::value) { \ - DISPATCH_KERNEL_na1d_in_cuda_naive_half(kernel_size, dilation, __VA_ARGS__); \ + DISPATCH_CM_na1d_in_cuda_naive_half(is_causal, __VA_ARGS__); \ } \ else if (std::is_same::value) { \ - DISPATCH_KERNEL_na1d_in_cuda_naive_bfloat16(kernel_size, dilation, __VA_ARGS__); \ + DISPATCH_CM_na1d_in_cuda_naive_bfloat16(is_causal, __VA_ARGS__); \ } \ else { \ std::cerr << "NATTEN kernel launch failed!" \ @@ -228,19 +228,19 @@ namespace naive { } \ }(); -#define DISPATCH_DTYPE_na2d_in_cuda_naive(dtype, kernel_size, dilation, ...) \ +#define DISPATCH_DTYPE_na2d_in_cuda_naive(dtype, is_causal, ...) \ [&] { \ if (std::is_same::value) { \ - DISPATCH_KERNEL_na2d_in_cuda_naive_double(kernel_size, dilation, __VA_ARGS__); \ + DISPATCH_CM_na2d_in_cuda_naive_double(is_causal, __VA_ARGS__); \ } \ else if (std::is_same::value) { \ - DISPATCH_KERNEL_na2d_in_cuda_naive_float(kernel_size, dilation, __VA_ARGS__); \ + DISPATCH_CM_na2d_in_cuda_naive_float(is_causal, __VA_ARGS__); \ } \ else if (std::is_same::value) { \ - DISPATCH_KERNEL_na2d_in_cuda_naive_half(kernel_size, dilation, __VA_ARGS__); \ + DISPATCH_CM_na2d_in_cuda_naive_half(is_causal, __VA_ARGS__); \ } \ else if (std::is_same::value) { \ - DISPATCH_KERNEL_na2d_in_cuda_naive_bfloat16(kernel_size, dilation, __VA_ARGS__); \ + DISPATCH_CM_na2d_in_cuda_naive_bfloat16(is_causal, __VA_ARGS__); \ } \ else { \ std::cerr << "NATTEN kernel launch failed!" \ @@ -250,19 +250,19 @@ namespace naive { } \ }(); -#define DISPATCH_DTYPE_na3d_in_cuda_naive(dtype, kernel_size, dilation, ...) \ +#define DISPATCH_DTYPE_na3d_in_cuda_naive(dtype, is_causal, ...) \ [&] { \ if (std::is_same::value) { \ - DISPATCH_KERNEL_na3d_in_cuda_naive_double(kernel_size, dilation, __VA_ARGS__); \ + DISPATCH_CM_na3d_in_cuda_naive_double(is_causal, __VA_ARGS__); \ } \ else if (std::is_same::value) { \ - DISPATCH_KERNEL_na3d_in_cuda_naive_float(kernel_size, dilation, __VA_ARGS__); \ + DISPATCH_CM_na3d_in_cuda_naive_float(is_causal, __VA_ARGS__); \ } \ else if (std::is_same::value) { \ - DISPATCH_KERNEL_na3d_in_cuda_naive_half(kernel_size, dilation, __VA_ARGS__); \ + DISPATCH_CM_na3d_in_cuda_naive_half(is_causal, __VA_ARGS__); \ } \ else if (std::is_same::value) { \ - DISPATCH_KERNEL_na3d_in_cuda_naive_bfloat16(kernel_size, dilation, __VA_ARGS__); \ + DISPATCH_CM_na3d_in_cuda_naive_bfloat16(is_causal, __VA_ARGS__); \ } \ else { \ std::cerr << "NATTEN kernel launch failed!" \ @@ -272,19 +272,19 @@ namespace naive { } \ }(); -#define DISPATCH_DTYPE_na1d_rpbgrad_cuda_naive(dtype, kernel_size, dilation, ...) \ +#define DISPATCH_DTYPE_na1d_rpbgrad_cuda_naive(dtype, is_causal, ...) \ [&] { \ if (std::is_same::value) { \ - DISPATCH_KERNEL_na1d_rpbgrad_cuda_naive_double(kernel_size, dilation, __VA_ARGS__); \ + DISPATCH_CM_na1d_rpbgrad_cuda_naive_double(is_causal, __VA_ARGS__); \ } \ else if (std::is_same::value) { \ - DISPATCH_KERNEL_na1d_rpbgrad_cuda_naive_float(kernel_size, dilation, __VA_ARGS__); \ + DISPATCH_CM_na1d_rpbgrad_cuda_naive_float(is_causal, __VA_ARGS__); \ } \ else if (std::is_same::value) { \ - DISPATCH_KERNEL_na1d_rpbgrad_cuda_naive_half(kernel_size, dilation, __VA_ARGS__); \ + DISPATCH_CM_na1d_rpbgrad_cuda_naive_half(is_causal, __VA_ARGS__); \ } \ else if (std::is_same::value) { \ - DISPATCH_KERNEL_na1d_rpbgrad_cuda_naive_bfloat16(kernel_size, dilation, __VA_ARGS__); \ + DISPATCH_CM_na1d_rpbgrad_cuda_naive_bfloat16(is_causal, __VA_ARGS__); \ } \ else { \ std::cerr << "NATTEN kernel launch failed!" \ @@ -294,19 +294,19 @@ namespace naive { } \ }(); -#define DISPATCH_DTYPE_na2d_rpbgrad_cuda_naive(dtype, kernel_size, dilation, ...) \ +#define DISPATCH_DTYPE_na2d_rpbgrad_cuda_naive(dtype, is_causal, ...) \ [&] { \ if (std::is_same::value) { \ - DISPATCH_KERNEL_na2d_rpbgrad_cuda_naive_double(kernel_size, dilation, __VA_ARGS__); \ + DISPATCH_CM_na2d_rpbgrad_cuda_naive_double(is_causal, __VA_ARGS__); \ } \ else if (std::is_same::value) { \ - DISPATCH_KERNEL_na2d_rpbgrad_cuda_naive_float(kernel_size, dilation, __VA_ARGS__); \ + DISPATCH_CM_na2d_rpbgrad_cuda_naive_float(is_causal, __VA_ARGS__); \ } \ else if (std::is_same::value) { \ - DISPATCH_KERNEL_na2d_rpbgrad_cuda_naive_half(kernel_size, dilation, __VA_ARGS__); \ + DISPATCH_CM_na2d_rpbgrad_cuda_naive_half(is_causal, __VA_ARGS__); \ } \ else if (std::is_same::value) { \ - DISPATCH_KERNEL_na2d_rpbgrad_cuda_naive_bfloat16(kernel_size, dilation, __VA_ARGS__); \ + DISPATCH_CM_na2d_rpbgrad_cuda_naive_bfloat16(is_causal, __VA_ARGS__); \ } \ else { \ std::cerr << "NATTEN kernel launch failed!" \ @@ -316,19 +316,19 @@ namespace naive { } \ }(); -#define DISPATCH_DTYPE_na3d_rpbgrad_cuda_naive(dtype, kernel_size, dilation, ...) \ +#define DISPATCH_DTYPE_na3d_rpbgrad_cuda_naive(dtype, is_causal, ...) \ [&] { \ if (std::is_same::value) { \ - DISPATCH_KERNEL_na3d_rpbgrad_cuda_naive_double(kernel_size, dilation, __VA_ARGS__); \ + DISPATCH_CM_na3d_rpbgrad_cuda_naive_double(is_causal, __VA_ARGS__); \ } \ else if (std::is_same::value) { \ - DISPATCH_KERNEL_na3d_rpbgrad_cuda_naive_float(kernel_size, dilation, __VA_ARGS__); \ + DISPATCH_CM_na3d_rpbgrad_cuda_naive_float(is_causal, __VA_ARGS__); \ } \ else if (std::is_same::value) { \ - DISPATCH_KERNEL_na3d_rpbgrad_cuda_naive_half(kernel_size, dilation, __VA_ARGS__); \ + DISPATCH_CM_na3d_rpbgrad_cuda_naive_half(is_causal, __VA_ARGS__); \ } \ else if (std::is_same::value) { \ - DISPATCH_KERNEL_na3d_rpbgrad_cuda_naive_bfloat16(kernel_size, dilation, __VA_ARGS__); \ + DISPATCH_CM_na3d_rpbgrad_cuda_naive_bfloat16(is_causal, __VA_ARGS__); \ } \ else { \ std::cerr << "NATTEN kernel launch failed!" \ @@ -340,7 +340,7 @@ namespace naive { -} // namespace {namespace} -} // namespace {namespace} -} // namespace {namespace} +} // namespace natten +} // namespace cuda +} // namespace naive diff --git a/csrc/autogen/include/natten_autogen/cuda/naive/kernels.h b/csrc/autogen/include/natten_autogen/cuda/naive/kernels.h index 9ce4638..06ea164 100644 --- a/csrc/autogen/include/natten_autogen/cuda/naive/kernels.h +++ b/csrc/autogen/include/natten_autogen/cuda/naive/kernels.h @@ -7,15689 +7,3665 @@ namespace natten { namespace cuda { namespace naive { -void na1d_pn_cuda_naive_double_ks_any_di_any( - const int cc, +void na1d_pn_cuda_naive_double_cm_0( + int32_t cc, cudaStream_t stream, + bool is_grad, void * query_ptr, void * key_ptr, void * attn_ptr, - int batch_size, - int heads, - int length, - int dim, + int32_t batch_size, + int32_t heads, + int32_t length, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, - int kernel_size, - int dilation); + const std::tuple& kernel_size, + const std::tuple& dilation); -void na1d_pn_cuda_naive_double_ks_any_di_1( - const int cc, +void na1d_pn_cuda_naive_double_cm_1( + int32_t cc, cudaStream_t stream, + bool is_grad, void * query_ptr, void * key_ptr, void * attn_ptr, - int batch_size, - int heads, - int length, - int dim, + int32_t batch_size, + int32_t heads, + int32_t length, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, - int kernel_size, - int dilation); + const std::tuple& kernel_size, + const std::tuple& dilation); -void na1d_pn_cuda_naive_double_ks_3_di_any( - const int cc, +void na1d_pn_cuda_naive_float_cm_0( + int32_t cc, cudaStream_t stream, + bool is_grad, void * query_ptr, void * key_ptr, void * attn_ptr, - int batch_size, - int heads, - int length, - int dim, + int32_t batch_size, + int32_t heads, + int32_t length, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, - int kernel_size, - int dilation); + const std::tuple& kernel_size, + const std::tuple& dilation); -void na1d_pn_cuda_naive_double_ks_3_di_1( - const int cc, +void na1d_pn_cuda_naive_float_cm_1( + int32_t cc, cudaStream_t stream, + bool is_grad, void * query_ptr, void * key_ptr, void * attn_ptr, - int batch_size, - int heads, - int length, - int dim, + int32_t batch_size, + int32_t heads, + int32_t length, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, - int kernel_size, - int dilation); + const std::tuple& kernel_size, + const std::tuple& dilation); -void na1d_pn_cuda_naive_double_ks_5_di_any( - const int cc, +void na1d_pn_cuda_naive_half_cm_0( + int32_t cc, cudaStream_t stream, + bool is_grad, void * query_ptr, void * key_ptr, void * attn_ptr, - int batch_size, - int heads, - int length, - int dim, + int32_t batch_size, + int32_t heads, + int32_t length, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, - int kernel_size, - int dilation); + const std::tuple& kernel_size, + const std::tuple& dilation); -void na1d_pn_cuda_naive_double_ks_5_di_1( - const int cc, +void na1d_pn_cuda_naive_half_cm_1( + int32_t cc, cudaStream_t stream, + bool is_grad, void * query_ptr, void * key_ptr, void * attn_ptr, - int batch_size, - int heads, - int length, - int dim, + int32_t batch_size, + int32_t heads, + int32_t length, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, - int kernel_size, - int dilation); + const std::tuple& kernel_size, + const std::tuple& dilation); -void na1d_pn_cuda_naive_double_ks_7_di_any( - const int cc, +void na1d_pn_cuda_naive_bfloat16_cm_0( + int32_t cc, cudaStream_t stream, + bool is_grad, void * query_ptr, void * key_ptr, void * attn_ptr, - int batch_size, - int heads, - int length, - int dim, + int32_t batch_size, + int32_t heads, + int32_t length, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, - int kernel_size, - int dilation); + const std::tuple& kernel_size, + const std::tuple& dilation); -void na1d_pn_cuda_naive_double_ks_7_di_1( - const int cc, +void na1d_pn_cuda_naive_bfloat16_cm_1( + int32_t cc, cudaStream_t stream, + bool is_grad, void * query_ptr, void * key_ptr, void * attn_ptr, - int batch_size, - int heads, - int length, - int dim, + int32_t batch_size, + int32_t heads, + int32_t length, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, - int kernel_size, - int dilation); + const std::tuple& kernel_size, + const std::tuple& dilation); -void na1d_pn_cuda_naive_double_ks_9_di_any( - const int cc, +void na2d_pn_cuda_naive_double_cm_0_0( + int32_t cc, cudaStream_t stream, + bool is_grad, void * query_ptr, void * key_ptr, void * attn_ptr, - int batch_size, - int heads, - int length, - int dim, + int32_t batch_size, + int32_t heads, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, - int kernel_size, - int dilation); + int64_t attn_stride_3, + const std::tuple& kernel_size, + const std::tuple& dilation); -void na1d_pn_cuda_naive_double_ks_9_di_1( - const int cc, +void na2d_pn_cuda_naive_double_cm_0_1( + int32_t cc, cudaStream_t stream, + bool is_grad, void * query_ptr, void * key_ptr, void * attn_ptr, - int batch_size, - int heads, - int length, - int dim, + int32_t batch_size, + int32_t heads, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, - int kernel_size, - int dilation); + int64_t attn_stride_3, + const std::tuple& kernel_size, + const std::tuple& dilation); -void na1d_pn_cuda_naive_double_ks_11_di_any( - const int cc, +void na2d_pn_cuda_naive_double_cm_1_0( + int32_t cc, cudaStream_t stream, + bool is_grad, void * query_ptr, void * key_ptr, void * attn_ptr, - int batch_size, - int heads, - int length, - int dim, + int32_t batch_size, + int32_t heads, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, - int kernel_size, - int dilation); + int64_t attn_stride_3, + const std::tuple& kernel_size, + const std::tuple& dilation); -void na1d_pn_cuda_naive_double_ks_11_di_1( - const int cc, +void na2d_pn_cuda_naive_double_cm_1_1( + int32_t cc, cudaStream_t stream, + bool is_grad, void * query_ptr, void * key_ptr, void * attn_ptr, - int batch_size, - int heads, - int length, - int dim, + int32_t batch_size, + int32_t heads, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, - int kernel_size, - int dilation); + int64_t attn_stride_3, + const std::tuple& kernel_size, + const std::tuple& dilation); -void na1d_pn_cuda_naive_double_ks_13_di_any( - const int cc, +void na2d_pn_cuda_naive_float_cm_0_0( + int32_t cc, cudaStream_t stream, + bool is_grad, void * query_ptr, void * key_ptr, void * attn_ptr, - int batch_size, - int heads, - int length, - int dim, + int32_t batch_size, + int32_t heads, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, - int kernel_size, - int dilation); + int64_t attn_stride_3, + const std::tuple& kernel_size, + const std::tuple& dilation); -void na1d_pn_cuda_naive_double_ks_13_di_1( - const int cc, +void na2d_pn_cuda_naive_float_cm_0_1( + int32_t cc, cudaStream_t stream, + bool is_grad, void * query_ptr, void * key_ptr, void * attn_ptr, - int batch_size, - int heads, - int length, - int dim, + int32_t batch_size, + int32_t heads, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, - int kernel_size, - int dilation); + int64_t attn_stride_3, + const std::tuple& kernel_size, + const std::tuple& dilation); -void na1d_pn_cuda_naive_float_ks_any_di_any( - const int cc, +void na2d_pn_cuda_naive_float_cm_1_0( + int32_t cc, cudaStream_t stream, + bool is_grad, void * query_ptr, void * key_ptr, void * attn_ptr, - int batch_size, - int heads, - int length, - int dim, + int32_t batch_size, + int32_t heads, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, - int kernel_size, - int dilation); + int64_t attn_stride_3, + const std::tuple& kernel_size, + const std::tuple& dilation); -void na1d_pn_cuda_naive_float_ks_any_di_1( - const int cc, +void na2d_pn_cuda_naive_float_cm_1_1( + int32_t cc, cudaStream_t stream, + bool is_grad, void * query_ptr, void * key_ptr, void * attn_ptr, - int batch_size, - int heads, - int length, - int dim, + int32_t batch_size, + int32_t heads, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, - int kernel_size, - int dilation); + int64_t attn_stride_3, + const std::tuple& kernel_size, + const std::tuple& dilation); -void na1d_pn_cuda_naive_float_ks_3_di_any( - const int cc, +void na2d_pn_cuda_naive_half_cm_0_0( + int32_t cc, cudaStream_t stream, + bool is_grad, void * query_ptr, void * key_ptr, void * attn_ptr, - int batch_size, - int heads, - int length, - int dim, + int32_t batch_size, + int32_t heads, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, - int kernel_size, - int dilation); + int64_t attn_stride_3, + const std::tuple& kernel_size, + const std::tuple& dilation); -void na1d_pn_cuda_naive_float_ks_3_di_1( - const int cc, +void na2d_pn_cuda_naive_half_cm_0_1( + int32_t cc, cudaStream_t stream, + bool is_grad, void * query_ptr, void * key_ptr, void * attn_ptr, - int batch_size, - int heads, - int length, - int dim, + int32_t batch_size, + int32_t heads, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, - int kernel_size, - int dilation); + int64_t attn_stride_3, + const std::tuple& kernel_size, + const std::tuple& dilation); -void na1d_pn_cuda_naive_float_ks_5_di_any( - const int cc, +void na2d_pn_cuda_naive_half_cm_1_0( + int32_t cc, cudaStream_t stream, + bool is_grad, void * query_ptr, void * key_ptr, void * attn_ptr, - int batch_size, - int heads, - int length, - int dim, + int32_t batch_size, + int32_t heads, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, - int kernel_size, - int dilation); + int64_t attn_stride_3, + const std::tuple& kernel_size, + const std::tuple& dilation); -void na1d_pn_cuda_naive_float_ks_5_di_1( - const int cc, +void na2d_pn_cuda_naive_half_cm_1_1( + int32_t cc, cudaStream_t stream, + bool is_grad, void * query_ptr, void * key_ptr, void * attn_ptr, - int batch_size, - int heads, - int length, - int dim, + int32_t batch_size, + int32_t heads, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, - int kernel_size, - int dilation); + int64_t attn_stride_3, + const std::tuple& kernel_size, + const std::tuple& dilation); -void na1d_pn_cuda_naive_float_ks_7_di_any( - const int cc, +void na2d_pn_cuda_naive_bfloat16_cm_0_0( + int32_t cc, cudaStream_t stream, + bool is_grad, void * query_ptr, void * key_ptr, void * attn_ptr, - int batch_size, - int heads, - int length, - int dim, + int32_t batch_size, + int32_t heads, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, - int kernel_size, - int dilation); + int64_t attn_stride_3, + const std::tuple& kernel_size, + const std::tuple& dilation); -void na1d_pn_cuda_naive_float_ks_7_di_1( - const int cc, +void na2d_pn_cuda_naive_bfloat16_cm_0_1( + int32_t cc, cudaStream_t stream, + bool is_grad, void * query_ptr, void * key_ptr, void * attn_ptr, - int batch_size, - int heads, - int length, - int dim, + int32_t batch_size, + int32_t heads, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, - int kernel_size, - int dilation); + int64_t attn_stride_3, + const std::tuple& kernel_size, + const std::tuple& dilation); -void na1d_pn_cuda_naive_float_ks_9_di_any( - const int cc, +void na2d_pn_cuda_naive_bfloat16_cm_1_0( + int32_t cc, cudaStream_t stream, + bool is_grad, void * query_ptr, void * key_ptr, void * attn_ptr, - int batch_size, - int heads, - int length, - int dim, + int32_t batch_size, + int32_t heads, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, - int kernel_size, - int dilation); + int64_t attn_stride_3, + const std::tuple& kernel_size, + const std::tuple& dilation); -void na1d_pn_cuda_naive_float_ks_9_di_1( - const int cc, +void na2d_pn_cuda_naive_bfloat16_cm_1_1( + int32_t cc, cudaStream_t stream, + bool is_grad, void * query_ptr, void * key_ptr, void * attn_ptr, - int batch_size, - int heads, - int length, - int dim, + int32_t batch_size, + int32_t heads, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, - int kernel_size, - int dilation); + int64_t attn_stride_3, + const std::tuple& kernel_size, + const std::tuple& dilation); -void na1d_pn_cuda_naive_float_ks_11_di_any( - const int cc, +void na3d_pn_cuda_naive_double_cm_0_0_0( + int32_t cc, cudaStream_t stream, + bool is_grad, void * query_ptr, void * key_ptr, void * attn_ptr, - int batch_size, - int heads, - int length, - int dim, + int32_t batch_size, + int32_t heads, + int32_t depth, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, - int kernel_size, - int dilation); + int64_t attn_stride_3, + int64_t attn_stride_4, + const std::tuple& kernel_size, + const std::tuple& dilation); -void na1d_pn_cuda_naive_float_ks_11_di_1( - const int cc, +void na3d_pn_cuda_naive_double_cm_0_0_1( + int32_t cc, cudaStream_t stream, + bool is_grad, void * query_ptr, void * key_ptr, void * attn_ptr, - int batch_size, - int heads, - int length, - int dim, + int32_t batch_size, + int32_t heads, + int32_t depth, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, - int kernel_size, - int dilation); + int64_t attn_stride_3, + int64_t attn_stride_4, + const std::tuple& kernel_size, + const std::tuple& dilation); -void na1d_pn_cuda_naive_float_ks_13_di_any( - const int cc, +void na3d_pn_cuda_naive_double_cm_0_1_0( + int32_t cc, cudaStream_t stream, + bool is_grad, void * query_ptr, void * key_ptr, void * attn_ptr, - int batch_size, - int heads, - int length, - int dim, + int32_t batch_size, + int32_t heads, + int32_t depth, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, - int kernel_size, - int dilation); + int64_t attn_stride_3, + int64_t attn_stride_4, + const std::tuple& kernel_size, + const std::tuple& dilation); -void na1d_pn_cuda_naive_float_ks_13_di_1( - const int cc, +void na3d_pn_cuda_naive_double_cm_0_1_1( + int32_t cc, cudaStream_t stream, + bool is_grad, void * query_ptr, void * key_ptr, void * attn_ptr, - int batch_size, - int heads, - int length, - int dim, + int32_t batch_size, + int32_t heads, + int32_t depth, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, - int kernel_size, - int dilation); + int64_t attn_stride_3, + int64_t attn_stride_4, + const std::tuple& kernel_size, + const std::tuple& dilation); -void na1d_pn_cuda_naive_half_ks_any_di_any( - const int cc, +void na3d_pn_cuda_naive_double_cm_1_0_0( + int32_t cc, cudaStream_t stream, + bool is_grad, void * query_ptr, void * key_ptr, void * attn_ptr, - int batch_size, - int heads, - int length, - int dim, + int32_t batch_size, + int32_t heads, + int32_t depth, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, - int kernel_size, - int dilation); + int64_t attn_stride_3, + int64_t attn_stride_4, + const std::tuple& kernel_size, + const std::tuple& dilation); -void na1d_pn_cuda_naive_half_ks_any_di_1( - const int cc, +void na3d_pn_cuda_naive_double_cm_1_0_1( + int32_t cc, cudaStream_t stream, + bool is_grad, void * query_ptr, void * key_ptr, void * attn_ptr, - int batch_size, - int heads, - int length, - int dim, + int32_t batch_size, + int32_t heads, + int32_t depth, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, - int kernel_size, - int dilation); + int64_t attn_stride_3, + int64_t attn_stride_4, + const std::tuple& kernel_size, + const std::tuple& dilation); -void na1d_pn_cuda_naive_half_ks_3_di_any( - const int cc, +void na3d_pn_cuda_naive_double_cm_1_1_0( + int32_t cc, cudaStream_t stream, + bool is_grad, void * query_ptr, void * key_ptr, void * attn_ptr, - int batch_size, - int heads, - int length, - int dim, + int32_t batch_size, + int32_t heads, + int32_t depth, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, - int kernel_size, - int dilation); + int64_t attn_stride_3, + int64_t attn_stride_4, + const std::tuple& kernel_size, + const std::tuple& dilation); -void na1d_pn_cuda_naive_half_ks_3_di_1( - const int cc, +void na3d_pn_cuda_naive_double_cm_1_1_1( + int32_t cc, cudaStream_t stream, + bool is_grad, void * query_ptr, void * key_ptr, void * attn_ptr, - int batch_size, - int heads, - int length, - int dim, + int32_t batch_size, + int32_t heads, + int32_t depth, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, - int kernel_size, - int dilation); + int64_t attn_stride_3, + int64_t attn_stride_4, + const std::tuple& kernel_size, + const std::tuple& dilation); -void na1d_pn_cuda_naive_half_ks_5_di_any( - const int cc, +void na3d_pn_cuda_naive_float_cm_0_0_0( + int32_t cc, cudaStream_t stream, + bool is_grad, void * query_ptr, void * key_ptr, void * attn_ptr, - int batch_size, - int heads, - int length, - int dim, + int32_t batch_size, + int32_t heads, + int32_t depth, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, - int kernel_size, - int dilation); + int64_t attn_stride_3, + int64_t attn_stride_4, + const std::tuple& kernel_size, + const std::tuple& dilation); -void na1d_pn_cuda_naive_half_ks_5_di_1( - const int cc, +void na3d_pn_cuda_naive_float_cm_0_0_1( + int32_t cc, cudaStream_t stream, + bool is_grad, void * query_ptr, void * key_ptr, void * attn_ptr, - int batch_size, - int heads, - int length, - int dim, + int32_t batch_size, + int32_t heads, + int32_t depth, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, - int kernel_size, - int dilation); + int64_t attn_stride_3, + int64_t attn_stride_4, + const std::tuple& kernel_size, + const std::tuple& dilation); -void na1d_pn_cuda_naive_half_ks_7_di_any( - const int cc, +void na3d_pn_cuda_naive_float_cm_0_1_0( + int32_t cc, cudaStream_t stream, + bool is_grad, void * query_ptr, void * key_ptr, void * attn_ptr, - int batch_size, - int heads, - int length, - int dim, + int32_t batch_size, + int32_t heads, + int32_t depth, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, - int kernel_size, - int dilation); + int64_t attn_stride_3, + int64_t attn_stride_4, + const std::tuple& kernel_size, + const std::tuple& dilation); -void na1d_pn_cuda_naive_half_ks_7_di_1( - const int cc, +void na3d_pn_cuda_naive_float_cm_0_1_1( + int32_t cc, cudaStream_t stream, + bool is_grad, void * query_ptr, void * key_ptr, void * attn_ptr, - int batch_size, - int heads, - int length, - int dim, + int32_t batch_size, + int32_t heads, + int32_t depth, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, - int kernel_size, - int dilation); + int64_t attn_stride_3, + int64_t attn_stride_4, + const std::tuple& kernel_size, + const std::tuple& dilation); -void na1d_pn_cuda_naive_half_ks_9_di_any( - const int cc, +void na3d_pn_cuda_naive_float_cm_1_0_0( + int32_t cc, cudaStream_t stream, + bool is_grad, void * query_ptr, void * key_ptr, void * attn_ptr, - int batch_size, - int heads, - int length, - int dim, + int32_t batch_size, + int32_t heads, + int32_t depth, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, - int kernel_size, - int dilation); + int64_t attn_stride_3, + int64_t attn_stride_4, + const std::tuple& kernel_size, + const std::tuple& dilation); -void na1d_pn_cuda_naive_half_ks_9_di_1( - const int cc, +void na3d_pn_cuda_naive_float_cm_1_0_1( + int32_t cc, cudaStream_t stream, + bool is_grad, void * query_ptr, void * key_ptr, void * attn_ptr, - int batch_size, - int heads, - int length, - int dim, + int32_t batch_size, + int32_t heads, + int32_t depth, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, - int kernel_size, - int dilation); + int64_t attn_stride_3, + int64_t attn_stride_4, + const std::tuple& kernel_size, + const std::tuple& dilation); -void na1d_pn_cuda_naive_half_ks_11_di_any( - const int cc, +void na3d_pn_cuda_naive_float_cm_1_1_0( + int32_t cc, cudaStream_t stream, + bool is_grad, void * query_ptr, void * key_ptr, void * attn_ptr, - int batch_size, - int heads, - int length, - int dim, + int32_t batch_size, + int32_t heads, + int32_t depth, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, - int kernel_size, - int dilation); + int64_t attn_stride_3, + int64_t attn_stride_4, + const std::tuple& kernel_size, + const std::tuple& dilation); -void na1d_pn_cuda_naive_half_ks_11_di_1( - const int cc, +void na3d_pn_cuda_naive_float_cm_1_1_1( + int32_t cc, cudaStream_t stream, + bool is_grad, void * query_ptr, void * key_ptr, void * attn_ptr, - int batch_size, - int heads, - int length, - int dim, + int32_t batch_size, + int32_t heads, + int32_t depth, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, - int kernel_size, - int dilation); + int64_t attn_stride_3, + int64_t attn_stride_4, + const std::tuple& kernel_size, + const std::tuple& dilation); -void na1d_pn_cuda_naive_half_ks_13_di_any( - const int cc, +void na3d_pn_cuda_naive_half_cm_0_0_0( + int32_t cc, cudaStream_t stream, + bool is_grad, void * query_ptr, void * key_ptr, void * attn_ptr, - int batch_size, - int heads, - int length, - int dim, + int32_t batch_size, + int32_t heads, + int32_t depth, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, - int kernel_size, - int dilation); + int64_t attn_stride_3, + int64_t attn_stride_4, + const std::tuple& kernel_size, + const std::tuple& dilation); -void na1d_pn_cuda_naive_half_ks_13_di_1( - const int cc, +void na3d_pn_cuda_naive_half_cm_0_0_1( + int32_t cc, cudaStream_t stream, + bool is_grad, void * query_ptr, void * key_ptr, void * attn_ptr, - int batch_size, - int heads, - int length, - int dim, + int32_t batch_size, + int32_t heads, + int32_t depth, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, - int kernel_size, - int dilation); + int64_t attn_stride_3, + int64_t attn_stride_4, + const std::tuple& kernel_size, + const std::tuple& dilation); -void na1d_pn_cuda_naive_bfloat16_ks_any_di_any( - const int cc, +void na3d_pn_cuda_naive_half_cm_0_1_0( + int32_t cc, cudaStream_t stream, + bool is_grad, void * query_ptr, void * key_ptr, void * attn_ptr, - int batch_size, - int heads, - int length, - int dim, + int32_t batch_size, + int32_t heads, + int32_t depth, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, - int kernel_size, - int dilation); + int64_t attn_stride_3, + int64_t attn_stride_4, + const std::tuple& kernel_size, + const std::tuple& dilation); -void na1d_pn_cuda_naive_bfloat16_ks_any_di_1( - const int cc, +void na3d_pn_cuda_naive_half_cm_0_1_1( + int32_t cc, cudaStream_t stream, + bool is_grad, void * query_ptr, void * key_ptr, void * attn_ptr, - int batch_size, - int heads, - int length, - int dim, + int32_t batch_size, + int32_t heads, + int32_t depth, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, - int kernel_size, - int dilation); + int64_t attn_stride_3, + int64_t attn_stride_4, + const std::tuple& kernel_size, + const std::tuple& dilation); -void na1d_pn_cuda_naive_bfloat16_ks_3_di_any( - const int cc, +void na3d_pn_cuda_naive_half_cm_1_0_0( + int32_t cc, cudaStream_t stream, + bool is_grad, void * query_ptr, void * key_ptr, void * attn_ptr, - int batch_size, - int heads, - int length, - int dim, + int32_t batch_size, + int32_t heads, + int32_t depth, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, - int kernel_size, - int dilation); + int64_t attn_stride_3, + int64_t attn_stride_4, + const std::tuple& kernel_size, + const std::tuple& dilation); -void na1d_pn_cuda_naive_bfloat16_ks_3_di_1( - const int cc, +void na3d_pn_cuda_naive_half_cm_1_0_1( + int32_t cc, cudaStream_t stream, + bool is_grad, void * query_ptr, void * key_ptr, void * attn_ptr, - int batch_size, - int heads, - int length, - int dim, + int32_t batch_size, + int32_t heads, + int32_t depth, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, - int kernel_size, - int dilation); + int64_t attn_stride_3, + int64_t attn_stride_4, + const std::tuple& kernel_size, + const std::tuple& dilation); -void na1d_pn_cuda_naive_bfloat16_ks_5_di_any( - const int cc, +void na3d_pn_cuda_naive_half_cm_1_1_0( + int32_t cc, cudaStream_t stream, + bool is_grad, void * query_ptr, void * key_ptr, void * attn_ptr, - int batch_size, - int heads, - int length, - int dim, + int32_t batch_size, + int32_t heads, + int32_t depth, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, - int kernel_size, - int dilation); + int64_t attn_stride_3, + int64_t attn_stride_4, + const std::tuple& kernel_size, + const std::tuple& dilation); -void na1d_pn_cuda_naive_bfloat16_ks_5_di_1( - const int cc, +void na3d_pn_cuda_naive_half_cm_1_1_1( + int32_t cc, cudaStream_t stream, + bool is_grad, void * query_ptr, void * key_ptr, void * attn_ptr, - int batch_size, - int heads, - int length, - int dim, + int32_t batch_size, + int32_t heads, + int32_t depth, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, - int kernel_size, - int dilation); + int64_t attn_stride_3, + int64_t attn_stride_4, + const std::tuple& kernel_size, + const std::tuple& dilation); -void na1d_pn_cuda_naive_bfloat16_ks_7_di_any( - const int cc, +void na3d_pn_cuda_naive_bfloat16_cm_0_0_0( + int32_t cc, cudaStream_t stream, + bool is_grad, void * query_ptr, void * key_ptr, void * attn_ptr, - int batch_size, - int heads, - int length, - int dim, + int32_t batch_size, + int32_t heads, + int32_t depth, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, - int kernel_size, - int dilation); + int64_t attn_stride_3, + int64_t attn_stride_4, + const std::tuple& kernel_size, + const std::tuple& dilation); -void na1d_pn_cuda_naive_bfloat16_ks_7_di_1( - const int cc, +void na3d_pn_cuda_naive_bfloat16_cm_0_0_1( + int32_t cc, cudaStream_t stream, + bool is_grad, void * query_ptr, void * key_ptr, void * attn_ptr, - int batch_size, - int heads, - int length, - int dim, + int32_t batch_size, + int32_t heads, + int32_t depth, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, - int kernel_size, - int dilation); + int64_t attn_stride_3, + int64_t attn_stride_4, + const std::tuple& kernel_size, + const std::tuple& dilation); -void na1d_pn_cuda_naive_bfloat16_ks_9_di_any( - const int cc, +void na3d_pn_cuda_naive_bfloat16_cm_0_1_0( + int32_t cc, cudaStream_t stream, + bool is_grad, void * query_ptr, void * key_ptr, void * attn_ptr, - int batch_size, - int heads, - int length, - int dim, + int32_t batch_size, + int32_t heads, + int32_t depth, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, - int kernel_size, - int dilation); + int64_t attn_stride_3, + int64_t attn_stride_4, + const std::tuple& kernel_size, + const std::tuple& dilation); -void na1d_pn_cuda_naive_bfloat16_ks_9_di_1( - const int cc, +void na3d_pn_cuda_naive_bfloat16_cm_0_1_1( + int32_t cc, cudaStream_t stream, + bool is_grad, void * query_ptr, void * key_ptr, void * attn_ptr, - int batch_size, - int heads, - int length, - int dim, + int32_t batch_size, + int32_t heads, + int32_t depth, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, - int kernel_size, - int dilation); + int64_t attn_stride_3, + int64_t attn_stride_4, + const std::tuple& kernel_size, + const std::tuple& dilation); -void na1d_pn_cuda_naive_bfloat16_ks_11_di_any( - const int cc, +void na3d_pn_cuda_naive_bfloat16_cm_1_0_0( + int32_t cc, cudaStream_t stream, + bool is_grad, void * query_ptr, void * key_ptr, void * attn_ptr, - int batch_size, - int heads, - int length, - int dim, + int32_t batch_size, + int32_t heads, + int32_t depth, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, - int kernel_size, - int dilation); + int64_t attn_stride_3, + int64_t attn_stride_4, + const std::tuple& kernel_size, + const std::tuple& dilation); -void na1d_pn_cuda_naive_bfloat16_ks_11_di_1( - const int cc, +void na3d_pn_cuda_naive_bfloat16_cm_1_0_1( + int32_t cc, cudaStream_t stream, + bool is_grad, void * query_ptr, void * key_ptr, void * attn_ptr, - int batch_size, - int heads, - int length, - int dim, + int32_t batch_size, + int32_t heads, + int32_t depth, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, - int kernel_size, - int dilation); + int64_t attn_stride_3, + int64_t attn_stride_4, + const std::tuple& kernel_size, + const std::tuple& dilation); -void na1d_pn_cuda_naive_bfloat16_ks_13_di_any( - const int cc, +void na3d_pn_cuda_naive_bfloat16_cm_1_1_0( + int32_t cc, cudaStream_t stream, + bool is_grad, void * query_ptr, void * key_ptr, void * attn_ptr, - int batch_size, - int heads, - int length, - int dim, + int32_t batch_size, + int32_t heads, + int32_t depth, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, - int kernel_size, - int dilation); + int64_t attn_stride_3, + int64_t attn_stride_4, + const std::tuple& kernel_size, + const std::tuple& dilation); -void na1d_pn_cuda_naive_bfloat16_ks_13_di_1( - const int cc, +void na3d_pn_cuda_naive_bfloat16_cm_1_1_1( + int32_t cc, cudaStream_t stream, + bool is_grad, void * query_ptr, void * key_ptr, void * attn_ptr, - int batch_size, - int heads, - int length, - int dim, + int32_t batch_size, + int32_t heads, + int32_t depth, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, - int kernel_size, - int dilation); + int64_t attn_stride_3, + int64_t attn_stride_4, + const std::tuple& kernel_size, + const std::tuple& dilation); -void na2d_pn_cuda_naive_double_ks_any_di_any( - const int cc, +void na1d_pn_bias_cuda_naive_double_cm_0( + int32_t cc, cudaStream_t stream, void * query_ptr, void * key_ptr, + void * bias_ptr, void * attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, + int32_t batch_size, + int32_t heads, + int32_t length, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation); + const std::tuple& kernel_size, + const std::tuple& dilation); -void na2d_pn_cuda_naive_double_ks_any_di_1( - const int cc, +void na1d_pn_bias_cuda_naive_float_cm_0( + int32_t cc, cudaStream_t stream, void * query_ptr, void * key_ptr, + void * bias_ptr, void * attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, + int32_t batch_size, + int32_t heads, + int32_t length, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation); + const std::tuple& kernel_size, + const std::tuple& dilation); -void na2d_pn_cuda_naive_double_ks_3_di_any( - const int cc, +void na1d_pn_bias_cuda_naive_half_cm_0( + int32_t cc, cudaStream_t stream, void * query_ptr, void * key_ptr, + void * bias_ptr, void * attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, + int32_t batch_size, + int32_t heads, + int32_t length, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation); + const std::tuple& kernel_size, + const std::tuple& dilation); -void na2d_pn_cuda_naive_double_ks_3_di_1( - const int cc, +void na1d_pn_bias_cuda_naive_bfloat16_cm_0( + int32_t cc, cudaStream_t stream, void * query_ptr, void * key_ptr, + void * bias_ptr, void * attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, + int32_t batch_size, + int32_t heads, + int32_t length, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation); + const std::tuple& kernel_size, + const std::tuple& dilation); -void na2d_pn_cuda_naive_double_ks_5_di_any( - const int cc, +void na2d_pn_bias_cuda_naive_double_cm_0_0( + int32_t cc, cudaStream_t stream, void * query_ptr, void * key_ptr, + void * bias_ptr, void * attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, + int32_t batch_size, + int32_t heads, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, - int kernel_size, - int dilation); + const std::tuple& kernel_size, + const std::tuple& dilation); -void na2d_pn_cuda_naive_double_ks_5_di_1( - const int cc, +void na2d_pn_bias_cuda_naive_float_cm_0_0( + int32_t cc, cudaStream_t stream, void * query_ptr, void * key_ptr, + void * bias_ptr, void * attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, + int32_t batch_size, + int32_t heads, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, - int kernel_size, - int dilation); + const std::tuple& kernel_size, + const std::tuple& dilation); -void na2d_pn_cuda_naive_double_ks_7_di_any( - const int cc, +void na2d_pn_bias_cuda_naive_half_cm_0_0( + int32_t cc, cudaStream_t stream, void * query_ptr, void * key_ptr, + void * bias_ptr, void * attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, + int32_t batch_size, + int32_t heads, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, - int kernel_size, - int dilation); + const std::tuple& kernel_size, + const std::tuple& dilation); -void na2d_pn_cuda_naive_double_ks_7_di_1( - const int cc, +void na2d_pn_bias_cuda_naive_bfloat16_cm_0_0( + int32_t cc, cudaStream_t stream, void * query_ptr, void * key_ptr, + void * bias_ptr, void * attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, + int32_t batch_size, + int32_t heads, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, - int kernel_size, - int dilation); + const std::tuple& kernel_size, + const std::tuple& dilation); -void na2d_pn_cuda_naive_double_ks_9_di_any( - const int cc, +void na3d_pn_bias_cuda_naive_double_cm_0_0_0( + int32_t cc, cudaStream_t stream, void * query_ptr, void * key_ptr, + void * bias_ptr, void * attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, + int32_t batch_size, + int32_t heads, + int32_t depth, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, - int kernel_size, - int dilation); + int64_t attn_stride_4, + const std::tuple& kernel_size, + const std::tuple& dilation); -void na2d_pn_cuda_naive_double_ks_9_di_1( - const int cc, +void na3d_pn_bias_cuda_naive_float_cm_0_0_0( + int32_t cc, cudaStream_t stream, void * query_ptr, void * key_ptr, + void * bias_ptr, void * attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, + int32_t batch_size, + int32_t heads, + int32_t depth, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, - int kernel_size, - int dilation); + int64_t attn_stride_4, + const std::tuple& kernel_size, + const std::tuple& dilation); -void na2d_pn_cuda_naive_double_ks_11_di_any( - const int cc, +void na3d_pn_bias_cuda_naive_half_cm_0_0_0( + int32_t cc, cudaStream_t stream, void * query_ptr, void * key_ptr, + void * bias_ptr, void * attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, + int32_t batch_size, + int32_t heads, + int32_t depth, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, - int kernel_size, - int dilation); + int64_t attn_stride_4, + const std::tuple& kernel_size, + const std::tuple& dilation); -void na2d_pn_cuda_naive_double_ks_11_di_1( - const int cc, +void na3d_pn_bias_cuda_naive_bfloat16_cm_0_0_0( + int32_t cc, cudaStream_t stream, void * query_ptr, void * key_ptr, + void * bias_ptr, void * attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, + int32_t batch_size, + int32_t heads, + int32_t depth, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, - int kernel_size, - int dilation); + int64_t attn_stride_4, + const std::tuple& kernel_size, + const std::tuple& dilation); -void na2d_pn_cuda_naive_double_ks_13_di_any( - const int cc, +void na1d_nn_cuda_naive_double_cm_0( + int32_t cc, cudaStream_t stream, - void * query_ptr, - void * key_ptr, void * attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, + void * value_ptr, + void * output_ptr, + int32_t batch_size, + int32_t heads, + int32_t length, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation); + const std::tuple& kernel_size, + const std::tuple& dilation); -void na2d_pn_cuda_naive_double_ks_13_di_1( - const int cc, +void na1d_nn_cuda_naive_double_cm_1( + int32_t cc, cudaStream_t stream, - void * query_ptr, - void * key_ptr, void * attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, + void * value_ptr, + void * output_ptr, + int32_t batch_size, + int32_t heads, + int32_t length, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation); + const std::tuple& kernel_size, + const std::tuple& dilation); -void na2d_pn_cuda_naive_float_ks_any_di_any( - const int cc, +void na1d_nn_cuda_naive_float_cm_0( + int32_t cc, cudaStream_t stream, - void * query_ptr, - void * key_ptr, void * attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, + void * value_ptr, + void * output_ptr, + int32_t batch_size, + int32_t heads, + int32_t length, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation); + const std::tuple& kernel_size, + const std::tuple& dilation); -void na2d_pn_cuda_naive_float_ks_any_di_1( - const int cc, +void na1d_nn_cuda_naive_float_cm_1( + int32_t cc, cudaStream_t stream, - void * query_ptr, - void * key_ptr, void * attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, + void * value_ptr, + void * output_ptr, + int32_t batch_size, + int32_t heads, + int32_t length, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation); + const std::tuple& kernel_size, + const std::tuple& dilation); -void na2d_pn_cuda_naive_float_ks_3_di_any( - const int cc, +void na1d_nn_cuda_naive_half_cm_0( + int32_t cc, cudaStream_t stream, - void * query_ptr, - void * key_ptr, void * attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, + void * value_ptr, + void * output_ptr, + int32_t batch_size, + int32_t heads, + int32_t length, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation); + const std::tuple& kernel_size, + const std::tuple& dilation); -void na2d_pn_cuda_naive_float_ks_3_di_1( - const int cc, +void na1d_nn_cuda_naive_half_cm_1( + int32_t cc, cudaStream_t stream, - void * query_ptr, - void * key_ptr, void * attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, + void * value_ptr, + void * output_ptr, + int32_t batch_size, + int32_t heads, + int32_t length, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation); + const std::tuple& kernel_size, + const std::tuple& dilation); -void na2d_pn_cuda_naive_float_ks_5_di_any( - const int cc, +void na1d_nn_cuda_naive_bfloat16_cm_0( + int32_t cc, cudaStream_t stream, - void * query_ptr, - void * key_ptr, void * attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, + void * value_ptr, + void * output_ptr, + int32_t batch_size, + int32_t heads, + int32_t length, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation); + const std::tuple& kernel_size, + const std::tuple& dilation); -void na2d_pn_cuda_naive_float_ks_5_di_1( - const int cc, +void na1d_nn_cuda_naive_bfloat16_cm_1( + int32_t cc, cudaStream_t stream, - void * query_ptr, - void * key_ptr, void * attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, + void * value_ptr, + void * output_ptr, + int32_t batch_size, + int32_t heads, + int32_t length, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation); + const std::tuple& kernel_size, + const std::tuple& dilation); -void na2d_pn_cuda_naive_float_ks_7_di_any( - const int cc, +void na2d_nn_cuda_naive_double_cm_0_0( + int32_t cc, cudaStream_t stream, - void * query_ptr, - void * key_ptr, void * attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, + void * value_ptr, + void * output_ptr, + int32_t batch_size, + int32_t heads, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, - int kernel_size, - int dilation); + const std::tuple& kernel_size, + const std::tuple& dilation); -void na2d_pn_cuda_naive_float_ks_7_di_1( - const int cc, +void na2d_nn_cuda_naive_double_cm_0_1( + int32_t cc, cudaStream_t stream, - void * query_ptr, - void * key_ptr, void * attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, + void * value_ptr, + void * output_ptr, + int32_t batch_size, + int32_t heads, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, - int kernel_size, - int dilation); + const std::tuple& kernel_size, + const std::tuple& dilation); -void na2d_pn_cuda_naive_float_ks_9_di_any( - const int cc, +void na2d_nn_cuda_naive_double_cm_1_0( + int32_t cc, cudaStream_t stream, - void * query_ptr, - void * key_ptr, void * attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, + void * value_ptr, + void * output_ptr, + int32_t batch_size, + int32_t heads, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, - int kernel_size, - int dilation); + const std::tuple& kernel_size, + const std::tuple& dilation); -void na2d_pn_cuda_naive_float_ks_9_di_1( - const int cc, +void na2d_nn_cuda_naive_double_cm_1_1( + int32_t cc, cudaStream_t stream, - void * query_ptr, - void * key_ptr, void * attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, + void * value_ptr, + void * output_ptr, + int32_t batch_size, + int32_t heads, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, - int kernel_size, - int dilation); + const std::tuple& kernel_size, + const std::tuple& dilation); -void na2d_pn_cuda_naive_float_ks_11_di_any( - const int cc, +void na2d_nn_cuda_naive_float_cm_0_0( + int32_t cc, cudaStream_t stream, - void * query_ptr, - void * key_ptr, void * attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, + void * value_ptr, + void * output_ptr, + int32_t batch_size, + int32_t heads, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, - int kernel_size, - int dilation); + const std::tuple& kernel_size, + const std::tuple& dilation); -void na2d_pn_cuda_naive_float_ks_11_di_1( - const int cc, +void na2d_nn_cuda_naive_float_cm_0_1( + int32_t cc, cudaStream_t stream, - void * query_ptr, - void * key_ptr, void * attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, + void * value_ptr, + void * output_ptr, + int32_t batch_size, + int32_t heads, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, - int kernel_size, - int dilation); + const std::tuple& kernel_size, + const std::tuple& dilation); -void na2d_pn_cuda_naive_float_ks_13_di_any( - const int cc, +void na2d_nn_cuda_naive_float_cm_1_0( + int32_t cc, cudaStream_t stream, - void * query_ptr, - void * key_ptr, void * attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, + void * value_ptr, + void * output_ptr, + int32_t batch_size, + int32_t heads, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, - int kernel_size, - int dilation); + const std::tuple& kernel_size, + const std::tuple& dilation); -void na2d_pn_cuda_naive_float_ks_13_di_1( - const int cc, +void na2d_nn_cuda_naive_float_cm_1_1( + int32_t cc, cudaStream_t stream, - void * query_ptr, - void * key_ptr, void * attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, + void * value_ptr, + void * output_ptr, + int32_t batch_size, + int32_t heads, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, - int kernel_size, - int dilation); + const std::tuple& kernel_size, + const std::tuple& dilation); -void na2d_pn_cuda_naive_half_ks_any_di_any( - const int cc, +void na2d_nn_cuda_naive_half_cm_0_0( + int32_t cc, cudaStream_t stream, - void * query_ptr, - void * key_ptr, void * attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, + void * value_ptr, + void * output_ptr, + int32_t batch_size, + int32_t heads, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, - int kernel_size, - int dilation); + const std::tuple& kernel_size, + const std::tuple& dilation); -void na2d_pn_cuda_naive_half_ks_any_di_1( - const int cc, +void na2d_nn_cuda_naive_half_cm_0_1( + int32_t cc, cudaStream_t stream, - void * query_ptr, - void * key_ptr, void * attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, + void * value_ptr, + void * output_ptr, + int32_t batch_size, + int32_t heads, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, - int kernel_size, - int dilation); + const std::tuple& kernel_size, + const std::tuple& dilation); -void na2d_pn_cuda_naive_half_ks_3_di_any( - const int cc, +void na2d_nn_cuda_naive_half_cm_1_0( + int32_t cc, cudaStream_t stream, - void * query_ptr, - void * key_ptr, void * attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, + void * value_ptr, + void * output_ptr, + int32_t batch_size, + int32_t heads, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, - int kernel_size, - int dilation); + const std::tuple& kernel_size, + const std::tuple& dilation); -void na2d_pn_cuda_naive_half_ks_3_di_1( - const int cc, +void na2d_nn_cuda_naive_half_cm_1_1( + int32_t cc, cudaStream_t stream, - void * query_ptr, - void * key_ptr, void * attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, + void * value_ptr, + void * output_ptr, + int32_t batch_size, + int32_t heads, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, - int kernel_size, - int dilation); + const std::tuple& kernel_size, + const std::tuple& dilation); -void na2d_pn_cuda_naive_half_ks_5_di_any( - const int cc, +void na2d_nn_cuda_naive_bfloat16_cm_0_0( + int32_t cc, cudaStream_t stream, - void * query_ptr, - void * key_ptr, void * attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, + void * value_ptr, + void * output_ptr, + int32_t batch_size, + int32_t heads, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, - int kernel_size, - int dilation); + const std::tuple& kernel_size, + const std::tuple& dilation); -void na2d_pn_cuda_naive_half_ks_5_di_1( - const int cc, +void na2d_nn_cuda_naive_bfloat16_cm_0_1( + int32_t cc, cudaStream_t stream, - void * query_ptr, - void * key_ptr, void * attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, + void * value_ptr, + void * output_ptr, + int32_t batch_size, + int32_t heads, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, - int kernel_size, - int dilation); + const std::tuple& kernel_size, + const std::tuple& dilation); -void na2d_pn_cuda_naive_half_ks_7_di_any( - const int cc, +void na2d_nn_cuda_naive_bfloat16_cm_1_0( + int32_t cc, cudaStream_t stream, - void * query_ptr, - void * key_ptr, void * attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, + void * value_ptr, + void * output_ptr, + int32_t batch_size, + int32_t heads, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, - int kernel_size, - int dilation); + const std::tuple& kernel_size, + const std::tuple& dilation); -void na2d_pn_cuda_naive_half_ks_7_di_1( - const int cc, +void na2d_nn_cuda_naive_bfloat16_cm_1_1( + int32_t cc, cudaStream_t stream, - void * query_ptr, - void * key_ptr, void * attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, + void * value_ptr, + void * output_ptr, + int32_t batch_size, + int32_t heads, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, - int kernel_size, - int dilation); + const std::tuple& kernel_size, + const std::tuple& dilation); -void na2d_pn_cuda_naive_half_ks_9_di_any( - const int cc, +void na3d_nn_cuda_naive_double_cm_0_0_0( + int32_t cc, cudaStream_t stream, - void * query_ptr, - void * key_ptr, void * attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, + void * value_ptr, + void * output_ptr, + int32_t batch_size, + int32_t heads, + int32_t depth, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, - int kernel_size, - int dilation); + int64_t attn_stride_4, + const std::tuple& kernel_size, + const std::tuple& dilation); -void na2d_pn_cuda_naive_half_ks_9_di_1( - const int cc, +void na3d_nn_cuda_naive_double_cm_0_0_1( + int32_t cc, cudaStream_t stream, - void * query_ptr, - void * key_ptr, void * attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, + void * value_ptr, + void * output_ptr, + int32_t batch_size, + int32_t heads, + int32_t depth, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, - int kernel_size, - int dilation); + int64_t attn_stride_4, + const std::tuple& kernel_size, + const std::tuple& dilation); -void na2d_pn_cuda_naive_half_ks_11_di_any( - const int cc, +void na3d_nn_cuda_naive_double_cm_0_1_0( + int32_t cc, cudaStream_t stream, - void * query_ptr, - void * key_ptr, void * attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, + void * value_ptr, + void * output_ptr, + int32_t batch_size, + int32_t heads, + int32_t depth, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, - int kernel_size, - int dilation); + int64_t attn_stride_4, + const std::tuple& kernel_size, + const std::tuple& dilation); -void na2d_pn_cuda_naive_half_ks_11_di_1( - const int cc, +void na3d_nn_cuda_naive_double_cm_0_1_1( + int32_t cc, cudaStream_t stream, - void * query_ptr, - void * key_ptr, void * attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, + void * value_ptr, + void * output_ptr, + int32_t batch_size, + int32_t heads, + int32_t depth, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, - int kernel_size, - int dilation); + int64_t attn_stride_4, + const std::tuple& kernel_size, + const std::tuple& dilation); -void na2d_pn_cuda_naive_half_ks_13_di_any( - const int cc, +void na3d_nn_cuda_naive_double_cm_1_0_0( + int32_t cc, cudaStream_t stream, - void * query_ptr, - void * key_ptr, void * attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, + void * value_ptr, + void * output_ptr, + int32_t batch_size, + int32_t heads, + int32_t depth, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, - int kernel_size, - int dilation); + int64_t attn_stride_4, + const std::tuple& kernel_size, + const std::tuple& dilation); -void na2d_pn_cuda_naive_half_ks_13_di_1( - const int cc, +void na3d_nn_cuda_naive_double_cm_1_0_1( + int32_t cc, cudaStream_t stream, - void * query_ptr, - void * key_ptr, void * attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, + void * value_ptr, + void * output_ptr, + int32_t batch_size, + int32_t heads, + int32_t depth, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, - int kernel_size, - int dilation); + int64_t attn_stride_4, + const std::tuple& kernel_size, + const std::tuple& dilation); -void na2d_pn_cuda_naive_bfloat16_ks_any_di_any( - const int cc, +void na3d_nn_cuda_naive_double_cm_1_1_0( + int32_t cc, cudaStream_t stream, - void * query_ptr, - void * key_ptr, void * attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, + void * value_ptr, + void * output_ptr, + int32_t batch_size, + int32_t heads, + int32_t depth, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, - int kernel_size, - int dilation); + int64_t attn_stride_4, + const std::tuple& kernel_size, + const std::tuple& dilation); -void na2d_pn_cuda_naive_bfloat16_ks_any_di_1( - const int cc, +void na3d_nn_cuda_naive_double_cm_1_1_1( + int32_t cc, cudaStream_t stream, - void * query_ptr, - void * key_ptr, void * attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, + void * value_ptr, + void * output_ptr, + int32_t batch_size, + int32_t heads, + int32_t depth, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, - int kernel_size, - int dilation); + int64_t attn_stride_4, + const std::tuple& kernel_size, + const std::tuple& dilation); -void na2d_pn_cuda_naive_bfloat16_ks_3_di_any( - const int cc, +void na3d_nn_cuda_naive_float_cm_0_0_0( + int32_t cc, cudaStream_t stream, - void * query_ptr, - void * key_ptr, void * attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, + void * value_ptr, + void * output_ptr, + int32_t batch_size, + int32_t heads, + int32_t depth, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, - int kernel_size, - int dilation); + int64_t attn_stride_4, + const std::tuple& kernel_size, + const std::tuple& dilation); -void na2d_pn_cuda_naive_bfloat16_ks_3_di_1( - const int cc, +void na3d_nn_cuda_naive_float_cm_0_0_1( + int32_t cc, cudaStream_t stream, - void * query_ptr, - void * key_ptr, void * attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, + void * value_ptr, + void * output_ptr, + int32_t batch_size, + int32_t heads, + int32_t depth, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, - int kernel_size, - int dilation); + int64_t attn_stride_4, + const std::tuple& kernel_size, + const std::tuple& dilation); -void na2d_pn_cuda_naive_bfloat16_ks_5_di_any( - const int cc, +void na3d_nn_cuda_naive_float_cm_0_1_0( + int32_t cc, cudaStream_t stream, - void * query_ptr, - void * key_ptr, void * attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, + void * value_ptr, + void * output_ptr, + int32_t batch_size, + int32_t heads, + int32_t depth, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, - int kernel_size, - int dilation); + int64_t attn_stride_4, + const std::tuple& kernel_size, + const std::tuple& dilation); -void na2d_pn_cuda_naive_bfloat16_ks_5_di_1( - const int cc, +void na3d_nn_cuda_naive_float_cm_0_1_1( + int32_t cc, cudaStream_t stream, - void * query_ptr, - void * key_ptr, void * attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, + void * value_ptr, + void * output_ptr, + int32_t batch_size, + int32_t heads, + int32_t depth, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, - int kernel_size, - int dilation); + int64_t attn_stride_4, + const std::tuple& kernel_size, + const std::tuple& dilation); -void na2d_pn_cuda_naive_bfloat16_ks_7_di_any( - const int cc, +void na3d_nn_cuda_naive_float_cm_1_0_0( + int32_t cc, cudaStream_t stream, - void * query_ptr, - void * key_ptr, void * attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, + void * value_ptr, + void * output_ptr, + int32_t batch_size, + int32_t heads, + int32_t depth, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, - int kernel_size, - int dilation); + int64_t attn_stride_4, + const std::tuple& kernel_size, + const std::tuple& dilation); -void na2d_pn_cuda_naive_bfloat16_ks_7_di_1( - const int cc, +void na3d_nn_cuda_naive_float_cm_1_0_1( + int32_t cc, cudaStream_t stream, - void * query_ptr, - void * key_ptr, void * attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, + void * value_ptr, + void * output_ptr, + int32_t batch_size, + int32_t heads, + int32_t depth, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, - int kernel_size, - int dilation); + int64_t attn_stride_4, + const std::tuple& kernel_size, + const std::tuple& dilation); -void na2d_pn_cuda_naive_bfloat16_ks_9_di_any( - const int cc, +void na3d_nn_cuda_naive_float_cm_1_1_0( + int32_t cc, cudaStream_t stream, - void * query_ptr, - void * key_ptr, void * attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, + void * value_ptr, + void * output_ptr, + int32_t batch_size, + int32_t heads, + int32_t depth, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, - int kernel_size, - int dilation); + int64_t attn_stride_4, + const std::tuple& kernel_size, + const std::tuple& dilation); -void na2d_pn_cuda_naive_bfloat16_ks_9_di_1( - const int cc, +void na3d_nn_cuda_naive_float_cm_1_1_1( + int32_t cc, cudaStream_t stream, - void * query_ptr, - void * key_ptr, void * attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, + void * value_ptr, + void * output_ptr, + int32_t batch_size, + int32_t heads, + int32_t depth, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, - int kernel_size, - int dilation); + int64_t attn_stride_4, + const std::tuple& kernel_size, + const std::tuple& dilation); -void na2d_pn_cuda_naive_bfloat16_ks_11_di_any( - const int cc, +void na3d_nn_cuda_naive_half_cm_0_0_0( + int32_t cc, cudaStream_t stream, - void * query_ptr, - void * key_ptr, void * attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, + void * value_ptr, + void * output_ptr, + int32_t batch_size, + int32_t heads, + int32_t depth, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, - int kernel_size, - int dilation); + int64_t attn_stride_4, + const std::tuple& kernel_size, + const std::tuple& dilation); -void na2d_pn_cuda_naive_bfloat16_ks_11_di_1( - const int cc, +void na3d_nn_cuda_naive_half_cm_0_0_1( + int32_t cc, cudaStream_t stream, - void * query_ptr, - void * key_ptr, void * attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, + void * value_ptr, + void * output_ptr, + int32_t batch_size, + int32_t heads, + int32_t depth, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, - int kernel_size, - int dilation); + int64_t attn_stride_4, + const std::tuple& kernel_size, + const std::tuple& dilation); -void na2d_pn_cuda_naive_bfloat16_ks_13_di_any( - const int cc, +void na3d_nn_cuda_naive_half_cm_0_1_0( + int32_t cc, cudaStream_t stream, - void * query_ptr, - void * key_ptr, void * attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, + void * value_ptr, + void * output_ptr, + int32_t batch_size, + int32_t heads, + int32_t depth, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, - int kernel_size, - int dilation); + int64_t attn_stride_4, + const std::tuple& kernel_size, + const std::tuple& dilation); -void na2d_pn_cuda_naive_bfloat16_ks_13_di_1( - const int cc, +void na3d_nn_cuda_naive_half_cm_0_1_1( + int32_t cc, cudaStream_t stream, - void * query_ptr, - void * key_ptr, void * attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, + void * value_ptr, + void * output_ptr, + int32_t batch_size, + int32_t heads, + int32_t depth, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, - int kernel_size, - int dilation); + int64_t attn_stride_4, + const std::tuple& kernel_size, + const std::tuple& dilation); -void na3d_pn_cuda_naive_double_ks_any_di_any( - const int cc, +void na3d_nn_cuda_naive_half_cm_1_0_0( + int32_t cc, cudaStream_t stream, - void * query_ptr, - void * key_ptr, void * attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, + void * value_ptr, + void * output_ptr, + int32_t batch_size, + int32_t heads, + int32_t depth, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); + const std::tuple& kernel_size, + const std::tuple& dilation); -void na3d_pn_cuda_naive_double_ks_any_di_1( - const int cc, +void na3d_nn_cuda_naive_half_cm_1_0_1( + int32_t cc, cudaStream_t stream, - void * query_ptr, - void * key_ptr, void * attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, + void * value_ptr, + void * output_ptr, + int32_t batch_size, + int32_t heads, + int32_t depth, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); + const std::tuple& kernel_size, + const std::tuple& dilation); -void na3d_pn_cuda_naive_double_ks_3_di_any( - const int cc, +void na3d_nn_cuda_naive_half_cm_1_1_0( + int32_t cc, cudaStream_t stream, - void * query_ptr, - void * key_ptr, void * attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, + void * value_ptr, + void * output_ptr, + int32_t batch_size, + int32_t heads, + int32_t depth, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); + const std::tuple& kernel_size, + const std::tuple& dilation); -void na3d_pn_cuda_naive_double_ks_3_di_1( - const int cc, +void na3d_nn_cuda_naive_half_cm_1_1_1( + int32_t cc, cudaStream_t stream, - void * query_ptr, - void * key_ptr, void * attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, + void * value_ptr, + void * output_ptr, + int32_t batch_size, + int32_t heads, + int32_t depth, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); + const std::tuple& kernel_size, + const std::tuple& dilation); -void na3d_pn_cuda_naive_double_ks_5_di_any( - const int cc, +void na3d_nn_cuda_naive_bfloat16_cm_0_0_0( + int32_t cc, cudaStream_t stream, - void * query_ptr, - void * key_ptr, void * attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, + void * value_ptr, + void * output_ptr, + int32_t batch_size, + int32_t heads, + int32_t depth, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); + const std::tuple& kernel_size, + const std::tuple& dilation); -void na3d_pn_cuda_naive_double_ks_5_di_1( - const int cc, +void na3d_nn_cuda_naive_bfloat16_cm_0_0_1( + int32_t cc, cudaStream_t stream, - void * query_ptr, - void * key_ptr, void * attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, + void * value_ptr, + void * output_ptr, + int32_t batch_size, + int32_t heads, + int32_t depth, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); + const std::tuple& kernel_size, + const std::tuple& dilation); -void na3d_pn_cuda_naive_double_ks_7_di_any( - const int cc, +void na3d_nn_cuda_naive_bfloat16_cm_0_1_0( + int32_t cc, cudaStream_t stream, - void * query_ptr, - void * key_ptr, void * attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, + void * value_ptr, + void * output_ptr, + int32_t batch_size, + int32_t heads, + int32_t depth, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); + const std::tuple& kernel_size, + const std::tuple& dilation); -void na3d_pn_cuda_naive_double_ks_7_di_1( - const int cc, +void na3d_nn_cuda_naive_bfloat16_cm_0_1_1( + int32_t cc, cudaStream_t stream, - void * query_ptr, - void * key_ptr, void * attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, + void * value_ptr, + void * output_ptr, + int32_t batch_size, + int32_t heads, + int32_t depth, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); + const std::tuple& kernel_size, + const std::tuple& dilation); -void na3d_pn_cuda_naive_double_ks_9_di_any( - const int cc, +void na3d_nn_cuda_naive_bfloat16_cm_1_0_0( + int32_t cc, cudaStream_t stream, - void * query_ptr, - void * key_ptr, void * attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, + void * value_ptr, + void * output_ptr, + int32_t batch_size, + int32_t heads, + int32_t depth, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); + const std::tuple& kernel_size, + const std::tuple& dilation); -void na3d_pn_cuda_naive_double_ks_9_di_1( - const int cc, +void na3d_nn_cuda_naive_bfloat16_cm_1_0_1( + int32_t cc, cudaStream_t stream, - void * query_ptr, - void * key_ptr, void * attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, + void * value_ptr, + void * output_ptr, + int32_t batch_size, + int32_t heads, + int32_t depth, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); + const std::tuple& kernel_size, + const std::tuple& dilation); -void na3d_pn_cuda_naive_double_ks_11_di_any( - const int cc, +void na3d_nn_cuda_naive_bfloat16_cm_1_1_0( + int32_t cc, cudaStream_t stream, - void * query_ptr, - void * key_ptr, void * attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, + void * value_ptr, + void * output_ptr, + int32_t batch_size, + int32_t heads, + int32_t depth, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); + const std::tuple& kernel_size, + const std::tuple& dilation); -void na3d_pn_cuda_naive_double_ks_11_di_1( - const int cc, +void na3d_nn_cuda_naive_bfloat16_cm_1_1_1( + int32_t cc, cudaStream_t stream, - void * query_ptr, - void * key_ptr, void * attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, + void * value_ptr, + void * output_ptr, + int32_t batch_size, + int32_t heads, + int32_t depth, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); + const std::tuple& kernel_size, + const std::tuple& dilation); -void na3d_pn_cuda_naive_double_ks_13_di_any( - const int cc, +void na1d_in_cuda_naive_double_cm_0( + int32_t cc, cudaStream_t stream, - void * query_ptr, - void * key_ptr, void * attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, + void * d_output_ptr, + void * d_value_ptr, + int32_t batch_size, + int32_t heads, + int32_t length, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); + const std::tuple& kernel_size, + const std::tuple& dilation); -void na3d_pn_cuda_naive_double_ks_13_di_1( - const int cc, +void na1d_in_cuda_naive_double_cm_1( + int32_t cc, cudaStream_t stream, - void * query_ptr, - void * key_ptr, void * attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, + void * d_output_ptr, + void * d_value_ptr, + int32_t batch_size, + int32_t heads, + int32_t length, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); + const std::tuple& kernel_size, + const std::tuple& dilation); -void na3d_pn_cuda_naive_float_ks_any_di_any( - const int cc, +void na1d_in_cuda_naive_float_cm_0( + int32_t cc, cudaStream_t stream, - void * query_ptr, - void * key_ptr, void * attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, + void * d_output_ptr, + void * d_value_ptr, + int32_t batch_size, + int32_t heads, + int32_t length, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); + const std::tuple& kernel_size, + const std::tuple& dilation); -void na3d_pn_cuda_naive_float_ks_any_di_1( - const int cc, +void na1d_in_cuda_naive_float_cm_1( + int32_t cc, cudaStream_t stream, - void * query_ptr, - void * key_ptr, void * attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, + void * d_output_ptr, + void * d_value_ptr, + int32_t batch_size, + int32_t heads, + int32_t length, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); + const std::tuple& kernel_size, + const std::tuple& dilation); -void na3d_pn_cuda_naive_float_ks_3_di_any( - const int cc, +void na1d_in_cuda_naive_half_cm_0( + int32_t cc, cudaStream_t stream, - void * query_ptr, - void * key_ptr, void * attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, + void * d_output_ptr, + void * d_value_ptr, + int32_t batch_size, + int32_t heads, + int32_t length, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); + const std::tuple& kernel_size, + const std::tuple& dilation); -void na3d_pn_cuda_naive_float_ks_3_di_1( - const int cc, +void na1d_in_cuda_naive_half_cm_1( + int32_t cc, cudaStream_t stream, - void * query_ptr, - void * key_ptr, void * attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, + void * d_output_ptr, + void * d_value_ptr, + int32_t batch_size, + int32_t heads, + int32_t length, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); + const std::tuple& kernel_size, + const std::tuple& dilation); -void na3d_pn_cuda_naive_float_ks_5_di_any( - const int cc, +void na1d_in_cuda_naive_bfloat16_cm_0( + int32_t cc, cudaStream_t stream, - void * query_ptr, - void * key_ptr, void * attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, + void * d_output_ptr, + void * d_value_ptr, + int32_t batch_size, + int32_t heads, + int32_t length, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); + const std::tuple& kernel_size, + const std::tuple& dilation); -void na3d_pn_cuda_naive_float_ks_5_di_1( - const int cc, +void na1d_in_cuda_naive_bfloat16_cm_1( + int32_t cc, cudaStream_t stream, - void * query_ptr, - void * key_ptr, void * attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, + void * d_output_ptr, + void * d_value_ptr, + int32_t batch_size, + int32_t heads, + int32_t length, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); + const std::tuple& kernel_size, + const std::tuple& dilation); -void na3d_pn_cuda_naive_float_ks_7_di_any( - const int cc, +void na2d_in_cuda_naive_double_cm_0_0( + int32_t cc, cudaStream_t stream, - void * query_ptr, - void * key_ptr, void * attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, + void * d_output_ptr, + void * d_value_ptr, + int32_t batch_size, + int32_t heads, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); + const std::tuple& kernel_size, + const std::tuple& dilation); -void na3d_pn_cuda_naive_float_ks_7_di_1( - const int cc, +void na2d_in_cuda_naive_double_cm_0_1( + int32_t cc, cudaStream_t stream, - void * query_ptr, - void * key_ptr, void * attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, + void * d_output_ptr, + void * d_value_ptr, + int32_t batch_size, + int32_t heads, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); + const std::tuple& kernel_size, + const std::tuple& dilation); -void na3d_pn_cuda_naive_float_ks_9_di_any( - const int cc, +void na2d_in_cuda_naive_double_cm_1_0( + int32_t cc, cudaStream_t stream, - void * query_ptr, - void * key_ptr, void * attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, + void * d_output_ptr, + void * d_value_ptr, + int32_t batch_size, + int32_t heads, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); + const std::tuple& kernel_size, + const std::tuple& dilation); -void na3d_pn_cuda_naive_float_ks_9_di_1( - const int cc, +void na2d_in_cuda_naive_double_cm_1_1( + int32_t cc, cudaStream_t stream, - void * query_ptr, - void * key_ptr, void * attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, + void * d_output_ptr, + void * d_value_ptr, + int32_t batch_size, + int32_t heads, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); + const std::tuple& kernel_size, + const std::tuple& dilation); -void na3d_pn_cuda_naive_float_ks_11_di_any( - const int cc, +void na2d_in_cuda_naive_float_cm_0_0( + int32_t cc, cudaStream_t stream, - void * query_ptr, - void * key_ptr, void * attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, + void * d_output_ptr, + void * d_value_ptr, + int32_t batch_size, + int32_t heads, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); + const std::tuple& kernel_size, + const std::tuple& dilation); -void na3d_pn_cuda_naive_float_ks_11_di_1( - const int cc, +void na2d_in_cuda_naive_float_cm_0_1( + int32_t cc, cudaStream_t stream, - void * query_ptr, - void * key_ptr, void * attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, + void * d_output_ptr, + void * d_value_ptr, + int32_t batch_size, + int32_t heads, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); + const std::tuple& kernel_size, + const std::tuple& dilation); -void na3d_pn_cuda_naive_float_ks_13_di_any( - const int cc, +void na2d_in_cuda_naive_float_cm_1_0( + int32_t cc, cudaStream_t stream, - void * query_ptr, - void * key_ptr, void * attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, + void * d_output_ptr, + void * d_value_ptr, + int32_t batch_size, + int32_t heads, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); + const std::tuple& kernel_size, + const std::tuple& dilation); -void na3d_pn_cuda_naive_float_ks_13_di_1( - const int cc, +void na2d_in_cuda_naive_float_cm_1_1( + int32_t cc, cudaStream_t stream, - void * query_ptr, - void * key_ptr, void * attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, + void * d_output_ptr, + void * d_value_ptr, + int32_t batch_size, + int32_t heads, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); + const std::tuple& kernel_size, + const std::tuple& dilation); -void na3d_pn_cuda_naive_half_ks_any_di_any( - const int cc, +void na2d_in_cuda_naive_half_cm_0_0( + int32_t cc, cudaStream_t stream, - void * query_ptr, - void * key_ptr, void * attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, + void * d_output_ptr, + void * d_value_ptr, + int32_t batch_size, + int32_t heads, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); + const std::tuple& kernel_size, + const std::tuple& dilation); -void na3d_pn_cuda_naive_half_ks_any_di_1( - const int cc, +void na2d_in_cuda_naive_half_cm_0_1( + int32_t cc, cudaStream_t stream, - void * query_ptr, - void * key_ptr, void * attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, + void * d_output_ptr, + void * d_value_ptr, + int32_t batch_size, + int32_t heads, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); + const std::tuple& kernel_size, + const std::tuple& dilation); -void na3d_pn_cuda_naive_half_ks_3_di_any( - const int cc, +void na2d_in_cuda_naive_half_cm_1_0( + int32_t cc, cudaStream_t stream, - void * query_ptr, - void * key_ptr, void * attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, + void * d_output_ptr, + void * d_value_ptr, + int32_t batch_size, + int32_t heads, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); + const std::tuple& kernel_size, + const std::tuple& dilation); -void na3d_pn_cuda_naive_half_ks_3_di_1( - const int cc, +void na2d_in_cuda_naive_half_cm_1_1( + int32_t cc, cudaStream_t stream, - void * query_ptr, - void * key_ptr, void * attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, + void * d_output_ptr, + void * d_value_ptr, + int32_t batch_size, + int32_t heads, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); + const std::tuple& kernel_size, + const std::tuple& dilation); -void na3d_pn_cuda_naive_half_ks_5_di_any( - const int cc, +void na2d_in_cuda_naive_bfloat16_cm_0_0( + int32_t cc, cudaStream_t stream, - void * query_ptr, - void * key_ptr, void * attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, + void * d_output_ptr, + void * d_value_ptr, + int32_t batch_size, + int32_t heads, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); + const std::tuple& kernel_size, + const std::tuple& dilation); -void na3d_pn_cuda_naive_half_ks_5_di_1( - const int cc, +void na2d_in_cuda_naive_bfloat16_cm_0_1( + int32_t cc, cudaStream_t stream, - void * query_ptr, - void * key_ptr, void * attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, + void * d_output_ptr, + void * d_value_ptr, + int32_t batch_size, + int32_t heads, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); + const std::tuple& kernel_size, + const std::tuple& dilation); -void na3d_pn_cuda_naive_half_ks_7_di_any( - const int cc, +void na2d_in_cuda_naive_bfloat16_cm_1_0( + int32_t cc, cudaStream_t stream, - void * query_ptr, - void * key_ptr, void * attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, + void * d_output_ptr, + void * d_value_ptr, + int32_t batch_size, + int32_t heads, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); + const std::tuple& kernel_size, + const std::tuple& dilation); -void na3d_pn_cuda_naive_half_ks_7_di_1( - const int cc, +void na2d_in_cuda_naive_bfloat16_cm_1_1( + int32_t cc, cudaStream_t stream, - void * query_ptr, - void * key_ptr, void * attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, + void * d_output_ptr, + void * d_value_ptr, + int32_t batch_size, + int32_t heads, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); + const std::tuple& kernel_size, + const std::tuple& dilation); -void na3d_pn_cuda_naive_half_ks_9_di_any( - const int cc, +void na3d_in_cuda_naive_double_cm_0_0_0( + int32_t cc, cudaStream_t stream, - void * query_ptr, - void * key_ptr, void * attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, + void * d_output_ptr, + void * d_value_ptr, + int32_t batch_size, + int32_t heads, + int32_t depth, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); + const std::tuple& kernel_size, + const std::tuple& dilation); -void na3d_pn_cuda_naive_half_ks_9_di_1( - const int cc, +void na3d_in_cuda_naive_double_cm_0_0_1( + int32_t cc, cudaStream_t stream, - void * query_ptr, - void * key_ptr, void * attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, + void * d_output_ptr, + void * d_value_ptr, + int32_t batch_size, + int32_t heads, + int32_t depth, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); + const std::tuple& kernel_size, + const std::tuple& dilation); -void na3d_pn_cuda_naive_half_ks_11_di_any( - const int cc, +void na3d_in_cuda_naive_double_cm_0_1_0( + int32_t cc, cudaStream_t stream, - void * query_ptr, - void * key_ptr, void * attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, + void * d_output_ptr, + void * d_value_ptr, + int32_t batch_size, + int32_t heads, + int32_t depth, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); + const std::tuple& kernel_size, + const std::tuple& dilation); -void na3d_pn_cuda_naive_half_ks_11_di_1( - const int cc, +void na3d_in_cuda_naive_double_cm_0_1_1( + int32_t cc, cudaStream_t stream, - void * query_ptr, - void * key_ptr, void * attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, + void * d_output_ptr, + void * d_value_ptr, + int32_t batch_size, + int32_t heads, + int32_t depth, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); + const std::tuple& kernel_size, + const std::tuple& dilation); -void na3d_pn_cuda_naive_half_ks_13_di_any( - const int cc, +void na3d_in_cuda_naive_double_cm_1_0_0( + int32_t cc, cudaStream_t stream, - void * query_ptr, - void * key_ptr, void * attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, + void * d_output_ptr, + void * d_value_ptr, + int32_t batch_size, + int32_t heads, + int32_t depth, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); + const std::tuple& kernel_size, + const std::tuple& dilation); -void na3d_pn_cuda_naive_half_ks_13_di_1( - const int cc, +void na3d_in_cuda_naive_double_cm_1_0_1( + int32_t cc, cudaStream_t stream, - void * query_ptr, - void * key_ptr, void * attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, + void * d_output_ptr, + void * d_value_ptr, + int32_t batch_size, + int32_t heads, + int32_t depth, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); + const std::tuple& kernel_size, + const std::tuple& dilation); -void na3d_pn_cuda_naive_bfloat16_ks_any_di_any( - const int cc, +void na3d_in_cuda_naive_double_cm_1_1_0( + int32_t cc, cudaStream_t stream, - void * query_ptr, - void * key_ptr, void * attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, + void * d_output_ptr, + void * d_value_ptr, + int32_t batch_size, + int32_t heads, + int32_t depth, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); + const std::tuple& kernel_size, + const std::tuple& dilation); -void na3d_pn_cuda_naive_bfloat16_ks_any_di_1( - const int cc, +void na3d_in_cuda_naive_double_cm_1_1_1( + int32_t cc, cudaStream_t stream, - void * query_ptr, - void * key_ptr, void * attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, + void * d_output_ptr, + void * d_value_ptr, + int32_t batch_size, + int32_t heads, + int32_t depth, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); + const std::tuple& kernel_size, + const std::tuple& dilation); -void na3d_pn_cuda_naive_bfloat16_ks_3_di_any( - const int cc, +void na3d_in_cuda_naive_float_cm_0_0_0( + int32_t cc, cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); - -void na3d_pn_cuda_naive_bfloat16_ks_3_di_1( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); - -void na3d_pn_cuda_naive_bfloat16_ks_5_di_any( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); - -void na3d_pn_cuda_naive_bfloat16_ks_5_di_1( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); - -void na3d_pn_cuda_naive_bfloat16_ks_7_di_any( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); - -void na3d_pn_cuda_naive_bfloat16_ks_7_di_1( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); - -void na3d_pn_cuda_naive_bfloat16_ks_9_di_any( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); - -void na3d_pn_cuda_naive_bfloat16_ks_9_di_1( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); - -void na3d_pn_cuda_naive_bfloat16_ks_11_di_any( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); - -void na3d_pn_cuda_naive_bfloat16_ks_11_di_1( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); - -void na3d_pn_cuda_naive_bfloat16_ks_13_di_any( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); - -void na3d_pn_cuda_naive_bfloat16_ks_13_di_1( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); - -void na1d_pn_bias_cuda_naive_double_ks_any_di_any( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation); - -void na1d_pn_bias_cuda_naive_double_ks_any_di_1( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation); - -void na1d_pn_bias_cuda_naive_double_ks_3_di_any( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation); - -void na1d_pn_bias_cuda_naive_double_ks_3_di_1( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation); - -void na1d_pn_bias_cuda_naive_double_ks_5_di_any( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation); - -void na1d_pn_bias_cuda_naive_double_ks_5_di_1( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation); - -void na1d_pn_bias_cuda_naive_double_ks_7_di_any( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation); - -void na1d_pn_bias_cuda_naive_double_ks_7_di_1( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation); - -void na1d_pn_bias_cuda_naive_double_ks_9_di_any( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation); - -void na1d_pn_bias_cuda_naive_double_ks_9_di_1( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation); - -void na1d_pn_bias_cuda_naive_double_ks_11_di_any( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation); - -void na1d_pn_bias_cuda_naive_double_ks_11_di_1( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation); - -void na1d_pn_bias_cuda_naive_double_ks_13_di_any( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation); - -void na1d_pn_bias_cuda_naive_double_ks_13_di_1( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation); - -void na1d_pn_bias_cuda_naive_float_ks_any_di_any( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation); - -void na1d_pn_bias_cuda_naive_float_ks_any_di_1( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation); - -void na1d_pn_bias_cuda_naive_float_ks_3_di_any( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation); - -void na1d_pn_bias_cuda_naive_float_ks_3_di_1( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation); - -void na1d_pn_bias_cuda_naive_float_ks_5_di_any( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation); - -void na1d_pn_bias_cuda_naive_float_ks_5_di_1( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation); - -void na1d_pn_bias_cuda_naive_float_ks_7_di_any( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation); - -void na1d_pn_bias_cuda_naive_float_ks_7_di_1( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation); - -void na1d_pn_bias_cuda_naive_float_ks_9_di_any( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation); - -void na1d_pn_bias_cuda_naive_float_ks_9_di_1( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation); - -void na1d_pn_bias_cuda_naive_float_ks_11_di_any( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation); - -void na1d_pn_bias_cuda_naive_float_ks_11_di_1( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation); - -void na1d_pn_bias_cuda_naive_float_ks_13_di_any( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation); - -void na1d_pn_bias_cuda_naive_float_ks_13_di_1( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation); - -void na1d_pn_bias_cuda_naive_half_ks_any_di_any( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation); - -void na1d_pn_bias_cuda_naive_half_ks_any_di_1( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation); - -void na1d_pn_bias_cuda_naive_half_ks_3_di_any( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation); - -void na1d_pn_bias_cuda_naive_half_ks_3_di_1( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation); - -void na1d_pn_bias_cuda_naive_half_ks_5_di_any( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation); - -void na1d_pn_bias_cuda_naive_half_ks_5_di_1( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation); - -void na1d_pn_bias_cuda_naive_half_ks_7_di_any( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation); - -void na1d_pn_bias_cuda_naive_half_ks_7_di_1( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation); - -void na1d_pn_bias_cuda_naive_half_ks_9_di_any( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation); - -void na1d_pn_bias_cuda_naive_half_ks_9_di_1( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation); - -void na1d_pn_bias_cuda_naive_half_ks_11_di_any( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation); - -void na1d_pn_bias_cuda_naive_half_ks_11_di_1( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation); - -void na1d_pn_bias_cuda_naive_half_ks_13_di_any( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation); - -void na1d_pn_bias_cuda_naive_half_ks_13_di_1( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation); - -void na1d_pn_bias_cuda_naive_bfloat16_ks_any_di_any( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation); - -void na1d_pn_bias_cuda_naive_bfloat16_ks_any_di_1( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation); - -void na1d_pn_bias_cuda_naive_bfloat16_ks_3_di_any( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation); - -void na1d_pn_bias_cuda_naive_bfloat16_ks_3_di_1( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation); - -void na1d_pn_bias_cuda_naive_bfloat16_ks_5_di_any( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation); - -void na1d_pn_bias_cuda_naive_bfloat16_ks_5_di_1( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation); - -void na1d_pn_bias_cuda_naive_bfloat16_ks_7_di_any( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation); - -void na1d_pn_bias_cuda_naive_bfloat16_ks_7_di_1( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation); - -void na1d_pn_bias_cuda_naive_bfloat16_ks_9_di_any( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation); - -void na1d_pn_bias_cuda_naive_bfloat16_ks_9_di_1( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation); - -void na1d_pn_bias_cuda_naive_bfloat16_ks_11_di_any( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation); - -void na1d_pn_bias_cuda_naive_bfloat16_ks_11_di_1( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation); - -void na1d_pn_bias_cuda_naive_bfloat16_ks_13_di_any( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation); - -void na1d_pn_bias_cuda_naive_bfloat16_ks_13_di_1( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation); - -void na2d_pn_bias_cuda_naive_double_ks_any_di_any( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation); - -void na2d_pn_bias_cuda_naive_double_ks_any_di_1( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation); - -void na2d_pn_bias_cuda_naive_double_ks_3_di_any( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation); - -void na2d_pn_bias_cuda_naive_double_ks_3_di_1( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation); - -void na2d_pn_bias_cuda_naive_double_ks_5_di_any( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation); - -void na2d_pn_bias_cuda_naive_double_ks_5_di_1( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation); - -void na2d_pn_bias_cuda_naive_double_ks_7_di_any( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation); - -void na2d_pn_bias_cuda_naive_double_ks_7_di_1( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation); - -void na2d_pn_bias_cuda_naive_double_ks_9_di_any( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation); - -void na2d_pn_bias_cuda_naive_double_ks_9_di_1( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation); - -void na2d_pn_bias_cuda_naive_double_ks_11_di_any( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation); - -void na2d_pn_bias_cuda_naive_double_ks_11_di_1( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation); - -void na2d_pn_bias_cuda_naive_double_ks_13_di_any( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation); - -void na2d_pn_bias_cuda_naive_double_ks_13_di_1( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation); - -void na2d_pn_bias_cuda_naive_float_ks_any_di_any( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation); - -void na2d_pn_bias_cuda_naive_float_ks_any_di_1( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation); - -void na2d_pn_bias_cuda_naive_float_ks_3_di_any( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation); - -void na2d_pn_bias_cuda_naive_float_ks_3_di_1( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation); - -void na2d_pn_bias_cuda_naive_float_ks_5_di_any( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation); - -void na2d_pn_bias_cuda_naive_float_ks_5_di_1( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation); - -void na2d_pn_bias_cuda_naive_float_ks_7_di_any( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation); - -void na2d_pn_bias_cuda_naive_float_ks_7_di_1( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation); - -void na2d_pn_bias_cuda_naive_float_ks_9_di_any( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation); - -void na2d_pn_bias_cuda_naive_float_ks_9_di_1( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation); - -void na2d_pn_bias_cuda_naive_float_ks_11_di_any( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation); - -void na2d_pn_bias_cuda_naive_float_ks_11_di_1( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation); - -void na2d_pn_bias_cuda_naive_float_ks_13_di_any( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation); - -void na2d_pn_bias_cuda_naive_float_ks_13_di_1( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation); - -void na2d_pn_bias_cuda_naive_half_ks_any_di_any( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation); - -void na2d_pn_bias_cuda_naive_half_ks_any_di_1( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation); - -void na2d_pn_bias_cuda_naive_half_ks_3_di_any( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation); - -void na2d_pn_bias_cuda_naive_half_ks_3_di_1( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation); - -void na2d_pn_bias_cuda_naive_half_ks_5_di_any( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation); - -void na2d_pn_bias_cuda_naive_half_ks_5_di_1( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation); - -void na2d_pn_bias_cuda_naive_half_ks_7_di_any( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation); - -void na2d_pn_bias_cuda_naive_half_ks_7_di_1( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation); - -void na2d_pn_bias_cuda_naive_half_ks_9_di_any( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation); - -void na2d_pn_bias_cuda_naive_half_ks_9_di_1( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation); - -void na2d_pn_bias_cuda_naive_half_ks_11_di_any( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation); - -void na2d_pn_bias_cuda_naive_half_ks_11_di_1( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation); - -void na2d_pn_bias_cuda_naive_half_ks_13_di_any( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation); - -void na2d_pn_bias_cuda_naive_half_ks_13_di_1( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation); - -void na2d_pn_bias_cuda_naive_bfloat16_ks_any_di_any( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation); - -void na2d_pn_bias_cuda_naive_bfloat16_ks_any_di_1( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation); - -void na2d_pn_bias_cuda_naive_bfloat16_ks_3_di_any( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation); - -void na2d_pn_bias_cuda_naive_bfloat16_ks_3_di_1( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation); - -void na2d_pn_bias_cuda_naive_bfloat16_ks_5_di_any( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation); - -void na2d_pn_bias_cuda_naive_bfloat16_ks_5_di_1( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation); - -void na2d_pn_bias_cuda_naive_bfloat16_ks_7_di_any( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation); - -void na2d_pn_bias_cuda_naive_bfloat16_ks_7_di_1( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation); - -void na2d_pn_bias_cuda_naive_bfloat16_ks_9_di_any( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation); - -void na2d_pn_bias_cuda_naive_bfloat16_ks_9_di_1( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation); - -void na2d_pn_bias_cuda_naive_bfloat16_ks_11_di_any( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation); - -void na2d_pn_bias_cuda_naive_bfloat16_ks_11_di_1( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation); - -void na2d_pn_bias_cuda_naive_bfloat16_ks_13_di_any( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation); - -void na2d_pn_bias_cuda_naive_bfloat16_ks_13_di_1( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation); - -void na3d_pn_bias_cuda_naive_double_ks_any_di_any( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); - -void na3d_pn_bias_cuda_naive_double_ks_any_di_1( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); - -void na3d_pn_bias_cuda_naive_double_ks_3_di_any( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); - -void na3d_pn_bias_cuda_naive_double_ks_3_di_1( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); - -void na3d_pn_bias_cuda_naive_double_ks_5_di_any( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); - -void na3d_pn_bias_cuda_naive_double_ks_5_di_1( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); - -void na3d_pn_bias_cuda_naive_double_ks_7_di_any( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); - -void na3d_pn_bias_cuda_naive_double_ks_7_di_1( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); - -void na3d_pn_bias_cuda_naive_double_ks_9_di_any( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); - -void na3d_pn_bias_cuda_naive_double_ks_9_di_1( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); - -void na3d_pn_bias_cuda_naive_double_ks_11_di_any( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); - -void na3d_pn_bias_cuda_naive_double_ks_11_di_1( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); - -void na3d_pn_bias_cuda_naive_double_ks_13_di_any( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); - -void na3d_pn_bias_cuda_naive_double_ks_13_di_1( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); - -void na3d_pn_bias_cuda_naive_float_ks_any_di_any( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); - -void na3d_pn_bias_cuda_naive_float_ks_any_di_1( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); - -void na3d_pn_bias_cuda_naive_float_ks_3_di_any( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); - -void na3d_pn_bias_cuda_naive_float_ks_3_di_1( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); - -void na3d_pn_bias_cuda_naive_float_ks_5_di_any( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); - -void na3d_pn_bias_cuda_naive_float_ks_5_di_1( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); - -void na3d_pn_bias_cuda_naive_float_ks_7_di_any( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); - -void na3d_pn_bias_cuda_naive_float_ks_7_di_1( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); - -void na3d_pn_bias_cuda_naive_float_ks_9_di_any( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); - -void na3d_pn_bias_cuda_naive_float_ks_9_di_1( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); - -void na3d_pn_bias_cuda_naive_float_ks_11_di_any( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); - -void na3d_pn_bias_cuda_naive_float_ks_11_di_1( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); - -void na3d_pn_bias_cuda_naive_float_ks_13_di_any( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); - -void na3d_pn_bias_cuda_naive_float_ks_13_di_1( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); - -void na3d_pn_bias_cuda_naive_half_ks_any_di_any( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); - -void na3d_pn_bias_cuda_naive_half_ks_any_di_1( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); - -void na3d_pn_bias_cuda_naive_half_ks_3_di_any( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); - -void na3d_pn_bias_cuda_naive_half_ks_3_di_1( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); - -void na3d_pn_bias_cuda_naive_half_ks_5_di_any( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); - -void na3d_pn_bias_cuda_naive_half_ks_5_di_1( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); - -void na3d_pn_bias_cuda_naive_half_ks_7_di_any( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); - -void na3d_pn_bias_cuda_naive_half_ks_7_di_1( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); - -void na3d_pn_bias_cuda_naive_half_ks_9_di_any( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); - -void na3d_pn_bias_cuda_naive_half_ks_9_di_1( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); - -void na3d_pn_bias_cuda_naive_half_ks_11_di_any( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); - -void na3d_pn_bias_cuda_naive_half_ks_11_di_1( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); - -void na3d_pn_bias_cuda_naive_half_ks_13_di_any( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); - -void na3d_pn_bias_cuda_naive_half_ks_13_di_1( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); - -void na3d_pn_bias_cuda_naive_bfloat16_ks_any_di_any( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); - -void na3d_pn_bias_cuda_naive_bfloat16_ks_any_di_1( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); - -void na3d_pn_bias_cuda_naive_bfloat16_ks_3_di_any( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); - -void na3d_pn_bias_cuda_naive_bfloat16_ks_3_di_1( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); - -void na3d_pn_bias_cuda_naive_bfloat16_ks_5_di_any( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); - -void na3d_pn_bias_cuda_naive_bfloat16_ks_5_di_1( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); - -void na3d_pn_bias_cuda_naive_bfloat16_ks_7_di_any( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); - -void na3d_pn_bias_cuda_naive_bfloat16_ks_7_di_1( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); - -void na3d_pn_bias_cuda_naive_bfloat16_ks_9_di_any( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); - -void na3d_pn_bias_cuda_naive_bfloat16_ks_9_di_1( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); - -void na3d_pn_bias_cuda_naive_bfloat16_ks_11_di_any( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); - -void na3d_pn_bias_cuda_naive_bfloat16_ks_11_di_1( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); - -void na3d_pn_bias_cuda_naive_bfloat16_ks_13_di_any( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); - -void na3d_pn_bias_cuda_naive_bfloat16_ks_13_di_1( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); - -void na1d_nn_cuda_naive_double_ks_any_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation); - -void na1d_nn_cuda_naive_double_ks_any_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation); - -void na1d_nn_cuda_naive_double_ks_3_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation); - -void na1d_nn_cuda_naive_double_ks_3_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation); - -void na1d_nn_cuda_naive_double_ks_5_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation); - -void na1d_nn_cuda_naive_double_ks_5_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation); - -void na1d_nn_cuda_naive_double_ks_7_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation); - -void na1d_nn_cuda_naive_double_ks_7_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation); - -void na1d_nn_cuda_naive_double_ks_9_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation); - -void na1d_nn_cuda_naive_double_ks_9_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation); - -void na1d_nn_cuda_naive_double_ks_11_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation); - -void na1d_nn_cuda_naive_double_ks_11_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation); - -void na1d_nn_cuda_naive_double_ks_13_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation); - -void na1d_nn_cuda_naive_double_ks_13_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation); - -void na1d_nn_cuda_naive_float_ks_any_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation); - -void na1d_nn_cuda_naive_float_ks_any_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation); - -void na1d_nn_cuda_naive_float_ks_3_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation); - -void na1d_nn_cuda_naive_float_ks_3_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation); - -void na1d_nn_cuda_naive_float_ks_5_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation); - -void na1d_nn_cuda_naive_float_ks_5_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation); - -void na1d_nn_cuda_naive_float_ks_7_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation); - -void na1d_nn_cuda_naive_float_ks_7_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation); - -void na1d_nn_cuda_naive_float_ks_9_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation); - -void na1d_nn_cuda_naive_float_ks_9_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation); - -void na1d_nn_cuda_naive_float_ks_11_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation); - -void na1d_nn_cuda_naive_float_ks_11_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation); - -void na1d_nn_cuda_naive_float_ks_13_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation); - -void na1d_nn_cuda_naive_float_ks_13_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation); - -void na1d_nn_cuda_naive_half_ks_any_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation); - -void na1d_nn_cuda_naive_half_ks_any_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation); - -void na1d_nn_cuda_naive_half_ks_3_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation); - -void na1d_nn_cuda_naive_half_ks_3_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation); - -void na1d_nn_cuda_naive_half_ks_5_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation); - -void na1d_nn_cuda_naive_half_ks_5_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation); - -void na1d_nn_cuda_naive_half_ks_7_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation); - -void na1d_nn_cuda_naive_half_ks_7_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation); - -void na1d_nn_cuda_naive_half_ks_9_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation); - -void na1d_nn_cuda_naive_half_ks_9_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation); - -void na1d_nn_cuda_naive_half_ks_11_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation); - -void na1d_nn_cuda_naive_half_ks_11_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation); - -void na1d_nn_cuda_naive_half_ks_13_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation); - -void na1d_nn_cuda_naive_half_ks_13_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation); - -void na1d_nn_cuda_naive_bfloat16_ks_any_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation); - -void na1d_nn_cuda_naive_bfloat16_ks_any_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation); - -void na1d_nn_cuda_naive_bfloat16_ks_3_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation); - -void na1d_nn_cuda_naive_bfloat16_ks_3_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation); - -void na1d_nn_cuda_naive_bfloat16_ks_5_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation); - -void na1d_nn_cuda_naive_bfloat16_ks_5_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation); - -void na1d_nn_cuda_naive_bfloat16_ks_7_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation); - -void na1d_nn_cuda_naive_bfloat16_ks_7_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation); - -void na1d_nn_cuda_naive_bfloat16_ks_9_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation); - -void na1d_nn_cuda_naive_bfloat16_ks_9_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation); - -void na1d_nn_cuda_naive_bfloat16_ks_11_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation); - -void na1d_nn_cuda_naive_bfloat16_ks_11_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation); - -void na1d_nn_cuda_naive_bfloat16_ks_13_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation); - -void na1d_nn_cuda_naive_bfloat16_ks_13_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation); - -void na2d_nn_cuda_naive_double_ks_any_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation); - -void na2d_nn_cuda_naive_double_ks_any_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation); - -void na2d_nn_cuda_naive_double_ks_3_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation); - -void na2d_nn_cuda_naive_double_ks_3_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation); - -void na2d_nn_cuda_naive_double_ks_5_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation); - -void na2d_nn_cuda_naive_double_ks_5_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation); - -void na2d_nn_cuda_naive_double_ks_7_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation); - -void na2d_nn_cuda_naive_double_ks_7_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation); - -void na2d_nn_cuda_naive_double_ks_9_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation); - -void na2d_nn_cuda_naive_double_ks_9_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation); - -void na2d_nn_cuda_naive_double_ks_11_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation); - -void na2d_nn_cuda_naive_double_ks_11_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation); - -void na2d_nn_cuda_naive_double_ks_13_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation); - -void na2d_nn_cuda_naive_double_ks_13_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation); - -void na2d_nn_cuda_naive_float_ks_any_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation); - -void na2d_nn_cuda_naive_float_ks_any_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation); - -void na2d_nn_cuda_naive_float_ks_3_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation); - -void na2d_nn_cuda_naive_float_ks_3_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation); - -void na2d_nn_cuda_naive_float_ks_5_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation); - -void na2d_nn_cuda_naive_float_ks_5_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation); - -void na2d_nn_cuda_naive_float_ks_7_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation); - -void na2d_nn_cuda_naive_float_ks_7_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation); - -void na2d_nn_cuda_naive_float_ks_9_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation); - -void na2d_nn_cuda_naive_float_ks_9_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation); - -void na2d_nn_cuda_naive_float_ks_11_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation); - -void na2d_nn_cuda_naive_float_ks_11_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation); - -void na2d_nn_cuda_naive_float_ks_13_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation); - -void na2d_nn_cuda_naive_float_ks_13_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation); - -void na2d_nn_cuda_naive_half_ks_any_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation); - -void na2d_nn_cuda_naive_half_ks_any_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation); - -void na2d_nn_cuda_naive_half_ks_3_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation); - -void na2d_nn_cuda_naive_half_ks_3_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation); - -void na2d_nn_cuda_naive_half_ks_5_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation); - -void na2d_nn_cuda_naive_half_ks_5_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation); - -void na2d_nn_cuda_naive_half_ks_7_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation); - -void na2d_nn_cuda_naive_half_ks_7_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation); - -void na2d_nn_cuda_naive_half_ks_9_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation); - -void na2d_nn_cuda_naive_half_ks_9_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation); - -void na2d_nn_cuda_naive_half_ks_11_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation); - -void na2d_nn_cuda_naive_half_ks_11_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation); - -void na2d_nn_cuda_naive_half_ks_13_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation); - -void na2d_nn_cuda_naive_half_ks_13_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation); - -void na2d_nn_cuda_naive_bfloat16_ks_any_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation); - -void na2d_nn_cuda_naive_bfloat16_ks_any_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation); - -void na2d_nn_cuda_naive_bfloat16_ks_3_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation); - -void na2d_nn_cuda_naive_bfloat16_ks_3_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation); - -void na2d_nn_cuda_naive_bfloat16_ks_5_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation); - -void na2d_nn_cuda_naive_bfloat16_ks_5_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation); - -void na2d_nn_cuda_naive_bfloat16_ks_7_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation); - -void na2d_nn_cuda_naive_bfloat16_ks_7_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation); - -void na2d_nn_cuda_naive_bfloat16_ks_9_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation); - -void na2d_nn_cuda_naive_bfloat16_ks_9_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation); - -void na2d_nn_cuda_naive_bfloat16_ks_11_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation); - -void na2d_nn_cuda_naive_bfloat16_ks_11_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation); - -void na2d_nn_cuda_naive_bfloat16_ks_13_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation); - -void na2d_nn_cuda_naive_bfloat16_ks_13_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation); - -void na3d_nn_cuda_naive_double_ks_any_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); - -void na3d_nn_cuda_naive_double_ks_any_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); - -void na3d_nn_cuda_naive_double_ks_3_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); - -void na3d_nn_cuda_naive_double_ks_3_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); - -void na3d_nn_cuda_naive_double_ks_5_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); - -void na3d_nn_cuda_naive_double_ks_5_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); - -void na3d_nn_cuda_naive_double_ks_7_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); - -void na3d_nn_cuda_naive_double_ks_7_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); - -void na3d_nn_cuda_naive_double_ks_9_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); - -void na3d_nn_cuda_naive_double_ks_9_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); - -void na3d_nn_cuda_naive_double_ks_11_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); - -void na3d_nn_cuda_naive_double_ks_11_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); - -void na3d_nn_cuda_naive_double_ks_13_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); - -void na3d_nn_cuda_naive_double_ks_13_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); - -void na3d_nn_cuda_naive_float_ks_any_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); - -void na3d_nn_cuda_naive_float_ks_any_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); - -void na3d_nn_cuda_naive_float_ks_3_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); - -void na3d_nn_cuda_naive_float_ks_3_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); - -void na3d_nn_cuda_naive_float_ks_5_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); - -void na3d_nn_cuda_naive_float_ks_5_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); - -void na3d_nn_cuda_naive_float_ks_7_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); - -void na3d_nn_cuda_naive_float_ks_7_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); - -void na3d_nn_cuda_naive_float_ks_9_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); - -void na3d_nn_cuda_naive_float_ks_9_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); - -void na3d_nn_cuda_naive_float_ks_11_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); - -void na3d_nn_cuda_naive_float_ks_11_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); - -void na3d_nn_cuda_naive_float_ks_13_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); - -void na3d_nn_cuda_naive_float_ks_13_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); - -void na3d_nn_cuda_naive_half_ks_any_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); - -void na3d_nn_cuda_naive_half_ks_any_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); - -void na3d_nn_cuda_naive_half_ks_3_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); - -void na3d_nn_cuda_naive_half_ks_3_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); - -void na3d_nn_cuda_naive_half_ks_5_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); - -void na3d_nn_cuda_naive_half_ks_5_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); - -void na3d_nn_cuda_naive_half_ks_7_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); - -void na3d_nn_cuda_naive_half_ks_7_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); - -void na3d_nn_cuda_naive_half_ks_9_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); - -void na3d_nn_cuda_naive_half_ks_9_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); - -void na3d_nn_cuda_naive_half_ks_11_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); - -void na3d_nn_cuda_naive_half_ks_11_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); - -void na3d_nn_cuda_naive_half_ks_13_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); - -void na3d_nn_cuda_naive_half_ks_13_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); - -void na3d_nn_cuda_naive_bfloat16_ks_any_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); - -void na3d_nn_cuda_naive_bfloat16_ks_any_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); - -void na3d_nn_cuda_naive_bfloat16_ks_3_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); - -void na3d_nn_cuda_naive_bfloat16_ks_3_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); - -void na3d_nn_cuda_naive_bfloat16_ks_5_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); - -void na3d_nn_cuda_naive_bfloat16_ks_5_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); - -void na3d_nn_cuda_naive_bfloat16_ks_7_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); - -void na3d_nn_cuda_naive_bfloat16_ks_7_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); - -void na3d_nn_cuda_naive_bfloat16_ks_9_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); - -void na3d_nn_cuda_naive_bfloat16_ks_9_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); - -void na3d_nn_cuda_naive_bfloat16_ks_11_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); - -void na3d_nn_cuda_naive_bfloat16_ks_11_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); - -void na3d_nn_cuda_naive_bfloat16_ks_13_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); - -void na3d_nn_cuda_naive_bfloat16_ks_13_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); - -void na1d_in_cuda_naive_double_ks_any_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation); - -void na1d_in_cuda_naive_double_ks_any_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation); - -void na1d_in_cuda_naive_double_ks_3_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation); - -void na1d_in_cuda_naive_double_ks_3_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation); - -void na1d_in_cuda_naive_double_ks_5_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation); - -void na1d_in_cuda_naive_double_ks_5_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation); - -void na1d_in_cuda_naive_double_ks_7_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation); - -void na1d_in_cuda_naive_double_ks_7_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation); - -void na1d_in_cuda_naive_double_ks_9_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation); - -void na1d_in_cuda_naive_double_ks_9_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation); - -void na1d_in_cuda_naive_double_ks_11_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation); - -void na1d_in_cuda_naive_double_ks_11_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation); - -void na1d_in_cuda_naive_double_ks_13_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation); - -void na1d_in_cuda_naive_double_ks_13_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation); - -void na1d_in_cuda_naive_float_ks_any_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation); - -void na1d_in_cuda_naive_float_ks_any_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation); - -void na1d_in_cuda_naive_float_ks_3_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation); - -void na1d_in_cuda_naive_float_ks_3_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation); - -void na1d_in_cuda_naive_float_ks_5_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation); - -void na1d_in_cuda_naive_float_ks_5_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation); - -void na1d_in_cuda_naive_float_ks_7_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation); - -void na1d_in_cuda_naive_float_ks_7_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation); - -void na1d_in_cuda_naive_float_ks_9_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation); - -void na1d_in_cuda_naive_float_ks_9_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation); - -void na1d_in_cuda_naive_float_ks_11_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation); - -void na1d_in_cuda_naive_float_ks_11_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation); - -void na1d_in_cuda_naive_float_ks_13_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation); - -void na1d_in_cuda_naive_float_ks_13_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation); - -void na1d_in_cuda_naive_half_ks_any_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation); - -void na1d_in_cuda_naive_half_ks_any_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation); - -void na1d_in_cuda_naive_half_ks_3_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation); - -void na1d_in_cuda_naive_half_ks_3_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation); - -void na1d_in_cuda_naive_half_ks_5_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation); - -void na1d_in_cuda_naive_half_ks_5_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation); - -void na1d_in_cuda_naive_half_ks_7_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation); - -void na1d_in_cuda_naive_half_ks_7_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation); - -void na1d_in_cuda_naive_half_ks_9_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation); - -void na1d_in_cuda_naive_half_ks_9_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation); - -void na1d_in_cuda_naive_half_ks_11_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation); - -void na1d_in_cuda_naive_half_ks_11_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation); - -void na1d_in_cuda_naive_half_ks_13_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation); - -void na1d_in_cuda_naive_half_ks_13_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation); - -void na1d_in_cuda_naive_bfloat16_ks_any_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation); - -void na1d_in_cuda_naive_bfloat16_ks_any_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation); - -void na1d_in_cuda_naive_bfloat16_ks_3_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation); - -void na1d_in_cuda_naive_bfloat16_ks_3_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation); - -void na1d_in_cuda_naive_bfloat16_ks_5_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation); - -void na1d_in_cuda_naive_bfloat16_ks_5_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation); - -void na1d_in_cuda_naive_bfloat16_ks_7_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation); - -void na1d_in_cuda_naive_bfloat16_ks_7_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation); - -void na1d_in_cuda_naive_bfloat16_ks_9_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation); - -void na1d_in_cuda_naive_bfloat16_ks_9_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation); - -void na1d_in_cuda_naive_bfloat16_ks_11_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation); - -void na1d_in_cuda_naive_bfloat16_ks_11_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation); - -void na1d_in_cuda_naive_bfloat16_ks_13_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation); - -void na1d_in_cuda_naive_bfloat16_ks_13_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation); - -void na2d_in_cuda_naive_double_ks_any_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation); - -void na2d_in_cuda_naive_double_ks_any_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation); - -void na2d_in_cuda_naive_double_ks_3_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation); - -void na2d_in_cuda_naive_double_ks_3_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation); - -void na2d_in_cuda_naive_double_ks_5_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation); - -void na2d_in_cuda_naive_double_ks_5_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation); - -void na2d_in_cuda_naive_double_ks_7_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation); - -void na2d_in_cuda_naive_double_ks_7_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation); - -void na2d_in_cuda_naive_double_ks_9_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation); - -void na2d_in_cuda_naive_double_ks_9_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation); - -void na2d_in_cuda_naive_double_ks_11_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation); - -void na2d_in_cuda_naive_double_ks_11_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation); - -void na2d_in_cuda_naive_double_ks_13_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation); - -void na2d_in_cuda_naive_double_ks_13_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation); - -void na2d_in_cuda_naive_float_ks_any_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation); - -void na2d_in_cuda_naive_float_ks_any_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation); - -void na2d_in_cuda_naive_float_ks_3_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation); - -void na2d_in_cuda_naive_float_ks_3_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation); - -void na2d_in_cuda_naive_float_ks_5_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation); - -void na2d_in_cuda_naive_float_ks_5_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation); - -void na2d_in_cuda_naive_float_ks_7_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation); - -void na2d_in_cuda_naive_float_ks_7_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation); - -void na2d_in_cuda_naive_float_ks_9_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation); - -void na2d_in_cuda_naive_float_ks_9_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation); - -void na2d_in_cuda_naive_float_ks_11_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation); - -void na2d_in_cuda_naive_float_ks_11_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation); - -void na2d_in_cuda_naive_float_ks_13_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation); - -void na2d_in_cuda_naive_float_ks_13_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation); - -void na2d_in_cuda_naive_half_ks_any_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation); - -void na2d_in_cuda_naive_half_ks_any_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation); - -void na2d_in_cuda_naive_half_ks_3_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation); - -void na2d_in_cuda_naive_half_ks_3_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation); - -void na2d_in_cuda_naive_half_ks_5_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation); - -void na2d_in_cuda_naive_half_ks_5_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation); - -void na2d_in_cuda_naive_half_ks_7_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation); - -void na2d_in_cuda_naive_half_ks_7_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation); - -void na2d_in_cuda_naive_half_ks_9_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation); - -void na2d_in_cuda_naive_half_ks_9_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation); - -void na2d_in_cuda_naive_half_ks_11_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation); - -void na2d_in_cuda_naive_half_ks_11_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation); - -void na2d_in_cuda_naive_half_ks_13_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation); - -void na2d_in_cuda_naive_half_ks_13_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation); - -void na2d_in_cuda_naive_bfloat16_ks_any_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation); - -void na2d_in_cuda_naive_bfloat16_ks_any_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation); - -void na2d_in_cuda_naive_bfloat16_ks_3_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation); - -void na2d_in_cuda_naive_bfloat16_ks_3_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation); - -void na2d_in_cuda_naive_bfloat16_ks_5_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation); - -void na2d_in_cuda_naive_bfloat16_ks_5_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation); - -void na2d_in_cuda_naive_bfloat16_ks_7_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation); - -void na2d_in_cuda_naive_bfloat16_ks_7_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation); - -void na2d_in_cuda_naive_bfloat16_ks_9_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation); - -void na2d_in_cuda_naive_bfloat16_ks_9_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation); - -void na2d_in_cuda_naive_bfloat16_ks_11_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation); - -void na2d_in_cuda_naive_bfloat16_ks_11_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation); - -void na2d_in_cuda_naive_bfloat16_ks_13_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation); - -void na2d_in_cuda_naive_bfloat16_ks_13_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation); - -void na3d_in_cuda_naive_double_ks_any_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); - -void na3d_in_cuda_naive_double_ks_any_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); - -void na3d_in_cuda_naive_double_ks_3_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); - -void na3d_in_cuda_naive_double_ks_3_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); - -void na3d_in_cuda_naive_double_ks_5_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); - -void na3d_in_cuda_naive_double_ks_5_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); - -void na3d_in_cuda_naive_double_ks_7_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); - -void na3d_in_cuda_naive_double_ks_7_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); - -void na3d_in_cuda_naive_double_ks_9_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); - -void na3d_in_cuda_naive_double_ks_9_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); - -void na3d_in_cuda_naive_double_ks_11_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); - -void na3d_in_cuda_naive_double_ks_11_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); - -void na3d_in_cuda_naive_double_ks_13_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); - -void na3d_in_cuda_naive_double_ks_13_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); - -void na3d_in_cuda_naive_float_ks_any_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); - -void na3d_in_cuda_naive_float_ks_any_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); - -void na3d_in_cuda_naive_float_ks_3_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); - -void na3d_in_cuda_naive_float_ks_3_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); - -void na3d_in_cuda_naive_float_ks_5_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); - -void na3d_in_cuda_naive_float_ks_5_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); - -void na3d_in_cuda_naive_float_ks_7_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); - -void na3d_in_cuda_naive_float_ks_7_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); - -void na3d_in_cuda_naive_float_ks_9_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); - -void na3d_in_cuda_naive_float_ks_9_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); - -void na3d_in_cuda_naive_float_ks_11_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); - -void na3d_in_cuda_naive_float_ks_11_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); - -void na3d_in_cuda_naive_float_ks_13_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); - -void na3d_in_cuda_naive_float_ks_13_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); - -void na3d_in_cuda_naive_half_ks_any_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); - -void na3d_in_cuda_naive_half_ks_any_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); - -void na3d_in_cuda_naive_half_ks_3_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); - -void na3d_in_cuda_naive_half_ks_3_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); - -void na3d_in_cuda_naive_half_ks_5_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); - -void na3d_in_cuda_naive_half_ks_5_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); - -void na3d_in_cuda_naive_half_ks_7_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); - -void na3d_in_cuda_naive_half_ks_7_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); - -void na3d_in_cuda_naive_half_ks_9_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); - -void na3d_in_cuda_naive_half_ks_9_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); - -void na3d_in_cuda_naive_half_ks_11_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); - -void na3d_in_cuda_naive_half_ks_11_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); - -void na3d_in_cuda_naive_half_ks_13_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); - -void na3d_in_cuda_naive_half_ks_13_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); - -void na3d_in_cuda_naive_bfloat16_ks_any_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); - -void na3d_in_cuda_naive_bfloat16_ks_any_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); - -void na3d_in_cuda_naive_bfloat16_ks_3_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); - -void na3d_in_cuda_naive_bfloat16_ks_3_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); - -void na3d_in_cuda_naive_bfloat16_ks_5_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); - -void na3d_in_cuda_naive_bfloat16_ks_5_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); - -void na3d_in_cuda_naive_bfloat16_ks_7_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); - -void na3d_in_cuda_naive_bfloat16_ks_7_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); - -void na3d_in_cuda_naive_bfloat16_ks_9_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); - -void na3d_in_cuda_naive_bfloat16_ks_9_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); - -void na3d_in_cuda_naive_bfloat16_ks_11_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); - -void na3d_in_cuda_naive_bfloat16_ks_11_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); - -void na3d_in_cuda_naive_bfloat16_ks_13_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); - -void na3d_in_cuda_naive_bfloat16_ks_13_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); - -void na1d_rpbgrad_cuda_naive_double_ks_any_di_any( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation); - -void na1d_rpbgrad_cuda_naive_double_ks_any_di_1( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation); - -void na1d_rpbgrad_cuda_naive_double_ks_3_di_any( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation); - -void na1d_rpbgrad_cuda_naive_double_ks_3_di_1( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation); - -void na1d_rpbgrad_cuda_naive_double_ks_5_di_any( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation); - -void na1d_rpbgrad_cuda_naive_double_ks_5_di_1( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation); - -void na1d_rpbgrad_cuda_naive_double_ks_7_di_any( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation); - -void na1d_rpbgrad_cuda_naive_double_ks_7_di_1( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation); - -void na1d_rpbgrad_cuda_naive_double_ks_9_di_any( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation); - -void na1d_rpbgrad_cuda_naive_double_ks_9_di_1( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation); - -void na1d_rpbgrad_cuda_naive_double_ks_11_di_any( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation); - -void na1d_rpbgrad_cuda_naive_double_ks_11_di_1( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation); - -void na1d_rpbgrad_cuda_naive_double_ks_13_di_any( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation); - -void na1d_rpbgrad_cuda_naive_double_ks_13_di_1( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation); - -void na1d_rpbgrad_cuda_naive_float_ks_any_di_any( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation); - -void na1d_rpbgrad_cuda_naive_float_ks_any_di_1( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation); - -void na1d_rpbgrad_cuda_naive_float_ks_3_di_any( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation); - -void na1d_rpbgrad_cuda_naive_float_ks_3_di_1( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation); - -void na1d_rpbgrad_cuda_naive_float_ks_5_di_any( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation); - -void na1d_rpbgrad_cuda_naive_float_ks_5_di_1( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation); - -void na1d_rpbgrad_cuda_naive_float_ks_7_di_any( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation); - -void na1d_rpbgrad_cuda_naive_float_ks_7_di_1( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation); - -void na1d_rpbgrad_cuda_naive_float_ks_9_di_any( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation); - -void na1d_rpbgrad_cuda_naive_float_ks_9_di_1( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation); - -void na1d_rpbgrad_cuda_naive_float_ks_11_di_any( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation); - -void na1d_rpbgrad_cuda_naive_float_ks_11_di_1( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation); - -void na1d_rpbgrad_cuda_naive_float_ks_13_di_any( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation); - -void na1d_rpbgrad_cuda_naive_float_ks_13_di_1( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation); - -void na1d_rpbgrad_cuda_naive_half_ks_any_di_any( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation); - -void na1d_rpbgrad_cuda_naive_half_ks_any_di_1( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation); - -void na1d_rpbgrad_cuda_naive_half_ks_3_di_any( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation); - -void na1d_rpbgrad_cuda_naive_half_ks_3_di_1( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation); - -void na1d_rpbgrad_cuda_naive_half_ks_5_di_any( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation); - -void na1d_rpbgrad_cuda_naive_half_ks_5_di_1( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation); - -void na1d_rpbgrad_cuda_naive_half_ks_7_di_any( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation); - -void na1d_rpbgrad_cuda_naive_half_ks_7_di_1( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation); - -void na1d_rpbgrad_cuda_naive_half_ks_9_di_any( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation); - -void na1d_rpbgrad_cuda_naive_half_ks_9_di_1( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation); - -void na1d_rpbgrad_cuda_naive_half_ks_11_di_any( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation); - -void na1d_rpbgrad_cuda_naive_half_ks_11_di_1( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation); - -void na1d_rpbgrad_cuda_naive_half_ks_13_di_any( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation); - -void na1d_rpbgrad_cuda_naive_half_ks_13_di_1( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation); - -void na1d_rpbgrad_cuda_naive_bfloat16_ks_any_di_any( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation); - -void na1d_rpbgrad_cuda_naive_bfloat16_ks_any_di_1( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation); - -void na1d_rpbgrad_cuda_naive_bfloat16_ks_3_di_any( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation); - -void na1d_rpbgrad_cuda_naive_bfloat16_ks_3_di_1( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation); - -void na1d_rpbgrad_cuda_naive_bfloat16_ks_5_di_any( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation); - -void na1d_rpbgrad_cuda_naive_bfloat16_ks_5_di_1( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation); - -void na1d_rpbgrad_cuda_naive_bfloat16_ks_7_di_any( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation); - -void na1d_rpbgrad_cuda_naive_bfloat16_ks_7_di_1( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation); - -void na1d_rpbgrad_cuda_naive_bfloat16_ks_9_di_any( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation); - -void na1d_rpbgrad_cuda_naive_bfloat16_ks_9_di_1( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation); - -void na1d_rpbgrad_cuda_naive_bfloat16_ks_11_di_any( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation); - -void na1d_rpbgrad_cuda_naive_bfloat16_ks_11_di_1( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation); - -void na1d_rpbgrad_cuda_naive_bfloat16_ks_13_di_any( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation); - -void na1d_rpbgrad_cuda_naive_bfloat16_ks_13_di_1( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation); - -void na2d_rpbgrad_cuda_naive_double_ks_any_di_any( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation); - -void na2d_rpbgrad_cuda_naive_double_ks_any_di_1( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation); - -void na2d_rpbgrad_cuda_naive_double_ks_3_di_any( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation); - -void na2d_rpbgrad_cuda_naive_double_ks_3_di_1( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation); - -void na2d_rpbgrad_cuda_naive_double_ks_5_di_any( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation); - -void na2d_rpbgrad_cuda_naive_double_ks_5_di_1( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation); - -void na2d_rpbgrad_cuda_naive_double_ks_7_di_any( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation); - -void na2d_rpbgrad_cuda_naive_double_ks_7_di_1( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation); - -void na2d_rpbgrad_cuda_naive_double_ks_9_di_any( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation); - -void na2d_rpbgrad_cuda_naive_double_ks_9_di_1( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation); - -void na2d_rpbgrad_cuda_naive_double_ks_11_di_any( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation); - -void na2d_rpbgrad_cuda_naive_double_ks_11_di_1( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation); - -void na2d_rpbgrad_cuda_naive_double_ks_13_di_any( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation); - -void na2d_rpbgrad_cuda_naive_double_ks_13_di_1( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation); - -void na2d_rpbgrad_cuda_naive_float_ks_any_di_any( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation); - -void na2d_rpbgrad_cuda_naive_float_ks_any_di_1( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation); - -void na2d_rpbgrad_cuda_naive_float_ks_3_di_any( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation); - -void na2d_rpbgrad_cuda_naive_float_ks_3_di_1( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation); - -void na2d_rpbgrad_cuda_naive_float_ks_5_di_any( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation); - -void na2d_rpbgrad_cuda_naive_float_ks_5_di_1( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation); - -void na2d_rpbgrad_cuda_naive_float_ks_7_di_any( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation); - -void na2d_rpbgrad_cuda_naive_float_ks_7_di_1( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation); - -void na2d_rpbgrad_cuda_naive_float_ks_9_di_any( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation); - -void na2d_rpbgrad_cuda_naive_float_ks_9_di_1( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation); - -void na2d_rpbgrad_cuda_naive_float_ks_11_di_any( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation); - -void na2d_rpbgrad_cuda_naive_float_ks_11_di_1( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation); - -void na2d_rpbgrad_cuda_naive_float_ks_13_di_any( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation); - -void na2d_rpbgrad_cuda_naive_float_ks_13_di_1( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation); - -void na2d_rpbgrad_cuda_naive_half_ks_any_di_any( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation); - -void na2d_rpbgrad_cuda_naive_half_ks_any_di_1( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation); - -void na2d_rpbgrad_cuda_naive_half_ks_3_di_any( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation); - -void na2d_rpbgrad_cuda_naive_half_ks_3_di_1( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation); - -void na2d_rpbgrad_cuda_naive_half_ks_5_di_any( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation); - -void na2d_rpbgrad_cuda_naive_half_ks_5_di_1( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation); - -void na2d_rpbgrad_cuda_naive_half_ks_7_di_any( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation); - -void na2d_rpbgrad_cuda_naive_half_ks_7_di_1( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation); - -void na2d_rpbgrad_cuda_naive_half_ks_9_di_any( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation); - -void na2d_rpbgrad_cuda_naive_half_ks_9_di_1( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation); - -void na2d_rpbgrad_cuda_naive_half_ks_11_di_any( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation); - -void na2d_rpbgrad_cuda_naive_half_ks_11_di_1( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation); - -void na2d_rpbgrad_cuda_naive_half_ks_13_di_any( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation); - -void na2d_rpbgrad_cuda_naive_half_ks_13_di_1( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation); - -void na2d_rpbgrad_cuda_naive_bfloat16_ks_any_di_any( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation); - -void na2d_rpbgrad_cuda_naive_bfloat16_ks_any_di_1( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation); - -void na2d_rpbgrad_cuda_naive_bfloat16_ks_3_di_any( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation); - -void na2d_rpbgrad_cuda_naive_bfloat16_ks_3_di_1( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation); - -void na2d_rpbgrad_cuda_naive_bfloat16_ks_5_di_any( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation); - -void na2d_rpbgrad_cuda_naive_bfloat16_ks_5_di_1( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation); - -void na2d_rpbgrad_cuda_naive_bfloat16_ks_7_di_any( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation); - -void na2d_rpbgrad_cuda_naive_bfloat16_ks_7_di_1( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation); - -void na2d_rpbgrad_cuda_naive_bfloat16_ks_9_di_any( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation); - -void na2d_rpbgrad_cuda_naive_bfloat16_ks_9_di_1( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation); - -void na2d_rpbgrad_cuda_naive_bfloat16_ks_11_di_any( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation); - -void na2d_rpbgrad_cuda_naive_bfloat16_ks_11_di_1( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation); - -void na2d_rpbgrad_cuda_naive_bfloat16_ks_13_di_any( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation); - -void na2d_rpbgrad_cuda_naive_bfloat16_ks_13_di_1( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation); - -void na3d_rpbgrad_cuda_naive_double_ks_any_di_any( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); - -void na3d_rpbgrad_cuda_naive_double_ks_any_di_1( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); - -void na3d_rpbgrad_cuda_naive_double_ks_3_di_any( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); - -void na3d_rpbgrad_cuda_naive_double_ks_3_di_1( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); - -void na3d_rpbgrad_cuda_naive_double_ks_5_di_any( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); - -void na3d_rpbgrad_cuda_naive_double_ks_5_di_1( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); - -void na3d_rpbgrad_cuda_naive_double_ks_7_di_any( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); - -void na3d_rpbgrad_cuda_naive_double_ks_7_di_1( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); - -void na3d_rpbgrad_cuda_naive_double_ks_9_di_any( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); - -void na3d_rpbgrad_cuda_naive_double_ks_9_di_1( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); - -void na3d_rpbgrad_cuda_naive_double_ks_11_di_any( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); - -void na3d_rpbgrad_cuda_naive_double_ks_11_di_1( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); - -void na3d_rpbgrad_cuda_naive_double_ks_13_di_any( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); - -void na3d_rpbgrad_cuda_naive_double_ks_13_di_1( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); - -void na3d_rpbgrad_cuda_naive_float_ks_any_di_any( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); - -void na3d_rpbgrad_cuda_naive_float_ks_any_di_1( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); - -void na3d_rpbgrad_cuda_naive_float_ks_3_di_any( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); - -void na3d_rpbgrad_cuda_naive_float_ks_3_di_1( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); - -void na3d_rpbgrad_cuda_naive_float_ks_5_di_any( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); - -void na3d_rpbgrad_cuda_naive_float_ks_5_di_1( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); - -void na3d_rpbgrad_cuda_naive_float_ks_7_di_any( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int32_t batch_size, + int32_t heads, + int32_t depth, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); + const std::tuple& kernel_size, + const std::tuple& dilation); -void na3d_rpbgrad_cuda_naive_float_ks_7_di_1( - const int cc, +void na3d_in_cuda_naive_float_cm_0_0_1( + int32_t cc, cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int32_t batch_size, + int32_t heads, + int32_t depth, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); + const std::tuple& kernel_size, + const std::tuple& dilation); -void na3d_rpbgrad_cuda_naive_float_ks_9_di_any( - const int cc, +void na3d_in_cuda_naive_float_cm_0_1_0( + int32_t cc, cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int32_t batch_size, + int32_t heads, + int32_t depth, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); + const std::tuple& kernel_size, + const std::tuple& dilation); -void na3d_rpbgrad_cuda_naive_float_ks_9_di_1( - const int cc, +void na3d_in_cuda_naive_float_cm_0_1_1( + int32_t cc, cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int32_t batch_size, + int32_t heads, + int32_t depth, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); + const std::tuple& kernel_size, + const std::tuple& dilation); -void na3d_rpbgrad_cuda_naive_float_ks_11_di_any( - const int cc, +void na3d_in_cuda_naive_float_cm_1_0_0( + int32_t cc, cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int32_t batch_size, + int32_t heads, + int32_t depth, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); + const std::tuple& kernel_size, + const std::tuple& dilation); -void na3d_rpbgrad_cuda_naive_float_ks_11_di_1( - const int cc, +void na3d_in_cuda_naive_float_cm_1_0_1( + int32_t cc, cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int32_t batch_size, + int32_t heads, + int32_t depth, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); + const std::tuple& kernel_size, + const std::tuple& dilation); -void na3d_rpbgrad_cuda_naive_float_ks_13_di_any( - const int cc, +void na3d_in_cuda_naive_float_cm_1_1_0( + int32_t cc, cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int32_t batch_size, + int32_t heads, + int32_t depth, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); + const std::tuple& kernel_size, + const std::tuple& dilation); -void na3d_rpbgrad_cuda_naive_float_ks_13_di_1( - const int cc, +void na3d_in_cuda_naive_float_cm_1_1_1( + int32_t cc, cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int32_t batch_size, + int32_t heads, + int32_t depth, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); + const std::tuple& kernel_size, + const std::tuple& dilation); -void na3d_rpbgrad_cuda_naive_half_ks_any_di_any( - const int cc, +void na3d_in_cuda_naive_half_cm_0_0_0( + int32_t cc, cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int32_t batch_size, + int32_t heads, + int32_t depth, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); + const std::tuple& kernel_size, + const std::tuple& dilation); -void na3d_rpbgrad_cuda_naive_half_ks_any_di_1( - const int cc, +void na3d_in_cuda_naive_half_cm_0_0_1( + int32_t cc, cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int32_t batch_size, + int32_t heads, + int32_t depth, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); + const std::tuple& kernel_size, + const std::tuple& dilation); -void na3d_rpbgrad_cuda_naive_half_ks_3_di_any( - const int cc, +void na3d_in_cuda_naive_half_cm_0_1_0( + int32_t cc, cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int32_t batch_size, + int32_t heads, + int32_t depth, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); + const std::tuple& kernel_size, + const std::tuple& dilation); -void na3d_rpbgrad_cuda_naive_half_ks_3_di_1( - const int cc, +void na3d_in_cuda_naive_half_cm_0_1_1( + int32_t cc, cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int32_t batch_size, + int32_t heads, + int32_t depth, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); + const std::tuple& kernel_size, + const std::tuple& dilation); -void na3d_rpbgrad_cuda_naive_half_ks_5_di_any( - const int cc, +void na3d_in_cuda_naive_half_cm_1_0_0( + int32_t cc, cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int32_t batch_size, + int32_t heads, + int32_t depth, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); + const std::tuple& kernel_size, + const std::tuple& dilation); -void na3d_rpbgrad_cuda_naive_half_ks_5_di_1( - const int cc, +void na3d_in_cuda_naive_half_cm_1_0_1( + int32_t cc, cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int32_t batch_size, + int32_t heads, + int32_t depth, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); + const std::tuple& kernel_size, + const std::tuple& dilation); -void na3d_rpbgrad_cuda_naive_half_ks_7_di_any( - const int cc, +void na3d_in_cuda_naive_half_cm_1_1_0( + int32_t cc, cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int32_t batch_size, + int32_t heads, + int32_t depth, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); + const std::tuple& kernel_size, + const std::tuple& dilation); -void na3d_rpbgrad_cuda_naive_half_ks_7_di_1( - const int cc, +void na3d_in_cuda_naive_half_cm_1_1_1( + int32_t cc, cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int32_t batch_size, + int32_t heads, + int32_t depth, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); + const std::tuple& kernel_size, + const std::tuple& dilation); -void na3d_rpbgrad_cuda_naive_half_ks_9_di_any( - const int cc, +void na3d_in_cuda_naive_bfloat16_cm_0_0_0( + int32_t cc, cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int32_t batch_size, + int32_t heads, + int32_t depth, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); + const std::tuple& kernel_size, + const std::tuple& dilation); -void na3d_rpbgrad_cuda_naive_half_ks_9_di_1( - const int cc, +void na3d_in_cuda_naive_bfloat16_cm_0_0_1( + int32_t cc, cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int32_t batch_size, + int32_t heads, + int32_t depth, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); + const std::tuple& kernel_size, + const std::tuple& dilation); -void na3d_rpbgrad_cuda_naive_half_ks_11_di_any( - const int cc, +void na3d_in_cuda_naive_bfloat16_cm_0_1_0( + int32_t cc, cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int32_t batch_size, + int32_t heads, + int32_t depth, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); + const std::tuple& kernel_size, + const std::tuple& dilation); -void na3d_rpbgrad_cuda_naive_half_ks_11_di_1( - const int cc, +void na3d_in_cuda_naive_bfloat16_cm_0_1_1( + int32_t cc, cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int32_t batch_size, + int32_t heads, + int32_t depth, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); + const std::tuple& kernel_size, + const std::tuple& dilation); -void na3d_rpbgrad_cuda_naive_half_ks_13_di_any( - const int cc, +void na3d_in_cuda_naive_bfloat16_cm_1_0_0( + int32_t cc, cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int32_t batch_size, + int32_t heads, + int32_t depth, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); + const std::tuple& kernel_size, + const std::tuple& dilation); -void na3d_rpbgrad_cuda_naive_half_ks_13_di_1( - const int cc, +void na3d_in_cuda_naive_bfloat16_cm_1_0_1( + int32_t cc, cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int32_t batch_size, + int32_t heads, + int32_t depth, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); + const std::tuple& kernel_size, + const std::tuple& dilation); -void na3d_rpbgrad_cuda_naive_bfloat16_ks_any_di_any( - const int cc, +void na3d_in_cuda_naive_bfloat16_cm_1_1_0( + int32_t cc, cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int32_t batch_size, + int32_t heads, + int32_t depth, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); + const std::tuple& kernel_size, + const std::tuple& dilation); -void na3d_rpbgrad_cuda_naive_bfloat16_ks_any_di_1( - const int cc, +void na3d_in_cuda_naive_bfloat16_cm_1_1_1( + int32_t cc, cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int32_t batch_size, + int32_t heads, + int32_t depth, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); + const std::tuple& kernel_size, + const std::tuple& dilation); -void na3d_rpbgrad_cuda_naive_bfloat16_ks_3_di_any( - const int cc, +void na1d_rpbgrad_cuda_naive_double_cm_0( + int32_t cc, cudaStream_t stream, void * d_bias_ptr, void * d_attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, + int32_t batch_size, + int32_t heads, + int32_t length, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); + const std::tuple& kernel_size, + const std::tuple& dilation); -void na3d_rpbgrad_cuda_naive_bfloat16_ks_3_di_1( - const int cc, +void na1d_rpbgrad_cuda_naive_float_cm_0( + int32_t cc, cudaStream_t stream, void * d_bias_ptr, void * d_attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, + int32_t batch_size, + int32_t heads, + int32_t length, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); + const std::tuple& kernel_size, + const std::tuple& dilation); -void na3d_rpbgrad_cuda_naive_bfloat16_ks_5_di_any( - const int cc, +void na1d_rpbgrad_cuda_naive_half_cm_0( + int32_t cc, cudaStream_t stream, void * d_bias_ptr, void * d_attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, + int32_t batch_size, + int32_t heads, + int32_t length, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); + const std::tuple& kernel_size, + const std::tuple& dilation); -void na3d_rpbgrad_cuda_naive_bfloat16_ks_5_di_1( - const int cc, +void na1d_rpbgrad_cuda_naive_bfloat16_cm_0( + int32_t cc, cudaStream_t stream, void * d_bias_ptr, void * d_attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, + int32_t batch_size, + int32_t heads, + int32_t length, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); + const std::tuple& kernel_size, + const std::tuple& dilation); -void na3d_rpbgrad_cuda_naive_bfloat16_ks_7_di_any( - const int cc, +void na2d_rpbgrad_cuda_naive_double_cm_0_0( + int32_t cc, cudaStream_t stream, void * d_bias_ptr, void * d_attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, + int32_t batch_size, + int32_t heads, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); + const std::tuple& kernel_size, + const std::tuple& dilation); -void na3d_rpbgrad_cuda_naive_bfloat16_ks_7_di_1( - const int cc, +void na2d_rpbgrad_cuda_naive_float_cm_0_0( + int32_t cc, cudaStream_t stream, void * d_bias_ptr, void * d_attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, + int32_t batch_size, + int32_t heads, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); + const std::tuple& kernel_size, + const std::tuple& dilation); -void na3d_rpbgrad_cuda_naive_bfloat16_ks_9_di_any( - const int cc, +void na2d_rpbgrad_cuda_naive_half_cm_0_0( + int32_t cc, cudaStream_t stream, void * d_bias_ptr, void * d_attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, + int32_t batch_size, + int32_t heads, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); + const std::tuple& kernel_size, + const std::tuple& dilation); -void na3d_rpbgrad_cuda_naive_bfloat16_ks_9_di_1( - const int cc, +void na2d_rpbgrad_cuda_naive_bfloat16_cm_0_0( + int32_t cc, cudaStream_t stream, void * d_bias_ptr, void * d_attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, + int32_t batch_size, + int32_t heads, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); + const std::tuple& kernel_size, + const std::tuple& dilation); -void na3d_rpbgrad_cuda_naive_bfloat16_ks_11_di_any( - const int cc, +void na3d_rpbgrad_cuda_naive_double_cm_0_0_0( + int32_t cc, cudaStream_t stream, void * d_bias_ptr, void * d_attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, + int32_t batch_size, + int32_t heads, + int32_t depth, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); + const std::tuple& kernel_size, + const std::tuple& dilation); -void na3d_rpbgrad_cuda_naive_bfloat16_ks_11_di_1( - const int cc, +void na3d_rpbgrad_cuda_naive_float_cm_0_0_0( + int32_t cc, cudaStream_t stream, void * d_bias_ptr, void * d_attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, + int32_t batch_size, + int32_t heads, + int32_t depth, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); + const std::tuple& kernel_size, + const std::tuple& dilation); -void na3d_rpbgrad_cuda_naive_bfloat16_ks_13_di_any( - const int cc, +void na3d_rpbgrad_cuda_naive_half_cm_0_0_0( + int32_t cc, cudaStream_t stream, void * d_bias_ptr, void * d_attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, + int32_t batch_size, + int32_t heads, + int32_t depth, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); + const std::tuple& kernel_size, + const std::tuple& dilation); -void na3d_rpbgrad_cuda_naive_bfloat16_ks_13_di_1( - const int cc, +void na3d_rpbgrad_cuda_naive_bfloat16_cm_0_0_0( + int32_t cc, cudaStream_t stream, void * d_bias_ptr, void * d_attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, + int32_t batch_size, + int32_t heads, + int32_t depth, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d); + const std::tuple& kernel_size, + const std::tuple& dilation); -} // namespace {namespace} -} // namespace {namespace} -} // namespace {namespace} +} // namespace natten +} // namespace cuda +} // namespace naive diff --git a/csrc/autogen/src/cpu/naive/source_0.cpp b/csrc/autogen/src/cpu/naive/source_0.cpp index 0f24746..6893809 100644 --- a/csrc/autogen/src/cpu/naive/source_0.cpp +++ b/csrc/autogen/src/cpu/naive/source_0.cpp @@ -1,141 +1,149 @@ #include -#include #include #include -#include +#include #include +#include namespace natten { namespace cpu { namespace naive { void na1d_pn_cpu_naive_double( + bool is_grad, void * query_ptr, void * key_ptr, void * attn_ptr, - int batch_size, - int heads, - int length, - int dim, + int32_t batch_size, + int32_t heads, + int32_t length, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, - int kernel_size, - int dilation) { + const std::tuple& kernel_size, + const std::tuple& dilation, + const std::tuple& is_causal) { using Kernel = PointwiseNeighborhood1D; Kernel kernel; kernel( -query_ptr, key_ptr, attn_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); +is_grad, query_ptr, key_ptr, attn_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation, is_causal); } void na1d_pn_cpu_naive_float( + bool is_grad, void * query_ptr, void * key_ptr, void * attn_ptr, - int batch_size, - int heads, - int length, - int dim, + int32_t batch_size, + int32_t heads, + int32_t length, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, - int kernel_size, - int dilation) { + const std::tuple& kernel_size, + const std::tuple& dilation, + const std::tuple& is_causal) { using Kernel = PointwiseNeighborhood1D; Kernel kernel; kernel( -query_ptr, key_ptr, attn_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); +is_grad, query_ptr, key_ptr, attn_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation, is_causal); } void na2d_pn_cpu_naive_double( + bool is_grad, void * query_ptr, void * key_ptr, void * attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, + int32_t batch_size, + int32_t heads, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, - int kernel_size, - int dilation) { + const std::tuple& kernel_size, + const std::tuple& dilation, + const std::tuple& is_causal) { using Kernel = PointwiseNeighborhood2D; Kernel kernel; kernel( -query_ptr, key_ptr, attn_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); +is_grad, query_ptr, key_ptr, attn_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, is_causal); } void na2d_pn_cpu_naive_float( + bool is_grad, void * query_ptr, void * key_ptr, void * attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, + int32_t batch_size, + int32_t heads, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, - int kernel_size, - int dilation) { + const std::tuple& kernel_size, + const std::tuple& dilation, + const std::tuple& is_causal) { using Kernel = PointwiseNeighborhood2D; Kernel kernel; kernel( -query_ptr, key_ptr, attn_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); +is_grad, query_ptr, key_ptr, attn_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, is_causal); } void na3d_pn_cpu_naive_double( + bool is_grad, void * query_ptr, void * key_ptr, void * attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, + int32_t batch_size, + int32_t heads, + int32_t depth, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { + const std::tuple& kernel_size, + const std::tuple& dilation, + const std::tuple& is_causal) { using Kernel = PointwiseNeighborhood3D; Kernel kernel; kernel( -query_ptr, key_ptr, attn_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); +is_grad, query_ptr, key_ptr, attn_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, is_causal); } void na3d_pn_cpu_naive_float( + bool is_grad, void * query_ptr, void * key_ptr, void * attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, + int32_t batch_size, + int32_t heads, + int32_t depth, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { + const std::tuple& kernel_size, + const std::tuple& dilation, + const std::tuple& is_causal) { using Kernel = PointwiseNeighborhood3D; Kernel kernel; kernel( -query_ptr, key_ptr, attn_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); +is_grad, query_ptr, key_ptr, attn_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, is_causal); } void na1d_pn_bias_cpu_naive_double( @@ -143,19 +151,20 @@ void na1d_pn_bias_cpu_naive_double( void * key_ptr, void * bias_ptr, void * attn_ptr, - int batch_size, - int heads, - int length, - int dim, + int32_t batch_size, + int32_t heads, + int32_t length, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, - int kernel_size, - int dilation) { + const std::tuple& kernel_size, + const std::tuple& dilation, + const std::tuple& is_causal) { using Kernel = PointwiseNeighborhood1DWithBias; Kernel kernel; kernel( -query_ptr, key_ptr, bias_ptr, attn_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); +query_ptr, key_ptr, bias_ptr, attn_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation, is_causal); } void na1d_pn_bias_cpu_naive_float( @@ -163,19 +172,20 @@ void na1d_pn_bias_cpu_naive_float( void * key_ptr, void * bias_ptr, void * attn_ptr, - int batch_size, - int heads, - int length, - int dim, + int32_t batch_size, + int32_t heads, + int32_t length, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, - int kernel_size, - int dilation) { + const std::tuple& kernel_size, + const std::tuple& dilation, + const std::tuple& is_causal) { using Kernel = PointwiseNeighborhood1DWithBias; Kernel kernel; kernel( -query_ptr, key_ptr, bias_ptr, attn_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); +query_ptr, key_ptr, bias_ptr, attn_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation, is_causal); } void na2d_pn_bias_cpu_naive_double( @@ -183,21 +193,22 @@ void na2d_pn_bias_cpu_naive_double( void * key_ptr, void * bias_ptr, void * attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, + int32_t batch_size, + int32_t heads, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, - int kernel_size, - int dilation) { + const std::tuple& kernel_size, + const std::tuple& dilation, + const std::tuple& is_causal) { using Kernel = PointwiseNeighborhood2DWithBias; Kernel kernel; kernel( -query_ptr, key_ptr, bias_ptr, attn_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); +query_ptr, key_ptr, bias_ptr, attn_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, is_causal); } void na2d_pn_bias_cpu_naive_float( @@ -205,21 +216,22 @@ void na2d_pn_bias_cpu_naive_float( void * key_ptr, void * bias_ptr, void * attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, + int32_t batch_size, + int32_t heads, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, - int kernel_size, - int dilation) { + const std::tuple& kernel_size, + const std::tuple& dilation, + const std::tuple& is_causal) { using Kernel = PointwiseNeighborhood2DWithBias; Kernel kernel; kernel( -query_ptr, key_ptr, bias_ptr, attn_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); +query_ptr, key_ptr, bias_ptr, attn_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, is_causal); } void na3d_pn_bias_cpu_naive_double( @@ -227,25 +239,24 @@ void na3d_pn_bias_cpu_naive_double( void * key_ptr, void * bias_ptr, void * attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, + int32_t batch_size, + int32_t heads, + int32_t depth, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { + const std::tuple& kernel_size, + const std::tuple& dilation, + const std::tuple& is_causal) { using Kernel = PointwiseNeighborhood3DWithBias; Kernel kernel; kernel( -query_ptr, key_ptr, bias_ptr, attn_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); +query_ptr, key_ptr, bias_ptr, attn_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, is_causal); } void na3d_pn_bias_cpu_naive_float( @@ -253,84 +264,86 @@ void na3d_pn_bias_cpu_naive_float( void * key_ptr, void * bias_ptr, void * attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, + int32_t batch_size, + int32_t heads, + int32_t depth, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { + const std::tuple& kernel_size, + const std::tuple& dilation, + const std::tuple& is_causal) { using Kernel = PointwiseNeighborhood3DWithBias; Kernel kernel; kernel( -query_ptr, key_ptr, bias_ptr, attn_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); +query_ptr, key_ptr, bias_ptr, attn_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, is_causal); } void na1d_nn_cpu_naive_double( void * attn_ptr, void * value_ptr, void * output_ptr, - int batch_size, - int heads, - int length, - int dim, + int32_t batch_size, + int32_t heads, + int32_t length, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, - int kernel_size, - int dilation) { + const std::tuple& kernel_size, + const std::tuple& dilation, + const std::tuple& is_causal) { using Kernel = NeighborhoodNeighborhood1D; Kernel kernel; kernel( -attn_ptr, value_ptr, output_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); +attn_ptr, value_ptr, output_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation, is_causal); } void na1d_nn_cpu_naive_float( void * attn_ptr, void * value_ptr, void * output_ptr, - int batch_size, - int heads, - int length, - int dim, + int32_t batch_size, + int32_t heads, + int32_t length, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, - int kernel_size, - int dilation) { + const std::tuple& kernel_size, + const std::tuple& dilation, + const std::tuple& is_causal) { using Kernel = NeighborhoodNeighborhood1D; Kernel kernel; kernel( -attn_ptr, value_ptr, output_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); +attn_ptr, value_ptr, output_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation, is_causal); } void na2d_nn_cpu_naive_double( void * attn_ptr, void * value_ptr, void * output_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, + int32_t batch_size, + int32_t heads, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, - int kernel_size, - int dilation) { + const std::tuple& kernel_size, + const std::tuple& dilation, + const std::tuple& is_causal) { using Kernel = NeighborhoodNeighborhood2D; Kernel kernel; kernel( -attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, is_causal); } } diff --git a/csrc/autogen/src/cpu/naive/source_1.cpp b/csrc/autogen/src/cpu/naive/source_1.cpp index b61cfad..142fcaa 100644 --- a/csrc/autogen/src/cpu/naive/source_1.cpp +++ b/csrc/autogen/src/cpu/naive/source_1.cpp @@ -1,12 +1,12 @@ #include -#include -#include #include -#include -#include #include +#include #include +#include +#include #include +#include namespace natten { namespace cpu { namespace naive { @@ -15,325 +15,328 @@ void na2d_nn_cpu_naive_float( void * attn_ptr, void * value_ptr, void * output_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, + int32_t batch_size, + int32_t heads, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, - int kernel_size, - int dilation) { + const std::tuple& kernel_size, + const std::tuple& dilation, + const std::tuple& is_causal) { using Kernel = NeighborhoodNeighborhood2D; Kernel kernel; kernel( -attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, is_causal); } void na3d_nn_cpu_naive_double( void * attn_ptr, void * value_ptr, void * output_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, + int32_t batch_size, + int32_t heads, + int32_t depth, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { + const std::tuple& kernel_size, + const std::tuple& dilation, + const std::tuple& is_causal) { using Kernel = NeighborhoodNeighborhood3D; Kernel kernel; kernel( -attn_ptr, value_ptr, output_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); +attn_ptr, value_ptr, output_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, is_causal); } void na3d_nn_cpu_naive_float( void * attn_ptr, void * value_ptr, void * output_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, + int32_t batch_size, + int32_t heads, + int32_t depth, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { + const std::tuple& kernel_size, + const std::tuple& dilation, + const std::tuple& is_causal) { using Kernel = NeighborhoodNeighborhood3D; Kernel kernel; kernel( -attn_ptr, value_ptr, output_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); +attn_ptr, value_ptr, output_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, is_causal); } void na1d_in_cpu_naive_double( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, - int batch_size, - int heads, - int length, - int dim, + int32_t batch_size, + int32_t heads, + int32_t length, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, - int kernel_size, - int dilation) { + const std::tuple& kernel_size, + const std::tuple& dilation, + const std::tuple& is_causal) { using Kernel = InverseNeighborhood1D; Kernel kernel; kernel( -attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation, is_causal); } void na1d_in_cpu_naive_float( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, - int batch_size, - int heads, - int length, - int dim, + int32_t batch_size, + int32_t heads, + int32_t length, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, - int kernel_size, - int dilation) { + const std::tuple& kernel_size, + const std::tuple& dilation, + const std::tuple& is_causal) { using Kernel = InverseNeighborhood1D; Kernel kernel; kernel( -attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation, is_causal); } void na2d_in_cpu_naive_double( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, + int32_t batch_size, + int32_t heads, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, - int kernel_size, - int dilation) { + const std::tuple& kernel_size, + const std::tuple& dilation, + const std::tuple& is_causal) { using Kernel = InverseNeighborhood2D; Kernel kernel; kernel( -attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, is_causal); } void na2d_in_cpu_naive_float( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, + int32_t batch_size, + int32_t heads, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, - int kernel_size, - int dilation) { + const std::tuple& kernel_size, + const std::tuple& dilation, + const std::tuple& is_causal) { using Kernel = InverseNeighborhood2D; Kernel kernel; kernel( -attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, is_causal); } void na3d_in_cpu_naive_double( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, + int32_t batch_size, + int32_t heads, + int32_t depth, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { + const std::tuple& kernel_size, + const std::tuple& dilation, + const std::tuple& is_causal) { using Kernel = InverseNeighborhood3D; Kernel kernel; kernel( -attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, is_causal); } void na3d_in_cpu_naive_float( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, + int32_t batch_size, + int32_t heads, + int32_t depth, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { + const std::tuple& kernel_size, + const std::tuple& dilation, + const std::tuple& is_causal) { using Kernel = InverseNeighborhood3D; Kernel kernel; kernel( -attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, is_causal); } void na1d_rpbgrad_cpu_naive_double( void * d_bias_ptr, void * d_attn_ptr, - int batch_size, - int heads, - int length, - int dim, + int32_t batch_size, + int32_t heads, + int32_t length, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, - int kernel_size, - int dilation) { + const std::tuple& kernel_size, + const std::tuple& dilation, + const std::tuple& is_causal) { using Kernel = RelPosBiasGradient1D; Kernel kernel; kernel( -d_bias_ptr, d_attn_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); +d_bias_ptr, d_attn_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation, is_causal); } void na1d_rpbgrad_cpu_naive_float( void * d_bias_ptr, void * d_attn_ptr, - int batch_size, - int heads, - int length, - int dim, + int32_t batch_size, + int32_t heads, + int32_t length, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, - int kernel_size, - int dilation) { + const std::tuple& kernel_size, + const std::tuple& dilation, + const std::tuple& is_causal) { using Kernel = RelPosBiasGradient1D; Kernel kernel; kernel( -d_bias_ptr, d_attn_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); +d_bias_ptr, d_attn_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation, is_causal); } void na2d_rpbgrad_cpu_naive_double( void * d_bias_ptr, void * d_attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, + int32_t batch_size, + int32_t heads, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, - int kernel_size, - int dilation) { + const std::tuple& kernel_size, + const std::tuple& dilation, + const std::tuple& is_causal) { using Kernel = RelPosBiasGradient2D; Kernel kernel; kernel( -d_bias_ptr, d_attn_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); +d_bias_ptr, d_attn_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, is_causal); } void na2d_rpbgrad_cpu_naive_float( void * d_bias_ptr, void * d_attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, + int32_t batch_size, + int32_t heads, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, - int kernel_size, - int dilation) { + const std::tuple& kernel_size, + const std::tuple& dilation, + const std::tuple& is_causal) { using Kernel = RelPosBiasGradient2D; Kernel kernel; kernel( -d_bias_ptr, d_attn_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); +d_bias_ptr, d_attn_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, is_causal); } void na3d_rpbgrad_cpu_naive_double( void * d_bias_ptr, void * d_attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, + int32_t batch_size, + int32_t heads, + int32_t depth, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { + const std::tuple& kernel_size, + const std::tuple& dilation, + const std::tuple& is_causal) { using Kernel = RelPosBiasGradient3D; Kernel kernel; kernel( -d_bias_ptr, d_attn_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); +d_bias_ptr, d_attn_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, is_causal); } void na3d_rpbgrad_cpu_naive_float( void * d_bias_ptr, void * d_attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, + int32_t batch_size, + int32_t heads, + int32_t depth, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { + const std::tuple& kernel_size, + const std::tuple& dilation, + const std::tuple& is_causal) { using Kernel = RelPosBiasGradient3D; Kernel kernel; kernel( -d_bias_ptr, d_attn_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); +d_bias_ptr, d_attn_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, is_causal); } } diff --git a/csrc/autogen/src/cuda/fna/source_0.cu b/csrc/autogen/src/cuda/fna/source_0.cu new file mode 100644 index 0000000..aa069fc --- /dev/null +++ b/csrc/autogen/src/cuda/fna/source_0.cu @@ -0,0 +1,2573 @@ +#include +#include +#include +#include +#include +namespace natten { +namespace cuda { +namespace fna { + + + +/////////////////////////////////////////////////////////////////// +// FNA-1D / float32 / SM50 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 32, false>::kMinBlocksPerSm) +fna1_32x128x32_sm50_float32_cm_0(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_32x128x32_sm50_float32_cm_0` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 32, false>::kMinBlocksPerSm) +fna1_64x64x32_sm50_float32_cm_0(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_64x64x32_sm50_float32_cm_0` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 32, false>::kMinBlocksPerSm) +fna1_64x128x32_sm50_float32_cm_0(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_64x128x32_sm50_float32_cm_0` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 64, false>::kMinBlocksPerSm) +fna1_32x128x64_sm50_float32_cm_0(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_32x128x64_sm50_float32_cm_0` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 64, false>::kMinBlocksPerSm) +fna1_64x64x64_sm50_float32_cm_0(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_64x64x64_sm50_float32_cm_0` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 64, false>::kMinBlocksPerSm) +fna1_64x128x64_sm50_float32_cm_0(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_64x128x64_sm50_float32_cm_0` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 128, false>::kMinBlocksPerSm) +fna1_32x128x128_sm50_float32_cm_0(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_32x128x128_sm50_float32_cm_0` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 128, false>::kMinBlocksPerSm) +fna1_64x64x128_sm50_float32_cm_0(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_64x64x128_sm50_float32_cm_0` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 128, false>::kMinBlocksPerSm) +fna1_64x128x128_sm50_float32_cm_0(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_64x128x128_sm50_float32_cm_0` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 65536, false>::kMinBlocksPerSm) +fna1_32x128x65536_sm50_float32_cm_0(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_32x128x65536_sm50_float32_cm_0` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 65536, false>::kMinBlocksPerSm) +fna1_64x128x65536_sm50_float32_cm_0(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_64x128x65536_sm50_float32_cm_0` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 65536, false>::kMinBlocksPerSm) +fna1_64x64x65536_sm50_float32_cm_0(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_64x64x65536_sm50_float32_cm_0` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +/////////////////////////////////////////////////////////////////// +// FNA-1D / float32 / SM50 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 32, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 32, true>::kMinBlocksPerSm) +fna1_32x128x32_sm50_float32_cm_0_rpb(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 32, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 32, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_32x128x32_sm50_float32_cm_0_rpb` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 32, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 32, true>::kMinBlocksPerSm) +fna1_64x64x32_sm50_float32_cm_0_rpb(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 32, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 32, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_64x64x32_sm50_float32_cm_0_rpb` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 32, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 32, true>::kMinBlocksPerSm) +fna1_64x128x32_sm50_float32_cm_0_rpb(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 32, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 32, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_64x128x32_sm50_float32_cm_0_rpb` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 64, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 64, true>::kMinBlocksPerSm) +fna1_32x128x64_sm50_float32_cm_0_rpb(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 64, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 64, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_32x128x64_sm50_float32_cm_0_rpb` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 64, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 64, true>::kMinBlocksPerSm) +fna1_64x64x64_sm50_float32_cm_0_rpb(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 64, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 64, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_64x64x64_sm50_float32_cm_0_rpb` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 64, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 64, true>::kMinBlocksPerSm) +fna1_64x128x64_sm50_float32_cm_0_rpb(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 64, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 64, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_64x128x64_sm50_float32_cm_0_rpb` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 128, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 128, true>::kMinBlocksPerSm) +fna1_32x128x128_sm50_float32_cm_0_rpb(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 128, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 128, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_32x128x128_sm50_float32_cm_0_rpb` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 128, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 128, true>::kMinBlocksPerSm) +fna1_64x64x128_sm50_float32_cm_0_rpb(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 128, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 128, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_64x64x128_sm50_float32_cm_0_rpb` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 128, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 128, true>::kMinBlocksPerSm) +fna1_64x128x128_sm50_float32_cm_0_rpb(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 128, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 128, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_64x128x128_sm50_float32_cm_0_rpb` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 65536, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 65536, true>::kMinBlocksPerSm) +fna1_32x128x65536_sm50_float32_cm_0_rpb(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 65536, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 65536, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_32x128x65536_sm50_float32_cm_0_rpb` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 65536, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 65536, true>::kMinBlocksPerSm) +fna1_64x128x65536_sm50_float32_cm_0_rpb(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 65536, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 65536, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_64x128x65536_sm50_float32_cm_0_rpb` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 65536, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 65536, true>::kMinBlocksPerSm) +fna1_64x64x65536_sm50_float32_cm_0_rpb(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 65536, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 65536, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_64x64x65536_sm50_float32_cm_0_rpb` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +/////////////////////////////////////////////////////////////////// +// FNA-1D / float32 / SM50 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 32, false>::kMinBlocksPerSm) +fna1_32x128x32_sm50_float32_cm_1(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_32x128x32_sm50_float32_cm_1` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 32, false>::kMinBlocksPerSm) +fna1_64x64x32_sm50_float32_cm_1(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_64x64x32_sm50_float32_cm_1` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 32, false>::kMinBlocksPerSm) +fna1_64x128x32_sm50_float32_cm_1(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_64x128x32_sm50_float32_cm_1` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 64, false>::kMinBlocksPerSm) +fna1_32x128x64_sm50_float32_cm_1(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_32x128x64_sm50_float32_cm_1` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 64, false>::kMinBlocksPerSm) +fna1_64x64x64_sm50_float32_cm_1(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_64x64x64_sm50_float32_cm_1` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 64, false>::kMinBlocksPerSm) +fna1_64x128x64_sm50_float32_cm_1(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_64x128x64_sm50_float32_cm_1` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 128, false>::kMinBlocksPerSm) +fna1_32x128x128_sm50_float32_cm_1(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_32x128x128_sm50_float32_cm_1` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 128, false>::kMinBlocksPerSm) +fna1_64x64x128_sm50_float32_cm_1(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_64x64x128_sm50_float32_cm_1` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 128, false>::kMinBlocksPerSm) +fna1_64x128x128_sm50_float32_cm_1(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_64x128x128_sm50_float32_cm_1` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 65536, false>::kMinBlocksPerSm) +fna1_32x128x65536_sm50_float32_cm_1(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_32x128x65536_sm50_float32_cm_1` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 65536, false>::kMinBlocksPerSm) +fna1_64x128x65536_sm50_float32_cm_1(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_64x128x65536_sm50_float32_cm_1` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 65536, false>::kMinBlocksPerSm) +fna1_64x64x65536_sm50_float32_cm_1(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_64x64x65536_sm50_float32_cm_1` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +/////////////////////////////////////////////////////////////////// +// FNA-1D / float16 / SM50 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 32, false>::kMinBlocksPerSm) +fna1_32x128x32_sm50_float16_cm_0(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_32x128x32_sm50_float16_cm_0` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 32, false>::kMinBlocksPerSm) +fna1_64x64x32_sm50_float16_cm_0(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_64x64x32_sm50_float16_cm_0` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 32, false>::kMinBlocksPerSm) +fna1_64x128x32_sm50_float16_cm_0(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_64x128x32_sm50_float16_cm_0` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 64, false>::kMinBlocksPerSm) +fna1_32x128x64_sm50_float16_cm_0(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_32x128x64_sm50_float16_cm_0` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 64, false>::kMinBlocksPerSm) +fna1_64x64x64_sm50_float16_cm_0(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_64x64x64_sm50_float16_cm_0` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 64, false>::kMinBlocksPerSm) +fna1_64x128x64_sm50_float16_cm_0(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_64x128x64_sm50_float16_cm_0` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 128, false>::kMinBlocksPerSm) +fna1_32x128x128_sm50_float16_cm_0(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_32x128x128_sm50_float16_cm_0` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 128, false>::kMinBlocksPerSm) +fna1_64x64x128_sm50_float16_cm_0(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_64x64x128_sm50_float16_cm_0` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 128, false>::kMinBlocksPerSm) +fna1_64x128x128_sm50_float16_cm_0(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_64x128x128_sm50_float16_cm_0` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 65536, false>::kMinBlocksPerSm) +fna1_32x128x65536_sm50_float16_cm_0(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_32x128x65536_sm50_float16_cm_0` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 65536, false>::kMinBlocksPerSm) +fna1_64x128x65536_sm50_float16_cm_0(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_64x128x65536_sm50_float16_cm_0` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 65536, false>::kMinBlocksPerSm) +fna1_64x64x65536_sm50_float16_cm_0(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_64x64x65536_sm50_float16_cm_0` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +/////////////////////////////////////////////////////////////////// +// FNA-1D / float16 / SM50 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 32, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 32, true>::kMinBlocksPerSm) +fna1_32x128x32_sm50_float16_cm_0_rpb(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 32, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 32, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_32x128x32_sm50_float16_cm_0_rpb` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 32, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 32, true>::kMinBlocksPerSm) +fna1_64x64x32_sm50_float16_cm_0_rpb(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 32, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 32, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_64x64x32_sm50_float16_cm_0_rpb` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 32, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 32, true>::kMinBlocksPerSm) +fna1_64x128x32_sm50_float16_cm_0_rpb(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 32, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 32, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_64x128x32_sm50_float16_cm_0_rpb` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 64, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 64, true>::kMinBlocksPerSm) +fna1_32x128x64_sm50_float16_cm_0_rpb(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 64, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 64, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_32x128x64_sm50_float16_cm_0_rpb` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 64, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 64, true>::kMinBlocksPerSm) +fna1_64x64x64_sm50_float16_cm_0_rpb(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 64, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 64, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_64x64x64_sm50_float16_cm_0_rpb` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 64, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 64, true>::kMinBlocksPerSm) +fna1_64x128x64_sm50_float16_cm_0_rpb(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 64, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 64, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_64x128x64_sm50_float16_cm_0_rpb` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 128, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 128, true>::kMinBlocksPerSm) +fna1_32x128x128_sm50_float16_cm_0_rpb(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 128, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 128, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_32x128x128_sm50_float16_cm_0_rpb` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 128, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 128, true>::kMinBlocksPerSm) +fna1_64x64x128_sm50_float16_cm_0_rpb(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 128, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 128, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_64x64x128_sm50_float16_cm_0_rpb` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 128, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 128, true>::kMinBlocksPerSm) +fna1_64x128x128_sm50_float16_cm_0_rpb(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 128, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 128, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_64x128x128_sm50_float16_cm_0_rpb` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 65536, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 65536, true>::kMinBlocksPerSm) +fna1_32x128x65536_sm50_float16_cm_0_rpb(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 65536, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 65536, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_32x128x65536_sm50_float16_cm_0_rpb` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 65536, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 65536, true>::kMinBlocksPerSm) +fna1_64x128x65536_sm50_float16_cm_0_rpb(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 65536, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 65536, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_64x128x65536_sm50_float16_cm_0_rpb` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 65536, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 65536, true>::kMinBlocksPerSm) +fna1_64x64x65536_sm50_float16_cm_0_rpb(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 65536, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 65536, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_64x64x65536_sm50_float16_cm_0_rpb` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +/////////////////////////////////////////////////////////////////// +// FNA-1D / float16 / SM50 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 32, false>::kMinBlocksPerSm) +fna1_32x128x32_sm50_float16_cm_1(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_32x128x32_sm50_float16_cm_1` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 32, false>::kMinBlocksPerSm) +fna1_64x64x32_sm50_float16_cm_1(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_64x64x32_sm50_float16_cm_1` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 32, false>::kMinBlocksPerSm) +fna1_64x128x32_sm50_float16_cm_1(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_64x128x32_sm50_float16_cm_1` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 64, false>::kMinBlocksPerSm) +fna1_32x128x64_sm50_float16_cm_1(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_32x128x64_sm50_float16_cm_1` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 64, false>::kMinBlocksPerSm) +fna1_64x64x64_sm50_float16_cm_1(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_64x64x64_sm50_float16_cm_1` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 64, false>::kMinBlocksPerSm) +fna1_64x128x64_sm50_float16_cm_1(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_64x128x64_sm50_float16_cm_1` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 128, false>::kMinBlocksPerSm) +fna1_32x128x128_sm50_float16_cm_1(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_32x128x128_sm50_float16_cm_1` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 128, false>::kMinBlocksPerSm) +fna1_64x64x128_sm50_float16_cm_1(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_64x64x128_sm50_float16_cm_1` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 128, false>::kMinBlocksPerSm) +fna1_64x128x128_sm50_float16_cm_1(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_64x128x128_sm50_float16_cm_1` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 65536, false>::kMinBlocksPerSm) +fna1_32x128x65536_sm50_float16_cm_1(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_32x128x65536_sm50_float16_cm_1` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 65536, false>::kMinBlocksPerSm) +fna1_64x128x65536_sm50_float16_cm_1(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_64x128x65536_sm50_float16_cm_1` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 65536, false>::kMinBlocksPerSm) +fna1_64x64x65536_sm50_float16_cm_1(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_64x64x65536_sm50_float16_cm_1` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +/////////////////////////////////////////////////////////////////// +// FNA-1D / float32 / SM70 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 32, false>::kMinBlocksPerSm) +fna1_32x128x32_sm70_float32_cm_0(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_32x128x32_sm70_float32_cm_0` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 32, false>::kMinBlocksPerSm) +fna1_64x64x32_sm70_float32_cm_0(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_64x64x32_sm70_float32_cm_0` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 32, false>::kMinBlocksPerSm) +fna1_64x128x32_sm70_float32_cm_0(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_64x128x32_sm70_float32_cm_0` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 64, false>::kMinBlocksPerSm) +fna1_32x128x64_sm70_float32_cm_0(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_32x128x64_sm70_float32_cm_0` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 64, false>::kMinBlocksPerSm) +fna1_64x64x64_sm70_float32_cm_0(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_64x64x64_sm70_float32_cm_0` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 64, false>::kMinBlocksPerSm) +fna1_64x128x64_sm70_float32_cm_0(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_64x128x64_sm70_float32_cm_0` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 128, false>::kMinBlocksPerSm) +fna1_32x128x128_sm70_float32_cm_0(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_32x128x128_sm70_float32_cm_0` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 128, false>::kMinBlocksPerSm) +fna1_64x64x128_sm70_float32_cm_0(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_64x64x128_sm70_float32_cm_0` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 128, false>::kMinBlocksPerSm) +fna1_64x128x128_sm70_float32_cm_0(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_64x128x128_sm70_float32_cm_0` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 65536, false>::kMinBlocksPerSm) +fna1_32x128x65536_sm70_float32_cm_0(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_32x128x65536_sm70_float32_cm_0` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 65536, false>::kMinBlocksPerSm) +fna1_64x128x65536_sm70_float32_cm_0(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_64x128x65536_sm70_float32_cm_0` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 65536, false>::kMinBlocksPerSm) +fna1_64x64x65536_sm70_float32_cm_0(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_64x64x65536_sm70_float32_cm_0` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +/////////////////////////////////////////////////////////////////// +// FNA-1D / float32 / SM70 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 32, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 32, true>::kMinBlocksPerSm) +fna1_32x128x32_sm70_float32_cm_0_rpb(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 32, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 32, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_32x128x32_sm70_float32_cm_0_rpb` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 32, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 32, true>::kMinBlocksPerSm) +fna1_64x64x32_sm70_float32_cm_0_rpb(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 32, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 32, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_64x64x32_sm70_float32_cm_0_rpb` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 32, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 32, true>::kMinBlocksPerSm) +fna1_64x128x32_sm70_float32_cm_0_rpb(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 32, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 32, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_64x128x32_sm70_float32_cm_0_rpb` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 64, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 64, true>::kMinBlocksPerSm) +fna1_32x128x64_sm70_float32_cm_0_rpb(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 64, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 64, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_32x128x64_sm70_float32_cm_0_rpb` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 64, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 64, true>::kMinBlocksPerSm) +fna1_64x64x64_sm70_float32_cm_0_rpb(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 64, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 64, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_64x64x64_sm70_float32_cm_0_rpb` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 64, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 64, true>::kMinBlocksPerSm) +fna1_64x128x64_sm70_float32_cm_0_rpb(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 64, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 64, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_64x128x64_sm70_float32_cm_0_rpb` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 128, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 128, true>::kMinBlocksPerSm) +fna1_32x128x128_sm70_float32_cm_0_rpb(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 128, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 128, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_32x128x128_sm70_float32_cm_0_rpb` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 128, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 128, true>::kMinBlocksPerSm) +fna1_64x64x128_sm70_float32_cm_0_rpb(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 128, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 128, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_64x64x128_sm70_float32_cm_0_rpb` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 128, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 128, true>::kMinBlocksPerSm) +fna1_64x128x128_sm70_float32_cm_0_rpb(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 128, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 128, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_64x128x128_sm70_float32_cm_0_rpb` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 65536, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 65536, true>::kMinBlocksPerSm) +fna1_32x128x65536_sm70_float32_cm_0_rpb(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 65536, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 65536, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_32x128x65536_sm70_float32_cm_0_rpb` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 65536, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 65536, true>::kMinBlocksPerSm) +fna1_64x128x65536_sm70_float32_cm_0_rpb(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 65536, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 65536, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_64x128x65536_sm70_float32_cm_0_rpb` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 65536, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 65536, true>::kMinBlocksPerSm) +fna1_64x64x65536_sm70_float32_cm_0_rpb(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 65536, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 65536, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_64x64x65536_sm70_float32_cm_0_rpb` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +/////////////////////////////////////////////////////////////////// +// FNA-1D / float32 / SM70 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 32, false>::kMinBlocksPerSm) +fna1_32x128x32_sm70_float32_cm_1(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_32x128x32_sm70_float32_cm_1` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 32, false>::kMinBlocksPerSm) +fna1_64x64x32_sm70_float32_cm_1(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_64x64x32_sm70_float32_cm_1` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 32, false>::kMinBlocksPerSm) +fna1_64x128x32_sm70_float32_cm_1(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_64x128x32_sm70_float32_cm_1` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 64, false>::kMinBlocksPerSm) +fna1_32x128x64_sm70_float32_cm_1(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_32x128x64_sm70_float32_cm_1` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 64, false>::kMinBlocksPerSm) +fna1_64x64x64_sm70_float32_cm_1(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_64x64x64_sm70_float32_cm_1` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 64, false>::kMinBlocksPerSm) +fna1_64x128x64_sm70_float32_cm_1(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_64x128x64_sm70_float32_cm_1` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 128, false>::kMinBlocksPerSm) +fna1_32x128x128_sm70_float32_cm_1(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_32x128x128_sm70_float32_cm_1` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 128, false>::kMinBlocksPerSm) +fna1_64x64x128_sm70_float32_cm_1(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_64x64x128_sm70_float32_cm_1` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 128, false>::kMinBlocksPerSm) +fna1_64x128x128_sm70_float32_cm_1(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_64x128x128_sm70_float32_cm_1` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 65536, false>::kMinBlocksPerSm) +fna1_32x128x65536_sm70_float32_cm_1(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_32x128x65536_sm70_float32_cm_1` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 65536, false>::kMinBlocksPerSm) +fna1_64x128x65536_sm70_float32_cm_1(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_64x128x65536_sm70_float32_cm_1` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 65536, false>::kMinBlocksPerSm) +fna1_64x64x65536_sm70_float32_cm_1(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_64x64x65536_sm70_float32_cm_1` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +/////////////////////////////////////////////////////////////////// +// FNA-1D / float16 / SM70 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 32, false>::kMinBlocksPerSm) +fna1_32x128x32_sm70_float16_cm_0(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_32x128x32_sm70_float16_cm_0` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 32, false>::kMinBlocksPerSm) +fna1_64x64x32_sm70_float16_cm_0(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_64x64x32_sm70_float16_cm_0` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 32, false>::kMinBlocksPerSm) +fna1_64x128x32_sm70_float16_cm_0(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_64x128x32_sm70_float16_cm_0` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 64, false>::kMinBlocksPerSm) +fna1_32x128x64_sm70_float16_cm_0(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_32x128x64_sm70_float16_cm_0` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 64, false>::kMinBlocksPerSm) +fna1_64x64x64_sm70_float16_cm_0(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_64x64x64_sm70_float16_cm_0` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 64, false>::kMinBlocksPerSm) +fna1_64x128x64_sm70_float16_cm_0(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_64x128x64_sm70_float16_cm_0` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 128, false>::kMinBlocksPerSm) +fna1_32x128x128_sm70_float16_cm_0(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_32x128x128_sm70_float16_cm_0` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 128, false>::kMinBlocksPerSm) +fna1_64x64x128_sm70_float16_cm_0(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_64x64x128_sm70_float16_cm_0` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 128, false>::kMinBlocksPerSm) +fna1_64x128x128_sm70_float16_cm_0(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_64x128x128_sm70_float16_cm_0` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 65536, false>::kMinBlocksPerSm) +fna1_32x128x65536_sm70_float16_cm_0(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_32x128x65536_sm70_float16_cm_0` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 65536, false>::kMinBlocksPerSm) +fna1_64x128x65536_sm70_float16_cm_0(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_64x128x65536_sm70_float16_cm_0` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 65536, false>::kMinBlocksPerSm) +fna1_64x64x65536_sm70_float16_cm_0(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_64x64x65536_sm70_float16_cm_0` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} +} +} +} + diff --git a/csrc/autogen/src/cuda/fna/source_1.cu b/csrc/autogen/src/cuda/fna/source_1.cu new file mode 100644 index 0000000..e0ebca4 --- /dev/null +++ b/csrc/autogen/src/cuda/fna/source_1.cu @@ -0,0 +1,2573 @@ +#include +#include +#include +#include +#include +namespace natten { +namespace cuda { +namespace fna { + + + +/////////////////////////////////////////////////////////////////// +// FNA-1D / float16 / SM70 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 32, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 32, true>::kMinBlocksPerSm) +fna1_32x128x32_sm70_float16_cm_0_rpb(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 32, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 32, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_32x128x32_sm70_float16_cm_0_rpb` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 32, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 32, true>::kMinBlocksPerSm) +fna1_64x64x32_sm70_float16_cm_0_rpb(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 32, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 32, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_64x64x32_sm70_float16_cm_0_rpb` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 32, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 32, true>::kMinBlocksPerSm) +fna1_64x128x32_sm70_float16_cm_0_rpb(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 32, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 32, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_64x128x32_sm70_float16_cm_0_rpb` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 64, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 64, true>::kMinBlocksPerSm) +fna1_32x128x64_sm70_float16_cm_0_rpb(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 64, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 64, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_32x128x64_sm70_float16_cm_0_rpb` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 64, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 64, true>::kMinBlocksPerSm) +fna1_64x64x64_sm70_float16_cm_0_rpb(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 64, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 64, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_64x64x64_sm70_float16_cm_0_rpb` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 64, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 64, true>::kMinBlocksPerSm) +fna1_64x128x64_sm70_float16_cm_0_rpb(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 64, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 64, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_64x128x64_sm70_float16_cm_0_rpb` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 128, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 128, true>::kMinBlocksPerSm) +fna1_32x128x128_sm70_float16_cm_0_rpb(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 128, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 128, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_32x128x128_sm70_float16_cm_0_rpb` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 128, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 128, true>::kMinBlocksPerSm) +fna1_64x64x128_sm70_float16_cm_0_rpb(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 128, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 128, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_64x64x128_sm70_float16_cm_0_rpb` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 128, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 128, true>::kMinBlocksPerSm) +fna1_64x128x128_sm70_float16_cm_0_rpb(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 128, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 128, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_64x128x128_sm70_float16_cm_0_rpb` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 65536, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 65536, true>::kMinBlocksPerSm) +fna1_32x128x65536_sm70_float16_cm_0_rpb(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 65536, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 65536, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_32x128x65536_sm70_float16_cm_0_rpb` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 65536, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 65536, true>::kMinBlocksPerSm) +fna1_64x128x65536_sm70_float16_cm_0_rpb(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 65536, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 65536, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_64x128x65536_sm70_float16_cm_0_rpb` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 65536, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 65536, true>::kMinBlocksPerSm) +fna1_64x64x65536_sm70_float16_cm_0_rpb(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 65536, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 65536, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_64x64x65536_sm70_float16_cm_0_rpb` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +/////////////////////////////////////////////////////////////////// +// FNA-1D / float16 / SM70 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 32, false>::kMinBlocksPerSm) +fna1_32x128x32_sm70_float16_cm_1(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_32x128x32_sm70_float16_cm_1` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 32, false>::kMinBlocksPerSm) +fna1_64x64x32_sm70_float16_cm_1(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_64x64x32_sm70_float16_cm_1` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 32, false>::kMinBlocksPerSm) +fna1_64x128x32_sm70_float16_cm_1(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_64x128x32_sm70_float16_cm_1` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 64, false>::kMinBlocksPerSm) +fna1_32x128x64_sm70_float16_cm_1(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_32x128x64_sm70_float16_cm_1` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 64, false>::kMinBlocksPerSm) +fna1_64x64x64_sm70_float16_cm_1(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_64x64x64_sm70_float16_cm_1` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 64, false>::kMinBlocksPerSm) +fna1_64x128x64_sm70_float16_cm_1(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_64x128x64_sm70_float16_cm_1` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 128, false>::kMinBlocksPerSm) +fna1_32x128x128_sm70_float16_cm_1(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_32x128x128_sm70_float16_cm_1` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 128, false>::kMinBlocksPerSm) +fna1_64x64x128_sm70_float16_cm_1(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_64x64x128_sm70_float16_cm_1` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 128, false>::kMinBlocksPerSm) +fna1_64x128x128_sm70_float16_cm_1(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_64x128x128_sm70_float16_cm_1` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 65536, false>::kMinBlocksPerSm) +fna1_32x128x65536_sm70_float16_cm_1(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_32x128x65536_sm70_float16_cm_1` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 65536, false>::kMinBlocksPerSm) +fna1_64x128x65536_sm70_float16_cm_1(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_64x128x65536_sm70_float16_cm_1` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 65536, false>::kMinBlocksPerSm) +fna1_64x64x65536_sm70_float16_cm_1(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_64x64x65536_sm70_float16_cm_1` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +/////////////////////////////////////////////////////////////////// +// FNA-1D / float32 / SM75 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 32, false>::kMinBlocksPerSm) +fna1_32x128x32_sm75_float32_cm_0(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_32x128x32_sm75_float32_cm_0` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 32, false>::kMinBlocksPerSm) +fna1_64x64x32_sm75_float32_cm_0(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_64x64x32_sm75_float32_cm_0` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 32, false>::kMinBlocksPerSm) +fna1_64x128x32_sm75_float32_cm_0(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_64x128x32_sm75_float32_cm_0` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 64, false>::kMinBlocksPerSm) +fna1_32x128x64_sm75_float32_cm_0(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_32x128x64_sm75_float32_cm_0` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 64, false>::kMinBlocksPerSm) +fna1_64x64x64_sm75_float32_cm_0(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_64x64x64_sm75_float32_cm_0` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 64, false>::kMinBlocksPerSm) +fna1_64x128x64_sm75_float32_cm_0(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_64x128x64_sm75_float32_cm_0` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 128, false>::kMinBlocksPerSm) +fna1_32x128x128_sm75_float32_cm_0(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_32x128x128_sm75_float32_cm_0` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 128, false>::kMinBlocksPerSm) +fna1_64x64x128_sm75_float32_cm_0(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_64x64x128_sm75_float32_cm_0` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 128, false>::kMinBlocksPerSm) +fna1_64x128x128_sm75_float32_cm_0(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_64x128x128_sm75_float32_cm_0` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 65536, false>::kMinBlocksPerSm) +fna1_32x128x65536_sm75_float32_cm_0(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_32x128x65536_sm75_float32_cm_0` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 65536, false>::kMinBlocksPerSm) +fna1_64x128x65536_sm75_float32_cm_0(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_64x128x65536_sm75_float32_cm_0` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 65536, false>::kMinBlocksPerSm) +fna1_64x64x65536_sm75_float32_cm_0(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_64x64x65536_sm75_float32_cm_0` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +/////////////////////////////////////////////////////////////////// +// FNA-1D / float32 / SM75 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 32, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 32, true>::kMinBlocksPerSm) +fna1_32x128x32_sm75_float32_cm_0_rpb(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 32, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 32, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_32x128x32_sm75_float32_cm_0_rpb` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 32, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 32, true>::kMinBlocksPerSm) +fna1_64x64x32_sm75_float32_cm_0_rpb(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 32, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 32, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_64x64x32_sm75_float32_cm_0_rpb` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 32, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 32, true>::kMinBlocksPerSm) +fna1_64x128x32_sm75_float32_cm_0_rpb(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 32, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 32, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_64x128x32_sm75_float32_cm_0_rpb` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 64, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 64, true>::kMinBlocksPerSm) +fna1_32x128x64_sm75_float32_cm_0_rpb(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 64, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 64, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_32x128x64_sm75_float32_cm_0_rpb` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 64, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 64, true>::kMinBlocksPerSm) +fna1_64x64x64_sm75_float32_cm_0_rpb(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 64, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 64, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_64x64x64_sm75_float32_cm_0_rpb` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 64, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 64, true>::kMinBlocksPerSm) +fna1_64x128x64_sm75_float32_cm_0_rpb(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 64, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 64, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_64x128x64_sm75_float32_cm_0_rpb` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 128, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 128, true>::kMinBlocksPerSm) +fna1_32x128x128_sm75_float32_cm_0_rpb(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 128, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 128, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_32x128x128_sm75_float32_cm_0_rpb` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 128, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 128, true>::kMinBlocksPerSm) +fna1_64x64x128_sm75_float32_cm_0_rpb(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 128, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 128, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_64x64x128_sm75_float32_cm_0_rpb` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 128, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 128, true>::kMinBlocksPerSm) +fna1_64x128x128_sm75_float32_cm_0_rpb(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 128, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 128, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_64x128x128_sm75_float32_cm_0_rpb` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 65536, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 65536, true>::kMinBlocksPerSm) +fna1_32x128x65536_sm75_float32_cm_0_rpb(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 65536, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 65536, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_32x128x65536_sm75_float32_cm_0_rpb` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 65536, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 65536, true>::kMinBlocksPerSm) +fna1_64x128x65536_sm75_float32_cm_0_rpb(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 65536, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 65536, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_64x128x65536_sm75_float32_cm_0_rpb` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 65536, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 65536, true>::kMinBlocksPerSm) +fna1_64x64x65536_sm75_float32_cm_0_rpb(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 65536, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 65536, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_64x64x65536_sm75_float32_cm_0_rpb` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +/////////////////////////////////////////////////////////////////// +// FNA-1D / float32 / SM75 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 32, false>::kMinBlocksPerSm) +fna1_32x128x32_sm75_float32_cm_1(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_32x128x32_sm75_float32_cm_1` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 32, false>::kMinBlocksPerSm) +fna1_64x64x32_sm75_float32_cm_1(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_64x64x32_sm75_float32_cm_1` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 32, false>::kMinBlocksPerSm) +fna1_64x128x32_sm75_float32_cm_1(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_64x128x32_sm75_float32_cm_1` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 64, false>::kMinBlocksPerSm) +fna1_32x128x64_sm75_float32_cm_1(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_32x128x64_sm75_float32_cm_1` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 64, false>::kMinBlocksPerSm) +fna1_64x64x64_sm75_float32_cm_1(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_64x64x64_sm75_float32_cm_1` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 64, false>::kMinBlocksPerSm) +fna1_64x128x64_sm75_float32_cm_1(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_64x128x64_sm75_float32_cm_1` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 128, false>::kMinBlocksPerSm) +fna1_32x128x128_sm75_float32_cm_1(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_32x128x128_sm75_float32_cm_1` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 128, false>::kMinBlocksPerSm) +fna1_64x64x128_sm75_float32_cm_1(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_64x64x128_sm75_float32_cm_1` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 128, false>::kMinBlocksPerSm) +fna1_64x128x128_sm75_float32_cm_1(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_64x128x128_sm75_float32_cm_1` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 65536, false>::kMinBlocksPerSm) +fna1_32x128x65536_sm75_float32_cm_1(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_32x128x65536_sm75_float32_cm_1` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 65536, false>::kMinBlocksPerSm) +fna1_64x128x65536_sm75_float32_cm_1(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_64x128x65536_sm75_float32_cm_1` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 65536, false>::kMinBlocksPerSm) +fna1_64x64x65536_sm75_float32_cm_1(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_64x64x65536_sm75_float32_cm_1` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +/////////////////////////////////////////////////////////////////// +// FNA-1D / float16 / SM75 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 32, false>::kMinBlocksPerSm) +fna1_32x128x32_sm75_float16_cm_0(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_32x128x32_sm75_float16_cm_0` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 32, false>::kMinBlocksPerSm) +fna1_64x64x32_sm75_float16_cm_0(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_64x64x32_sm75_float16_cm_0` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 32, false>::kMinBlocksPerSm) +fna1_64x128x32_sm75_float16_cm_0(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_64x128x32_sm75_float16_cm_0` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 64, false>::kMinBlocksPerSm) +fna1_32x128x64_sm75_float16_cm_0(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_32x128x64_sm75_float16_cm_0` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 64, false>::kMinBlocksPerSm) +fna1_64x64x64_sm75_float16_cm_0(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_64x64x64_sm75_float16_cm_0` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 64, false>::kMinBlocksPerSm) +fna1_64x128x64_sm75_float16_cm_0(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_64x128x64_sm75_float16_cm_0` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 128, false>::kMinBlocksPerSm) +fna1_32x128x128_sm75_float16_cm_0(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_32x128x128_sm75_float16_cm_0` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 128, false>::kMinBlocksPerSm) +fna1_64x64x128_sm75_float16_cm_0(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_64x64x128_sm75_float16_cm_0` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 128, false>::kMinBlocksPerSm) +fna1_64x128x128_sm75_float16_cm_0(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_64x128x128_sm75_float16_cm_0` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 65536, false>::kMinBlocksPerSm) +fna1_32x128x65536_sm75_float16_cm_0(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_32x128x65536_sm75_float16_cm_0` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 65536, false>::kMinBlocksPerSm) +fna1_64x128x65536_sm75_float16_cm_0(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_64x128x65536_sm75_float16_cm_0` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 65536, false>::kMinBlocksPerSm) +fna1_64x64x65536_sm75_float16_cm_0(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_64x64x65536_sm75_float16_cm_0` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +/////////////////////////////////////////////////////////////////// +// FNA-1D / float16 / SM75 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 32, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 32, true>::kMinBlocksPerSm) +fna1_32x128x32_sm75_float16_cm_0_rpb(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 32, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 32, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_32x128x32_sm75_float16_cm_0_rpb` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 32, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 32, true>::kMinBlocksPerSm) +fna1_64x64x32_sm75_float16_cm_0_rpb(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 32, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 32, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_64x64x32_sm75_float16_cm_0_rpb` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 32, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 32, true>::kMinBlocksPerSm) +fna1_64x128x32_sm75_float16_cm_0_rpb(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 32, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 32, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_64x128x32_sm75_float16_cm_0_rpb` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 64, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 64, true>::kMinBlocksPerSm) +fna1_32x128x64_sm75_float16_cm_0_rpb(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 64, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 64, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_32x128x64_sm75_float16_cm_0_rpb` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 64, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 64, true>::kMinBlocksPerSm) +fna1_64x64x64_sm75_float16_cm_0_rpb(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 64, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 64, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_64x64x64_sm75_float16_cm_0_rpb` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 64, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 64, true>::kMinBlocksPerSm) +fna1_64x128x64_sm75_float16_cm_0_rpb(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 64, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 64, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_64x128x64_sm75_float16_cm_0_rpb` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 128, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 128, true>::kMinBlocksPerSm) +fna1_32x128x128_sm75_float16_cm_0_rpb(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 128, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 128, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_32x128x128_sm75_float16_cm_0_rpb` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 128, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 128, true>::kMinBlocksPerSm) +fna1_64x64x128_sm75_float16_cm_0_rpb(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 128, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 128, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_64x64x128_sm75_float16_cm_0_rpb` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 128, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 128, true>::kMinBlocksPerSm) +fna1_64x128x128_sm75_float16_cm_0_rpb(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 128, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 128, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_64x128x128_sm75_float16_cm_0_rpb` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 65536, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 65536, true>::kMinBlocksPerSm) +fna1_32x128x65536_sm75_float16_cm_0_rpb(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 65536, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 65536, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_32x128x65536_sm75_float16_cm_0_rpb` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 65536, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 65536, true>::kMinBlocksPerSm) +fna1_64x128x65536_sm75_float16_cm_0_rpb(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 65536, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 65536, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_64x128x65536_sm75_float16_cm_0_rpb` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 65536, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 65536, true>::kMinBlocksPerSm) +fna1_64x64x65536_sm75_float16_cm_0_rpb(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 65536, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 65536, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_64x64x65536_sm75_float16_cm_0_rpb` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +/////////////////////////////////////////////////////////////////// +// FNA-1D / float16 / SM75 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 32, false>::kMinBlocksPerSm) +fna1_32x128x32_sm75_float16_cm_1(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_32x128x32_sm75_float16_cm_1` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 32, false>::kMinBlocksPerSm) +fna1_64x64x32_sm75_float16_cm_1(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_64x64x32_sm75_float16_cm_1` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 32, false>::kMinBlocksPerSm) +fna1_64x128x32_sm75_float16_cm_1(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_64x128x32_sm75_float16_cm_1` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 64, false>::kMinBlocksPerSm) +fna1_32x128x64_sm75_float16_cm_1(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_32x128x64_sm75_float16_cm_1` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 64, false>::kMinBlocksPerSm) +fna1_64x64x64_sm75_float16_cm_1(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_64x64x64_sm75_float16_cm_1` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 64, false>::kMinBlocksPerSm) +fna1_64x128x64_sm75_float16_cm_1(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_64x128x64_sm75_float16_cm_1` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 128, false>::kMinBlocksPerSm) +fna1_32x128x128_sm75_float16_cm_1(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_32x128x128_sm75_float16_cm_1` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 128, false>::kMinBlocksPerSm) +fna1_64x64x128_sm75_float16_cm_1(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_64x64x128_sm75_float16_cm_1` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 128, false>::kMinBlocksPerSm) +fna1_64x128x128_sm75_float16_cm_1(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_64x128x128_sm75_float16_cm_1` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 65536, false>::kMinBlocksPerSm) +fna1_32x128x65536_sm75_float16_cm_1(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_32x128x65536_sm75_float16_cm_1` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 65536, false>::kMinBlocksPerSm) +fna1_64x128x65536_sm75_float16_cm_1(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_64x128x65536_sm75_float16_cm_1` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 65536, false>::kMinBlocksPerSm) +fna1_64x64x65536_sm75_float16_cm_1(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_64x64x65536_sm75_float16_cm_1` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +/////////////////////////////////////////////////////////////////// +// FNA-1D / float32 / SM80 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 32, false>::kMinBlocksPerSm) +fna1_32x128x32_sm80_float32_cm_0(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_32x128x32_sm80_float32_cm_0` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 32, false>::kMinBlocksPerSm) +fna1_64x64x32_sm80_float32_cm_0(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_64x64x32_sm80_float32_cm_0` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 32, false>::kMinBlocksPerSm) +fna1_64x128x32_sm80_float32_cm_0(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_64x128x32_sm80_float32_cm_0` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 64, false>::kMinBlocksPerSm) +fna1_32x128x64_sm80_float32_cm_0(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_32x128x64_sm80_float32_cm_0` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 64, false>::kMinBlocksPerSm) +fna1_64x64x64_sm80_float32_cm_0(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_64x64x64_sm80_float32_cm_0` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 64, false>::kMinBlocksPerSm) +fna1_64x128x64_sm80_float32_cm_0(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_64x128x64_sm80_float32_cm_0` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 128, false>::kMinBlocksPerSm) +fna1_32x128x128_sm80_float32_cm_0(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_32x128x128_sm80_float32_cm_0` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 128, false>::kMinBlocksPerSm) +fna1_64x64x128_sm80_float32_cm_0(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_64x64x128_sm80_float32_cm_0` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 128, false>::kMinBlocksPerSm) +fna1_64x128x128_sm80_float32_cm_0(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_64x128x128_sm80_float32_cm_0` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 65536, false>::kMinBlocksPerSm) +fna1_32x128x65536_sm80_float32_cm_0(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_32x128x65536_sm80_float32_cm_0` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 65536, false>::kMinBlocksPerSm) +fna1_64x128x65536_sm80_float32_cm_0(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_64x128x65536_sm80_float32_cm_0` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 65536, false>::kMinBlocksPerSm) +fna1_64x64x65536_sm80_float32_cm_0(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_64x64x65536_sm80_float32_cm_0` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +/////////////////////////////////////////////////////////////////// +// FNA-1D / float32 / SM80 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 32, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 32, true>::kMinBlocksPerSm) +fna1_32x128x32_sm80_float32_cm_0_rpb(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 32, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 32, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_32x128x32_sm80_float32_cm_0_rpb` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 32, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 32, true>::kMinBlocksPerSm) +fna1_64x64x32_sm80_float32_cm_0_rpb(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 32, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 32, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_64x64x32_sm80_float32_cm_0_rpb` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 32, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 32, true>::kMinBlocksPerSm) +fna1_64x128x32_sm80_float32_cm_0_rpb(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 32, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 32, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_64x128x32_sm80_float32_cm_0_rpb` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 64, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 64, true>::kMinBlocksPerSm) +fna1_32x128x64_sm80_float32_cm_0_rpb(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 64, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 64, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_32x128x64_sm80_float32_cm_0_rpb` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 64, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 64, true>::kMinBlocksPerSm) +fna1_64x64x64_sm80_float32_cm_0_rpb(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 64, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 64, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_64x64x64_sm80_float32_cm_0_rpb` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 64, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 64, true>::kMinBlocksPerSm) +fna1_64x128x64_sm80_float32_cm_0_rpb(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 64, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 64, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_64x128x64_sm80_float32_cm_0_rpb` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 128, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 128, true>::kMinBlocksPerSm) +fna1_32x128x128_sm80_float32_cm_0_rpb(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 128, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 128, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_32x128x128_sm80_float32_cm_0_rpb` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 128, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 128, true>::kMinBlocksPerSm) +fna1_64x64x128_sm80_float32_cm_0_rpb(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 128, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 128, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_64x64x128_sm80_float32_cm_0_rpb` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 128, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 128, true>::kMinBlocksPerSm) +fna1_64x128x128_sm80_float32_cm_0_rpb(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 128, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 128, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_64x128x128_sm80_float32_cm_0_rpb` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 65536, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 65536, true>::kMinBlocksPerSm) +fna1_32x128x65536_sm80_float32_cm_0_rpb(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 65536, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 65536, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_32x128x65536_sm80_float32_cm_0_rpb` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 65536, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 65536, true>::kMinBlocksPerSm) +fna1_64x128x65536_sm80_float32_cm_0_rpb(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 65536, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 65536, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_64x128x65536_sm80_float32_cm_0_rpb` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 65536, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 65536, true>::kMinBlocksPerSm) +fna1_64x64x65536_sm80_float32_cm_0_rpb(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 65536, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 65536, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_64x64x65536_sm80_float32_cm_0_rpb` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} +} +} +} + diff --git a/csrc/autogen/src/cuda/fna/source_10.cu b/csrc/autogen/src/cuda/fna/source_10.cu new file mode 100644 index 0000000..48d1041 --- /dev/null +++ b/csrc/autogen/src/cuda/fna/source_10.cu @@ -0,0 +1,2573 @@ +#include +#include +#include +#include +#include +namespace natten { +namespace cuda { +namespace fna { + + + +/////////////////////////////////////////////////////////////////// +// FNA-3D / float16 / SM70 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 32, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 32, true>::kMinBlocksPerSm) +fna3_32x128x32_sm70_float16_cm_0_0_0_rpb(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 32, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 32, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x32_sm70_float16_cm_0_0_0_rpb` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 32, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 32, true>::kMinBlocksPerSm) +fna3_64x64x32_sm70_float16_cm_0_0_0_rpb(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 32, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 32, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x32_sm70_float16_cm_0_0_0_rpb` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 32, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 32, true>::kMinBlocksPerSm) +fna3_64x128x32_sm70_float16_cm_0_0_0_rpb(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 32, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 32, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x32_sm70_float16_cm_0_0_0_rpb` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 64, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 64, true>::kMinBlocksPerSm) +fna3_32x128x64_sm70_float16_cm_0_0_0_rpb(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 64, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 64, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x64_sm70_float16_cm_0_0_0_rpb` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 64, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 64, true>::kMinBlocksPerSm) +fna3_64x64x64_sm70_float16_cm_0_0_0_rpb(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 64, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 64, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x64_sm70_float16_cm_0_0_0_rpb` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 64, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 64, true>::kMinBlocksPerSm) +fna3_64x128x64_sm70_float16_cm_0_0_0_rpb(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 64, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 64, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x64_sm70_float16_cm_0_0_0_rpb` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 128, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 128, true>::kMinBlocksPerSm) +fna3_32x128x128_sm70_float16_cm_0_0_0_rpb(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 128, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 128, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x128_sm70_float16_cm_0_0_0_rpb` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 128, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 128, true>::kMinBlocksPerSm) +fna3_64x64x128_sm70_float16_cm_0_0_0_rpb(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 128, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 128, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x128_sm70_float16_cm_0_0_0_rpb` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 128, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 128, true>::kMinBlocksPerSm) +fna3_64x128x128_sm70_float16_cm_0_0_0_rpb(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 128, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 128, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x128_sm70_float16_cm_0_0_0_rpb` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 65536, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 65536, true>::kMinBlocksPerSm) +fna3_32x128x65536_sm70_float16_cm_0_0_0_rpb(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 65536, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 65536, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x65536_sm70_float16_cm_0_0_0_rpb` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 65536, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 65536, true>::kMinBlocksPerSm) +fna3_64x128x65536_sm70_float16_cm_0_0_0_rpb(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 65536, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 65536, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x65536_sm70_float16_cm_0_0_0_rpb` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 65536, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 65536, true>::kMinBlocksPerSm) +fna3_64x64x65536_sm70_float16_cm_0_0_0_rpb(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 65536, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 65536, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x65536_sm70_float16_cm_0_0_0_rpb` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +/////////////////////////////////////////////////////////////////// +// FNA-3D / float16 / SM70 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 32, false>::kMinBlocksPerSm) +fna3_32x128x32_sm70_float16_cm_0_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x32_sm70_float16_cm_0_0_1` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 32, false>::kMinBlocksPerSm) +fna3_64x64x32_sm70_float16_cm_0_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x32_sm70_float16_cm_0_0_1` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 32, false>::kMinBlocksPerSm) +fna3_64x128x32_sm70_float16_cm_0_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x32_sm70_float16_cm_0_0_1` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 64, false>::kMinBlocksPerSm) +fna3_32x128x64_sm70_float16_cm_0_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x64_sm70_float16_cm_0_0_1` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 64, false>::kMinBlocksPerSm) +fna3_64x64x64_sm70_float16_cm_0_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x64_sm70_float16_cm_0_0_1` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 64, false>::kMinBlocksPerSm) +fna3_64x128x64_sm70_float16_cm_0_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x64_sm70_float16_cm_0_0_1` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 128, false>::kMinBlocksPerSm) +fna3_32x128x128_sm70_float16_cm_0_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x128_sm70_float16_cm_0_0_1` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 128, false>::kMinBlocksPerSm) +fna3_64x64x128_sm70_float16_cm_0_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x128_sm70_float16_cm_0_0_1` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 128, false>::kMinBlocksPerSm) +fna3_64x128x128_sm70_float16_cm_0_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x128_sm70_float16_cm_0_0_1` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 65536, false>::kMinBlocksPerSm) +fna3_32x128x65536_sm70_float16_cm_0_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x65536_sm70_float16_cm_0_0_1` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 65536, false>::kMinBlocksPerSm) +fna3_64x128x65536_sm70_float16_cm_0_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x65536_sm70_float16_cm_0_0_1` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 65536, false>::kMinBlocksPerSm) +fna3_64x64x65536_sm70_float16_cm_0_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x65536_sm70_float16_cm_0_0_1` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +/////////////////////////////////////////////////////////////////// +// FNA-3D / float16 / SM70 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 32, false>::kMinBlocksPerSm) +fna3_32x128x32_sm70_float16_cm_0_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x32_sm70_float16_cm_0_1_0` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 32, false>::kMinBlocksPerSm) +fna3_64x64x32_sm70_float16_cm_0_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x32_sm70_float16_cm_0_1_0` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 32, false>::kMinBlocksPerSm) +fna3_64x128x32_sm70_float16_cm_0_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x32_sm70_float16_cm_0_1_0` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 64, false>::kMinBlocksPerSm) +fna3_32x128x64_sm70_float16_cm_0_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x64_sm70_float16_cm_0_1_0` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 64, false>::kMinBlocksPerSm) +fna3_64x64x64_sm70_float16_cm_0_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x64_sm70_float16_cm_0_1_0` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 64, false>::kMinBlocksPerSm) +fna3_64x128x64_sm70_float16_cm_0_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x64_sm70_float16_cm_0_1_0` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 128, false>::kMinBlocksPerSm) +fna3_32x128x128_sm70_float16_cm_0_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x128_sm70_float16_cm_0_1_0` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 128, false>::kMinBlocksPerSm) +fna3_64x64x128_sm70_float16_cm_0_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x128_sm70_float16_cm_0_1_0` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 128, false>::kMinBlocksPerSm) +fna3_64x128x128_sm70_float16_cm_0_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x128_sm70_float16_cm_0_1_0` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 65536, false>::kMinBlocksPerSm) +fna3_32x128x65536_sm70_float16_cm_0_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x65536_sm70_float16_cm_0_1_0` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 65536, false>::kMinBlocksPerSm) +fna3_64x128x65536_sm70_float16_cm_0_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x65536_sm70_float16_cm_0_1_0` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 65536, false>::kMinBlocksPerSm) +fna3_64x64x65536_sm70_float16_cm_0_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x65536_sm70_float16_cm_0_1_0` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +/////////////////////////////////////////////////////////////////// +// FNA-3D / float16 / SM70 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 32, false>::kMinBlocksPerSm) +fna3_32x128x32_sm70_float16_cm_0_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x32_sm70_float16_cm_0_1_1` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 32, false>::kMinBlocksPerSm) +fna3_64x64x32_sm70_float16_cm_0_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x32_sm70_float16_cm_0_1_1` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 32, false>::kMinBlocksPerSm) +fna3_64x128x32_sm70_float16_cm_0_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x32_sm70_float16_cm_0_1_1` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 64, false>::kMinBlocksPerSm) +fna3_32x128x64_sm70_float16_cm_0_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x64_sm70_float16_cm_0_1_1` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 64, false>::kMinBlocksPerSm) +fna3_64x64x64_sm70_float16_cm_0_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x64_sm70_float16_cm_0_1_1` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 64, false>::kMinBlocksPerSm) +fna3_64x128x64_sm70_float16_cm_0_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x64_sm70_float16_cm_0_1_1` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 128, false>::kMinBlocksPerSm) +fna3_32x128x128_sm70_float16_cm_0_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x128_sm70_float16_cm_0_1_1` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 128, false>::kMinBlocksPerSm) +fna3_64x64x128_sm70_float16_cm_0_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x128_sm70_float16_cm_0_1_1` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 128, false>::kMinBlocksPerSm) +fna3_64x128x128_sm70_float16_cm_0_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x128_sm70_float16_cm_0_1_1` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 65536, false>::kMinBlocksPerSm) +fna3_32x128x65536_sm70_float16_cm_0_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x65536_sm70_float16_cm_0_1_1` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 65536, false>::kMinBlocksPerSm) +fna3_64x128x65536_sm70_float16_cm_0_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x65536_sm70_float16_cm_0_1_1` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 65536, false>::kMinBlocksPerSm) +fna3_64x64x65536_sm70_float16_cm_0_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x65536_sm70_float16_cm_0_1_1` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +/////////////////////////////////////////////////////////////////// +// FNA-3D / float16 / SM70 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 32, false>::kMinBlocksPerSm) +fna3_32x128x32_sm70_float16_cm_1_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x32_sm70_float16_cm_1_0_0` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 32, false>::kMinBlocksPerSm) +fna3_64x64x32_sm70_float16_cm_1_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x32_sm70_float16_cm_1_0_0` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 32, false>::kMinBlocksPerSm) +fna3_64x128x32_sm70_float16_cm_1_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x32_sm70_float16_cm_1_0_0` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 64, false>::kMinBlocksPerSm) +fna3_32x128x64_sm70_float16_cm_1_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x64_sm70_float16_cm_1_0_0` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 64, false>::kMinBlocksPerSm) +fna3_64x64x64_sm70_float16_cm_1_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x64_sm70_float16_cm_1_0_0` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 64, false>::kMinBlocksPerSm) +fna3_64x128x64_sm70_float16_cm_1_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x64_sm70_float16_cm_1_0_0` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 128, false>::kMinBlocksPerSm) +fna3_32x128x128_sm70_float16_cm_1_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x128_sm70_float16_cm_1_0_0` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 128, false>::kMinBlocksPerSm) +fna3_64x64x128_sm70_float16_cm_1_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x128_sm70_float16_cm_1_0_0` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 128, false>::kMinBlocksPerSm) +fna3_64x128x128_sm70_float16_cm_1_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x128_sm70_float16_cm_1_0_0` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 65536, false>::kMinBlocksPerSm) +fna3_32x128x65536_sm70_float16_cm_1_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x65536_sm70_float16_cm_1_0_0` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 65536, false>::kMinBlocksPerSm) +fna3_64x128x65536_sm70_float16_cm_1_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x65536_sm70_float16_cm_1_0_0` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 65536, false>::kMinBlocksPerSm) +fna3_64x64x65536_sm70_float16_cm_1_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x65536_sm70_float16_cm_1_0_0` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +/////////////////////////////////////////////////////////////////// +// FNA-3D / float16 / SM70 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 32, false>::kMinBlocksPerSm) +fna3_32x128x32_sm70_float16_cm_1_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x32_sm70_float16_cm_1_0_1` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 32, false>::kMinBlocksPerSm) +fna3_64x64x32_sm70_float16_cm_1_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x32_sm70_float16_cm_1_0_1` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 32, false>::kMinBlocksPerSm) +fna3_64x128x32_sm70_float16_cm_1_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x32_sm70_float16_cm_1_0_1` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 64, false>::kMinBlocksPerSm) +fna3_32x128x64_sm70_float16_cm_1_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x64_sm70_float16_cm_1_0_1` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 64, false>::kMinBlocksPerSm) +fna3_64x64x64_sm70_float16_cm_1_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x64_sm70_float16_cm_1_0_1` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 64, false>::kMinBlocksPerSm) +fna3_64x128x64_sm70_float16_cm_1_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x64_sm70_float16_cm_1_0_1` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 128, false>::kMinBlocksPerSm) +fna3_32x128x128_sm70_float16_cm_1_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x128_sm70_float16_cm_1_0_1` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 128, false>::kMinBlocksPerSm) +fna3_64x64x128_sm70_float16_cm_1_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x128_sm70_float16_cm_1_0_1` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 128, false>::kMinBlocksPerSm) +fna3_64x128x128_sm70_float16_cm_1_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x128_sm70_float16_cm_1_0_1` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 65536, false>::kMinBlocksPerSm) +fna3_32x128x65536_sm70_float16_cm_1_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x65536_sm70_float16_cm_1_0_1` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 65536, false>::kMinBlocksPerSm) +fna3_64x128x65536_sm70_float16_cm_1_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x65536_sm70_float16_cm_1_0_1` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 65536, false>::kMinBlocksPerSm) +fna3_64x64x65536_sm70_float16_cm_1_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x65536_sm70_float16_cm_1_0_1` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +/////////////////////////////////////////////////////////////////// +// FNA-3D / float16 / SM70 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 32, false>::kMinBlocksPerSm) +fna3_32x128x32_sm70_float16_cm_1_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x32_sm70_float16_cm_1_1_0` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 32, false>::kMinBlocksPerSm) +fna3_64x64x32_sm70_float16_cm_1_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x32_sm70_float16_cm_1_1_0` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 32, false>::kMinBlocksPerSm) +fna3_64x128x32_sm70_float16_cm_1_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x32_sm70_float16_cm_1_1_0` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 64, false>::kMinBlocksPerSm) +fna3_32x128x64_sm70_float16_cm_1_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x64_sm70_float16_cm_1_1_0` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 64, false>::kMinBlocksPerSm) +fna3_64x64x64_sm70_float16_cm_1_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x64_sm70_float16_cm_1_1_0` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 64, false>::kMinBlocksPerSm) +fna3_64x128x64_sm70_float16_cm_1_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x64_sm70_float16_cm_1_1_0` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 128, false>::kMinBlocksPerSm) +fna3_32x128x128_sm70_float16_cm_1_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x128_sm70_float16_cm_1_1_0` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 128, false>::kMinBlocksPerSm) +fna3_64x64x128_sm70_float16_cm_1_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x128_sm70_float16_cm_1_1_0` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 128, false>::kMinBlocksPerSm) +fna3_64x128x128_sm70_float16_cm_1_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x128_sm70_float16_cm_1_1_0` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 65536, false>::kMinBlocksPerSm) +fna3_32x128x65536_sm70_float16_cm_1_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x65536_sm70_float16_cm_1_1_0` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 65536, false>::kMinBlocksPerSm) +fna3_64x128x65536_sm70_float16_cm_1_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x65536_sm70_float16_cm_1_1_0` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 65536, false>::kMinBlocksPerSm) +fna3_64x64x65536_sm70_float16_cm_1_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x65536_sm70_float16_cm_1_1_0` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +/////////////////////////////////////////////////////////////////// +// FNA-3D / float16 / SM70 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 32, false>::kMinBlocksPerSm) +fna3_32x128x32_sm70_float16_cm_1_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x32_sm70_float16_cm_1_1_1` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 32, false>::kMinBlocksPerSm) +fna3_64x64x32_sm70_float16_cm_1_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x32_sm70_float16_cm_1_1_1` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 32, false>::kMinBlocksPerSm) +fna3_64x128x32_sm70_float16_cm_1_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x32_sm70_float16_cm_1_1_1` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 64, false>::kMinBlocksPerSm) +fna3_32x128x64_sm70_float16_cm_1_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x64_sm70_float16_cm_1_1_1` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 64, false>::kMinBlocksPerSm) +fna3_64x64x64_sm70_float16_cm_1_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x64_sm70_float16_cm_1_1_1` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 64, false>::kMinBlocksPerSm) +fna3_64x128x64_sm70_float16_cm_1_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x64_sm70_float16_cm_1_1_1` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 128, false>::kMinBlocksPerSm) +fna3_32x128x128_sm70_float16_cm_1_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x128_sm70_float16_cm_1_1_1` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 128, false>::kMinBlocksPerSm) +fna3_64x64x128_sm70_float16_cm_1_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x128_sm70_float16_cm_1_1_1` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 128, false>::kMinBlocksPerSm) +fna3_64x128x128_sm70_float16_cm_1_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x128_sm70_float16_cm_1_1_1` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 65536, false>::kMinBlocksPerSm) +fna3_32x128x65536_sm70_float16_cm_1_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x65536_sm70_float16_cm_1_1_1` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 65536, false>::kMinBlocksPerSm) +fna3_64x128x65536_sm70_float16_cm_1_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x65536_sm70_float16_cm_1_1_1` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 65536, false>::kMinBlocksPerSm) +fna3_64x64x65536_sm70_float16_cm_1_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x65536_sm70_float16_cm_1_1_1` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +/////////////////////////////////////////////////////////////////// +// FNA-3D / float32 / SM75 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 32, false>::kMinBlocksPerSm) +fna3_32x128x32_sm75_float32_cm_0_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x32_sm75_float32_cm_0_0_0` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 32, false>::kMinBlocksPerSm) +fna3_64x64x32_sm75_float32_cm_0_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x32_sm75_float32_cm_0_0_0` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 32, false>::kMinBlocksPerSm) +fna3_64x128x32_sm75_float32_cm_0_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x32_sm75_float32_cm_0_0_0` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 64, false>::kMinBlocksPerSm) +fna3_32x128x64_sm75_float32_cm_0_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x64_sm75_float32_cm_0_0_0` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 64, false>::kMinBlocksPerSm) +fna3_64x64x64_sm75_float32_cm_0_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x64_sm75_float32_cm_0_0_0` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 64, false>::kMinBlocksPerSm) +fna3_64x128x64_sm75_float32_cm_0_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x64_sm75_float32_cm_0_0_0` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 128, false>::kMinBlocksPerSm) +fna3_32x128x128_sm75_float32_cm_0_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x128_sm75_float32_cm_0_0_0` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 128, false>::kMinBlocksPerSm) +fna3_64x64x128_sm75_float32_cm_0_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x128_sm75_float32_cm_0_0_0` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 128, false>::kMinBlocksPerSm) +fna3_64x128x128_sm75_float32_cm_0_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x128_sm75_float32_cm_0_0_0` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 65536, false>::kMinBlocksPerSm) +fna3_32x128x65536_sm75_float32_cm_0_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x65536_sm75_float32_cm_0_0_0` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 65536, false>::kMinBlocksPerSm) +fna3_64x128x65536_sm75_float32_cm_0_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x65536_sm75_float32_cm_0_0_0` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 65536, false>::kMinBlocksPerSm) +fna3_64x64x65536_sm75_float32_cm_0_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x65536_sm75_float32_cm_0_0_0` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +/////////////////////////////////////////////////////////////////// +// FNA-3D / float32 / SM75 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 32, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 32, true>::kMinBlocksPerSm) +fna3_32x128x32_sm75_float32_cm_0_0_0_rpb(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 32, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 32, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x32_sm75_float32_cm_0_0_0_rpb` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 32, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 32, true>::kMinBlocksPerSm) +fna3_64x64x32_sm75_float32_cm_0_0_0_rpb(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 32, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 32, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x32_sm75_float32_cm_0_0_0_rpb` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 32, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 32, true>::kMinBlocksPerSm) +fna3_64x128x32_sm75_float32_cm_0_0_0_rpb(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 32, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 32, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x32_sm75_float32_cm_0_0_0_rpb` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 64, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 64, true>::kMinBlocksPerSm) +fna3_32x128x64_sm75_float32_cm_0_0_0_rpb(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 64, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 64, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x64_sm75_float32_cm_0_0_0_rpb` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 64, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 64, true>::kMinBlocksPerSm) +fna3_64x64x64_sm75_float32_cm_0_0_0_rpb(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 64, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 64, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x64_sm75_float32_cm_0_0_0_rpb` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 64, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 64, true>::kMinBlocksPerSm) +fna3_64x128x64_sm75_float32_cm_0_0_0_rpb(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 64, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 64, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x64_sm75_float32_cm_0_0_0_rpb` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 128, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 128, true>::kMinBlocksPerSm) +fna3_32x128x128_sm75_float32_cm_0_0_0_rpb(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 128, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 128, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x128_sm75_float32_cm_0_0_0_rpb` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 128, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 128, true>::kMinBlocksPerSm) +fna3_64x64x128_sm75_float32_cm_0_0_0_rpb(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 128, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 128, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x128_sm75_float32_cm_0_0_0_rpb` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 128, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 128, true>::kMinBlocksPerSm) +fna3_64x128x128_sm75_float32_cm_0_0_0_rpb(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 128, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 128, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x128_sm75_float32_cm_0_0_0_rpb` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 65536, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 65536, true>::kMinBlocksPerSm) +fna3_32x128x65536_sm75_float32_cm_0_0_0_rpb(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 65536, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 65536, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x65536_sm75_float32_cm_0_0_0_rpb` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 65536, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 65536, true>::kMinBlocksPerSm) +fna3_64x128x65536_sm75_float32_cm_0_0_0_rpb(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 65536, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 65536, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x65536_sm75_float32_cm_0_0_0_rpb` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 65536, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 65536, true>::kMinBlocksPerSm) +fna3_64x64x65536_sm75_float32_cm_0_0_0_rpb(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 65536, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 65536, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x65536_sm75_float32_cm_0_0_0_rpb` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} +} +} +} + diff --git a/csrc/autogen/src/cuda/fna/source_11.cu b/csrc/autogen/src/cuda/fna/source_11.cu new file mode 100644 index 0000000..c4fc950 --- /dev/null +++ b/csrc/autogen/src/cuda/fna/source_11.cu @@ -0,0 +1,2573 @@ +#include +#include +#include +#include +#include +namespace natten { +namespace cuda { +namespace fna { + + + +/////////////////////////////////////////////////////////////////// +// FNA-3D / float32 / SM75 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 32, false>::kMinBlocksPerSm) +fna3_32x128x32_sm75_float32_cm_0_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x32_sm75_float32_cm_0_0_1` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 32, false>::kMinBlocksPerSm) +fna3_64x64x32_sm75_float32_cm_0_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x32_sm75_float32_cm_0_0_1` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 32, false>::kMinBlocksPerSm) +fna3_64x128x32_sm75_float32_cm_0_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x32_sm75_float32_cm_0_0_1` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 64, false>::kMinBlocksPerSm) +fna3_32x128x64_sm75_float32_cm_0_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x64_sm75_float32_cm_0_0_1` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 64, false>::kMinBlocksPerSm) +fna3_64x64x64_sm75_float32_cm_0_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x64_sm75_float32_cm_0_0_1` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 64, false>::kMinBlocksPerSm) +fna3_64x128x64_sm75_float32_cm_0_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x64_sm75_float32_cm_0_0_1` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 128, false>::kMinBlocksPerSm) +fna3_32x128x128_sm75_float32_cm_0_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x128_sm75_float32_cm_0_0_1` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 128, false>::kMinBlocksPerSm) +fna3_64x64x128_sm75_float32_cm_0_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x128_sm75_float32_cm_0_0_1` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 128, false>::kMinBlocksPerSm) +fna3_64x128x128_sm75_float32_cm_0_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x128_sm75_float32_cm_0_0_1` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 65536, false>::kMinBlocksPerSm) +fna3_32x128x65536_sm75_float32_cm_0_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x65536_sm75_float32_cm_0_0_1` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 65536, false>::kMinBlocksPerSm) +fna3_64x128x65536_sm75_float32_cm_0_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x65536_sm75_float32_cm_0_0_1` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 65536, false>::kMinBlocksPerSm) +fna3_64x64x65536_sm75_float32_cm_0_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x65536_sm75_float32_cm_0_0_1` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +/////////////////////////////////////////////////////////////////// +// FNA-3D / float32 / SM75 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 32, false>::kMinBlocksPerSm) +fna3_32x128x32_sm75_float32_cm_0_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x32_sm75_float32_cm_0_1_0` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 32, false>::kMinBlocksPerSm) +fna3_64x64x32_sm75_float32_cm_0_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x32_sm75_float32_cm_0_1_0` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 32, false>::kMinBlocksPerSm) +fna3_64x128x32_sm75_float32_cm_0_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x32_sm75_float32_cm_0_1_0` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 64, false>::kMinBlocksPerSm) +fna3_32x128x64_sm75_float32_cm_0_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x64_sm75_float32_cm_0_1_0` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 64, false>::kMinBlocksPerSm) +fna3_64x64x64_sm75_float32_cm_0_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x64_sm75_float32_cm_0_1_0` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 64, false>::kMinBlocksPerSm) +fna3_64x128x64_sm75_float32_cm_0_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x64_sm75_float32_cm_0_1_0` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 128, false>::kMinBlocksPerSm) +fna3_32x128x128_sm75_float32_cm_0_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x128_sm75_float32_cm_0_1_0` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 128, false>::kMinBlocksPerSm) +fna3_64x64x128_sm75_float32_cm_0_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x128_sm75_float32_cm_0_1_0` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 128, false>::kMinBlocksPerSm) +fna3_64x128x128_sm75_float32_cm_0_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x128_sm75_float32_cm_0_1_0` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 65536, false>::kMinBlocksPerSm) +fna3_32x128x65536_sm75_float32_cm_0_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x65536_sm75_float32_cm_0_1_0` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 65536, false>::kMinBlocksPerSm) +fna3_64x128x65536_sm75_float32_cm_0_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x65536_sm75_float32_cm_0_1_0` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 65536, false>::kMinBlocksPerSm) +fna3_64x64x65536_sm75_float32_cm_0_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x65536_sm75_float32_cm_0_1_0` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +/////////////////////////////////////////////////////////////////// +// FNA-3D / float32 / SM75 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 32, false>::kMinBlocksPerSm) +fna3_32x128x32_sm75_float32_cm_0_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x32_sm75_float32_cm_0_1_1` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 32, false>::kMinBlocksPerSm) +fna3_64x64x32_sm75_float32_cm_0_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x32_sm75_float32_cm_0_1_1` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 32, false>::kMinBlocksPerSm) +fna3_64x128x32_sm75_float32_cm_0_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x32_sm75_float32_cm_0_1_1` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 64, false>::kMinBlocksPerSm) +fna3_32x128x64_sm75_float32_cm_0_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x64_sm75_float32_cm_0_1_1` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 64, false>::kMinBlocksPerSm) +fna3_64x64x64_sm75_float32_cm_0_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x64_sm75_float32_cm_0_1_1` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 64, false>::kMinBlocksPerSm) +fna3_64x128x64_sm75_float32_cm_0_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x64_sm75_float32_cm_0_1_1` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 128, false>::kMinBlocksPerSm) +fna3_32x128x128_sm75_float32_cm_0_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x128_sm75_float32_cm_0_1_1` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 128, false>::kMinBlocksPerSm) +fna3_64x64x128_sm75_float32_cm_0_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x128_sm75_float32_cm_0_1_1` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 128, false>::kMinBlocksPerSm) +fna3_64x128x128_sm75_float32_cm_0_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x128_sm75_float32_cm_0_1_1` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 65536, false>::kMinBlocksPerSm) +fna3_32x128x65536_sm75_float32_cm_0_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x65536_sm75_float32_cm_0_1_1` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 65536, false>::kMinBlocksPerSm) +fna3_64x128x65536_sm75_float32_cm_0_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x65536_sm75_float32_cm_0_1_1` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 65536, false>::kMinBlocksPerSm) +fna3_64x64x65536_sm75_float32_cm_0_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x65536_sm75_float32_cm_0_1_1` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +/////////////////////////////////////////////////////////////////// +// FNA-3D / float32 / SM75 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 32, false>::kMinBlocksPerSm) +fna3_32x128x32_sm75_float32_cm_1_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x32_sm75_float32_cm_1_0_0` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 32, false>::kMinBlocksPerSm) +fna3_64x64x32_sm75_float32_cm_1_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x32_sm75_float32_cm_1_0_0` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 32, false>::kMinBlocksPerSm) +fna3_64x128x32_sm75_float32_cm_1_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x32_sm75_float32_cm_1_0_0` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 64, false>::kMinBlocksPerSm) +fna3_32x128x64_sm75_float32_cm_1_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x64_sm75_float32_cm_1_0_0` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 64, false>::kMinBlocksPerSm) +fna3_64x64x64_sm75_float32_cm_1_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x64_sm75_float32_cm_1_0_0` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 64, false>::kMinBlocksPerSm) +fna3_64x128x64_sm75_float32_cm_1_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x64_sm75_float32_cm_1_0_0` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 128, false>::kMinBlocksPerSm) +fna3_32x128x128_sm75_float32_cm_1_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x128_sm75_float32_cm_1_0_0` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 128, false>::kMinBlocksPerSm) +fna3_64x64x128_sm75_float32_cm_1_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x128_sm75_float32_cm_1_0_0` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 128, false>::kMinBlocksPerSm) +fna3_64x128x128_sm75_float32_cm_1_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x128_sm75_float32_cm_1_0_0` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 65536, false>::kMinBlocksPerSm) +fna3_32x128x65536_sm75_float32_cm_1_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x65536_sm75_float32_cm_1_0_0` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 65536, false>::kMinBlocksPerSm) +fna3_64x128x65536_sm75_float32_cm_1_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x65536_sm75_float32_cm_1_0_0` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 65536, false>::kMinBlocksPerSm) +fna3_64x64x65536_sm75_float32_cm_1_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x65536_sm75_float32_cm_1_0_0` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +/////////////////////////////////////////////////////////////////// +// FNA-3D / float32 / SM75 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 32, false>::kMinBlocksPerSm) +fna3_32x128x32_sm75_float32_cm_1_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x32_sm75_float32_cm_1_0_1` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 32, false>::kMinBlocksPerSm) +fna3_64x64x32_sm75_float32_cm_1_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x32_sm75_float32_cm_1_0_1` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 32, false>::kMinBlocksPerSm) +fna3_64x128x32_sm75_float32_cm_1_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x32_sm75_float32_cm_1_0_1` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 64, false>::kMinBlocksPerSm) +fna3_32x128x64_sm75_float32_cm_1_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x64_sm75_float32_cm_1_0_1` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 64, false>::kMinBlocksPerSm) +fna3_64x64x64_sm75_float32_cm_1_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x64_sm75_float32_cm_1_0_1` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 64, false>::kMinBlocksPerSm) +fna3_64x128x64_sm75_float32_cm_1_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x64_sm75_float32_cm_1_0_1` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 128, false>::kMinBlocksPerSm) +fna3_32x128x128_sm75_float32_cm_1_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x128_sm75_float32_cm_1_0_1` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 128, false>::kMinBlocksPerSm) +fna3_64x64x128_sm75_float32_cm_1_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x128_sm75_float32_cm_1_0_1` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 128, false>::kMinBlocksPerSm) +fna3_64x128x128_sm75_float32_cm_1_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x128_sm75_float32_cm_1_0_1` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 65536, false>::kMinBlocksPerSm) +fna3_32x128x65536_sm75_float32_cm_1_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x65536_sm75_float32_cm_1_0_1` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 65536, false>::kMinBlocksPerSm) +fna3_64x128x65536_sm75_float32_cm_1_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x65536_sm75_float32_cm_1_0_1` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 65536, false>::kMinBlocksPerSm) +fna3_64x64x65536_sm75_float32_cm_1_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x65536_sm75_float32_cm_1_0_1` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +/////////////////////////////////////////////////////////////////// +// FNA-3D / float32 / SM75 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 32, false>::kMinBlocksPerSm) +fna3_32x128x32_sm75_float32_cm_1_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x32_sm75_float32_cm_1_1_0` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 32, false>::kMinBlocksPerSm) +fna3_64x64x32_sm75_float32_cm_1_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x32_sm75_float32_cm_1_1_0` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 32, false>::kMinBlocksPerSm) +fna3_64x128x32_sm75_float32_cm_1_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x32_sm75_float32_cm_1_1_0` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 64, false>::kMinBlocksPerSm) +fna3_32x128x64_sm75_float32_cm_1_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x64_sm75_float32_cm_1_1_0` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 64, false>::kMinBlocksPerSm) +fna3_64x64x64_sm75_float32_cm_1_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x64_sm75_float32_cm_1_1_0` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 64, false>::kMinBlocksPerSm) +fna3_64x128x64_sm75_float32_cm_1_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x64_sm75_float32_cm_1_1_0` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 128, false>::kMinBlocksPerSm) +fna3_32x128x128_sm75_float32_cm_1_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x128_sm75_float32_cm_1_1_0` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 128, false>::kMinBlocksPerSm) +fna3_64x64x128_sm75_float32_cm_1_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x128_sm75_float32_cm_1_1_0` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 128, false>::kMinBlocksPerSm) +fna3_64x128x128_sm75_float32_cm_1_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x128_sm75_float32_cm_1_1_0` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 65536, false>::kMinBlocksPerSm) +fna3_32x128x65536_sm75_float32_cm_1_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x65536_sm75_float32_cm_1_1_0` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 65536, false>::kMinBlocksPerSm) +fna3_64x128x65536_sm75_float32_cm_1_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x65536_sm75_float32_cm_1_1_0` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 65536, false>::kMinBlocksPerSm) +fna3_64x64x65536_sm75_float32_cm_1_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x65536_sm75_float32_cm_1_1_0` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +/////////////////////////////////////////////////////////////////// +// FNA-3D / float32 / SM75 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 32, false>::kMinBlocksPerSm) +fna3_32x128x32_sm75_float32_cm_1_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x32_sm75_float32_cm_1_1_1` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 32, false>::kMinBlocksPerSm) +fna3_64x64x32_sm75_float32_cm_1_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x32_sm75_float32_cm_1_1_1` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 32, false>::kMinBlocksPerSm) +fna3_64x128x32_sm75_float32_cm_1_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x32_sm75_float32_cm_1_1_1` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 64, false>::kMinBlocksPerSm) +fna3_32x128x64_sm75_float32_cm_1_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x64_sm75_float32_cm_1_1_1` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 64, false>::kMinBlocksPerSm) +fna3_64x64x64_sm75_float32_cm_1_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x64_sm75_float32_cm_1_1_1` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 64, false>::kMinBlocksPerSm) +fna3_64x128x64_sm75_float32_cm_1_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x64_sm75_float32_cm_1_1_1` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 128, false>::kMinBlocksPerSm) +fna3_32x128x128_sm75_float32_cm_1_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x128_sm75_float32_cm_1_1_1` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 128, false>::kMinBlocksPerSm) +fna3_64x64x128_sm75_float32_cm_1_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x128_sm75_float32_cm_1_1_1` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 128, false>::kMinBlocksPerSm) +fna3_64x128x128_sm75_float32_cm_1_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x128_sm75_float32_cm_1_1_1` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 65536, false>::kMinBlocksPerSm) +fna3_32x128x65536_sm75_float32_cm_1_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x65536_sm75_float32_cm_1_1_1` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 65536, false>::kMinBlocksPerSm) +fna3_64x128x65536_sm75_float32_cm_1_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x65536_sm75_float32_cm_1_1_1` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 65536, false>::kMinBlocksPerSm) +fna3_64x64x65536_sm75_float32_cm_1_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x65536_sm75_float32_cm_1_1_1` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +/////////////////////////////////////////////////////////////////// +// FNA-3D / float16 / SM75 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 32, false>::kMinBlocksPerSm) +fna3_32x128x32_sm75_float16_cm_0_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x32_sm75_float16_cm_0_0_0` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 32, false>::kMinBlocksPerSm) +fna3_64x64x32_sm75_float16_cm_0_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x32_sm75_float16_cm_0_0_0` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 32, false>::kMinBlocksPerSm) +fna3_64x128x32_sm75_float16_cm_0_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x32_sm75_float16_cm_0_0_0` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 64, false>::kMinBlocksPerSm) +fna3_32x128x64_sm75_float16_cm_0_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x64_sm75_float16_cm_0_0_0` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 64, false>::kMinBlocksPerSm) +fna3_64x64x64_sm75_float16_cm_0_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x64_sm75_float16_cm_0_0_0` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 64, false>::kMinBlocksPerSm) +fna3_64x128x64_sm75_float16_cm_0_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x64_sm75_float16_cm_0_0_0` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 128, false>::kMinBlocksPerSm) +fna3_32x128x128_sm75_float16_cm_0_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x128_sm75_float16_cm_0_0_0` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 128, false>::kMinBlocksPerSm) +fna3_64x64x128_sm75_float16_cm_0_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x128_sm75_float16_cm_0_0_0` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 128, false>::kMinBlocksPerSm) +fna3_64x128x128_sm75_float16_cm_0_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x128_sm75_float16_cm_0_0_0` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 65536, false>::kMinBlocksPerSm) +fna3_32x128x65536_sm75_float16_cm_0_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x65536_sm75_float16_cm_0_0_0` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 65536, false>::kMinBlocksPerSm) +fna3_64x128x65536_sm75_float16_cm_0_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x65536_sm75_float16_cm_0_0_0` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 65536, false>::kMinBlocksPerSm) +fna3_64x64x65536_sm75_float16_cm_0_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x65536_sm75_float16_cm_0_0_0` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +/////////////////////////////////////////////////////////////////// +// FNA-3D / float16 / SM75 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 32, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 32, true>::kMinBlocksPerSm) +fna3_32x128x32_sm75_float16_cm_0_0_0_rpb(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 32, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 32, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x32_sm75_float16_cm_0_0_0_rpb` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 32, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 32, true>::kMinBlocksPerSm) +fna3_64x64x32_sm75_float16_cm_0_0_0_rpb(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 32, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 32, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x32_sm75_float16_cm_0_0_0_rpb` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 32, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 32, true>::kMinBlocksPerSm) +fna3_64x128x32_sm75_float16_cm_0_0_0_rpb(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 32, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 32, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x32_sm75_float16_cm_0_0_0_rpb` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 64, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 64, true>::kMinBlocksPerSm) +fna3_32x128x64_sm75_float16_cm_0_0_0_rpb(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 64, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 64, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x64_sm75_float16_cm_0_0_0_rpb` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 64, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 64, true>::kMinBlocksPerSm) +fna3_64x64x64_sm75_float16_cm_0_0_0_rpb(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 64, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 64, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x64_sm75_float16_cm_0_0_0_rpb` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 64, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 64, true>::kMinBlocksPerSm) +fna3_64x128x64_sm75_float16_cm_0_0_0_rpb(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 64, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 64, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x64_sm75_float16_cm_0_0_0_rpb` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 128, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 128, true>::kMinBlocksPerSm) +fna3_32x128x128_sm75_float16_cm_0_0_0_rpb(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 128, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 128, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x128_sm75_float16_cm_0_0_0_rpb` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 128, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 128, true>::kMinBlocksPerSm) +fna3_64x64x128_sm75_float16_cm_0_0_0_rpb(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 128, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 128, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x128_sm75_float16_cm_0_0_0_rpb` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 128, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 128, true>::kMinBlocksPerSm) +fna3_64x128x128_sm75_float16_cm_0_0_0_rpb(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 128, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 128, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x128_sm75_float16_cm_0_0_0_rpb` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 65536, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 65536, true>::kMinBlocksPerSm) +fna3_32x128x65536_sm75_float16_cm_0_0_0_rpb(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 65536, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 65536, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x65536_sm75_float16_cm_0_0_0_rpb` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 65536, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 65536, true>::kMinBlocksPerSm) +fna3_64x128x65536_sm75_float16_cm_0_0_0_rpb(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 65536, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 65536, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x65536_sm75_float16_cm_0_0_0_rpb` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 65536, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 65536, true>::kMinBlocksPerSm) +fna3_64x64x65536_sm75_float16_cm_0_0_0_rpb(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 65536, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 65536, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x65536_sm75_float16_cm_0_0_0_rpb` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +/////////////////////////////////////////////////////////////////// +// FNA-3D / float16 / SM75 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 32, false>::kMinBlocksPerSm) +fna3_32x128x32_sm75_float16_cm_0_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x32_sm75_float16_cm_0_0_1` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 32, false>::kMinBlocksPerSm) +fna3_64x64x32_sm75_float16_cm_0_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x32_sm75_float16_cm_0_0_1` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 32, false>::kMinBlocksPerSm) +fna3_64x128x32_sm75_float16_cm_0_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x32_sm75_float16_cm_0_0_1` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 64, false>::kMinBlocksPerSm) +fna3_32x128x64_sm75_float16_cm_0_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x64_sm75_float16_cm_0_0_1` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 64, false>::kMinBlocksPerSm) +fna3_64x64x64_sm75_float16_cm_0_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x64_sm75_float16_cm_0_0_1` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 64, false>::kMinBlocksPerSm) +fna3_64x128x64_sm75_float16_cm_0_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x64_sm75_float16_cm_0_0_1` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 128, false>::kMinBlocksPerSm) +fna3_32x128x128_sm75_float16_cm_0_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x128_sm75_float16_cm_0_0_1` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 128, false>::kMinBlocksPerSm) +fna3_64x64x128_sm75_float16_cm_0_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x128_sm75_float16_cm_0_0_1` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 128, false>::kMinBlocksPerSm) +fna3_64x128x128_sm75_float16_cm_0_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x128_sm75_float16_cm_0_0_1` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 65536, false>::kMinBlocksPerSm) +fna3_32x128x65536_sm75_float16_cm_0_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x65536_sm75_float16_cm_0_0_1` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 65536, false>::kMinBlocksPerSm) +fna3_64x128x65536_sm75_float16_cm_0_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x65536_sm75_float16_cm_0_0_1` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 65536, false>::kMinBlocksPerSm) +fna3_64x64x65536_sm75_float16_cm_0_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x65536_sm75_float16_cm_0_0_1` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} +} +} +} + diff --git a/csrc/autogen/src/cuda/fna/source_12.cu b/csrc/autogen/src/cuda/fna/source_12.cu new file mode 100644 index 0000000..e770d0f --- /dev/null +++ b/csrc/autogen/src/cuda/fna/source_12.cu @@ -0,0 +1,2573 @@ +#include +#include +#include +#include +#include +namespace natten { +namespace cuda { +namespace fna { + + + +/////////////////////////////////////////////////////////////////// +// FNA-3D / float16 / SM75 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 32, false>::kMinBlocksPerSm) +fna3_32x128x32_sm75_float16_cm_0_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x32_sm75_float16_cm_0_1_0` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 32, false>::kMinBlocksPerSm) +fna3_64x64x32_sm75_float16_cm_0_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x32_sm75_float16_cm_0_1_0` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 32, false>::kMinBlocksPerSm) +fna3_64x128x32_sm75_float16_cm_0_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x32_sm75_float16_cm_0_1_0` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 64, false>::kMinBlocksPerSm) +fna3_32x128x64_sm75_float16_cm_0_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x64_sm75_float16_cm_0_1_0` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 64, false>::kMinBlocksPerSm) +fna3_64x64x64_sm75_float16_cm_0_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x64_sm75_float16_cm_0_1_0` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 64, false>::kMinBlocksPerSm) +fna3_64x128x64_sm75_float16_cm_0_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x64_sm75_float16_cm_0_1_0` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 128, false>::kMinBlocksPerSm) +fna3_32x128x128_sm75_float16_cm_0_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x128_sm75_float16_cm_0_1_0` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 128, false>::kMinBlocksPerSm) +fna3_64x64x128_sm75_float16_cm_0_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x128_sm75_float16_cm_0_1_0` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 128, false>::kMinBlocksPerSm) +fna3_64x128x128_sm75_float16_cm_0_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x128_sm75_float16_cm_0_1_0` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 65536, false>::kMinBlocksPerSm) +fna3_32x128x65536_sm75_float16_cm_0_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x65536_sm75_float16_cm_0_1_0` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 65536, false>::kMinBlocksPerSm) +fna3_64x128x65536_sm75_float16_cm_0_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x65536_sm75_float16_cm_0_1_0` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 65536, false>::kMinBlocksPerSm) +fna3_64x64x65536_sm75_float16_cm_0_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x65536_sm75_float16_cm_0_1_0` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +/////////////////////////////////////////////////////////////////// +// FNA-3D / float16 / SM75 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 32, false>::kMinBlocksPerSm) +fna3_32x128x32_sm75_float16_cm_0_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x32_sm75_float16_cm_0_1_1` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 32, false>::kMinBlocksPerSm) +fna3_64x64x32_sm75_float16_cm_0_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x32_sm75_float16_cm_0_1_1` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 32, false>::kMinBlocksPerSm) +fna3_64x128x32_sm75_float16_cm_0_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x32_sm75_float16_cm_0_1_1` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 64, false>::kMinBlocksPerSm) +fna3_32x128x64_sm75_float16_cm_0_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x64_sm75_float16_cm_0_1_1` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 64, false>::kMinBlocksPerSm) +fna3_64x64x64_sm75_float16_cm_0_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x64_sm75_float16_cm_0_1_1` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 64, false>::kMinBlocksPerSm) +fna3_64x128x64_sm75_float16_cm_0_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x64_sm75_float16_cm_0_1_1` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 128, false>::kMinBlocksPerSm) +fna3_32x128x128_sm75_float16_cm_0_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x128_sm75_float16_cm_0_1_1` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 128, false>::kMinBlocksPerSm) +fna3_64x64x128_sm75_float16_cm_0_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x128_sm75_float16_cm_0_1_1` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 128, false>::kMinBlocksPerSm) +fna3_64x128x128_sm75_float16_cm_0_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x128_sm75_float16_cm_0_1_1` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 65536, false>::kMinBlocksPerSm) +fna3_32x128x65536_sm75_float16_cm_0_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x65536_sm75_float16_cm_0_1_1` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 65536, false>::kMinBlocksPerSm) +fna3_64x128x65536_sm75_float16_cm_0_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x65536_sm75_float16_cm_0_1_1` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 65536, false>::kMinBlocksPerSm) +fna3_64x64x65536_sm75_float16_cm_0_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x65536_sm75_float16_cm_0_1_1` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +/////////////////////////////////////////////////////////////////// +// FNA-3D / float16 / SM75 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 32, false>::kMinBlocksPerSm) +fna3_32x128x32_sm75_float16_cm_1_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x32_sm75_float16_cm_1_0_0` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 32, false>::kMinBlocksPerSm) +fna3_64x64x32_sm75_float16_cm_1_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x32_sm75_float16_cm_1_0_0` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 32, false>::kMinBlocksPerSm) +fna3_64x128x32_sm75_float16_cm_1_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x32_sm75_float16_cm_1_0_0` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 64, false>::kMinBlocksPerSm) +fna3_32x128x64_sm75_float16_cm_1_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x64_sm75_float16_cm_1_0_0` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 64, false>::kMinBlocksPerSm) +fna3_64x64x64_sm75_float16_cm_1_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x64_sm75_float16_cm_1_0_0` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 64, false>::kMinBlocksPerSm) +fna3_64x128x64_sm75_float16_cm_1_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x64_sm75_float16_cm_1_0_0` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 128, false>::kMinBlocksPerSm) +fna3_32x128x128_sm75_float16_cm_1_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x128_sm75_float16_cm_1_0_0` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 128, false>::kMinBlocksPerSm) +fna3_64x64x128_sm75_float16_cm_1_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x128_sm75_float16_cm_1_0_0` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 128, false>::kMinBlocksPerSm) +fna3_64x128x128_sm75_float16_cm_1_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x128_sm75_float16_cm_1_0_0` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 65536, false>::kMinBlocksPerSm) +fna3_32x128x65536_sm75_float16_cm_1_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x65536_sm75_float16_cm_1_0_0` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 65536, false>::kMinBlocksPerSm) +fna3_64x128x65536_sm75_float16_cm_1_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x65536_sm75_float16_cm_1_0_0` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 65536, false>::kMinBlocksPerSm) +fna3_64x64x65536_sm75_float16_cm_1_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x65536_sm75_float16_cm_1_0_0` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +/////////////////////////////////////////////////////////////////// +// FNA-3D / float16 / SM75 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 32, false>::kMinBlocksPerSm) +fna3_32x128x32_sm75_float16_cm_1_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x32_sm75_float16_cm_1_0_1` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 32, false>::kMinBlocksPerSm) +fna3_64x64x32_sm75_float16_cm_1_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x32_sm75_float16_cm_1_0_1` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 32, false>::kMinBlocksPerSm) +fna3_64x128x32_sm75_float16_cm_1_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x32_sm75_float16_cm_1_0_1` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 64, false>::kMinBlocksPerSm) +fna3_32x128x64_sm75_float16_cm_1_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x64_sm75_float16_cm_1_0_1` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 64, false>::kMinBlocksPerSm) +fna3_64x64x64_sm75_float16_cm_1_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x64_sm75_float16_cm_1_0_1` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 64, false>::kMinBlocksPerSm) +fna3_64x128x64_sm75_float16_cm_1_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x64_sm75_float16_cm_1_0_1` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 128, false>::kMinBlocksPerSm) +fna3_32x128x128_sm75_float16_cm_1_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x128_sm75_float16_cm_1_0_1` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 128, false>::kMinBlocksPerSm) +fna3_64x64x128_sm75_float16_cm_1_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x128_sm75_float16_cm_1_0_1` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 128, false>::kMinBlocksPerSm) +fna3_64x128x128_sm75_float16_cm_1_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x128_sm75_float16_cm_1_0_1` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 65536, false>::kMinBlocksPerSm) +fna3_32x128x65536_sm75_float16_cm_1_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x65536_sm75_float16_cm_1_0_1` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 65536, false>::kMinBlocksPerSm) +fna3_64x128x65536_sm75_float16_cm_1_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x65536_sm75_float16_cm_1_0_1` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 65536, false>::kMinBlocksPerSm) +fna3_64x64x65536_sm75_float16_cm_1_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x65536_sm75_float16_cm_1_0_1` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +/////////////////////////////////////////////////////////////////// +// FNA-3D / float16 / SM75 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 32, false>::kMinBlocksPerSm) +fna3_32x128x32_sm75_float16_cm_1_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x32_sm75_float16_cm_1_1_0` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 32, false>::kMinBlocksPerSm) +fna3_64x64x32_sm75_float16_cm_1_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x32_sm75_float16_cm_1_1_0` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 32, false>::kMinBlocksPerSm) +fna3_64x128x32_sm75_float16_cm_1_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x32_sm75_float16_cm_1_1_0` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 64, false>::kMinBlocksPerSm) +fna3_32x128x64_sm75_float16_cm_1_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x64_sm75_float16_cm_1_1_0` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 64, false>::kMinBlocksPerSm) +fna3_64x64x64_sm75_float16_cm_1_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x64_sm75_float16_cm_1_1_0` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 64, false>::kMinBlocksPerSm) +fna3_64x128x64_sm75_float16_cm_1_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x64_sm75_float16_cm_1_1_0` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 128, false>::kMinBlocksPerSm) +fna3_32x128x128_sm75_float16_cm_1_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x128_sm75_float16_cm_1_1_0` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 128, false>::kMinBlocksPerSm) +fna3_64x64x128_sm75_float16_cm_1_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x128_sm75_float16_cm_1_1_0` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 128, false>::kMinBlocksPerSm) +fna3_64x128x128_sm75_float16_cm_1_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x128_sm75_float16_cm_1_1_0` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 65536, false>::kMinBlocksPerSm) +fna3_32x128x65536_sm75_float16_cm_1_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x65536_sm75_float16_cm_1_1_0` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 65536, false>::kMinBlocksPerSm) +fna3_64x128x65536_sm75_float16_cm_1_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x65536_sm75_float16_cm_1_1_0` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 65536, false>::kMinBlocksPerSm) +fna3_64x64x65536_sm75_float16_cm_1_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x65536_sm75_float16_cm_1_1_0` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +/////////////////////////////////////////////////////////////////// +// FNA-3D / float16 / SM75 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 32, false>::kMinBlocksPerSm) +fna3_32x128x32_sm75_float16_cm_1_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x32_sm75_float16_cm_1_1_1` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 32, false>::kMinBlocksPerSm) +fna3_64x64x32_sm75_float16_cm_1_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x32_sm75_float16_cm_1_1_1` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 32, false>::kMinBlocksPerSm) +fna3_64x128x32_sm75_float16_cm_1_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x32_sm75_float16_cm_1_1_1` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 64, false>::kMinBlocksPerSm) +fna3_32x128x64_sm75_float16_cm_1_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x64_sm75_float16_cm_1_1_1` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 64, false>::kMinBlocksPerSm) +fna3_64x64x64_sm75_float16_cm_1_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x64_sm75_float16_cm_1_1_1` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 64, false>::kMinBlocksPerSm) +fna3_64x128x64_sm75_float16_cm_1_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x64_sm75_float16_cm_1_1_1` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 128, false>::kMinBlocksPerSm) +fna3_32x128x128_sm75_float16_cm_1_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x128_sm75_float16_cm_1_1_1` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 128, false>::kMinBlocksPerSm) +fna3_64x64x128_sm75_float16_cm_1_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x128_sm75_float16_cm_1_1_1` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 128, false>::kMinBlocksPerSm) +fna3_64x128x128_sm75_float16_cm_1_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x128_sm75_float16_cm_1_1_1` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 65536, false>::kMinBlocksPerSm) +fna3_32x128x65536_sm75_float16_cm_1_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x65536_sm75_float16_cm_1_1_1` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 65536, false>::kMinBlocksPerSm) +fna3_64x128x65536_sm75_float16_cm_1_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x65536_sm75_float16_cm_1_1_1` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 65536, false>::kMinBlocksPerSm) +fna3_64x64x65536_sm75_float16_cm_1_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x65536_sm75_float16_cm_1_1_1` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +/////////////////////////////////////////////////////////////////// +// FNA-3D / float32 / SM80 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 32, false>::kMinBlocksPerSm) +fna3_32x128x32_sm80_float32_cm_0_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x32_sm80_float32_cm_0_0_0` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 32, false>::kMinBlocksPerSm) +fna3_64x64x32_sm80_float32_cm_0_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x32_sm80_float32_cm_0_0_0` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 32, false>::kMinBlocksPerSm) +fna3_64x128x32_sm80_float32_cm_0_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x32_sm80_float32_cm_0_0_0` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 64, false>::kMinBlocksPerSm) +fna3_32x128x64_sm80_float32_cm_0_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x64_sm80_float32_cm_0_0_0` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 64, false>::kMinBlocksPerSm) +fna3_64x64x64_sm80_float32_cm_0_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x64_sm80_float32_cm_0_0_0` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 64, false>::kMinBlocksPerSm) +fna3_64x128x64_sm80_float32_cm_0_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x64_sm80_float32_cm_0_0_0` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 128, false>::kMinBlocksPerSm) +fna3_32x128x128_sm80_float32_cm_0_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x128_sm80_float32_cm_0_0_0` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 128, false>::kMinBlocksPerSm) +fna3_64x64x128_sm80_float32_cm_0_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x128_sm80_float32_cm_0_0_0` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 128, false>::kMinBlocksPerSm) +fna3_64x128x128_sm80_float32_cm_0_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x128_sm80_float32_cm_0_0_0` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 65536, false>::kMinBlocksPerSm) +fna3_32x128x65536_sm80_float32_cm_0_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x65536_sm80_float32_cm_0_0_0` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 65536, false>::kMinBlocksPerSm) +fna3_64x128x65536_sm80_float32_cm_0_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x65536_sm80_float32_cm_0_0_0` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 65536, false>::kMinBlocksPerSm) +fna3_64x64x65536_sm80_float32_cm_0_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x65536_sm80_float32_cm_0_0_0` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +/////////////////////////////////////////////////////////////////// +// FNA-3D / float32 / SM80 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 32, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 32, true>::kMinBlocksPerSm) +fna3_32x128x32_sm80_float32_cm_0_0_0_rpb(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 32, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 32, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x32_sm80_float32_cm_0_0_0_rpb` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 32, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 32, true>::kMinBlocksPerSm) +fna3_64x64x32_sm80_float32_cm_0_0_0_rpb(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 32, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 32, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x32_sm80_float32_cm_0_0_0_rpb` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 32, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 32, true>::kMinBlocksPerSm) +fna3_64x128x32_sm80_float32_cm_0_0_0_rpb(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 32, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 32, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x32_sm80_float32_cm_0_0_0_rpb` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 64, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 64, true>::kMinBlocksPerSm) +fna3_32x128x64_sm80_float32_cm_0_0_0_rpb(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 64, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 64, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x64_sm80_float32_cm_0_0_0_rpb` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 64, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 64, true>::kMinBlocksPerSm) +fna3_64x64x64_sm80_float32_cm_0_0_0_rpb(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 64, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 64, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x64_sm80_float32_cm_0_0_0_rpb` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 64, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 64, true>::kMinBlocksPerSm) +fna3_64x128x64_sm80_float32_cm_0_0_0_rpb(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 64, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 64, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x64_sm80_float32_cm_0_0_0_rpb` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 128, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 128, true>::kMinBlocksPerSm) +fna3_32x128x128_sm80_float32_cm_0_0_0_rpb(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 128, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 128, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x128_sm80_float32_cm_0_0_0_rpb` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 128, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 128, true>::kMinBlocksPerSm) +fna3_64x64x128_sm80_float32_cm_0_0_0_rpb(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 128, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 128, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x128_sm80_float32_cm_0_0_0_rpb` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 128, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 128, true>::kMinBlocksPerSm) +fna3_64x128x128_sm80_float32_cm_0_0_0_rpb(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 128, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 128, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x128_sm80_float32_cm_0_0_0_rpb` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 65536, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 65536, true>::kMinBlocksPerSm) +fna3_32x128x65536_sm80_float32_cm_0_0_0_rpb(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 65536, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 65536, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x65536_sm80_float32_cm_0_0_0_rpb` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 65536, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 65536, true>::kMinBlocksPerSm) +fna3_64x128x65536_sm80_float32_cm_0_0_0_rpb(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 65536, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 65536, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x65536_sm80_float32_cm_0_0_0_rpb` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 65536, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 65536, true>::kMinBlocksPerSm) +fna3_64x64x65536_sm80_float32_cm_0_0_0_rpb(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 65536, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 65536, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x65536_sm80_float32_cm_0_0_0_rpb` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +/////////////////////////////////////////////////////////////////// +// FNA-3D / float32 / SM80 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 32, false>::kMinBlocksPerSm) +fna3_32x128x32_sm80_float32_cm_0_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x32_sm80_float32_cm_0_0_1` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 32, false>::kMinBlocksPerSm) +fna3_64x64x32_sm80_float32_cm_0_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x32_sm80_float32_cm_0_0_1` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 32, false>::kMinBlocksPerSm) +fna3_64x128x32_sm80_float32_cm_0_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x32_sm80_float32_cm_0_0_1` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 64, false>::kMinBlocksPerSm) +fna3_32x128x64_sm80_float32_cm_0_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x64_sm80_float32_cm_0_0_1` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 64, false>::kMinBlocksPerSm) +fna3_64x64x64_sm80_float32_cm_0_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x64_sm80_float32_cm_0_0_1` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 64, false>::kMinBlocksPerSm) +fna3_64x128x64_sm80_float32_cm_0_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x64_sm80_float32_cm_0_0_1` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 128, false>::kMinBlocksPerSm) +fna3_32x128x128_sm80_float32_cm_0_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x128_sm80_float32_cm_0_0_1` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 128, false>::kMinBlocksPerSm) +fna3_64x64x128_sm80_float32_cm_0_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x128_sm80_float32_cm_0_0_1` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 128, false>::kMinBlocksPerSm) +fna3_64x128x128_sm80_float32_cm_0_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x128_sm80_float32_cm_0_0_1` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 65536, false>::kMinBlocksPerSm) +fna3_32x128x65536_sm80_float32_cm_0_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x65536_sm80_float32_cm_0_0_1` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 65536, false>::kMinBlocksPerSm) +fna3_64x128x65536_sm80_float32_cm_0_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x65536_sm80_float32_cm_0_0_1` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 65536, false>::kMinBlocksPerSm) +fna3_64x64x65536_sm80_float32_cm_0_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x65536_sm80_float32_cm_0_0_1` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +/////////////////////////////////////////////////////////////////// +// FNA-3D / float32 / SM80 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 32, false>::kMinBlocksPerSm) +fna3_32x128x32_sm80_float32_cm_0_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x32_sm80_float32_cm_0_1_0` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 32, false>::kMinBlocksPerSm) +fna3_64x64x32_sm80_float32_cm_0_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x32_sm80_float32_cm_0_1_0` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 32, false>::kMinBlocksPerSm) +fna3_64x128x32_sm80_float32_cm_0_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x32_sm80_float32_cm_0_1_0` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 64, false>::kMinBlocksPerSm) +fna3_32x128x64_sm80_float32_cm_0_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x64_sm80_float32_cm_0_1_0` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 64, false>::kMinBlocksPerSm) +fna3_64x64x64_sm80_float32_cm_0_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x64_sm80_float32_cm_0_1_0` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 64, false>::kMinBlocksPerSm) +fna3_64x128x64_sm80_float32_cm_0_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x64_sm80_float32_cm_0_1_0` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 128, false>::kMinBlocksPerSm) +fna3_32x128x128_sm80_float32_cm_0_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x128_sm80_float32_cm_0_1_0` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 128, false>::kMinBlocksPerSm) +fna3_64x64x128_sm80_float32_cm_0_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x128_sm80_float32_cm_0_1_0` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 128, false>::kMinBlocksPerSm) +fna3_64x128x128_sm80_float32_cm_0_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x128_sm80_float32_cm_0_1_0` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 65536, false>::kMinBlocksPerSm) +fna3_32x128x65536_sm80_float32_cm_0_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x65536_sm80_float32_cm_0_1_0` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 65536, false>::kMinBlocksPerSm) +fna3_64x128x65536_sm80_float32_cm_0_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x65536_sm80_float32_cm_0_1_0` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 65536, false>::kMinBlocksPerSm) +fna3_64x64x65536_sm80_float32_cm_0_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x65536_sm80_float32_cm_0_1_0` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} +} +} +} + diff --git a/csrc/autogen/src/cuda/fna/source_13.cu b/csrc/autogen/src/cuda/fna/source_13.cu new file mode 100644 index 0000000..c6b600d --- /dev/null +++ b/csrc/autogen/src/cuda/fna/source_13.cu @@ -0,0 +1,2573 @@ +#include +#include +#include +#include +#include +namespace natten { +namespace cuda { +namespace fna { + + + +/////////////////////////////////////////////////////////////////// +// FNA-3D / float32 / SM80 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 32, false>::kMinBlocksPerSm) +fna3_32x128x32_sm80_float32_cm_0_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x32_sm80_float32_cm_0_1_1` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 32, false>::kMinBlocksPerSm) +fna3_64x64x32_sm80_float32_cm_0_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x32_sm80_float32_cm_0_1_1` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 32, false>::kMinBlocksPerSm) +fna3_64x128x32_sm80_float32_cm_0_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x32_sm80_float32_cm_0_1_1` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 64, false>::kMinBlocksPerSm) +fna3_32x128x64_sm80_float32_cm_0_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x64_sm80_float32_cm_0_1_1` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 64, false>::kMinBlocksPerSm) +fna3_64x64x64_sm80_float32_cm_0_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x64_sm80_float32_cm_0_1_1` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 64, false>::kMinBlocksPerSm) +fna3_64x128x64_sm80_float32_cm_0_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x64_sm80_float32_cm_0_1_1` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 128, false>::kMinBlocksPerSm) +fna3_32x128x128_sm80_float32_cm_0_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x128_sm80_float32_cm_0_1_1` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 128, false>::kMinBlocksPerSm) +fna3_64x64x128_sm80_float32_cm_0_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x128_sm80_float32_cm_0_1_1` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 128, false>::kMinBlocksPerSm) +fna3_64x128x128_sm80_float32_cm_0_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x128_sm80_float32_cm_0_1_1` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 65536, false>::kMinBlocksPerSm) +fna3_32x128x65536_sm80_float32_cm_0_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x65536_sm80_float32_cm_0_1_1` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 65536, false>::kMinBlocksPerSm) +fna3_64x128x65536_sm80_float32_cm_0_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x65536_sm80_float32_cm_0_1_1` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 65536, false>::kMinBlocksPerSm) +fna3_64x64x65536_sm80_float32_cm_0_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x65536_sm80_float32_cm_0_1_1` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +/////////////////////////////////////////////////////////////////// +// FNA-3D / float32 / SM80 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 32, false>::kMinBlocksPerSm) +fna3_32x128x32_sm80_float32_cm_1_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x32_sm80_float32_cm_1_0_0` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 32, false>::kMinBlocksPerSm) +fna3_64x64x32_sm80_float32_cm_1_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x32_sm80_float32_cm_1_0_0` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 32, false>::kMinBlocksPerSm) +fna3_64x128x32_sm80_float32_cm_1_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x32_sm80_float32_cm_1_0_0` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 64, false>::kMinBlocksPerSm) +fna3_32x128x64_sm80_float32_cm_1_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x64_sm80_float32_cm_1_0_0` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 64, false>::kMinBlocksPerSm) +fna3_64x64x64_sm80_float32_cm_1_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x64_sm80_float32_cm_1_0_0` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 64, false>::kMinBlocksPerSm) +fna3_64x128x64_sm80_float32_cm_1_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x64_sm80_float32_cm_1_0_0` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 128, false>::kMinBlocksPerSm) +fna3_32x128x128_sm80_float32_cm_1_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x128_sm80_float32_cm_1_0_0` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 128, false>::kMinBlocksPerSm) +fna3_64x64x128_sm80_float32_cm_1_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x128_sm80_float32_cm_1_0_0` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 128, false>::kMinBlocksPerSm) +fna3_64x128x128_sm80_float32_cm_1_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x128_sm80_float32_cm_1_0_0` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 65536, false>::kMinBlocksPerSm) +fna3_32x128x65536_sm80_float32_cm_1_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x65536_sm80_float32_cm_1_0_0` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 65536, false>::kMinBlocksPerSm) +fna3_64x128x65536_sm80_float32_cm_1_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x65536_sm80_float32_cm_1_0_0` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 65536, false>::kMinBlocksPerSm) +fna3_64x64x65536_sm80_float32_cm_1_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x65536_sm80_float32_cm_1_0_0` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +/////////////////////////////////////////////////////////////////// +// FNA-3D / float32 / SM80 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 32, false>::kMinBlocksPerSm) +fna3_32x128x32_sm80_float32_cm_1_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x32_sm80_float32_cm_1_0_1` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 32, false>::kMinBlocksPerSm) +fna3_64x64x32_sm80_float32_cm_1_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x32_sm80_float32_cm_1_0_1` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 32, false>::kMinBlocksPerSm) +fna3_64x128x32_sm80_float32_cm_1_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x32_sm80_float32_cm_1_0_1` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 64, false>::kMinBlocksPerSm) +fna3_32x128x64_sm80_float32_cm_1_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x64_sm80_float32_cm_1_0_1` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 64, false>::kMinBlocksPerSm) +fna3_64x64x64_sm80_float32_cm_1_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x64_sm80_float32_cm_1_0_1` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 64, false>::kMinBlocksPerSm) +fna3_64x128x64_sm80_float32_cm_1_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x64_sm80_float32_cm_1_0_1` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 128, false>::kMinBlocksPerSm) +fna3_32x128x128_sm80_float32_cm_1_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x128_sm80_float32_cm_1_0_1` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 128, false>::kMinBlocksPerSm) +fna3_64x64x128_sm80_float32_cm_1_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x128_sm80_float32_cm_1_0_1` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 128, false>::kMinBlocksPerSm) +fna3_64x128x128_sm80_float32_cm_1_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x128_sm80_float32_cm_1_0_1` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 65536, false>::kMinBlocksPerSm) +fna3_32x128x65536_sm80_float32_cm_1_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x65536_sm80_float32_cm_1_0_1` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 65536, false>::kMinBlocksPerSm) +fna3_64x128x65536_sm80_float32_cm_1_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x65536_sm80_float32_cm_1_0_1` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 65536, false>::kMinBlocksPerSm) +fna3_64x64x65536_sm80_float32_cm_1_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x65536_sm80_float32_cm_1_0_1` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +/////////////////////////////////////////////////////////////////// +// FNA-3D / float32 / SM80 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 32, false>::kMinBlocksPerSm) +fna3_32x128x32_sm80_float32_cm_1_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x32_sm80_float32_cm_1_1_0` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 32, false>::kMinBlocksPerSm) +fna3_64x64x32_sm80_float32_cm_1_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x32_sm80_float32_cm_1_1_0` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 32, false>::kMinBlocksPerSm) +fna3_64x128x32_sm80_float32_cm_1_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x32_sm80_float32_cm_1_1_0` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 64, false>::kMinBlocksPerSm) +fna3_32x128x64_sm80_float32_cm_1_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x64_sm80_float32_cm_1_1_0` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 64, false>::kMinBlocksPerSm) +fna3_64x64x64_sm80_float32_cm_1_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x64_sm80_float32_cm_1_1_0` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 64, false>::kMinBlocksPerSm) +fna3_64x128x64_sm80_float32_cm_1_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x64_sm80_float32_cm_1_1_0` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 128, false>::kMinBlocksPerSm) +fna3_32x128x128_sm80_float32_cm_1_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x128_sm80_float32_cm_1_1_0` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 128, false>::kMinBlocksPerSm) +fna3_64x64x128_sm80_float32_cm_1_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x128_sm80_float32_cm_1_1_0` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 128, false>::kMinBlocksPerSm) +fna3_64x128x128_sm80_float32_cm_1_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x128_sm80_float32_cm_1_1_0` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 65536, false>::kMinBlocksPerSm) +fna3_32x128x65536_sm80_float32_cm_1_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x65536_sm80_float32_cm_1_1_0` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 65536, false>::kMinBlocksPerSm) +fna3_64x128x65536_sm80_float32_cm_1_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x65536_sm80_float32_cm_1_1_0` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 65536, false>::kMinBlocksPerSm) +fna3_64x64x65536_sm80_float32_cm_1_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x65536_sm80_float32_cm_1_1_0` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +/////////////////////////////////////////////////////////////////// +// FNA-3D / float32 / SM80 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 32, false>::kMinBlocksPerSm) +fna3_32x128x32_sm80_float32_cm_1_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x32_sm80_float32_cm_1_1_1` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 32, false>::kMinBlocksPerSm) +fna3_64x64x32_sm80_float32_cm_1_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x32_sm80_float32_cm_1_1_1` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 32, false>::kMinBlocksPerSm) +fna3_64x128x32_sm80_float32_cm_1_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x32_sm80_float32_cm_1_1_1` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 64, false>::kMinBlocksPerSm) +fna3_32x128x64_sm80_float32_cm_1_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x64_sm80_float32_cm_1_1_1` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 64, false>::kMinBlocksPerSm) +fna3_64x64x64_sm80_float32_cm_1_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x64_sm80_float32_cm_1_1_1` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 64, false>::kMinBlocksPerSm) +fna3_64x128x64_sm80_float32_cm_1_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x64_sm80_float32_cm_1_1_1` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 128, false>::kMinBlocksPerSm) +fna3_32x128x128_sm80_float32_cm_1_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x128_sm80_float32_cm_1_1_1` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 128, false>::kMinBlocksPerSm) +fna3_64x64x128_sm80_float32_cm_1_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x128_sm80_float32_cm_1_1_1` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 128, false>::kMinBlocksPerSm) +fna3_64x128x128_sm80_float32_cm_1_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x128_sm80_float32_cm_1_1_1` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 65536, false>::kMinBlocksPerSm) +fna3_32x128x65536_sm80_float32_cm_1_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x65536_sm80_float32_cm_1_1_1` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 65536, false>::kMinBlocksPerSm) +fna3_64x128x65536_sm80_float32_cm_1_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x65536_sm80_float32_cm_1_1_1` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 65536, false>::kMinBlocksPerSm) +fna3_64x64x65536_sm80_float32_cm_1_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x65536_sm80_float32_cm_1_1_1` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +/////////////////////////////////////////////////////////////////// +// FNA-3D / float16 / SM80 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 32, false>::kMinBlocksPerSm) +fna3_32x128x32_sm80_float16_cm_0_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x32_sm80_float16_cm_0_0_0` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 32, false>::kMinBlocksPerSm) +fna3_64x64x32_sm80_float16_cm_0_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x32_sm80_float16_cm_0_0_0` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 32, false>::kMinBlocksPerSm) +fna3_64x128x32_sm80_float16_cm_0_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x32_sm80_float16_cm_0_0_0` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 64, false>::kMinBlocksPerSm) +fna3_32x128x64_sm80_float16_cm_0_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x64_sm80_float16_cm_0_0_0` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 64, false>::kMinBlocksPerSm) +fna3_64x64x64_sm80_float16_cm_0_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x64_sm80_float16_cm_0_0_0` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 64, false>::kMinBlocksPerSm) +fna3_64x128x64_sm80_float16_cm_0_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x64_sm80_float16_cm_0_0_0` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 128, false>::kMinBlocksPerSm) +fna3_32x128x128_sm80_float16_cm_0_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x128_sm80_float16_cm_0_0_0` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 128, false>::kMinBlocksPerSm) +fna3_64x64x128_sm80_float16_cm_0_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x128_sm80_float16_cm_0_0_0` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 128, false>::kMinBlocksPerSm) +fna3_64x128x128_sm80_float16_cm_0_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x128_sm80_float16_cm_0_0_0` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 65536, false>::kMinBlocksPerSm) +fna3_32x128x65536_sm80_float16_cm_0_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x65536_sm80_float16_cm_0_0_0` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 65536, false>::kMinBlocksPerSm) +fna3_64x128x65536_sm80_float16_cm_0_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x65536_sm80_float16_cm_0_0_0` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 65536, false>::kMinBlocksPerSm) +fna3_64x64x65536_sm80_float16_cm_0_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x65536_sm80_float16_cm_0_0_0` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +/////////////////////////////////////////////////////////////////// +// FNA-3D / float16 / SM80 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 32, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 32, true>::kMinBlocksPerSm) +fna3_32x128x32_sm80_float16_cm_0_0_0_rpb(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 32, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 32, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x32_sm80_float16_cm_0_0_0_rpb` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 32, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 32, true>::kMinBlocksPerSm) +fna3_64x64x32_sm80_float16_cm_0_0_0_rpb(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 32, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 32, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x32_sm80_float16_cm_0_0_0_rpb` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 32, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 32, true>::kMinBlocksPerSm) +fna3_64x128x32_sm80_float16_cm_0_0_0_rpb(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 32, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 32, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x32_sm80_float16_cm_0_0_0_rpb` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 64, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 64, true>::kMinBlocksPerSm) +fna3_32x128x64_sm80_float16_cm_0_0_0_rpb(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 64, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 64, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x64_sm80_float16_cm_0_0_0_rpb` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 64, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 64, true>::kMinBlocksPerSm) +fna3_64x64x64_sm80_float16_cm_0_0_0_rpb(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 64, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 64, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x64_sm80_float16_cm_0_0_0_rpb` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 64, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 64, true>::kMinBlocksPerSm) +fna3_64x128x64_sm80_float16_cm_0_0_0_rpb(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 64, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 64, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x64_sm80_float16_cm_0_0_0_rpb` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 128, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 128, true>::kMinBlocksPerSm) +fna3_32x128x128_sm80_float16_cm_0_0_0_rpb(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 128, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 128, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x128_sm80_float16_cm_0_0_0_rpb` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 128, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 128, true>::kMinBlocksPerSm) +fna3_64x64x128_sm80_float16_cm_0_0_0_rpb(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 128, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 128, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x128_sm80_float16_cm_0_0_0_rpb` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 128, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 128, true>::kMinBlocksPerSm) +fna3_64x128x128_sm80_float16_cm_0_0_0_rpb(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 128, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 128, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x128_sm80_float16_cm_0_0_0_rpb` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 65536, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 65536, true>::kMinBlocksPerSm) +fna3_32x128x65536_sm80_float16_cm_0_0_0_rpb(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 65536, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 65536, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x65536_sm80_float16_cm_0_0_0_rpb` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 65536, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 65536, true>::kMinBlocksPerSm) +fna3_64x128x65536_sm80_float16_cm_0_0_0_rpb(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 65536, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 65536, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x65536_sm80_float16_cm_0_0_0_rpb` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 65536, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 65536, true>::kMinBlocksPerSm) +fna3_64x64x65536_sm80_float16_cm_0_0_0_rpb(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 65536, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 65536, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x65536_sm80_float16_cm_0_0_0_rpb` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +/////////////////////////////////////////////////////////////////// +// FNA-3D / float16 / SM80 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 32, false>::kMinBlocksPerSm) +fna3_32x128x32_sm80_float16_cm_0_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x32_sm80_float16_cm_0_0_1` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 32, false>::kMinBlocksPerSm) +fna3_64x64x32_sm80_float16_cm_0_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x32_sm80_float16_cm_0_0_1` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 32, false>::kMinBlocksPerSm) +fna3_64x128x32_sm80_float16_cm_0_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x32_sm80_float16_cm_0_0_1` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 64, false>::kMinBlocksPerSm) +fna3_32x128x64_sm80_float16_cm_0_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x64_sm80_float16_cm_0_0_1` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 64, false>::kMinBlocksPerSm) +fna3_64x64x64_sm80_float16_cm_0_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x64_sm80_float16_cm_0_0_1` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 64, false>::kMinBlocksPerSm) +fna3_64x128x64_sm80_float16_cm_0_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x64_sm80_float16_cm_0_0_1` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 128, false>::kMinBlocksPerSm) +fna3_32x128x128_sm80_float16_cm_0_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x128_sm80_float16_cm_0_0_1` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 128, false>::kMinBlocksPerSm) +fna3_64x64x128_sm80_float16_cm_0_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x128_sm80_float16_cm_0_0_1` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 128, false>::kMinBlocksPerSm) +fna3_64x128x128_sm80_float16_cm_0_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x128_sm80_float16_cm_0_0_1` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 65536, false>::kMinBlocksPerSm) +fna3_32x128x65536_sm80_float16_cm_0_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x65536_sm80_float16_cm_0_0_1` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 65536, false>::kMinBlocksPerSm) +fna3_64x128x65536_sm80_float16_cm_0_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x65536_sm80_float16_cm_0_0_1` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 65536, false>::kMinBlocksPerSm) +fna3_64x64x65536_sm80_float16_cm_0_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x65536_sm80_float16_cm_0_0_1` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +/////////////////////////////////////////////////////////////////// +// FNA-3D / float16 / SM80 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 32, false>::kMinBlocksPerSm) +fna3_32x128x32_sm80_float16_cm_0_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x32_sm80_float16_cm_0_1_0` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 32, false>::kMinBlocksPerSm) +fna3_64x64x32_sm80_float16_cm_0_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x32_sm80_float16_cm_0_1_0` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 32, false>::kMinBlocksPerSm) +fna3_64x128x32_sm80_float16_cm_0_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x32_sm80_float16_cm_0_1_0` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 64, false>::kMinBlocksPerSm) +fna3_32x128x64_sm80_float16_cm_0_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x64_sm80_float16_cm_0_1_0` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 64, false>::kMinBlocksPerSm) +fna3_64x64x64_sm80_float16_cm_0_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x64_sm80_float16_cm_0_1_0` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 64, false>::kMinBlocksPerSm) +fna3_64x128x64_sm80_float16_cm_0_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x64_sm80_float16_cm_0_1_0` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 128, false>::kMinBlocksPerSm) +fna3_32x128x128_sm80_float16_cm_0_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x128_sm80_float16_cm_0_1_0` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 128, false>::kMinBlocksPerSm) +fna3_64x64x128_sm80_float16_cm_0_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x128_sm80_float16_cm_0_1_0` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 128, false>::kMinBlocksPerSm) +fna3_64x128x128_sm80_float16_cm_0_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x128_sm80_float16_cm_0_1_0` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 65536, false>::kMinBlocksPerSm) +fna3_32x128x65536_sm80_float16_cm_0_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x65536_sm80_float16_cm_0_1_0` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 65536, false>::kMinBlocksPerSm) +fna3_64x128x65536_sm80_float16_cm_0_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x65536_sm80_float16_cm_0_1_0` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 65536, false>::kMinBlocksPerSm) +fna3_64x64x65536_sm80_float16_cm_0_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x65536_sm80_float16_cm_0_1_0` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +/////////////////////////////////////////////////////////////////// +// FNA-3D / float16 / SM80 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 32, false>::kMinBlocksPerSm) +fna3_32x128x32_sm80_float16_cm_0_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x32_sm80_float16_cm_0_1_1` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 32, false>::kMinBlocksPerSm) +fna3_64x64x32_sm80_float16_cm_0_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x32_sm80_float16_cm_0_1_1` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 32, false>::kMinBlocksPerSm) +fna3_64x128x32_sm80_float16_cm_0_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x32_sm80_float16_cm_0_1_1` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 64, false>::kMinBlocksPerSm) +fna3_32x128x64_sm80_float16_cm_0_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x64_sm80_float16_cm_0_1_1` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 64, false>::kMinBlocksPerSm) +fna3_64x64x64_sm80_float16_cm_0_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x64_sm80_float16_cm_0_1_1` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 64, false>::kMinBlocksPerSm) +fna3_64x128x64_sm80_float16_cm_0_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x64_sm80_float16_cm_0_1_1` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 128, false>::kMinBlocksPerSm) +fna3_32x128x128_sm80_float16_cm_0_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x128_sm80_float16_cm_0_1_1` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 128, false>::kMinBlocksPerSm) +fna3_64x64x128_sm80_float16_cm_0_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x128_sm80_float16_cm_0_1_1` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 128, false>::kMinBlocksPerSm) +fna3_64x128x128_sm80_float16_cm_0_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x128_sm80_float16_cm_0_1_1` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 65536, false>::kMinBlocksPerSm) +fna3_32x128x65536_sm80_float16_cm_0_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x65536_sm80_float16_cm_0_1_1` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 65536, false>::kMinBlocksPerSm) +fna3_64x128x65536_sm80_float16_cm_0_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x65536_sm80_float16_cm_0_1_1` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 65536, false>::kMinBlocksPerSm) +fna3_64x64x65536_sm80_float16_cm_0_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x65536_sm80_float16_cm_0_1_1` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} +} +} +} + diff --git a/csrc/autogen/src/cuda/fna/source_14.cu b/csrc/autogen/src/cuda/fna/source_14.cu new file mode 100644 index 0000000..43f111d --- /dev/null +++ b/csrc/autogen/src/cuda/fna/source_14.cu @@ -0,0 +1,2573 @@ +#include +#include +#include +#include +#include +namespace natten { +namespace cuda { +namespace fna { + + + +/////////////////////////////////////////////////////////////////// +// FNA-3D / float16 / SM80 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 32, false>::kMinBlocksPerSm) +fna3_32x128x32_sm80_float16_cm_1_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x32_sm80_float16_cm_1_0_0` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 32, false>::kMinBlocksPerSm) +fna3_64x64x32_sm80_float16_cm_1_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x32_sm80_float16_cm_1_0_0` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 32, false>::kMinBlocksPerSm) +fna3_64x128x32_sm80_float16_cm_1_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x32_sm80_float16_cm_1_0_0` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 64, false>::kMinBlocksPerSm) +fna3_32x128x64_sm80_float16_cm_1_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x64_sm80_float16_cm_1_0_0` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 64, false>::kMinBlocksPerSm) +fna3_64x64x64_sm80_float16_cm_1_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x64_sm80_float16_cm_1_0_0` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 64, false>::kMinBlocksPerSm) +fna3_64x128x64_sm80_float16_cm_1_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x64_sm80_float16_cm_1_0_0` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 128, false>::kMinBlocksPerSm) +fna3_32x128x128_sm80_float16_cm_1_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x128_sm80_float16_cm_1_0_0` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 128, false>::kMinBlocksPerSm) +fna3_64x64x128_sm80_float16_cm_1_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x128_sm80_float16_cm_1_0_0` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 128, false>::kMinBlocksPerSm) +fna3_64x128x128_sm80_float16_cm_1_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x128_sm80_float16_cm_1_0_0` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 65536, false>::kMinBlocksPerSm) +fna3_32x128x65536_sm80_float16_cm_1_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x65536_sm80_float16_cm_1_0_0` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 65536, false>::kMinBlocksPerSm) +fna3_64x128x65536_sm80_float16_cm_1_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x65536_sm80_float16_cm_1_0_0` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 65536, false>::kMinBlocksPerSm) +fna3_64x64x65536_sm80_float16_cm_1_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x65536_sm80_float16_cm_1_0_0` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +/////////////////////////////////////////////////////////////////// +// FNA-3D / float16 / SM80 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 32, false>::kMinBlocksPerSm) +fna3_32x128x32_sm80_float16_cm_1_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x32_sm80_float16_cm_1_0_1` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 32, false>::kMinBlocksPerSm) +fna3_64x64x32_sm80_float16_cm_1_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x32_sm80_float16_cm_1_0_1` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 32, false>::kMinBlocksPerSm) +fna3_64x128x32_sm80_float16_cm_1_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x32_sm80_float16_cm_1_0_1` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 64, false>::kMinBlocksPerSm) +fna3_32x128x64_sm80_float16_cm_1_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x64_sm80_float16_cm_1_0_1` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 64, false>::kMinBlocksPerSm) +fna3_64x64x64_sm80_float16_cm_1_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x64_sm80_float16_cm_1_0_1` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 64, false>::kMinBlocksPerSm) +fna3_64x128x64_sm80_float16_cm_1_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x64_sm80_float16_cm_1_0_1` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 128, false>::kMinBlocksPerSm) +fna3_32x128x128_sm80_float16_cm_1_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x128_sm80_float16_cm_1_0_1` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 128, false>::kMinBlocksPerSm) +fna3_64x64x128_sm80_float16_cm_1_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x128_sm80_float16_cm_1_0_1` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 128, false>::kMinBlocksPerSm) +fna3_64x128x128_sm80_float16_cm_1_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x128_sm80_float16_cm_1_0_1` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 65536, false>::kMinBlocksPerSm) +fna3_32x128x65536_sm80_float16_cm_1_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x65536_sm80_float16_cm_1_0_1` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 65536, false>::kMinBlocksPerSm) +fna3_64x128x65536_sm80_float16_cm_1_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x65536_sm80_float16_cm_1_0_1` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 65536, false>::kMinBlocksPerSm) +fna3_64x64x65536_sm80_float16_cm_1_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x65536_sm80_float16_cm_1_0_1` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +/////////////////////////////////////////////////////////////////// +// FNA-3D / float16 / SM80 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 32, false>::kMinBlocksPerSm) +fna3_32x128x32_sm80_float16_cm_1_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x32_sm80_float16_cm_1_1_0` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 32, false>::kMinBlocksPerSm) +fna3_64x64x32_sm80_float16_cm_1_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x32_sm80_float16_cm_1_1_0` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 32, false>::kMinBlocksPerSm) +fna3_64x128x32_sm80_float16_cm_1_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x32_sm80_float16_cm_1_1_0` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 64, false>::kMinBlocksPerSm) +fna3_32x128x64_sm80_float16_cm_1_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x64_sm80_float16_cm_1_1_0` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 64, false>::kMinBlocksPerSm) +fna3_64x64x64_sm80_float16_cm_1_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x64_sm80_float16_cm_1_1_0` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 64, false>::kMinBlocksPerSm) +fna3_64x128x64_sm80_float16_cm_1_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x64_sm80_float16_cm_1_1_0` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 128, false>::kMinBlocksPerSm) +fna3_32x128x128_sm80_float16_cm_1_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x128_sm80_float16_cm_1_1_0` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 128, false>::kMinBlocksPerSm) +fna3_64x64x128_sm80_float16_cm_1_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x128_sm80_float16_cm_1_1_0` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 128, false>::kMinBlocksPerSm) +fna3_64x128x128_sm80_float16_cm_1_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x128_sm80_float16_cm_1_1_0` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 65536, false>::kMinBlocksPerSm) +fna3_32x128x65536_sm80_float16_cm_1_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x65536_sm80_float16_cm_1_1_0` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 65536, false>::kMinBlocksPerSm) +fna3_64x128x65536_sm80_float16_cm_1_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x65536_sm80_float16_cm_1_1_0` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 65536, false>::kMinBlocksPerSm) +fna3_64x64x65536_sm80_float16_cm_1_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x65536_sm80_float16_cm_1_1_0` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +/////////////////////////////////////////////////////////////////// +// FNA-3D / float16 / SM80 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 32, false>::kMinBlocksPerSm) +fna3_32x128x32_sm80_float16_cm_1_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x32_sm80_float16_cm_1_1_1` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 32, false>::kMinBlocksPerSm) +fna3_64x64x32_sm80_float16_cm_1_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x32_sm80_float16_cm_1_1_1` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 32, false>::kMinBlocksPerSm) +fna3_64x128x32_sm80_float16_cm_1_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x32_sm80_float16_cm_1_1_1` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 64, false>::kMinBlocksPerSm) +fna3_32x128x64_sm80_float16_cm_1_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x64_sm80_float16_cm_1_1_1` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 64, false>::kMinBlocksPerSm) +fna3_64x64x64_sm80_float16_cm_1_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x64_sm80_float16_cm_1_1_1` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 64, false>::kMinBlocksPerSm) +fna3_64x128x64_sm80_float16_cm_1_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x64_sm80_float16_cm_1_1_1` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 128, false>::kMinBlocksPerSm) +fna3_32x128x128_sm80_float16_cm_1_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x128_sm80_float16_cm_1_1_1` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 128, false>::kMinBlocksPerSm) +fna3_64x64x128_sm80_float16_cm_1_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x128_sm80_float16_cm_1_1_1` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 128, false>::kMinBlocksPerSm) +fna3_64x128x128_sm80_float16_cm_1_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x128_sm80_float16_cm_1_1_1` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 65536, false>::kMinBlocksPerSm) +fna3_32x128x65536_sm80_float16_cm_1_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x65536_sm80_float16_cm_1_1_1` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 65536, false>::kMinBlocksPerSm) +fna3_64x128x65536_sm80_float16_cm_1_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x65536_sm80_float16_cm_1_1_1` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 65536, false>::kMinBlocksPerSm) +fna3_64x64x65536_sm80_float16_cm_1_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x65536_sm80_float16_cm_1_1_1` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +/////////////////////////////////////////////////////////////////// +// FNA-3D / bfloat16 / SM80 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 32, false>::kMinBlocksPerSm) +fna3_32x128x32_sm80_bfloat16_cm_0_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x32_sm80_bfloat16_cm_0_0_0` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 32, false>::kMinBlocksPerSm) +fna3_64x64x32_sm80_bfloat16_cm_0_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x32_sm80_bfloat16_cm_0_0_0` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 32, false>::kMinBlocksPerSm) +fna3_64x128x32_sm80_bfloat16_cm_0_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x32_sm80_bfloat16_cm_0_0_0` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 64, false>::kMinBlocksPerSm) +fna3_32x128x64_sm80_bfloat16_cm_0_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x64_sm80_bfloat16_cm_0_0_0` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 64, false>::kMinBlocksPerSm) +fna3_64x64x64_sm80_bfloat16_cm_0_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x64_sm80_bfloat16_cm_0_0_0` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 64, false>::kMinBlocksPerSm) +fna3_64x128x64_sm80_bfloat16_cm_0_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x64_sm80_bfloat16_cm_0_0_0` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 128, false>::kMinBlocksPerSm) +fna3_32x128x128_sm80_bfloat16_cm_0_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x128_sm80_bfloat16_cm_0_0_0` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 128, false>::kMinBlocksPerSm) +fna3_64x64x128_sm80_bfloat16_cm_0_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x128_sm80_bfloat16_cm_0_0_0` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 128, false>::kMinBlocksPerSm) +fna3_64x128x128_sm80_bfloat16_cm_0_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x128_sm80_bfloat16_cm_0_0_0` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 65536, false>::kMinBlocksPerSm) +fna3_32x128x65536_sm80_bfloat16_cm_0_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x65536_sm80_bfloat16_cm_0_0_0` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 65536, false>::kMinBlocksPerSm) +fna3_64x128x65536_sm80_bfloat16_cm_0_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x65536_sm80_bfloat16_cm_0_0_0` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 65536, false>::kMinBlocksPerSm) +fna3_64x64x65536_sm80_bfloat16_cm_0_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x65536_sm80_bfloat16_cm_0_0_0` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +/////////////////////////////////////////////////////////////////// +// FNA-3D / bfloat16 / SM80 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 32, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 32, true>::kMinBlocksPerSm) +fna3_32x128x32_sm80_bfloat16_cm_0_0_0_rpb(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 32, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 32, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x32_sm80_bfloat16_cm_0_0_0_rpb` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 32, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 32, true>::kMinBlocksPerSm) +fna3_64x64x32_sm80_bfloat16_cm_0_0_0_rpb(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 32, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 32, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x32_sm80_bfloat16_cm_0_0_0_rpb` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 32, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 32, true>::kMinBlocksPerSm) +fna3_64x128x32_sm80_bfloat16_cm_0_0_0_rpb(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 32, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 32, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x32_sm80_bfloat16_cm_0_0_0_rpb` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 64, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 64, true>::kMinBlocksPerSm) +fna3_32x128x64_sm80_bfloat16_cm_0_0_0_rpb(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 64, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 64, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x64_sm80_bfloat16_cm_0_0_0_rpb` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 64, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 64, true>::kMinBlocksPerSm) +fna3_64x64x64_sm80_bfloat16_cm_0_0_0_rpb(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 64, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 64, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x64_sm80_bfloat16_cm_0_0_0_rpb` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 64, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 64, true>::kMinBlocksPerSm) +fna3_64x128x64_sm80_bfloat16_cm_0_0_0_rpb(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 64, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 64, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x64_sm80_bfloat16_cm_0_0_0_rpb` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 128, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 128, true>::kMinBlocksPerSm) +fna3_32x128x128_sm80_bfloat16_cm_0_0_0_rpb(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 128, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 128, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x128_sm80_bfloat16_cm_0_0_0_rpb` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 128, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 128, true>::kMinBlocksPerSm) +fna3_64x64x128_sm80_bfloat16_cm_0_0_0_rpb(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 128, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 128, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x128_sm80_bfloat16_cm_0_0_0_rpb` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 128, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 128, true>::kMinBlocksPerSm) +fna3_64x128x128_sm80_bfloat16_cm_0_0_0_rpb(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 128, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 128, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x128_sm80_bfloat16_cm_0_0_0_rpb` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 65536, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 65536, true>::kMinBlocksPerSm) +fna3_32x128x65536_sm80_bfloat16_cm_0_0_0_rpb(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 65536, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 65536, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x65536_sm80_bfloat16_cm_0_0_0_rpb` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 65536, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 65536, true>::kMinBlocksPerSm) +fna3_64x128x65536_sm80_bfloat16_cm_0_0_0_rpb(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 65536, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 65536, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x65536_sm80_bfloat16_cm_0_0_0_rpb` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 65536, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 65536, true>::kMinBlocksPerSm) +fna3_64x64x65536_sm80_bfloat16_cm_0_0_0_rpb(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 65536, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 65536, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x65536_sm80_bfloat16_cm_0_0_0_rpb` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +/////////////////////////////////////////////////////////////////// +// FNA-3D / bfloat16 / SM80 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 32, false>::kMinBlocksPerSm) +fna3_32x128x32_sm80_bfloat16_cm_0_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x32_sm80_bfloat16_cm_0_0_1` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 32, false>::kMinBlocksPerSm) +fna3_64x64x32_sm80_bfloat16_cm_0_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x32_sm80_bfloat16_cm_0_0_1` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 32, false>::kMinBlocksPerSm) +fna3_64x128x32_sm80_bfloat16_cm_0_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x32_sm80_bfloat16_cm_0_0_1` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 64, false>::kMinBlocksPerSm) +fna3_32x128x64_sm80_bfloat16_cm_0_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x64_sm80_bfloat16_cm_0_0_1` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 64, false>::kMinBlocksPerSm) +fna3_64x64x64_sm80_bfloat16_cm_0_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x64_sm80_bfloat16_cm_0_0_1` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 64, false>::kMinBlocksPerSm) +fna3_64x128x64_sm80_bfloat16_cm_0_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x64_sm80_bfloat16_cm_0_0_1` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 128, false>::kMinBlocksPerSm) +fna3_32x128x128_sm80_bfloat16_cm_0_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x128_sm80_bfloat16_cm_0_0_1` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 128, false>::kMinBlocksPerSm) +fna3_64x64x128_sm80_bfloat16_cm_0_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x128_sm80_bfloat16_cm_0_0_1` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 128, false>::kMinBlocksPerSm) +fna3_64x128x128_sm80_bfloat16_cm_0_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x128_sm80_bfloat16_cm_0_0_1` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 65536, false>::kMinBlocksPerSm) +fna3_32x128x65536_sm80_bfloat16_cm_0_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x65536_sm80_bfloat16_cm_0_0_1` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 65536, false>::kMinBlocksPerSm) +fna3_64x128x65536_sm80_bfloat16_cm_0_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x65536_sm80_bfloat16_cm_0_0_1` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 65536, false>::kMinBlocksPerSm) +fna3_64x64x65536_sm80_bfloat16_cm_0_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x65536_sm80_bfloat16_cm_0_0_1` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +/////////////////////////////////////////////////////////////////// +// FNA-3D / bfloat16 / SM80 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 32, false>::kMinBlocksPerSm) +fna3_32x128x32_sm80_bfloat16_cm_0_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x32_sm80_bfloat16_cm_0_1_0` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 32, false>::kMinBlocksPerSm) +fna3_64x64x32_sm80_bfloat16_cm_0_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x32_sm80_bfloat16_cm_0_1_0` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 32, false>::kMinBlocksPerSm) +fna3_64x128x32_sm80_bfloat16_cm_0_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x32_sm80_bfloat16_cm_0_1_0` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 64, false>::kMinBlocksPerSm) +fna3_32x128x64_sm80_bfloat16_cm_0_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x64_sm80_bfloat16_cm_0_1_0` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 64, false>::kMinBlocksPerSm) +fna3_64x64x64_sm80_bfloat16_cm_0_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x64_sm80_bfloat16_cm_0_1_0` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 64, false>::kMinBlocksPerSm) +fna3_64x128x64_sm80_bfloat16_cm_0_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x64_sm80_bfloat16_cm_0_1_0` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 128, false>::kMinBlocksPerSm) +fna3_32x128x128_sm80_bfloat16_cm_0_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x128_sm80_bfloat16_cm_0_1_0` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 128, false>::kMinBlocksPerSm) +fna3_64x64x128_sm80_bfloat16_cm_0_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x128_sm80_bfloat16_cm_0_1_0` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 128, false>::kMinBlocksPerSm) +fna3_64x128x128_sm80_bfloat16_cm_0_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x128_sm80_bfloat16_cm_0_1_0` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 65536, false>::kMinBlocksPerSm) +fna3_32x128x65536_sm80_bfloat16_cm_0_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x65536_sm80_bfloat16_cm_0_1_0` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 65536, false>::kMinBlocksPerSm) +fna3_64x128x65536_sm80_bfloat16_cm_0_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x65536_sm80_bfloat16_cm_0_1_0` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 65536, false>::kMinBlocksPerSm) +fna3_64x64x65536_sm80_bfloat16_cm_0_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x65536_sm80_bfloat16_cm_0_1_0` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +/////////////////////////////////////////////////////////////////// +// FNA-3D / bfloat16 / SM80 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 32, false>::kMinBlocksPerSm) +fna3_32x128x32_sm80_bfloat16_cm_0_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x32_sm80_bfloat16_cm_0_1_1` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 32, false>::kMinBlocksPerSm) +fna3_64x64x32_sm80_bfloat16_cm_0_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x32_sm80_bfloat16_cm_0_1_1` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 32, false>::kMinBlocksPerSm) +fna3_64x128x32_sm80_bfloat16_cm_0_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x32_sm80_bfloat16_cm_0_1_1` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 64, false>::kMinBlocksPerSm) +fna3_32x128x64_sm80_bfloat16_cm_0_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x64_sm80_bfloat16_cm_0_1_1` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 64, false>::kMinBlocksPerSm) +fna3_64x64x64_sm80_bfloat16_cm_0_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x64_sm80_bfloat16_cm_0_1_1` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 64, false>::kMinBlocksPerSm) +fna3_64x128x64_sm80_bfloat16_cm_0_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x64_sm80_bfloat16_cm_0_1_1` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 128, false>::kMinBlocksPerSm) +fna3_32x128x128_sm80_bfloat16_cm_0_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x128_sm80_bfloat16_cm_0_1_1` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 128, false>::kMinBlocksPerSm) +fna3_64x64x128_sm80_bfloat16_cm_0_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x128_sm80_bfloat16_cm_0_1_1` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 128, false>::kMinBlocksPerSm) +fna3_64x128x128_sm80_bfloat16_cm_0_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x128_sm80_bfloat16_cm_0_1_1` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 65536, false>::kMinBlocksPerSm) +fna3_32x128x65536_sm80_bfloat16_cm_0_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x65536_sm80_bfloat16_cm_0_1_1` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 65536, false>::kMinBlocksPerSm) +fna3_64x128x65536_sm80_bfloat16_cm_0_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x65536_sm80_bfloat16_cm_0_1_1` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 65536, false>::kMinBlocksPerSm) +fna3_64x64x65536_sm80_bfloat16_cm_0_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x65536_sm80_bfloat16_cm_0_1_1` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +/////////////////////////////////////////////////////////////////// +// FNA-3D / bfloat16 / SM80 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 32, false>::kMinBlocksPerSm) +fna3_32x128x32_sm80_bfloat16_cm_1_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x32_sm80_bfloat16_cm_1_0_0` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 32, false>::kMinBlocksPerSm) +fna3_64x64x32_sm80_bfloat16_cm_1_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x32_sm80_bfloat16_cm_1_0_0` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 32, false>::kMinBlocksPerSm) +fna3_64x128x32_sm80_bfloat16_cm_1_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x32_sm80_bfloat16_cm_1_0_0` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 64, false>::kMinBlocksPerSm) +fna3_32x128x64_sm80_bfloat16_cm_1_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x64_sm80_bfloat16_cm_1_0_0` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 64, false>::kMinBlocksPerSm) +fna3_64x64x64_sm80_bfloat16_cm_1_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x64_sm80_bfloat16_cm_1_0_0` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 64, false>::kMinBlocksPerSm) +fna3_64x128x64_sm80_bfloat16_cm_1_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x64_sm80_bfloat16_cm_1_0_0` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 128, false>::kMinBlocksPerSm) +fna3_32x128x128_sm80_bfloat16_cm_1_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x128_sm80_bfloat16_cm_1_0_0` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 128, false>::kMinBlocksPerSm) +fna3_64x64x128_sm80_bfloat16_cm_1_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x128_sm80_bfloat16_cm_1_0_0` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 128, false>::kMinBlocksPerSm) +fna3_64x128x128_sm80_bfloat16_cm_1_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x128_sm80_bfloat16_cm_1_0_0` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 65536, false>::kMinBlocksPerSm) +fna3_32x128x65536_sm80_bfloat16_cm_1_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x65536_sm80_bfloat16_cm_1_0_0` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 65536, false>::kMinBlocksPerSm) +fna3_64x128x65536_sm80_bfloat16_cm_1_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x65536_sm80_bfloat16_cm_1_0_0` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 65536, false>::kMinBlocksPerSm) +fna3_64x64x65536_sm80_bfloat16_cm_1_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x65536_sm80_bfloat16_cm_1_0_0` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} +} +} +} + diff --git a/csrc/autogen/src/cuda/fna/source_15.cu b/csrc/autogen/src/cuda/fna/source_15.cu new file mode 100644 index 0000000..694c092 --- /dev/null +++ b/csrc/autogen/src/cuda/fna/source_15.cu @@ -0,0 +1,781 @@ +#include +#include +#include +#include +#include +namespace natten { +namespace cuda { +namespace fna { + + + +/////////////////////////////////////////////////////////////////// +// FNA-3D / bfloat16 / SM80 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 32, false>::kMinBlocksPerSm) +fna3_32x128x32_sm80_bfloat16_cm_1_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x32_sm80_bfloat16_cm_1_0_1` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 32, false>::kMinBlocksPerSm) +fna3_64x64x32_sm80_bfloat16_cm_1_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x32_sm80_bfloat16_cm_1_0_1` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 32, false>::kMinBlocksPerSm) +fna3_64x128x32_sm80_bfloat16_cm_1_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x32_sm80_bfloat16_cm_1_0_1` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 64, false>::kMinBlocksPerSm) +fna3_32x128x64_sm80_bfloat16_cm_1_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x64_sm80_bfloat16_cm_1_0_1` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 64, false>::kMinBlocksPerSm) +fna3_64x64x64_sm80_bfloat16_cm_1_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x64_sm80_bfloat16_cm_1_0_1` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 64, false>::kMinBlocksPerSm) +fna3_64x128x64_sm80_bfloat16_cm_1_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x64_sm80_bfloat16_cm_1_0_1` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 128, false>::kMinBlocksPerSm) +fna3_32x128x128_sm80_bfloat16_cm_1_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x128_sm80_bfloat16_cm_1_0_1` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 128, false>::kMinBlocksPerSm) +fna3_64x64x128_sm80_bfloat16_cm_1_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x128_sm80_bfloat16_cm_1_0_1` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 128, false>::kMinBlocksPerSm) +fna3_64x128x128_sm80_bfloat16_cm_1_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x128_sm80_bfloat16_cm_1_0_1` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 65536, false>::kMinBlocksPerSm) +fna3_32x128x65536_sm80_bfloat16_cm_1_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x65536_sm80_bfloat16_cm_1_0_1` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 65536, false>::kMinBlocksPerSm) +fna3_64x128x65536_sm80_bfloat16_cm_1_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x65536_sm80_bfloat16_cm_1_0_1` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 65536, false>::kMinBlocksPerSm) +fna3_64x64x65536_sm80_bfloat16_cm_1_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x65536_sm80_bfloat16_cm_1_0_1` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +/////////////////////////////////////////////////////////////////// +// FNA-3D / bfloat16 / SM80 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 32, false>::kMinBlocksPerSm) +fna3_32x128x32_sm80_bfloat16_cm_1_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x32_sm80_bfloat16_cm_1_1_0` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 32, false>::kMinBlocksPerSm) +fna3_64x64x32_sm80_bfloat16_cm_1_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x32_sm80_bfloat16_cm_1_1_0` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 32, false>::kMinBlocksPerSm) +fna3_64x128x32_sm80_bfloat16_cm_1_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x32_sm80_bfloat16_cm_1_1_0` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 64, false>::kMinBlocksPerSm) +fna3_32x128x64_sm80_bfloat16_cm_1_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x64_sm80_bfloat16_cm_1_1_0` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 64, false>::kMinBlocksPerSm) +fna3_64x64x64_sm80_bfloat16_cm_1_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x64_sm80_bfloat16_cm_1_1_0` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 64, false>::kMinBlocksPerSm) +fna3_64x128x64_sm80_bfloat16_cm_1_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x64_sm80_bfloat16_cm_1_1_0` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 128, false>::kMinBlocksPerSm) +fna3_32x128x128_sm80_bfloat16_cm_1_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x128_sm80_bfloat16_cm_1_1_0` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 128, false>::kMinBlocksPerSm) +fna3_64x64x128_sm80_bfloat16_cm_1_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x128_sm80_bfloat16_cm_1_1_0` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 128, false>::kMinBlocksPerSm) +fna3_64x128x128_sm80_bfloat16_cm_1_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x128_sm80_bfloat16_cm_1_1_0` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 65536, false>::kMinBlocksPerSm) +fna3_32x128x65536_sm80_bfloat16_cm_1_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x65536_sm80_bfloat16_cm_1_1_0` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 65536, false>::kMinBlocksPerSm) +fna3_64x128x65536_sm80_bfloat16_cm_1_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x65536_sm80_bfloat16_cm_1_1_0` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 65536, false>::kMinBlocksPerSm) +fna3_64x64x65536_sm80_bfloat16_cm_1_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x65536_sm80_bfloat16_cm_1_1_0` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +/////////////////////////////////////////////////////////////////// +// FNA-3D / bfloat16 / SM80 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 32, false>::kMinBlocksPerSm) +fna3_32x128x32_sm80_bfloat16_cm_1_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x32_sm80_bfloat16_cm_1_1_1` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 32, false>::kMinBlocksPerSm) +fna3_64x64x32_sm80_bfloat16_cm_1_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x32_sm80_bfloat16_cm_1_1_1` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 32, false>::kMinBlocksPerSm) +fna3_64x128x32_sm80_bfloat16_cm_1_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x32_sm80_bfloat16_cm_1_1_1` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 64, false>::kMinBlocksPerSm) +fna3_32x128x64_sm80_bfloat16_cm_1_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x64_sm80_bfloat16_cm_1_1_1` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 64, false>::kMinBlocksPerSm) +fna3_64x64x64_sm80_bfloat16_cm_1_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x64_sm80_bfloat16_cm_1_1_1` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 64, false>::kMinBlocksPerSm) +fna3_64x128x64_sm80_bfloat16_cm_1_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x64_sm80_bfloat16_cm_1_1_1` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 128, false>::kMinBlocksPerSm) +fna3_32x128x128_sm80_bfloat16_cm_1_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x128_sm80_bfloat16_cm_1_1_1` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 128, false>::kMinBlocksPerSm) +fna3_64x64x128_sm80_bfloat16_cm_1_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x128_sm80_bfloat16_cm_1_1_1` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 128, false>::kMinBlocksPerSm) +fna3_64x128x128_sm80_bfloat16_cm_1_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x128_sm80_bfloat16_cm_1_1_1` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 65536, false>::kMinBlocksPerSm) +fna3_32x128x65536_sm80_bfloat16_cm_1_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x65536_sm80_bfloat16_cm_1_1_1` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 65536, false>::kMinBlocksPerSm) +fna3_64x128x65536_sm80_bfloat16_cm_1_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x65536_sm80_bfloat16_cm_1_1_1` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 65536, false>::kMinBlocksPerSm) +fna3_64x64x65536_sm80_bfloat16_cm_1_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x65536_sm80_bfloat16_cm_1_1_1` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} +} +} +} + diff --git a/csrc/autogen/src/cuda/fna/source_2.cu b/csrc/autogen/src/cuda/fna/source_2.cu new file mode 100644 index 0000000..7160a2a --- /dev/null +++ b/csrc/autogen/src/cuda/fna/source_2.cu @@ -0,0 +1,2573 @@ +#include +#include +#include +#include +#include +namespace natten { +namespace cuda { +namespace fna { + + + +/////////////////////////////////////////////////////////////////// +// FNA-1D / float32 / SM80 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 32, false>::kMinBlocksPerSm) +fna1_32x128x32_sm80_float32_cm_1(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_32x128x32_sm80_float32_cm_1` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 32, false>::kMinBlocksPerSm) +fna1_64x64x32_sm80_float32_cm_1(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_64x64x32_sm80_float32_cm_1` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 32, false>::kMinBlocksPerSm) +fna1_64x128x32_sm80_float32_cm_1(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_64x128x32_sm80_float32_cm_1` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 64, false>::kMinBlocksPerSm) +fna1_32x128x64_sm80_float32_cm_1(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_32x128x64_sm80_float32_cm_1` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 64, false>::kMinBlocksPerSm) +fna1_64x64x64_sm80_float32_cm_1(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_64x64x64_sm80_float32_cm_1` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 64, false>::kMinBlocksPerSm) +fna1_64x128x64_sm80_float32_cm_1(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_64x128x64_sm80_float32_cm_1` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 128, false>::kMinBlocksPerSm) +fna1_32x128x128_sm80_float32_cm_1(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_32x128x128_sm80_float32_cm_1` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 128, false>::kMinBlocksPerSm) +fna1_64x64x128_sm80_float32_cm_1(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_64x64x128_sm80_float32_cm_1` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 128, false>::kMinBlocksPerSm) +fna1_64x128x128_sm80_float32_cm_1(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_64x128x128_sm80_float32_cm_1` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 65536, false>::kMinBlocksPerSm) +fna1_32x128x65536_sm80_float32_cm_1(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_32x128x65536_sm80_float32_cm_1` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 65536, false>::kMinBlocksPerSm) +fna1_64x128x65536_sm80_float32_cm_1(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_64x128x65536_sm80_float32_cm_1` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 65536, false>::kMinBlocksPerSm) +fna1_64x64x65536_sm80_float32_cm_1(typename FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_64x64x65536_sm80_float32_cm_1` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +/////////////////////////////////////////////////////////////////// +// FNA-1D / float16 / SM80 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 32, false>::kMinBlocksPerSm) +fna1_32x128x32_sm80_float16_cm_0(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_32x128x32_sm80_float16_cm_0` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 32, false>::kMinBlocksPerSm) +fna1_64x64x32_sm80_float16_cm_0(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_64x64x32_sm80_float16_cm_0` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 32, false>::kMinBlocksPerSm) +fna1_64x128x32_sm80_float16_cm_0(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_64x128x32_sm80_float16_cm_0` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 64, false>::kMinBlocksPerSm) +fna1_32x128x64_sm80_float16_cm_0(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_32x128x64_sm80_float16_cm_0` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 64, false>::kMinBlocksPerSm) +fna1_64x64x64_sm80_float16_cm_0(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_64x64x64_sm80_float16_cm_0` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 64, false>::kMinBlocksPerSm) +fna1_64x128x64_sm80_float16_cm_0(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_64x128x64_sm80_float16_cm_0` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 128, false>::kMinBlocksPerSm) +fna1_32x128x128_sm80_float16_cm_0(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_32x128x128_sm80_float16_cm_0` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 128, false>::kMinBlocksPerSm) +fna1_64x64x128_sm80_float16_cm_0(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_64x64x128_sm80_float16_cm_0` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 128, false>::kMinBlocksPerSm) +fna1_64x128x128_sm80_float16_cm_0(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_64x128x128_sm80_float16_cm_0` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 65536, false>::kMinBlocksPerSm) +fna1_32x128x65536_sm80_float16_cm_0(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_32x128x65536_sm80_float16_cm_0` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 65536, false>::kMinBlocksPerSm) +fna1_64x128x65536_sm80_float16_cm_0(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_64x128x65536_sm80_float16_cm_0` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 65536, false>::kMinBlocksPerSm) +fna1_64x64x65536_sm80_float16_cm_0(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_64x64x65536_sm80_float16_cm_0` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +/////////////////////////////////////////////////////////////////// +// FNA-1D / float16 / SM80 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 32, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 32, true>::kMinBlocksPerSm) +fna1_32x128x32_sm80_float16_cm_0_rpb(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 32, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 32, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_32x128x32_sm80_float16_cm_0_rpb` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 32, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 32, true>::kMinBlocksPerSm) +fna1_64x64x32_sm80_float16_cm_0_rpb(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 32, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 32, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_64x64x32_sm80_float16_cm_0_rpb` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 32, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 32, true>::kMinBlocksPerSm) +fna1_64x128x32_sm80_float16_cm_0_rpb(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 32, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 32, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_64x128x32_sm80_float16_cm_0_rpb` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 64, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 64, true>::kMinBlocksPerSm) +fna1_32x128x64_sm80_float16_cm_0_rpb(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 64, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 64, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_32x128x64_sm80_float16_cm_0_rpb` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 64, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 64, true>::kMinBlocksPerSm) +fna1_64x64x64_sm80_float16_cm_0_rpb(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 64, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 64, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_64x64x64_sm80_float16_cm_0_rpb` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 64, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 64, true>::kMinBlocksPerSm) +fna1_64x128x64_sm80_float16_cm_0_rpb(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 64, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 64, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_64x128x64_sm80_float16_cm_0_rpb` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 128, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 128, true>::kMinBlocksPerSm) +fna1_32x128x128_sm80_float16_cm_0_rpb(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 128, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 128, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_32x128x128_sm80_float16_cm_0_rpb` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 128, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 128, true>::kMinBlocksPerSm) +fna1_64x64x128_sm80_float16_cm_0_rpb(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 128, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 128, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_64x64x128_sm80_float16_cm_0_rpb` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 128, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 128, true>::kMinBlocksPerSm) +fna1_64x128x128_sm80_float16_cm_0_rpb(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 128, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 128, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_64x128x128_sm80_float16_cm_0_rpb` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 65536, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 65536, true>::kMinBlocksPerSm) +fna1_32x128x65536_sm80_float16_cm_0_rpb(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 65536, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 65536, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_32x128x65536_sm80_float16_cm_0_rpb` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 65536, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 65536, true>::kMinBlocksPerSm) +fna1_64x128x65536_sm80_float16_cm_0_rpb(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 65536, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 65536, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_64x128x65536_sm80_float16_cm_0_rpb` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 65536, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 65536, true>::kMinBlocksPerSm) +fna1_64x64x65536_sm80_float16_cm_0_rpb(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 65536, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 65536, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_64x64x65536_sm80_float16_cm_0_rpb` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +/////////////////////////////////////////////////////////////////// +// FNA-1D / float16 / SM80 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 32, false>::kMinBlocksPerSm) +fna1_32x128x32_sm80_float16_cm_1(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_32x128x32_sm80_float16_cm_1` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 32, false>::kMinBlocksPerSm) +fna1_64x64x32_sm80_float16_cm_1(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_64x64x32_sm80_float16_cm_1` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 32, false>::kMinBlocksPerSm) +fna1_64x128x32_sm80_float16_cm_1(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_64x128x32_sm80_float16_cm_1` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 64, false>::kMinBlocksPerSm) +fna1_32x128x64_sm80_float16_cm_1(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_32x128x64_sm80_float16_cm_1` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 64, false>::kMinBlocksPerSm) +fna1_64x64x64_sm80_float16_cm_1(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_64x64x64_sm80_float16_cm_1` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 64, false>::kMinBlocksPerSm) +fna1_64x128x64_sm80_float16_cm_1(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_64x128x64_sm80_float16_cm_1` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 128, false>::kMinBlocksPerSm) +fna1_32x128x128_sm80_float16_cm_1(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_32x128x128_sm80_float16_cm_1` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 128, false>::kMinBlocksPerSm) +fna1_64x64x128_sm80_float16_cm_1(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_64x64x128_sm80_float16_cm_1` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 128, false>::kMinBlocksPerSm) +fna1_64x128x128_sm80_float16_cm_1(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_64x128x128_sm80_float16_cm_1` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 65536, false>::kMinBlocksPerSm) +fna1_32x128x65536_sm80_float16_cm_1(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_32x128x65536_sm80_float16_cm_1` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 65536, false>::kMinBlocksPerSm) +fna1_64x128x65536_sm80_float16_cm_1(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_64x128x65536_sm80_float16_cm_1` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 65536, false>::kMinBlocksPerSm) +fna1_64x64x65536_sm80_float16_cm_1(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_64x64x65536_sm80_float16_cm_1` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +/////////////////////////////////////////////////////////////////// +// FNA-1D / bfloat16 / SM80 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 32, false>::kMinBlocksPerSm) +fna1_32x128x32_sm80_bfloat16_cm_0(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_32x128x32_sm80_bfloat16_cm_0` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 32, false>::kMinBlocksPerSm) +fna1_64x64x32_sm80_bfloat16_cm_0(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_64x64x32_sm80_bfloat16_cm_0` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 32, false>::kMinBlocksPerSm) +fna1_64x128x32_sm80_bfloat16_cm_0(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_64x128x32_sm80_bfloat16_cm_0` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 64, false>::kMinBlocksPerSm) +fna1_32x128x64_sm80_bfloat16_cm_0(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_32x128x64_sm80_bfloat16_cm_0` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 64, false>::kMinBlocksPerSm) +fna1_64x64x64_sm80_bfloat16_cm_0(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_64x64x64_sm80_bfloat16_cm_0` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 64, false>::kMinBlocksPerSm) +fna1_64x128x64_sm80_bfloat16_cm_0(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_64x128x64_sm80_bfloat16_cm_0` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 128, false>::kMinBlocksPerSm) +fna1_32x128x128_sm80_bfloat16_cm_0(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_32x128x128_sm80_bfloat16_cm_0` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 128, false>::kMinBlocksPerSm) +fna1_64x64x128_sm80_bfloat16_cm_0(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_64x64x128_sm80_bfloat16_cm_0` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 128, false>::kMinBlocksPerSm) +fna1_64x128x128_sm80_bfloat16_cm_0(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_64x128x128_sm80_bfloat16_cm_0` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 65536, false>::kMinBlocksPerSm) +fna1_32x128x65536_sm80_bfloat16_cm_0(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_32x128x65536_sm80_bfloat16_cm_0` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 65536, false>::kMinBlocksPerSm) +fna1_64x128x65536_sm80_bfloat16_cm_0(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_64x128x65536_sm80_bfloat16_cm_0` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 65536, false>::kMinBlocksPerSm) +fna1_64x64x65536_sm80_bfloat16_cm_0(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_64x64x65536_sm80_bfloat16_cm_0` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +/////////////////////////////////////////////////////////////////// +// FNA-1D / bfloat16 / SM80 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 32, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 32, true>::kMinBlocksPerSm) +fna1_32x128x32_sm80_bfloat16_cm_0_rpb(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 32, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 32, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_32x128x32_sm80_bfloat16_cm_0_rpb` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 32, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 32, true>::kMinBlocksPerSm) +fna1_64x64x32_sm80_bfloat16_cm_0_rpb(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 32, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 32, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_64x64x32_sm80_bfloat16_cm_0_rpb` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 32, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 32, true>::kMinBlocksPerSm) +fna1_64x128x32_sm80_bfloat16_cm_0_rpb(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 32, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 32, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_64x128x32_sm80_bfloat16_cm_0_rpb` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 64, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 64, true>::kMinBlocksPerSm) +fna1_32x128x64_sm80_bfloat16_cm_0_rpb(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 64, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 64, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_32x128x64_sm80_bfloat16_cm_0_rpb` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 64, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 64, true>::kMinBlocksPerSm) +fna1_64x64x64_sm80_bfloat16_cm_0_rpb(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 64, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 64, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_64x64x64_sm80_bfloat16_cm_0_rpb` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 64, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 64, true>::kMinBlocksPerSm) +fna1_64x128x64_sm80_bfloat16_cm_0_rpb(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 64, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 64, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_64x128x64_sm80_bfloat16_cm_0_rpb` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 128, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 128, true>::kMinBlocksPerSm) +fna1_32x128x128_sm80_bfloat16_cm_0_rpb(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 128, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 128, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_32x128x128_sm80_bfloat16_cm_0_rpb` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 128, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 128, true>::kMinBlocksPerSm) +fna1_64x64x128_sm80_bfloat16_cm_0_rpb(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 128, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 128, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_64x64x128_sm80_bfloat16_cm_0_rpb` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 128, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 128, true>::kMinBlocksPerSm) +fna1_64x128x128_sm80_bfloat16_cm_0_rpb(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 128, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 128, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_64x128x128_sm80_bfloat16_cm_0_rpb` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 65536, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 65536, true>::kMinBlocksPerSm) +fna1_32x128x65536_sm80_bfloat16_cm_0_rpb(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 65536, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 65536, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_32x128x65536_sm80_bfloat16_cm_0_rpb` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 65536, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 65536, true>::kMinBlocksPerSm) +fna1_64x128x65536_sm80_bfloat16_cm_0_rpb(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 65536, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 65536, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_64x128x65536_sm80_bfloat16_cm_0_rpb` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 65536, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 65536, true>::kMinBlocksPerSm) +fna1_64x64x65536_sm80_bfloat16_cm_0_rpb(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 65536, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 65536, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_64x64x65536_sm80_bfloat16_cm_0_rpb` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +/////////////////////////////////////////////////////////////////// +// FNA-1D / bfloat16 / SM80 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 32, false>::kMinBlocksPerSm) +fna1_32x128x32_sm80_bfloat16_cm_1(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_32x128x32_sm80_bfloat16_cm_1` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 32, false>::kMinBlocksPerSm) +fna1_64x64x32_sm80_bfloat16_cm_1(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_64x64x32_sm80_bfloat16_cm_1` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 32, false>::kMinBlocksPerSm) +fna1_64x128x32_sm80_bfloat16_cm_1(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_64x128x32_sm80_bfloat16_cm_1` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 64, false>::kMinBlocksPerSm) +fna1_32x128x64_sm80_bfloat16_cm_1(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_32x128x64_sm80_bfloat16_cm_1` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 64, false>::kMinBlocksPerSm) +fna1_64x64x64_sm80_bfloat16_cm_1(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_64x64x64_sm80_bfloat16_cm_1` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 64, false>::kMinBlocksPerSm) +fna1_64x128x64_sm80_bfloat16_cm_1(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_64x128x64_sm80_bfloat16_cm_1` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 128, false>::kMinBlocksPerSm) +fna1_32x128x128_sm80_bfloat16_cm_1(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_32x128x128_sm80_bfloat16_cm_1` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 128, false>::kMinBlocksPerSm) +fna1_64x64x128_sm80_bfloat16_cm_1(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_64x64x128_sm80_bfloat16_cm_1` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 128, false>::kMinBlocksPerSm) +fna1_64x128x128_sm80_bfloat16_cm_1(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_64x128x128_sm80_bfloat16_cm_1` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 65536, false>::kMinBlocksPerSm) +fna1_32x128x65536_sm80_bfloat16_cm_1(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_32x128x65536_sm80_bfloat16_cm_1` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 65536, false>::kMinBlocksPerSm) +fna1_64x128x65536_sm80_bfloat16_cm_1(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_64x128x65536_sm80_bfloat16_cm_1` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 65536, false>::kMinBlocksPerSm) +fna1_64x64x65536_sm80_bfloat16_cm_1(typename FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<1, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna1_64x64x65536_sm80_bfloat16_cm_1` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +/////////////////////////////////////////////////////////////////// +// FNA-2D / float32 / SM50 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 32, false>::kMinBlocksPerSm) +fna2_32x128x32_sm50_float32_cm_0_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_32x128x32_sm50_float32_cm_0_0` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 32, false>::kMinBlocksPerSm) +fna2_64x64x32_sm50_float32_cm_0_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x64x32_sm50_float32_cm_0_0` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 32, false>::kMinBlocksPerSm) +fna2_64x128x32_sm50_float32_cm_0_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x128x32_sm50_float32_cm_0_0` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 64, false>::kMinBlocksPerSm) +fna2_32x128x64_sm50_float32_cm_0_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_32x128x64_sm50_float32_cm_0_0` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 64, false>::kMinBlocksPerSm) +fna2_64x64x64_sm50_float32_cm_0_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x64x64_sm50_float32_cm_0_0` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 64, false>::kMinBlocksPerSm) +fna2_64x128x64_sm50_float32_cm_0_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x128x64_sm50_float32_cm_0_0` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 128, false>::kMinBlocksPerSm) +fna2_32x128x128_sm50_float32_cm_0_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_32x128x128_sm50_float32_cm_0_0` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 128, false>::kMinBlocksPerSm) +fna2_64x64x128_sm50_float32_cm_0_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x64x128_sm50_float32_cm_0_0` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 128, false>::kMinBlocksPerSm) +fna2_64x128x128_sm50_float32_cm_0_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x128x128_sm50_float32_cm_0_0` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 65536, false>::kMinBlocksPerSm) +fna2_32x128x65536_sm50_float32_cm_0_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_32x128x65536_sm50_float32_cm_0_0` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 65536, false>::kMinBlocksPerSm) +fna2_64x128x65536_sm50_float32_cm_0_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x128x65536_sm50_float32_cm_0_0` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 65536, false>::kMinBlocksPerSm) +fna2_64x64x65536_sm50_float32_cm_0_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x64x65536_sm50_float32_cm_0_0` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +/////////////////////////////////////////////////////////////////// +// FNA-2D / float32 / SM50 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 32, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 32, true>::kMinBlocksPerSm) +fna2_32x128x32_sm50_float32_cm_0_0_rpb(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 32, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 32, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_32x128x32_sm50_float32_cm_0_0_rpb` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 32, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 32, true>::kMinBlocksPerSm) +fna2_64x64x32_sm50_float32_cm_0_0_rpb(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 32, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 32, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x64x32_sm50_float32_cm_0_0_rpb` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 32, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 32, true>::kMinBlocksPerSm) +fna2_64x128x32_sm50_float32_cm_0_0_rpb(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 32, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 32, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x128x32_sm50_float32_cm_0_0_rpb` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 64, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 64, true>::kMinBlocksPerSm) +fna2_32x128x64_sm50_float32_cm_0_0_rpb(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 64, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 64, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_32x128x64_sm50_float32_cm_0_0_rpb` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 64, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 64, true>::kMinBlocksPerSm) +fna2_64x64x64_sm50_float32_cm_0_0_rpb(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 64, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 64, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x64x64_sm50_float32_cm_0_0_rpb` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 64, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 64, true>::kMinBlocksPerSm) +fna2_64x128x64_sm50_float32_cm_0_0_rpb(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 64, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 64, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x128x64_sm50_float32_cm_0_0_rpb` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 128, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 128, true>::kMinBlocksPerSm) +fna2_32x128x128_sm50_float32_cm_0_0_rpb(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 128, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 128, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_32x128x128_sm50_float32_cm_0_0_rpb` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 128, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 128, true>::kMinBlocksPerSm) +fna2_64x64x128_sm50_float32_cm_0_0_rpb(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 128, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 128, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x64x128_sm50_float32_cm_0_0_rpb` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 128, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 128, true>::kMinBlocksPerSm) +fna2_64x128x128_sm50_float32_cm_0_0_rpb(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 128, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 128, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x128x128_sm50_float32_cm_0_0_rpb` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 65536, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 65536, true>::kMinBlocksPerSm) +fna2_32x128x65536_sm50_float32_cm_0_0_rpb(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 65536, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 65536, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_32x128x65536_sm50_float32_cm_0_0_rpb` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 65536, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 65536, true>::kMinBlocksPerSm) +fna2_64x128x65536_sm50_float32_cm_0_0_rpb(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 65536, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 65536, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x128x65536_sm50_float32_cm_0_0_rpb` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 65536, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 65536, true>::kMinBlocksPerSm) +fna2_64x64x65536_sm50_float32_cm_0_0_rpb(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 65536, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 65536, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x64x65536_sm50_float32_cm_0_0_rpb` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +/////////////////////////////////////////////////////////////////// +// FNA-2D / float32 / SM50 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 32, false>::kMinBlocksPerSm) +fna2_32x128x32_sm50_float32_cm_0_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_32x128x32_sm50_float32_cm_0_1` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 32, false>::kMinBlocksPerSm) +fna2_64x64x32_sm50_float32_cm_0_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x64x32_sm50_float32_cm_0_1` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 32, false>::kMinBlocksPerSm) +fna2_64x128x32_sm50_float32_cm_0_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x128x32_sm50_float32_cm_0_1` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 64, false>::kMinBlocksPerSm) +fna2_32x128x64_sm50_float32_cm_0_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_32x128x64_sm50_float32_cm_0_1` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 64, false>::kMinBlocksPerSm) +fna2_64x64x64_sm50_float32_cm_0_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x64x64_sm50_float32_cm_0_1` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 64, false>::kMinBlocksPerSm) +fna2_64x128x64_sm50_float32_cm_0_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x128x64_sm50_float32_cm_0_1` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 128, false>::kMinBlocksPerSm) +fna2_32x128x128_sm50_float32_cm_0_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_32x128x128_sm50_float32_cm_0_1` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 128, false>::kMinBlocksPerSm) +fna2_64x64x128_sm50_float32_cm_0_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x64x128_sm50_float32_cm_0_1` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 128, false>::kMinBlocksPerSm) +fna2_64x128x128_sm50_float32_cm_0_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x128x128_sm50_float32_cm_0_1` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 65536, false>::kMinBlocksPerSm) +fna2_32x128x65536_sm50_float32_cm_0_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_32x128x65536_sm50_float32_cm_0_1` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 65536, false>::kMinBlocksPerSm) +fna2_64x128x65536_sm50_float32_cm_0_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x128x65536_sm50_float32_cm_0_1` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 65536, false>::kMinBlocksPerSm) +fna2_64x64x65536_sm50_float32_cm_0_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x64x65536_sm50_float32_cm_0_1` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} +} +} +} + diff --git a/csrc/autogen/src/cuda/fna/source_3.cu b/csrc/autogen/src/cuda/fna/source_3.cu new file mode 100644 index 0000000..91e6312 --- /dev/null +++ b/csrc/autogen/src/cuda/fna/source_3.cu @@ -0,0 +1,2573 @@ +#include +#include +#include +#include +#include +namespace natten { +namespace cuda { +namespace fna { + + + +/////////////////////////////////////////////////////////////////// +// FNA-2D / float32 / SM50 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 32, false>::kMinBlocksPerSm) +fna2_32x128x32_sm50_float32_cm_1_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_32x128x32_sm50_float32_cm_1_0` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 32, false>::kMinBlocksPerSm) +fna2_64x64x32_sm50_float32_cm_1_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x64x32_sm50_float32_cm_1_0` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 32, false>::kMinBlocksPerSm) +fna2_64x128x32_sm50_float32_cm_1_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x128x32_sm50_float32_cm_1_0` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 64, false>::kMinBlocksPerSm) +fna2_32x128x64_sm50_float32_cm_1_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_32x128x64_sm50_float32_cm_1_0` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 64, false>::kMinBlocksPerSm) +fna2_64x64x64_sm50_float32_cm_1_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x64x64_sm50_float32_cm_1_0` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 64, false>::kMinBlocksPerSm) +fna2_64x128x64_sm50_float32_cm_1_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x128x64_sm50_float32_cm_1_0` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 128, false>::kMinBlocksPerSm) +fna2_32x128x128_sm50_float32_cm_1_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_32x128x128_sm50_float32_cm_1_0` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 128, false>::kMinBlocksPerSm) +fna2_64x64x128_sm50_float32_cm_1_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x64x128_sm50_float32_cm_1_0` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 128, false>::kMinBlocksPerSm) +fna2_64x128x128_sm50_float32_cm_1_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x128x128_sm50_float32_cm_1_0` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 65536, false>::kMinBlocksPerSm) +fna2_32x128x65536_sm50_float32_cm_1_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_32x128x65536_sm50_float32_cm_1_0` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 65536, false>::kMinBlocksPerSm) +fna2_64x128x65536_sm50_float32_cm_1_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x128x65536_sm50_float32_cm_1_0` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 65536, false>::kMinBlocksPerSm) +fna2_64x64x65536_sm50_float32_cm_1_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x64x65536_sm50_float32_cm_1_0` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +/////////////////////////////////////////////////////////////////// +// FNA-2D / float32 / SM50 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 32, false>::kMinBlocksPerSm) +fna2_32x128x32_sm50_float32_cm_1_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_32x128x32_sm50_float32_cm_1_1` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 32, false>::kMinBlocksPerSm) +fna2_64x64x32_sm50_float32_cm_1_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x64x32_sm50_float32_cm_1_1` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 32, false>::kMinBlocksPerSm) +fna2_64x128x32_sm50_float32_cm_1_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x128x32_sm50_float32_cm_1_1` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 64, false>::kMinBlocksPerSm) +fna2_32x128x64_sm50_float32_cm_1_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_32x128x64_sm50_float32_cm_1_1` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 64, false>::kMinBlocksPerSm) +fna2_64x64x64_sm50_float32_cm_1_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x64x64_sm50_float32_cm_1_1` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 64, false>::kMinBlocksPerSm) +fna2_64x128x64_sm50_float32_cm_1_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x128x64_sm50_float32_cm_1_1` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 128, false>::kMinBlocksPerSm) +fna2_32x128x128_sm50_float32_cm_1_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_32x128x128_sm50_float32_cm_1_1` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 128, false>::kMinBlocksPerSm) +fna2_64x64x128_sm50_float32_cm_1_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x64x128_sm50_float32_cm_1_1` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 128, false>::kMinBlocksPerSm) +fna2_64x128x128_sm50_float32_cm_1_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x128x128_sm50_float32_cm_1_1` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 65536, false>::kMinBlocksPerSm) +fna2_32x128x65536_sm50_float32_cm_1_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_32x128x65536_sm50_float32_cm_1_1` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 65536, false>::kMinBlocksPerSm) +fna2_64x128x65536_sm50_float32_cm_1_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x128x65536_sm50_float32_cm_1_1` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 65536, false>::kMinBlocksPerSm) +fna2_64x64x65536_sm50_float32_cm_1_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x64x65536_sm50_float32_cm_1_1` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +/////////////////////////////////////////////////////////////////// +// FNA-2D / float16 / SM50 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 32, false>::kMinBlocksPerSm) +fna2_32x128x32_sm50_float16_cm_0_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_32x128x32_sm50_float16_cm_0_0` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 32, false>::kMinBlocksPerSm) +fna2_64x64x32_sm50_float16_cm_0_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x64x32_sm50_float16_cm_0_0` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 32, false>::kMinBlocksPerSm) +fna2_64x128x32_sm50_float16_cm_0_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x128x32_sm50_float16_cm_0_0` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 64, false>::kMinBlocksPerSm) +fna2_32x128x64_sm50_float16_cm_0_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_32x128x64_sm50_float16_cm_0_0` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 64, false>::kMinBlocksPerSm) +fna2_64x64x64_sm50_float16_cm_0_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x64x64_sm50_float16_cm_0_0` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 64, false>::kMinBlocksPerSm) +fna2_64x128x64_sm50_float16_cm_0_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x128x64_sm50_float16_cm_0_0` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 128, false>::kMinBlocksPerSm) +fna2_32x128x128_sm50_float16_cm_0_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_32x128x128_sm50_float16_cm_0_0` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 128, false>::kMinBlocksPerSm) +fna2_64x64x128_sm50_float16_cm_0_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x64x128_sm50_float16_cm_0_0` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 128, false>::kMinBlocksPerSm) +fna2_64x128x128_sm50_float16_cm_0_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x128x128_sm50_float16_cm_0_0` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 65536, false>::kMinBlocksPerSm) +fna2_32x128x65536_sm50_float16_cm_0_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_32x128x65536_sm50_float16_cm_0_0` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 65536, false>::kMinBlocksPerSm) +fna2_64x128x65536_sm50_float16_cm_0_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x128x65536_sm50_float16_cm_0_0` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 65536, false>::kMinBlocksPerSm) +fna2_64x64x65536_sm50_float16_cm_0_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x64x65536_sm50_float16_cm_0_0` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +/////////////////////////////////////////////////////////////////// +// FNA-2D / float16 / SM50 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 32, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 32, true>::kMinBlocksPerSm) +fna2_32x128x32_sm50_float16_cm_0_0_rpb(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 32, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 32, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_32x128x32_sm50_float16_cm_0_0_rpb` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 32, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 32, true>::kMinBlocksPerSm) +fna2_64x64x32_sm50_float16_cm_0_0_rpb(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 32, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 32, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x64x32_sm50_float16_cm_0_0_rpb` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 32, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 32, true>::kMinBlocksPerSm) +fna2_64x128x32_sm50_float16_cm_0_0_rpb(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 32, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 32, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x128x32_sm50_float16_cm_0_0_rpb` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 64, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 64, true>::kMinBlocksPerSm) +fna2_32x128x64_sm50_float16_cm_0_0_rpb(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 64, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 64, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_32x128x64_sm50_float16_cm_0_0_rpb` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 64, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 64, true>::kMinBlocksPerSm) +fna2_64x64x64_sm50_float16_cm_0_0_rpb(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 64, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 64, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x64x64_sm50_float16_cm_0_0_rpb` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 64, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 64, true>::kMinBlocksPerSm) +fna2_64x128x64_sm50_float16_cm_0_0_rpb(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 64, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 64, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x128x64_sm50_float16_cm_0_0_rpb` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 128, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 128, true>::kMinBlocksPerSm) +fna2_32x128x128_sm50_float16_cm_0_0_rpb(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 128, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 128, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_32x128x128_sm50_float16_cm_0_0_rpb` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 128, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 128, true>::kMinBlocksPerSm) +fna2_64x64x128_sm50_float16_cm_0_0_rpb(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 128, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 128, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x64x128_sm50_float16_cm_0_0_rpb` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 128, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 128, true>::kMinBlocksPerSm) +fna2_64x128x128_sm50_float16_cm_0_0_rpb(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 128, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 128, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x128x128_sm50_float16_cm_0_0_rpb` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 65536, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 65536, true>::kMinBlocksPerSm) +fna2_32x128x65536_sm50_float16_cm_0_0_rpb(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 65536, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 65536, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_32x128x65536_sm50_float16_cm_0_0_rpb` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 65536, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 65536, true>::kMinBlocksPerSm) +fna2_64x128x65536_sm50_float16_cm_0_0_rpb(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 65536, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 65536, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x128x65536_sm50_float16_cm_0_0_rpb` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 65536, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 65536, true>::kMinBlocksPerSm) +fna2_64x64x65536_sm50_float16_cm_0_0_rpb(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 65536, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 65536, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x64x65536_sm50_float16_cm_0_0_rpb` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +/////////////////////////////////////////////////////////////////// +// FNA-2D / float16 / SM50 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 32, false>::kMinBlocksPerSm) +fna2_32x128x32_sm50_float16_cm_0_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_32x128x32_sm50_float16_cm_0_1` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 32, false>::kMinBlocksPerSm) +fna2_64x64x32_sm50_float16_cm_0_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x64x32_sm50_float16_cm_0_1` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 32, false>::kMinBlocksPerSm) +fna2_64x128x32_sm50_float16_cm_0_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x128x32_sm50_float16_cm_0_1` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 64, false>::kMinBlocksPerSm) +fna2_32x128x64_sm50_float16_cm_0_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_32x128x64_sm50_float16_cm_0_1` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 64, false>::kMinBlocksPerSm) +fna2_64x64x64_sm50_float16_cm_0_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x64x64_sm50_float16_cm_0_1` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 64, false>::kMinBlocksPerSm) +fna2_64x128x64_sm50_float16_cm_0_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x128x64_sm50_float16_cm_0_1` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 128, false>::kMinBlocksPerSm) +fna2_32x128x128_sm50_float16_cm_0_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_32x128x128_sm50_float16_cm_0_1` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 128, false>::kMinBlocksPerSm) +fna2_64x64x128_sm50_float16_cm_0_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x64x128_sm50_float16_cm_0_1` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 128, false>::kMinBlocksPerSm) +fna2_64x128x128_sm50_float16_cm_0_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x128x128_sm50_float16_cm_0_1` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 65536, false>::kMinBlocksPerSm) +fna2_32x128x65536_sm50_float16_cm_0_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_32x128x65536_sm50_float16_cm_0_1` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 65536, false>::kMinBlocksPerSm) +fna2_64x128x65536_sm50_float16_cm_0_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x128x65536_sm50_float16_cm_0_1` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 65536, false>::kMinBlocksPerSm) +fna2_64x64x65536_sm50_float16_cm_0_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x64x65536_sm50_float16_cm_0_1` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +/////////////////////////////////////////////////////////////////// +// FNA-2D / float16 / SM50 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 32, false>::kMinBlocksPerSm) +fna2_32x128x32_sm50_float16_cm_1_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_32x128x32_sm50_float16_cm_1_0` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 32, false>::kMinBlocksPerSm) +fna2_64x64x32_sm50_float16_cm_1_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x64x32_sm50_float16_cm_1_0` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 32, false>::kMinBlocksPerSm) +fna2_64x128x32_sm50_float16_cm_1_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x128x32_sm50_float16_cm_1_0` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 64, false>::kMinBlocksPerSm) +fna2_32x128x64_sm50_float16_cm_1_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_32x128x64_sm50_float16_cm_1_0` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 64, false>::kMinBlocksPerSm) +fna2_64x64x64_sm50_float16_cm_1_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x64x64_sm50_float16_cm_1_0` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 64, false>::kMinBlocksPerSm) +fna2_64x128x64_sm50_float16_cm_1_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x128x64_sm50_float16_cm_1_0` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 128, false>::kMinBlocksPerSm) +fna2_32x128x128_sm50_float16_cm_1_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_32x128x128_sm50_float16_cm_1_0` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 128, false>::kMinBlocksPerSm) +fna2_64x64x128_sm50_float16_cm_1_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x64x128_sm50_float16_cm_1_0` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 128, false>::kMinBlocksPerSm) +fna2_64x128x128_sm50_float16_cm_1_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x128x128_sm50_float16_cm_1_0` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 65536, false>::kMinBlocksPerSm) +fna2_32x128x65536_sm50_float16_cm_1_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_32x128x65536_sm50_float16_cm_1_0` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 65536, false>::kMinBlocksPerSm) +fna2_64x128x65536_sm50_float16_cm_1_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x128x65536_sm50_float16_cm_1_0` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 65536, false>::kMinBlocksPerSm) +fna2_64x64x65536_sm50_float16_cm_1_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x64x65536_sm50_float16_cm_1_0` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +/////////////////////////////////////////////////////////////////// +// FNA-2D / float16 / SM50 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 32, false>::kMinBlocksPerSm) +fna2_32x128x32_sm50_float16_cm_1_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_32x128x32_sm50_float16_cm_1_1` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 32, false>::kMinBlocksPerSm) +fna2_64x64x32_sm50_float16_cm_1_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x64x32_sm50_float16_cm_1_1` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 32, false>::kMinBlocksPerSm) +fna2_64x128x32_sm50_float16_cm_1_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x128x32_sm50_float16_cm_1_1` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 64, false>::kMinBlocksPerSm) +fna2_32x128x64_sm50_float16_cm_1_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_32x128x64_sm50_float16_cm_1_1` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 64, false>::kMinBlocksPerSm) +fna2_64x64x64_sm50_float16_cm_1_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x64x64_sm50_float16_cm_1_1` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 64, false>::kMinBlocksPerSm) +fna2_64x128x64_sm50_float16_cm_1_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x128x64_sm50_float16_cm_1_1` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 128, false>::kMinBlocksPerSm) +fna2_32x128x128_sm50_float16_cm_1_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_32x128x128_sm50_float16_cm_1_1` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 128, false>::kMinBlocksPerSm) +fna2_64x64x128_sm50_float16_cm_1_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x64x128_sm50_float16_cm_1_1` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 128, false>::kMinBlocksPerSm) +fna2_64x128x128_sm50_float16_cm_1_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x128x128_sm50_float16_cm_1_1` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 65536, false>::kMinBlocksPerSm) +fna2_32x128x65536_sm50_float16_cm_1_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_32x128x65536_sm50_float16_cm_1_1` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 65536, false>::kMinBlocksPerSm) +fna2_64x128x65536_sm50_float16_cm_1_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x128x65536_sm50_float16_cm_1_1` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 65536, false>::kMinBlocksPerSm) +fna2_64x64x65536_sm50_float16_cm_1_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x64x65536_sm50_float16_cm_1_1` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +/////////////////////////////////////////////////////////////////// +// FNA-2D / float32 / SM70 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 32, false>::kMinBlocksPerSm) +fna2_32x128x32_sm70_float32_cm_0_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_32x128x32_sm70_float32_cm_0_0` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 32, false>::kMinBlocksPerSm) +fna2_64x64x32_sm70_float32_cm_0_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x64x32_sm70_float32_cm_0_0` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 32, false>::kMinBlocksPerSm) +fna2_64x128x32_sm70_float32_cm_0_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x128x32_sm70_float32_cm_0_0` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 64, false>::kMinBlocksPerSm) +fna2_32x128x64_sm70_float32_cm_0_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_32x128x64_sm70_float32_cm_0_0` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 64, false>::kMinBlocksPerSm) +fna2_64x64x64_sm70_float32_cm_0_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x64x64_sm70_float32_cm_0_0` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 64, false>::kMinBlocksPerSm) +fna2_64x128x64_sm70_float32_cm_0_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x128x64_sm70_float32_cm_0_0` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 128, false>::kMinBlocksPerSm) +fna2_32x128x128_sm70_float32_cm_0_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_32x128x128_sm70_float32_cm_0_0` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 128, false>::kMinBlocksPerSm) +fna2_64x64x128_sm70_float32_cm_0_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x64x128_sm70_float32_cm_0_0` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 128, false>::kMinBlocksPerSm) +fna2_64x128x128_sm70_float32_cm_0_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x128x128_sm70_float32_cm_0_0` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 65536, false>::kMinBlocksPerSm) +fna2_32x128x65536_sm70_float32_cm_0_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_32x128x65536_sm70_float32_cm_0_0` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 65536, false>::kMinBlocksPerSm) +fna2_64x128x65536_sm70_float32_cm_0_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x128x65536_sm70_float32_cm_0_0` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 65536, false>::kMinBlocksPerSm) +fna2_64x64x65536_sm70_float32_cm_0_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x64x65536_sm70_float32_cm_0_0` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +/////////////////////////////////////////////////////////////////// +// FNA-2D / float32 / SM70 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 32, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 32, true>::kMinBlocksPerSm) +fna2_32x128x32_sm70_float32_cm_0_0_rpb(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 32, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 32, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_32x128x32_sm70_float32_cm_0_0_rpb` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 32, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 32, true>::kMinBlocksPerSm) +fna2_64x64x32_sm70_float32_cm_0_0_rpb(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 32, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 32, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x64x32_sm70_float32_cm_0_0_rpb` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 32, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 32, true>::kMinBlocksPerSm) +fna2_64x128x32_sm70_float32_cm_0_0_rpb(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 32, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 32, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x128x32_sm70_float32_cm_0_0_rpb` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 64, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 64, true>::kMinBlocksPerSm) +fna2_32x128x64_sm70_float32_cm_0_0_rpb(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 64, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 64, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_32x128x64_sm70_float32_cm_0_0_rpb` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 64, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 64, true>::kMinBlocksPerSm) +fna2_64x64x64_sm70_float32_cm_0_0_rpb(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 64, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 64, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x64x64_sm70_float32_cm_0_0_rpb` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 64, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 64, true>::kMinBlocksPerSm) +fna2_64x128x64_sm70_float32_cm_0_0_rpb(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 64, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 64, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x128x64_sm70_float32_cm_0_0_rpb` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 128, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 128, true>::kMinBlocksPerSm) +fna2_32x128x128_sm70_float32_cm_0_0_rpb(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 128, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 128, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_32x128x128_sm70_float32_cm_0_0_rpb` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 128, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 128, true>::kMinBlocksPerSm) +fna2_64x64x128_sm70_float32_cm_0_0_rpb(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 128, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 128, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x64x128_sm70_float32_cm_0_0_rpb` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 128, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 128, true>::kMinBlocksPerSm) +fna2_64x128x128_sm70_float32_cm_0_0_rpb(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 128, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 128, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x128x128_sm70_float32_cm_0_0_rpb` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 65536, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 65536, true>::kMinBlocksPerSm) +fna2_32x128x65536_sm70_float32_cm_0_0_rpb(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 65536, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 65536, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_32x128x65536_sm70_float32_cm_0_0_rpb` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 65536, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 65536, true>::kMinBlocksPerSm) +fna2_64x128x65536_sm70_float32_cm_0_0_rpb(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 65536, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 65536, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x128x65536_sm70_float32_cm_0_0_rpb` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 65536, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 65536, true>::kMinBlocksPerSm) +fna2_64x64x65536_sm70_float32_cm_0_0_rpb(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 65536, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 65536, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x64x65536_sm70_float32_cm_0_0_rpb` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +/////////////////////////////////////////////////////////////////// +// FNA-2D / float32 / SM70 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 32, false>::kMinBlocksPerSm) +fna2_32x128x32_sm70_float32_cm_0_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_32x128x32_sm70_float32_cm_0_1` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 32, false>::kMinBlocksPerSm) +fna2_64x64x32_sm70_float32_cm_0_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x64x32_sm70_float32_cm_0_1` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 32, false>::kMinBlocksPerSm) +fna2_64x128x32_sm70_float32_cm_0_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x128x32_sm70_float32_cm_0_1` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 64, false>::kMinBlocksPerSm) +fna2_32x128x64_sm70_float32_cm_0_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_32x128x64_sm70_float32_cm_0_1` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 64, false>::kMinBlocksPerSm) +fna2_64x64x64_sm70_float32_cm_0_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x64x64_sm70_float32_cm_0_1` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 64, false>::kMinBlocksPerSm) +fna2_64x128x64_sm70_float32_cm_0_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x128x64_sm70_float32_cm_0_1` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 128, false>::kMinBlocksPerSm) +fna2_32x128x128_sm70_float32_cm_0_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_32x128x128_sm70_float32_cm_0_1` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 128, false>::kMinBlocksPerSm) +fna2_64x64x128_sm70_float32_cm_0_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x64x128_sm70_float32_cm_0_1` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 128, false>::kMinBlocksPerSm) +fna2_64x128x128_sm70_float32_cm_0_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x128x128_sm70_float32_cm_0_1` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 65536, false>::kMinBlocksPerSm) +fna2_32x128x65536_sm70_float32_cm_0_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_32x128x65536_sm70_float32_cm_0_1` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 65536, false>::kMinBlocksPerSm) +fna2_64x128x65536_sm70_float32_cm_0_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x128x65536_sm70_float32_cm_0_1` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 65536, false>::kMinBlocksPerSm) +fna2_64x64x65536_sm70_float32_cm_0_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x64x65536_sm70_float32_cm_0_1` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} +} +} +} + diff --git a/csrc/autogen/src/cuda/fna/source_4.cu b/csrc/autogen/src/cuda/fna/source_4.cu new file mode 100644 index 0000000..476232d --- /dev/null +++ b/csrc/autogen/src/cuda/fna/source_4.cu @@ -0,0 +1,2573 @@ +#include +#include +#include +#include +#include +namespace natten { +namespace cuda { +namespace fna { + + + +/////////////////////////////////////////////////////////////////// +// FNA-2D / float32 / SM70 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 32, false>::kMinBlocksPerSm) +fna2_32x128x32_sm70_float32_cm_1_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_32x128x32_sm70_float32_cm_1_0` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 32, false>::kMinBlocksPerSm) +fna2_64x64x32_sm70_float32_cm_1_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x64x32_sm70_float32_cm_1_0` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 32, false>::kMinBlocksPerSm) +fna2_64x128x32_sm70_float32_cm_1_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x128x32_sm70_float32_cm_1_0` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 64, false>::kMinBlocksPerSm) +fna2_32x128x64_sm70_float32_cm_1_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_32x128x64_sm70_float32_cm_1_0` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 64, false>::kMinBlocksPerSm) +fna2_64x64x64_sm70_float32_cm_1_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x64x64_sm70_float32_cm_1_0` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 64, false>::kMinBlocksPerSm) +fna2_64x128x64_sm70_float32_cm_1_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x128x64_sm70_float32_cm_1_0` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 128, false>::kMinBlocksPerSm) +fna2_32x128x128_sm70_float32_cm_1_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_32x128x128_sm70_float32_cm_1_0` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 128, false>::kMinBlocksPerSm) +fna2_64x64x128_sm70_float32_cm_1_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x64x128_sm70_float32_cm_1_0` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 128, false>::kMinBlocksPerSm) +fna2_64x128x128_sm70_float32_cm_1_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x128x128_sm70_float32_cm_1_0` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 65536, false>::kMinBlocksPerSm) +fna2_32x128x65536_sm70_float32_cm_1_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_32x128x65536_sm70_float32_cm_1_0` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 65536, false>::kMinBlocksPerSm) +fna2_64x128x65536_sm70_float32_cm_1_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x128x65536_sm70_float32_cm_1_0` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 65536, false>::kMinBlocksPerSm) +fna2_64x64x65536_sm70_float32_cm_1_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x64x65536_sm70_float32_cm_1_0` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +/////////////////////////////////////////////////////////////////// +// FNA-2D / float32 / SM70 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 32, false>::kMinBlocksPerSm) +fna2_32x128x32_sm70_float32_cm_1_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_32x128x32_sm70_float32_cm_1_1` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 32, false>::kMinBlocksPerSm) +fna2_64x64x32_sm70_float32_cm_1_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x64x32_sm70_float32_cm_1_1` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 32, false>::kMinBlocksPerSm) +fna2_64x128x32_sm70_float32_cm_1_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x128x32_sm70_float32_cm_1_1` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 64, false>::kMinBlocksPerSm) +fna2_32x128x64_sm70_float32_cm_1_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_32x128x64_sm70_float32_cm_1_1` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 64, false>::kMinBlocksPerSm) +fna2_64x64x64_sm70_float32_cm_1_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x64x64_sm70_float32_cm_1_1` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 64, false>::kMinBlocksPerSm) +fna2_64x128x64_sm70_float32_cm_1_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x128x64_sm70_float32_cm_1_1` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 128, false>::kMinBlocksPerSm) +fna2_32x128x128_sm70_float32_cm_1_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_32x128x128_sm70_float32_cm_1_1` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 128, false>::kMinBlocksPerSm) +fna2_64x64x128_sm70_float32_cm_1_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x64x128_sm70_float32_cm_1_1` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 128, false>::kMinBlocksPerSm) +fna2_64x128x128_sm70_float32_cm_1_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x128x128_sm70_float32_cm_1_1` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 65536, false>::kMinBlocksPerSm) +fna2_32x128x65536_sm70_float32_cm_1_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_32x128x65536_sm70_float32_cm_1_1` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 65536, false>::kMinBlocksPerSm) +fna2_64x128x65536_sm70_float32_cm_1_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x128x65536_sm70_float32_cm_1_1` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 65536, false>::kMinBlocksPerSm) +fna2_64x64x65536_sm70_float32_cm_1_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x64x65536_sm70_float32_cm_1_1` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +/////////////////////////////////////////////////////////////////// +// FNA-2D / float16 / SM70 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 32, false>::kMinBlocksPerSm) +fna2_32x128x32_sm70_float16_cm_0_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_32x128x32_sm70_float16_cm_0_0` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 32, false>::kMinBlocksPerSm) +fna2_64x64x32_sm70_float16_cm_0_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x64x32_sm70_float16_cm_0_0` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 32, false>::kMinBlocksPerSm) +fna2_64x128x32_sm70_float16_cm_0_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x128x32_sm70_float16_cm_0_0` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 64, false>::kMinBlocksPerSm) +fna2_32x128x64_sm70_float16_cm_0_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_32x128x64_sm70_float16_cm_0_0` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 64, false>::kMinBlocksPerSm) +fna2_64x64x64_sm70_float16_cm_0_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x64x64_sm70_float16_cm_0_0` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 64, false>::kMinBlocksPerSm) +fna2_64x128x64_sm70_float16_cm_0_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x128x64_sm70_float16_cm_0_0` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 128, false>::kMinBlocksPerSm) +fna2_32x128x128_sm70_float16_cm_0_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_32x128x128_sm70_float16_cm_0_0` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 128, false>::kMinBlocksPerSm) +fna2_64x64x128_sm70_float16_cm_0_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x64x128_sm70_float16_cm_0_0` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 128, false>::kMinBlocksPerSm) +fna2_64x128x128_sm70_float16_cm_0_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x128x128_sm70_float16_cm_0_0` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 65536, false>::kMinBlocksPerSm) +fna2_32x128x65536_sm70_float16_cm_0_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_32x128x65536_sm70_float16_cm_0_0` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 65536, false>::kMinBlocksPerSm) +fna2_64x128x65536_sm70_float16_cm_0_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x128x65536_sm70_float16_cm_0_0` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 65536, false>::kMinBlocksPerSm) +fna2_64x64x65536_sm70_float16_cm_0_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x64x65536_sm70_float16_cm_0_0` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +/////////////////////////////////////////////////////////////////// +// FNA-2D / float16 / SM70 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 32, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 32, true>::kMinBlocksPerSm) +fna2_32x128x32_sm70_float16_cm_0_0_rpb(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 32, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 32, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_32x128x32_sm70_float16_cm_0_0_rpb` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 32, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 32, true>::kMinBlocksPerSm) +fna2_64x64x32_sm70_float16_cm_0_0_rpb(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 32, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 32, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x64x32_sm70_float16_cm_0_0_rpb` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 32, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 32, true>::kMinBlocksPerSm) +fna2_64x128x32_sm70_float16_cm_0_0_rpb(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 32, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 32, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x128x32_sm70_float16_cm_0_0_rpb` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 64, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 64, true>::kMinBlocksPerSm) +fna2_32x128x64_sm70_float16_cm_0_0_rpb(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 64, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 64, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_32x128x64_sm70_float16_cm_0_0_rpb` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 64, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 64, true>::kMinBlocksPerSm) +fna2_64x64x64_sm70_float16_cm_0_0_rpb(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 64, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 64, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x64x64_sm70_float16_cm_0_0_rpb` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 64, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 64, true>::kMinBlocksPerSm) +fna2_64x128x64_sm70_float16_cm_0_0_rpb(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 64, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 64, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x128x64_sm70_float16_cm_0_0_rpb` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 128, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 128, true>::kMinBlocksPerSm) +fna2_32x128x128_sm70_float16_cm_0_0_rpb(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 128, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 128, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_32x128x128_sm70_float16_cm_0_0_rpb` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 128, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 128, true>::kMinBlocksPerSm) +fna2_64x64x128_sm70_float16_cm_0_0_rpb(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 128, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 128, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x64x128_sm70_float16_cm_0_0_rpb` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 128, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 128, true>::kMinBlocksPerSm) +fna2_64x128x128_sm70_float16_cm_0_0_rpb(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 128, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 128, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x128x128_sm70_float16_cm_0_0_rpb` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 65536, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 65536, true>::kMinBlocksPerSm) +fna2_32x128x65536_sm70_float16_cm_0_0_rpb(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 65536, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 65536, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_32x128x65536_sm70_float16_cm_0_0_rpb` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 65536, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 65536, true>::kMinBlocksPerSm) +fna2_64x128x65536_sm70_float16_cm_0_0_rpb(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 65536, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 65536, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x128x65536_sm70_float16_cm_0_0_rpb` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 65536, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 65536, true>::kMinBlocksPerSm) +fna2_64x64x65536_sm70_float16_cm_0_0_rpb(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 65536, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 65536, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x64x65536_sm70_float16_cm_0_0_rpb` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +/////////////////////////////////////////////////////////////////// +// FNA-2D / float16 / SM70 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 32, false>::kMinBlocksPerSm) +fna2_32x128x32_sm70_float16_cm_0_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_32x128x32_sm70_float16_cm_0_1` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 32, false>::kMinBlocksPerSm) +fna2_64x64x32_sm70_float16_cm_0_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x64x32_sm70_float16_cm_0_1` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 32, false>::kMinBlocksPerSm) +fna2_64x128x32_sm70_float16_cm_0_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x128x32_sm70_float16_cm_0_1` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 64, false>::kMinBlocksPerSm) +fna2_32x128x64_sm70_float16_cm_0_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_32x128x64_sm70_float16_cm_0_1` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 64, false>::kMinBlocksPerSm) +fna2_64x64x64_sm70_float16_cm_0_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x64x64_sm70_float16_cm_0_1` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 64, false>::kMinBlocksPerSm) +fna2_64x128x64_sm70_float16_cm_0_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x128x64_sm70_float16_cm_0_1` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 128, false>::kMinBlocksPerSm) +fna2_32x128x128_sm70_float16_cm_0_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_32x128x128_sm70_float16_cm_0_1` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 128, false>::kMinBlocksPerSm) +fna2_64x64x128_sm70_float16_cm_0_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x64x128_sm70_float16_cm_0_1` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 128, false>::kMinBlocksPerSm) +fna2_64x128x128_sm70_float16_cm_0_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x128x128_sm70_float16_cm_0_1` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 65536, false>::kMinBlocksPerSm) +fna2_32x128x65536_sm70_float16_cm_0_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_32x128x65536_sm70_float16_cm_0_1` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 65536, false>::kMinBlocksPerSm) +fna2_64x128x65536_sm70_float16_cm_0_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x128x65536_sm70_float16_cm_0_1` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 65536, false>::kMinBlocksPerSm) +fna2_64x64x65536_sm70_float16_cm_0_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x64x65536_sm70_float16_cm_0_1` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +/////////////////////////////////////////////////////////////////// +// FNA-2D / float16 / SM70 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 32, false>::kMinBlocksPerSm) +fna2_32x128x32_sm70_float16_cm_1_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_32x128x32_sm70_float16_cm_1_0` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 32, false>::kMinBlocksPerSm) +fna2_64x64x32_sm70_float16_cm_1_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x64x32_sm70_float16_cm_1_0` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 32, false>::kMinBlocksPerSm) +fna2_64x128x32_sm70_float16_cm_1_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x128x32_sm70_float16_cm_1_0` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 64, false>::kMinBlocksPerSm) +fna2_32x128x64_sm70_float16_cm_1_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_32x128x64_sm70_float16_cm_1_0` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 64, false>::kMinBlocksPerSm) +fna2_64x64x64_sm70_float16_cm_1_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x64x64_sm70_float16_cm_1_0` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 64, false>::kMinBlocksPerSm) +fna2_64x128x64_sm70_float16_cm_1_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x128x64_sm70_float16_cm_1_0` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 128, false>::kMinBlocksPerSm) +fna2_32x128x128_sm70_float16_cm_1_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_32x128x128_sm70_float16_cm_1_0` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 128, false>::kMinBlocksPerSm) +fna2_64x64x128_sm70_float16_cm_1_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x64x128_sm70_float16_cm_1_0` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 128, false>::kMinBlocksPerSm) +fna2_64x128x128_sm70_float16_cm_1_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x128x128_sm70_float16_cm_1_0` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 65536, false>::kMinBlocksPerSm) +fna2_32x128x65536_sm70_float16_cm_1_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_32x128x65536_sm70_float16_cm_1_0` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 65536, false>::kMinBlocksPerSm) +fna2_64x128x65536_sm70_float16_cm_1_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x128x65536_sm70_float16_cm_1_0` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 65536, false>::kMinBlocksPerSm) +fna2_64x64x65536_sm70_float16_cm_1_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x64x65536_sm70_float16_cm_1_0` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +/////////////////////////////////////////////////////////////////// +// FNA-2D / float16 / SM70 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 32, false>::kMinBlocksPerSm) +fna2_32x128x32_sm70_float16_cm_1_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_32x128x32_sm70_float16_cm_1_1` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 32, false>::kMinBlocksPerSm) +fna2_64x64x32_sm70_float16_cm_1_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x64x32_sm70_float16_cm_1_1` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 32, false>::kMinBlocksPerSm) +fna2_64x128x32_sm70_float16_cm_1_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x128x32_sm70_float16_cm_1_1` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 64, false>::kMinBlocksPerSm) +fna2_32x128x64_sm70_float16_cm_1_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_32x128x64_sm70_float16_cm_1_1` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 64, false>::kMinBlocksPerSm) +fna2_64x64x64_sm70_float16_cm_1_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x64x64_sm70_float16_cm_1_1` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 64, false>::kMinBlocksPerSm) +fna2_64x128x64_sm70_float16_cm_1_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x128x64_sm70_float16_cm_1_1` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 128, false>::kMinBlocksPerSm) +fna2_32x128x128_sm70_float16_cm_1_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_32x128x128_sm70_float16_cm_1_1` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 128, false>::kMinBlocksPerSm) +fna2_64x64x128_sm70_float16_cm_1_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x64x128_sm70_float16_cm_1_1` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 128, false>::kMinBlocksPerSm) +fna2_64x128x128_sm70_float16_cm_1_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x128x128_sm70_float16_cm_1_1` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 65536, false>::kMinBlocksPerSm) +fna2_32x128x65536_sm70_float16_cm_1_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_32x128x65536_sm70_float16_cm_1_1` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 65536, false>::kMinBlocksPerSm) +fna2_64x128x65536_sm70_float16_cm_1_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x128x65536_sm70_float16_cm_1_1` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 65536, false>::kMinBlocksPerSm) +fna2_64x64x65536_sm70_float16_cm_1_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x64x65536_sm70_float16_cm_1_1` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +/////////////////////////////////////////////////////////////////// +// FNA-2D / float32 / SM75 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 32, false>::kMinBlocksPerSm) +fna2_32x128x32_sm75_float32_cm_0_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_32x128x32_sm75_float32_cm_0_0` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 32, false>::kMinBlocksPerSm) +fna2_64x64x32_sm75_float32_cm_0_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x64x32_sm75_float32_cm_0_0` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 32, false>::kMinBlocksPerSm) +fna2_64x128x32_sm75_float32_cm_0_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x128x32_sm75_float32_cm_0_0` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 64, false>::kMinBlocksPerSm) +fna2_32x128x64_sm75_float32_cm_0_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_32x128x64_sm75_float32_cm_0_0` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 64, false>::kMinBlocksPerSm) +fna2_64x64x64_sm75_float32_cm_0_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x64x64_sm75_float32_cm_0_0` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 64, false>::kMinBlocksPerSm) +fna2_64x128x64_sm75_float32_cm_0_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x128x64_sm75_float32_cm_0_0` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 128, false>::kMinBlocksPerSm) +fna2_32x128x128_sm75_float32_cm_0_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_32x128x128_sm75_float32_cm_0_0` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 128, false>::kMinBlocksPerSm) +fna2_64x64x128_sm75_float32_cm_0_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x64x128_sm75_float32_cm_0_0` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 128, false>::kMinBlocksPerSm) +fna2_64x128x128_sm75_float32_cm_0_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x128x128_sm75_float32_cm_0_0` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 65536, false>::kMinBlocksPerSm) +fna2_32x128x65536_sm75_float32_cm_0_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_32x128x65536_sm75_float32_cm_0_0` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 65536, false>::kMinBlocksPerSm) +fna2_64x128x65536_sm75_float32_cm_0_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x128x65536_sm75_float32_cm_0_0` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 65536, false>::kMinBlocksPerSm) +fna2_64x64x65536_sm75_float32_cm_0_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x64x65536_sm75_float32_cm_0_0` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +/////////////////////////////////////////////////////////////////// +// FNA-2D / float32 / SM75 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 32, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 32, true>::kMinBlocksPerSm) +fna2_32x128x32_sm75_float32_cm_0_0_rpb(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 32, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 32, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_32x128x32_sm75_float32_cm_0_0_rpb` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 32, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 32, true>::kMinBlocksPerSm) +fna2_64x64x32_sm75_float32_cm_0_0_rpb(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 32, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 32, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x64x32_sm75_float32_cm_0_0_rpb` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 32, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 32, true>::kMinBlocksPerSm) +fna2_64x128x32_sm75_float32_cm_0_0_rpb(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 32, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 32, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x128x32_sm75_float32_cm_0_0_rpb` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 64, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 64, true>::kMinBlocksPerSm) +fna2_32x128x64_sm75_float32_cm_0_0_rpb(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 64, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 64, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_32x128x64_sm75_float32_cm_0_0_rpb` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 64, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 64, true>::kMinBlocksPerSm) +fna2_64x64x64_sm75_float32_cm_0_0_rpb(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 64, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 64, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x64x64_sm75_float32_cm_0_0_rpb` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 64, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 64, true>::kMinBlocksPerSm) +fna2_64x128x64_sm75_float32_cm_0_0_rpb(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 64, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 64, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x128x64_sm75_float32_cm_0_0_rpb` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 128, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 128, true>::kMinBlocksPerSm) +fna2_32x128x128_sm75_float32_cm_0_0_rpb(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 128, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 128, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_32x128x128_sm75_float32_cm_0_0_rpb` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 128, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 128, true>::kMinBlocksPerSm) +fna2_64x64x128_sm75_float32_cm_0_0_rpb(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 128, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 128, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x64x128_sm75_float32_cm_0_0_rpb` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 128, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 128, true>::kMinBlocksPerSm) +fna2_64x128x128_sm75_float32_cm_0_0_rpb(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 128, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 128, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x128x128_sm75_float32_cm_0_0_rpb` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 65536, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 65536, true>::kMinBlocksPerSm) +fna2_32x128x65536_sm75_float32_cm_0_0_rpb(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 65536, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 65536, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_32x128x65536_sm75_float32_cm_0_0_rpb` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 65536, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 65536, true>::kMinBlocksPerSm) +fna2_64x128x65536_sm75_float32_cm_0_0_rpb(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 65536, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 65536, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x128x65536_sm75_float32_cm_0_0_rpb` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 65536, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 65536, true>::kMinBlocksPerSm) +fna2_64x64x65536_sm75_float32_cm_0_0_rpb(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 65536, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 65536, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x64x65536_sm75_float32_cm_0_0_rpb` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +/////////////////////////////////////////////////////////////////// +// FNA-2D / float32 / SM75 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 32, false>::kMinBlocksPerSm) +fna2_32x128x32_sm75_float32_cm_0_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_32x128x32_sm75_float32_cm_0_1` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 32, false>::kMinBlocksPerSm) +fna2_64x64x32_sm75_float32_cm_0_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x64x32_sm75_float32_cm_0_1` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 32, false>::kMinBlocksPerSm) +fna2_64x128x32_sm75_float32_cm_0_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x128x32_sm75_float32_cm_0_1` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 64, false>::kMinBlocksPerSm) +fna2_32x128x64_sm75_float32_cm_0_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_32x128x64_sm75_float32_cm_0_1` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 64, false>::kMinBlocksPerSm) +fna2_64x64x64_sm75_float32_cm_0_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x64x64_sm75_float32_cm_0_1` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 64, false>::kMinBlocksPerSm) +fna2_64x128x64_sm75_float32_cm_0_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x128x64_sm75_float32_cm_0_1` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 128, false>::kMinBlocksPerSm) +fna2_32x128x128_sm75_float32_cm_0_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_32x128x128_sm75_float32_cm_0_1` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 128, false>::kMinBlocksPerSm) +fna2_64x64x128_sm75_float32_cm_0_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x64x128_sm75_float32_cm_0_1` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 128, false>::kMinBlocksPerSm) +fna2_64x128x128_sm75_float32_cm_0_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x128x128_sm75_float32_cm_0_1` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 65536, false>::kMinBlocksPerSm) +fna2_32x128x65536_sm75_float32_cm_0_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_32x128x65536_sm75_float32_cm_0_1` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 65536, false>::kMinBlocksPerSm) +fna2_64x128x65536_sm75_float32_cm_0_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x128x65536_sm75_float32_cm_0_1` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 65536, false>::kMinBlocksPerSm) +fna2_64x64x65536_sm75_float32_cm_0_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x64x65536_sm75_float32_cm_0_1` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} +} +} +} + diff --git a/csrc/autogen/src/cuda/fna/source_5.cu b/csrc/autogen/src/cuda/fna/source_5.cu new file mode 100644 index 0000000..2d6e3bc --- /dev/null +++ b/csrc/autogen/src/cuda/fna/source_5.cu @@ -0,0 +1,2573 @@ +#include +#include +#include +#include +#include +namespace natten { +namespace cuda { +namespace fna { + + + +/////////////////////////////////////////////////////////////////// +// FNA-2D / float32 / SM75 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 32, false>::kMinBlocksPerSm) +fna2_32x128x32_sm75_float32_cm_1_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_32x128x32_sm75_float32_cm_1_0` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 32, false>::kMinBlocksPerSm) +fna2_64x64x32_sm75_float32_cm_1_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x64x32_sm75_float32_cm_1_0` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 32, false>::kMinBlocksPerSm) +fna2_64x128x32_sm75_float32_cm_1_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x128x32_sm75_float32_cm_1_0` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 64, false>::kMinBlocksPerSm) +fna2_32x128x64_sm75_float32_cm_1_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_32x128x64_sm75_float32_cm_1_0` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 64, false>::kMinBlocksPerSm) +fna2_64x64x64_sm75_float32_cm_1_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x64x64_sm75_float32_cm_1_0` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 64, false>::kMinBlocksPerSm) +fna2_64x128x64_sm75_float32_cm_1_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x128x64_sm75_float32_cm_1_0` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 128, false>::kMinBlocksPerSm) +fna2_32x128x128_sm75_float32_cm_1_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_32x128x128_sm75_float32_cm_1_0` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 128, false>::kMinBlocksPerSm) +fna2_64x64x128_sm75_float32_cm_1_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x64x128_sm75_float32_cm_1_0` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 128, false>::kMinBlocksPerSm) +fna2_64x128x128_sm75_float32_cm_1_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x128x128_sm75_float32_cm_1_0` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 65536, false>::kMinBlocksPerSm) +fna2_32x128x65536_sm75_float32_cm_1_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_32x128x65536_sm75_float32_cm_1_0` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 65536, false>::kMinBlocksPerSm) +fna2_64x128x65536_sm75_float32_cm_1_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x128x65536_sm75_float32_cm_1_0` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 65536, false>::kMinBlocksPerSm) +fna2_64x64x65536_sm75_float32_cm_1_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x64x65536_sm75_float32_cm_1_0` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +/////////////////////////////////////////////////////////////////// +// FNA-2D / float32 / SM75 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 32, false>::kMinBlocksPerSm) +fna2_32x128x32_sm75_float32_cm_1_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_32x128x32_sm75_float32_cm_1_1` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 32, false>::kMinBlocksPerSm) +fna2_64x64x32_sm75_float32_cm_1_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x64x32_sm75_float32_cm_1_1` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 32, false>::kMinBlocksPerSm) +fna2_64x128x32_sm75_float32_cm_1_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x128x32_sm75_float32_cm_1_1` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 64, false>::kMinBlocksPerSm) +fna2_32x128x64_sm75_float32_cm_1_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_32x128x64_sm75_float32_cm_1_1` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 64, false>::kMinBlocksPerSm) +fna2_64x64x64_sm75_float32_cm_1_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x64x64_sm75_float32_cm_1_1` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 64, false>::kMinBlocksPerSm) +fna2_64x128x64_sm75_float32_cm_1_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x128x64_sm75_float32_cm_1_1` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 128, false>::kMinBlocksPerSm) +fna2_32x128x128_sm75_float32_cm_1_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_32x128x128_sm75_float32_cm_1_1` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 128, false>::kMinBlocksPerSm) +fna2_64x64x128_sm75_float32_cm_1_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x64x128_sm75_float32_cm_1_1` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 128, false>::kMinBlocksPerSm) +fna2_64x128x128_sm75_float32_cm_1_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x128x128_sm75_float32_cm_1_1` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 65536, false>::kMinBlocksPerSm) +fna2_32x128x65536_sm75_float32_cm_1_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 32, 128, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_32x128x65536_sm75_float32_cm_1_1` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 65536, false>::kMinBlocksPerSm) +fna2_64x128x65536_sm75_float32_cm_1_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 128, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x128x65536_sm75_float32_cm_1_1` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 65536, false>::kMinBlocksPerSm) +fna2_64x64x65536_sm75_float32_cm_1_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm75, true, 64, 64, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x64x65536_sm75_float32_cm_1_1` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +/////////////////////////////////////////////////////////////////// +// FNA-2D / float16 / SM75 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 32, false>::kMinBlocksPerSm) +fna2_32x128x32_sm75_float16_cm_0_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_32x128x32_sm75_float16_cm_0_0` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 32, false>::kMinBlocksPerSm) +fna2_64x64x32_sm75_float16_cm_0_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x64x32_sm75_float16_cm_0_0` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 32, false>::kMinBlocksPerSm) +fna2_64x128x32_sm75_float16_cm_0_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x128x32_sm75_float16_cm_0_0` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 64, false>::kMinBlocksPerSm) +fna2_32x128x64_sm75_float16_cm_0_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_32x128x64_sm75_float16_cm_0_0` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 64, false>::kMinBlocksPerSm) +fna2_64x64x64_sm75_float16_cm_0_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x64x64_sm75_float16_cm_0_0` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 64, false>::kMinBlocksPerSm) +fna2_64x128x64_sm75_float16_cm_0_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x128x64_sm75_float16_cm_0_0` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 128, false>::kMinBlocksPerSm) +fna2_32x128x128_sm75_float16_cm_0_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_32x128x128_sm75_float16_cm_0_0` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 128, false>::kMinBlocksPerSm) +fna2_64x64x128_sm75_float16_cm_0_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x64x128_sm75_float16_cm_0_0` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 128, false>::kMinBlocksPerSm) +fna2_64x128x128_sm75_float16_cm_0_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x128x128_sm75_float16_cm_0_0` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 65536, false>::kMinBlocksPerSm) +fna2_32x128x65536_sm75_float16_cm_0_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_32x128x65536_sm75_float16_cm_0_0` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 65536, false>::kMinBlocksPerSm) +fna2_64x128x65536_sm75_float16_cm_0_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x128x65536_sm75_float16_cm_0_0` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 65536, false>::kMinBlocksPerSm) +fna2_64x64x65536_sm75_float16_cm_0_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x64x65536_sm75_float16_cm_0_0` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +/////////////////////////////////////////////////////////////////// +// FNA-2D / float16 / SM75 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 32, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 32, true>::kMinBlocksPerSm) +fna2_32x128x32_sm75_float16_cm_0_0_rpb(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 32, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 32, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_32x128x32_sm75_float16_cm_0_0_rpb` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 32, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 32, true>::kMinBlocksPerSm) +fna2_64x64x32_sm75_float16_cm_0_0_rpb(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 32, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 32, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x64x32_sm75_float16_cm_0_0_rpb` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 32, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 32, true>::kMinBlocksPerSm) +fna2_64x128x32_sm75_float16_cm_0_0_rpb(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 32, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 32, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x128x32_sm75_float16_cm_0_0_rpb` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 64, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 64, true>::kMinBlocksPerSm) +fna2_32x128x64_sm75_float16_cm_0_0_rpb(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 64, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 64, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_32x128x64_sm75_float16_cm_0_0_rpb` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 64, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 64, true>::kMinBlocksPerSm) +fna2_64x64x64_sm75_float16_cm_0_0_rpb(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 64, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 64, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x64x64_sm75_float16_cm_0_0_rpb` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 64, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 64, true>::kMinBlocksPerSm) +fna2_64x128x64_sm75_float16_cm_0_0_rpb(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 64, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 64, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x128x64_sm75_float16_cm_0_0_rpb` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 128, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 128, true>::kMinBlocksPerSm) +fna2_32x128x128_sm75_float16_cm_0_0_rpb(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 128, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 128, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_32x128x128_sm75_float16_cm_0_0_rpb` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 128, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 128, true>::kMinBlocksPerSm) +fna2_64x64x128_sm75_float16_cm_0_0_rpb(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 128, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 128, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x64x128_sm75_float16_cm_0_0_rpb` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 128, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 128, true>::kMinBlocksPerSm) +fna2_64x128x128_sm75_float16_cm_0_0_rpb(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 128, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 128, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x128x128_sm75_float16_cm_0_0_rpb` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 65536, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 65536, true>::kMinBlocksPerSm) +fna2_32x128x65536_sm75_float16_cm_0_0_rpb(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 65536, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 65536, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_32x128x65536_sm75_float16_cm_0_0_rpb` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 65536, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 65536, true>::kMinBlocksPerSm) +fna2_64x128x65536_sm75_float16_cm_0_0_rpb(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 65536, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 65536, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x128x65536_sm75_float16_cm_0_0_rpb` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 65536, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 65536, true>::kMinBlocksPerSm) +fna2_64x64x65536_sm75_float16_cm_0_0_rpb(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 65536, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 65536, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x64x65536_sm75_float16_cm_0_0_rpb` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +/////////////////////////////////////////////////////////////////// +// FNA-2D / float16 / SM75 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 32, false>::kMinBlocksPerSm) +fna2_32x128x32_sm75_float16_cm_0_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_32x128x32_sm75_float16_cm_0_1` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 32, false>::kMinBlocksPerSm) +fna2_64x64x32_sm75_float16_cm_0_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x64x32_sm75_float16_cm_0_1` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 32, false>::kMinBlocksPerSm) +fna2_64x128x32_sm75_float16_cm_0_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x128x32_sm75_float16_cm_0_1` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 64, false>::kMinBlocksPerSm) +fna2_32x128x64_sm75_float16_cm_0_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_32x128x64_sm75_float16_cm_0_1` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 64, false>::kMinBlocksPerSm) +fna2_64x64x64_sm75_float16_cm_0_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x64x64_sm75_float16_cm_0_1` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 64, false>::kMinBlocksPerSm) +fna2_64x128x64_sm75_float16_cm_0_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x128x64_sm75_float16_cm_0_1` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 128, false>::kMinBlocksPerSm) +fna2_32x128x128_sm75_float16_cm_0_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_32x128x128_sm75_float16_cm_0_1` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 128, false>::kMinBlocksPerSm) +fna2_64x64x128_sm75_float16_cm_0_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x64x128_sm75_float16_cm_0_1` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 128, false>::kMinBlocksPerSm) +fna2_64x128x128_sm75_float16_cm_0_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x128x128_sm75_float16_cm_0_1` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 65536, false>::kMinBlocksPerSm) +fna2_32x128x65536_sm75_float16_cm_0_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_32x128x65536_sm75_float16_cm_0_1` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 65536, false>::kMinBlocksPerSm) +fna2_64x128x65536_sm75_float16_cm_0_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x128x65536_sm75_float16_cm_0_1` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 65536, false>::kMinBlocksPerSm) +fna2_64x64x65536_sm75_float16_cm_0_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x64x65536_sm75_float16_cm_0_1` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +/////////////////////////////////////////////////////////////////// +// FNA-2D / float16 / SM75 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 32, false>::kMinBlocksPerSm) +fna2_32x128x32_sm75_float16_cm_1_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_32x128x32_sm75_float16_cm_1_0` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 32, false>::kMinBlocksPerSm) +fna2_64x64x32_sm75_float16_cm_1_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x64x32_sm75_float16_cm_1_0` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 32, false>::kMinBlocksPerSm) +fna2_64x128x32_sm75_float16_cm_1_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x128x32_sm75_float16_cm_1_0` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 64, false>::kMinBlocksPerSm) +fna2_32x128x64_sm75_float16_cm_1_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_32x128x64_sm75_float16_cm_1_0` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 64, false>::kMinBlocksPerSm) +fna2_64x64x64_sm75_float16_cm_1_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x64x64_sm75_float16_cm_1_0` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 64, false>::kMinBlocksPerSm) +fna2_64x128x64_sm75_float16_cm_1_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x128x64_sm75_float16_cm_1_0` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 128, false>::kMinBlocksPerSm) +fna2_32x128x128_sm75_float16_cm_1_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_32x128x128_sm75_float16_cm_1_0` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 128, false>::kMinBlocksPerSm) +fna2_64x64x128_sm75_float16_cm_1_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x64x128_sm75_float16_cm_1_0` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 128, false>::kMinBlocksPerSm) +fna2_64x128x128_sm75_float16_cm_1_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x128x128_sm75_float16_cm_1_0` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 65536, false>::kMinBlocksPerSm) +fna2_32x128x65536_sm75_float16_cm_1_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_32x128x65536_sm75_float16_cm_1_0` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 65536, false>::kMinBlocksPerSm) +fna2_64x128x65536_sm75_float16_cm_1_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x128x65536_sm75_float16_cm_1_0` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 65536, false>::kMinBlocksPerSm) +fna2_64x64x65536_sm75_float16_cm_1_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x64x65536_sm75_float16_cm_1_0` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +/////////////////////////////////////////////////////////////////// +// FNA-2D / float16 / SM75 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 32, false>::kMinBlocksPerSm) +fna2_32x128x32_sm75_float16_cm_1_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_32x128x32_sm75_float16_cm_1_1` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 32, false>::kMinBlocksPerSm) +fna2_64x64x32_sm75_float16_cm_1_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x64x32_sm75_float16_cm_1_1` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 32, false>::kMinBlocksPerSm) +fna2_64x128x32_sm75_float16_cm_1_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x128x32_sm75_float16_cm_1_1` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 64, false>::kMinBlocksPerSm) +fna2_32x128x64_sm75_float16_cm_1_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_32x128x64_sm75_float16_cm_1_1` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 64, false>::kMinBlocksPerSm) +fna2_64x64x64_sm75_float16_cm_1_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x64x64_sm75_float16_cm_1_1` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 64, false>::kMinBlocksPerSm) +fna2_64x128x64_sm75_float16_cm_1_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x128x64_sm75_float16_cm_1_1` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 128, false>::kMinBlocksPerSm) +fna2_32x128x128_sm75_float16_cm_1_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_32x128x128_sm75_float16_cm_1_1` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 128, false>::kMinBlocksPerSm) +fna2_64x64x128_sm75_float16_cm_1_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x64x128_sm75_float16_cm_1_1` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 128, false>::kMinBlocksPerSm) +fna2_64x128x128_sm75_float16_cm_1_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x128x128_sm75_float16_cm_1_1` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 65536, false>::kMinBlocksPerSm) +fna2_32x128x65536_sm75_float16_cm_1_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 32, 128, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_32x128x65536_sm75_float16_cm_1_1` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 65536, false>::kMinBlocksPerSm) +fna2_64x128x65536_sm75_float16_cm_1_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 128, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x128x65536_sm75_float16_cm_1_1` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 65536, false>::kMinBlocksPerSm) +fna2_64x64x65536_sm75_float16_cm_1_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 750 +#if __CUDA_ARCH__ < 800 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm75, true, 64, 64, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x64x65536_sm75_float16_cm_1_1` was built for SM75, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +/////////////////////////////////////////////////////////////////// +// FNA-2D / float32 / SM80 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 32, false>::kMinBlocksPerSm) +fna2_32x128x32_sm80_float32_cm_0_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_32x128x32_sm80_float32_cm_0_0` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 32, false>::kMinBlocksPerSm) +fna2_64x64x32_sm80_float32_cm_0_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x64x32_sm80_float32_cm_0_0` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 32, false>::kMinBlocksPerSm) +fna2_64x128x32_sm80_float32_cm_0_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x128x32_sm80_float32_cm_0_0` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 64, false>::kMinBlocksPerSm) +fna2_32x128x64_sm80_float32_cm_0_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_32x128x64_sm80_float32_cm_0_0` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 64, false>::kMinBlocksPerSm) +fna2_64x64x64_sm80_float32_cm_0_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x64x64_sm80_float32_cm_0_0` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 64, false>::kMinBlocksPerSm) +fna2_64x128x64_sm80_float32_cm_0_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x128x64_sm80_float32_cm_0_0` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 128, false>::kMinBlocksPerSm) +fna2_32x128x128_sm80_float32_cm_0_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_32x128x128_sm80_float32_cm_0_0` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 128, false>::kMinBlocksPerSm) +fna2_64x64x128_sm80_float32_cm_0_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x64x128_sm80_float32_cm_0_0` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 128, false>::kMinBlocksPerSm) +fna2_64x128x128_sm80_float32_cm_0_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x128x128_sm80_float32_cm_0_0` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 65536, false>::kMinBlocksPerSm) +fna2_32x128x65536_sm80_float32_cm_0_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_32x128x65536_sm80_float32_cm_0_0` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 65536, false>::kMinBlocksPerSm) +fna2_64x128x65536_sm80_float32_cm_0_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x128x65536_sm80_float32_cm_0_0` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 65536, false>::kMinBlocksPerSm) +fna2_64x64x65536_sm80_float32_cm_0_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x64x65536_sm80_float32_cm_0_0` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +/////////////////////////////////////////////////////////////////// +// FNA-2D / float32 / SM80 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 32, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 32, true>::kMinBlocksPerSm) +fna2_32x128x32_sm80_float32_cm_0_0_rpb(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 32, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 32, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_32x128x32_sm80_float32_cm_0_0_rpb` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 32, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 32, true>::kMinBlocksPerSm) +fna2_64x64x32_sm80_float32_cm_0_0_rpb(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 32, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 32, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x64x32_sm80_float32_cm_0_0_rpb` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 32, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 32, true>::kMinBlocksPerSm) +fna2_64x128x32_sm80_float32_cm_0_0_rpb(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 32, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 32, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x128x32_sm80_float32_cm_0_0_rpb` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 64, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 64, true>::kMinBlocksPerSm) +fna2_32x128x64_sm80_float32_cm_0_0_rpb(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 64, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 64, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_32x128x64_sm80_float32_cm_0_0_rpb` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 64, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 64, true>::kMinBlocksPerSm) +fna2_64x64x64_sm80_float32_cm_0_0_rpb(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 64, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 64, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x64x64_sm80_float32_cm_0_0_rpb` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 64, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 64, true>::kMinBlocksPerSm) +fna2_64x128x64_sm80_float32_cm_0_0_rpb(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 64, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 64, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x128x64_sm80_float32_cm_0_0_rpb` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 128, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 128, true>::kMinBlocksPerSm) +fna2_32x128x128_sm80_float32_cm_0_0_rpb(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 128, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 128, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_32x128x128_sm80_float32_cm_0_0_rpb` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 128, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 128, true>::kMinBlocksPerSm) +fna2_64x64x128_sm80_float32_cm_0_0_rpb(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 128, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 128, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x64x128_sm80_float32_cm_0_0_rpb` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 128, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 128, true>::kMinBlocksPerSm) +fna2_64x128x128_sm80_float32_cm_0_0_rpb(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 128, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 128, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x128x128_sm80_float32_cm_0_0_rpb` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 65536, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 65536, true>::kMinBlocksPerSm) +fna2_32x128x65536_sm80_float32_cm_0_0_rpb(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 65536, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 65536, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_32x128x65536_sm80_float32_cm_0_0_rpb` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 65536, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 65536, true>::kMinBlocksPerSm) +fna2_64x128x65536_sm80_float32_cm_0_0_rpb(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 65536, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 65536, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x128x65536_sm80_float32_cm_0_0_rpb` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 65536, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 65536, true>::kMinBlocksPerSm) +fna2_64x64x65536_sm80_float32_cm_0_0_rpb(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 65536, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 65536, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x64x65536_sm80_float32_cm_0_0_rpb` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +/////////////////////////////////////////////////////////////////// +// FNA-2D / float32 / SM80 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 32, false>::kMinBlocksPerSm) +fna2_32x128x32_sm80_float32_cm_0_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_32x128x32_sm80_float32_cm_0_1` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 32, false>::kMinBlocksPerSm) +fna2_64x64x32_sm80_float32_cm_0_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x64x32_sm80_float32_cm_0_1` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 32, false>::kMinBlocksPerSm) +fna2_64x128x32_sm80_float32_cm_0_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x128x32_sm80_float32_cm_0_1` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 64, false>::kMinBlocksPerSm) +fna2_32x128x64_sm80_float32_cm_0_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_32x128x64_sm80_float32_cm_0_1` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 64, false>::kMinBlocksPerSm) +fna2_64x64x64_sm80_float32_cm_0_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x64x64_sm80_float32_cm_0_1` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 64, false>::kMinBlocksPerSm) +fna2_64x128x64_sm80_float32_cm_0_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x128x64_sm80_float32_cm_0_1` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 128, false>::kMinBlocksPerSm) +fna2_32x128x128_sm80_float32_cm_0_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_32x128x128_sm80_float32_cm_0_1` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 128, false>::kMinBlocksPerSm) +fna2_64x64x128_sm80_float32_cm_0_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x64x128_sm80_float32_cm_0_1` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 128, false>::kMinBlocksPerSm) +fna2_64x128x128_sm80_float32_cm_0_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x128x128_sm80_float32_cm_0_1` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 65536, false>::kMinBlocksPerSm) +fna2_32x128x65536_sm80_float32_cm_0_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_32x128x65536_sm80_float32_cm_0_1` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 65536, false>::kMinBlocksPerSm) +fna2_64x128x65536_sm80_float32_cm_0_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x128x65536_sm80_float32_cm_0_1` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 65536, false>::kMinBlocksPerSm) +fna2_64x64x65536_sm80_float32_cm_0_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x64x65536_sm80_float32_cm_0_1` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} +} +} +} + diff --git a/csrc/autogen/src/cuda/fna/source_6.cu b/csrc/autogen/src/cuda/fna/source_6.cu new file mode 100644 index 0000000..083655b --- /dev/null +++ b/csrc/autogen/src/cuda/fna/source_6.cu @@ -0,0 +1,2573 @@ +#include +#include +#include +#include +#include +namespace natten { +namespace cuda { +namespace fna { + + + +/////////////////////////////////////////////////////////////////// +// FNA-2D / float32 / SM80 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 32, false>::kMinBlocksPerSm) +fna2_32x128x32_sm80_float32_cm_1_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_32x128x32_sm80_float32_cm_1_0` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 32, false>::kMinBlocksPerSm) +fna2_64x64x32_sm80_float32_cm_1_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x64x32_sm80_float32_cm_1_0` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 32, false>::kMinBlocksPerSm) +fna2_64x128x32_sm80_float32_cm_1_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x128x32_sm80_float32_cm_1_0` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 64, false>::kMinBlocksPerSm) +fna2_32x128x64_sm80_float32_cm_1_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_32x128x64_sm80_float32_cm_1_0` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 64, false>::kMinBlocksPerSm) +fna2_64x64x64_sm80_float32_cm_1_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x64x64_sm80_float32_cm_1_0` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 64, false>::kMinBlocksPerSm) +fna2_64x128x64_sm80_float32_cm_1_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x128x64_sm80_float32_cm_1_0` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 128, false>::kMinBlocksPerSm) +fna2_32x128x128_sm80_float32_cm_1_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_32x128x128_sm80_float32_cm_1_0` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 128, false>::kMinBlocksPerSm) +fna2_64x64x128_sm80_float32_cm_1_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x64x128_sm80_float32_cm_1_0` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 128, false>::kMinBlocksPerSm) +fna2_64x128x128_sm80_float32_cm_1_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x128x128_sm80_float32_cm_1_0` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 65536, false>::kMinBlocksPerSm) +fna2_32x128x65536_sm80_float32_cm_1_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_32x128x65536_sm80_float32_cm_1_0` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 65536, false>::kMinBlocksPerSm) +fna2_64x128x65536_sm80_float32_cm_1_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x128x65536_sm80_float32_cm_1_0` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 65536, false>::kMinBlocksPerSm) +fna2_64x64x65536_sm80_float32_cm_1_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x64x65536_sm80_float32_cm_1_0` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +/////////////////////////////////////////////////////////////////// +// FNA-2D / float32 / SM80 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 32, false>::kMinBlocksPerSm) +fna2_32x128x32_sm80_float32_cm_1_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_32x128x32_sm80_float32_cm_1_1` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 32, false>::kMinBlocksPerSm) +fna2_64x64x32_sm80_float32_cm_1_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x64x32_sm80_float32_cm_1_1` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 32, false>::kMinBlocksPerSm) +fna2_64x128x32_sm80_float32_cm_1_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x128x32_sm80_float32_cm_1_1` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 64, false>::kMinBlocksPerSm) +fna2_32x128x64_sm80_float32_cm_1_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_32x128x64_sm80_float32_cm_1_1` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 64, false>::kMinBlocksPerSm) +fna2_64x64x64_sm80_float32_cm_1_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x64x64_sm80_float32_cm_1_1` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 64, false>::kMinBlocksPerSm) +fna2_64x128x64_sm80_float32_cm_1_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x128x64_sm80_float32_cm_1_1` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 128, false>::kMinBlocksPerSm) +fna2_32x128x128_sm80_float32_cm_1_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_32x128x128_sm80_float32_cm_1_1` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 128, false>::kMinBlocksPerSm) +fna2_64x64x128_sm80_float32_cm_1_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x64x128_sm80_float32_cm_1_1` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 128, false>::kMinBlocksPerSm) +fna2_64x128x128_sm80_float32_cm_1_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x128x128_sm80_float32_cm_1_1` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 65536, false>::kMinBlocksPerSm) +fna2_32x128x65536_sm80_float32_cm_1_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 32, 128, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_32x128x65536_sm80_float32_cm_1_1` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 65536, false>::kMinBlocksPerSm) +fna2_64x128x65536_sm80_float32_cm_1_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 128, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x128x65536_sm80_float32_cm_1_1` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 65536, false>::kMinBlocksPerSm) +fna2_64x64x65536_sm80_float32_cm_1_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, float, cutlass::arch::Sm80, true, 64, 64, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x64x65536_sm80_float32_cm_1_1` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +/////////////////////////////////////////////////////////////////// +// FNA-2D / float16 / SM80 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 32, false>::kMinBlocksPerSm) +fna2_32x128x32_sm80_float16_cm_0_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_32x128x32_sm80_float16_cm_0_0` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 32, false>::kMinBlocksPerSm) +fna2_64x64x32_sm80_float16_cm_0_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x64x32_sm80_float16_cm_0_0` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 32, false>::kMinBlocksPerSm) +fna2_64x128x32_sm80_float16_cm_0_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x128x32_sm80_float16_cm_0_0` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 64, false>::kMinBlocksPerSm) +fna2_32x128x64_sm80_float16_cm_0_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_32x128x64_sm80_float16_cm_0_0` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 64, false>::kMinBlocksPerSm) +fna2_64x64x64_sm80_float16_cm_0_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x64x64_sm80_float16_cm_0_0` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 64, false>::kMinBlocksPerSm) +fna2_64x128x64_sm80_float16_cm_0_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x128x64_sm80_float16_cm_0_0` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 128, false>::kMinBlocksPerSm) +fna2_32x128x128_sm80_float16_cm_0_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_32x128x128_sm80_float16_cm_0_0` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 128, false>::kMinBlocksPerSm) +fna2_64x64x128_sm80_float16_cm_0_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x64x128_sm80_float16_cm_0_0` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 128, false>::kMinBlocksPerSm) +fna2_64x128x128_sm80_float16_cm_0_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x128x128_sm80_float16_cm_0_0` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 65536, false>::kMinBlocksPerSm) +fna2_32x128x65536_sm80_float16_cm_0_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_32x128x65536_sm80_float16_cm_0_0` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 65536, false>::kMinBlocksPerSm) +fna2_64x128x65536_sm80_float16_cm_0_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x128x65536_sm80_float16_cm_0_0` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 65536, false>::kMinBlocksPerSm) +fna2_64x64x65536_sm80_float16_cm_0_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x64x65536_sm80_float16_cm_0_0` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +/////////////////////////////////////////////////////////////////// +// FNA-2D / float16 / SM80 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 32, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 32, true>::kMinBlocksPerSm) +fna2_32x128x32_sm80_float16_cm_0_0_rpb(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 32, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 32, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_32x128x32_sm80_float16_cm_0_0_rpb` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 32, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 32, true>::kMinBlocksPerSm) +fna2_64x64x32_sm80_float16_cm_0_0_rpb(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 32, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 32, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x64x32_sm80_float16_cm_0_0_rpb` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 32, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 32, true>::kMinBlocksPerSm) +fna2_64x128x32_sm80_float16_cm_0_0_rpb(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 32, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 32, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x128x32_sm80_float16_cm_0_0_rpb` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 64, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 64, true>::kMinBlocksPerSm) +fna2_32x128x64_sm80_float16_cm_0_0_rpb(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 64, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 64, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_32x128x64_sm80_float16_cm_0_0_rpb` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 64, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 64, true>::kMinBlocksPerSm) +fna2_64x64x64_sm80_float16_cm_0_0_rpb(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 64, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 64, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x64x64_sm80_float16_cm_0_0_rpb` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 64, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 64, true>::kMinBlocksPerSm) +fna2_64x128x64_sm80_float16_cm_0_0_rpb(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 64, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 64, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x128x64_sm80_float16_cm_0_0_rpb` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 128, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 128, true>::kMinBlocksPerSm) +fna2_32x128x128_sm80_float16_cm_0_0_rpb(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 128, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 128, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_32x128x128_sm80_float16_cm_0_0_rpb` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 128, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 128, true>::kMinBlocksPerSm) +fna2_64x64x128_sm80_float16_cm_0_0_rpb(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 128, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 128, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x64x128_sm80_float16_cm_0_0_rpb` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 128, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 128, true>::kMinBlocksPerSm) +fna2_64x128x128_sm80_float16_cm_0_0_rpb(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 128, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 128, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x128x128_sm80_float16_cm_0_0_rpb` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 65536, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 65536, true>::kMinBlocksPerSm) +fna2_32x128x65536_sm80_float16_cm_0_0_rpb(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 65536, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 65536, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_32x128x65536_sm80_float16_cm_0_0_rpb` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 65536, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 65536, true>::kMinBlocksPerSm) +fna2_64x128x65536_sm80_float16_cm_0_0_rpb(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 65536, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 65536, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x128x65536_sm80_float16_cm_0_0_rpb` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 65536, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 65536, true>::kMinBlocksPerSm) +fna2_64x64x65536_sm80_float16_cm_0_0_rpb(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 65536, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 65536, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x64x65536_sm80_float16_cm_0_0_rpb` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +/////////////////////////////////////////////////////////////////// +// FNA-2D / float16 / SM80 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 32, false>::kMinBlocksPerSm) +fna2_32x128x32_sm80_float16_cm_0_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_32x128x32_sm80_float16_cm_0_1` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 32, false>::kMinBlocksPerSm) +fna2_64x64x32_sm80_float16_cm_0_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x64x32_sm80_float16_cm_0_1` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 32, false>::kMinBlocksPerSm) +fna2_64x128x32_sm80_float16_cm_0_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x128x32_sm80_float16_cm_0_1` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 64, false>::kMinBlocksPerSm) +fna2_32x128x64_sm80_float16_cm_0_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_32x128x64_sm80_float16_cm_0_1` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 64, false>::kMinBlocksPerSm) +fna2_64x64x64_sm80_float16_cm_0_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x64x64_sm80_float16_cm_0_1` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 64, false>::kMinBlocksPerSm) +fna2_64x128x64_sm80_float16_cm_0_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x128x64_sm80_float16_cm_0_1` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 128, false>::kMinBlocksPerSm) +fna2_32x128x128_sm80_float16_cm_0_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_32x128x128_sm80_float16_cm_0_1` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 128, false>::kMinBlocksPerSm) +fna2_64x64x128_sm80_float16_cm_0_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x64x128_sm80_float16_cm_0_1` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 128, false>::kMinBlocksPerSm) +fna2_64x128x128_sm80_float16_cm_0_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x128x128_sm80_float16_cm_0_1` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 65536, false>::kMinBlocksPerSm) +fna2_32x128x65536_sm80_float16_cm_0_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_32x128x65536_sm80_float16_cm_0_1` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 65536, false>::kMinBlocksPerSm) +fna2_64x128x65536_sm80_float16_cm_0_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x128x65536_sm80_float16_cm_0_1` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 65536, false>::kMinBlocksPerSm) +fna2_64x64x65536_sm80_float16_cm_0_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x64x65536_sm80_float16_cm_0_1` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +/////////////////////////////////////////////////////////////////// +// FNA-2D / float16 / SM80 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 32, false>::kMinBlocksPerSm) +fna2_32x128x32_sm80_float16_cm_1_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_32x128x32_sm80_float16_cm_1_0` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 32, false>::kMinBlocksPerSm) +fna2_64x64x32_sm80_float16_cm_1_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x64x32_sm80_float16_cm_1_0` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 32, false>::kMinBlocksPerSm) +fna2_64x128x32_sm80_float16_cm_1_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x128x32_sm80_float16_cm_1_0` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 64, false>::kMinBlocksPerSm) +fna2_32x128x64_sm80_float16_cm_1_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_32x128x64_sm80_float16_cm_1_0` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 64, false>::kMinBlocksPerSm) +fna2_64x64x64_sm80_float16_cm_1_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x64x64_sm80_float16_cm_1_0` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 64, false>::kMinBlocksPerSm) +fna2_64x128x64_sm80_float16_cm_1_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x128x64_sm80_float16_cm_1_0` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 128, false>::kMinBlocksPerSm) +fna2_32x128x128_sm80_float16_cm_1_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_32x128x128_sm80_float16_cm_1_0` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 128, false>::kMinBlocksPerSm) +fna2_64x64x128_sm80_float16_cm_1_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x64x128_sm80_float16_cm_1_0` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 128, false>::kMinBlocksPerSm) +fna2_64x128x128_sm80_float16_cm_1_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x128x128_sm80_float16_cm_1_0` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 65536, false>::kMinBlocksPerSm) +fna2_32x128x65536_sm80_float16_cm_1_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_32x128x65536_sm80_float16_cm_1_0` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 65536, false>::kMinBlocksPerSm) +fna2_64x128x65536_sm80_float16_cm_1_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x128x65536_sm80_float16_cm_1_0` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 65536, false>::kMinBlocksPerSm) +fna2_64x64x65536_sm80_float16_cm_1_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x64x65536_sm80_float16_cm_1_0` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +/////////////////////////////////////////////////////////////////// +// FNA-2D / float16 / SM80 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 32, false>::kMinBlocksPerSm) +fna2_32x128x32_sm80_float16_cm_1_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_32x128x32_sm80_float16_cm_1_1` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 32, false>::kMinBlocksPerSm) +fna2_64x64x32_sm80_float16_cm_1_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x64x32_sm80_float16_cm_1_1` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 32, false>::kMinBlocksPerSm) +fna2_64x128x32_sm80_float16_cm_1_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x128x32_sm80_float16_cm_1_1` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 64, false>::kMinBlocksPerSm) +fna2_32x128x64_sm80_float16_cm_1_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_32x128x64_sm80_float16_cm_1_1` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 64, false>::kMinBlocksPerSm) +fna2_64x64x64_sm80_float16_cm_1_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x64x64_sm80_float16_cm_1_1` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 64, false>::kMinBlocksPerSm) +fna2_64x128x64_sm80_float16_cm_1_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x128x64_sm80_float16_cm_1_1` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 128, false>::kMinBlocksPerSm) +fna2_32x128x128_sm80_float16_cm_1_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_32x128x128_sm80_float16_cm_1_1` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 128, false>::kMinBlocksPerSm) +fna2_64x64x128_sm80_float16_cm_1_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x64x128_sm80_float16_cm_1_1` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 128, false>::kMinBlocksPerSm) +fna2_64x128x128_sm80_float16_cm_1_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x128x128_sm80_float16_cm_1_1` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 65536, false>::kMinBlocksPerSm) +fna2_32x128x65536_sm80_float16_cm_1_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 32, 128, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_32x128x65536_sm80_float16_cm_1_1` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 65536, false>::kMinBlocksPerSm) +fna2_64x128x65536_sm80_float16_cm_1_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 128, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x128x65536_sm80_float16_cm_1_1` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 65536, false>::kMinBlocksPerSm) +fna2_64x64x65536_sm80_float16_cm_1_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::half_t, cutlass::arch::Sm80, true, 64, 64, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x64x65536_sm80_float16_cm_1_1` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +/////////////////////////////////////////////////////////////////// +// FNA-2D / bfloat16 / SM80 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 32, false>::kMinBlocksPerSm) +fna2_32x128x32_sm80_bfloat16_cm_0_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_32x128x32_sm80_bfloat16_cm_0_0` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 32, false>::kMinBlocksPerSm) +fna2_64x64x32_sm80_bfloat16_cm_0_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x64x32_sm80_bfloat16_cm_0_0` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 32, false>::kMinBlocksPerSm) +fna2_64x128x32_sm80_bfloat16_cm_0_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x128x32_sm80_bfloat16_cm_0_0` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 64, false>::kMinBlocksPerSm) +fna2_32x128x64_sm80_bfloat16_cm_0_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_32x128x64_sm80_bfloat16_cm_0_0` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 64, false>::kMinBlocksPerSm) +fna2_64x64x64_sm80_bfloat16_cm_0_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x64x64_sm80_bfloat16_cm_0_0` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 64, false>::kMinBlocksPerSm) +fna2_64x128x64_sm80_bfloat16_cm_0_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x128x64_sm80_bfloat16_cm_0_0` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 128, false>::kMinBlocksPerSm) +fna2_32x128x128_sm80_bfloat16_cm_0_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_32x128x128_sm80_bfloat16_cm_0_0` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 128, false>::kMinBlocksPerSm) +fna2_64x64x128_sm80_bfloat16_cm_0_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x64x128_sm80_bfloat16_cm_0_0` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 128, false>::kMinBlocksPerSm) +fna2_64x128x128_sm80_bfloat16_cm_0_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x128x128_sm80_bfloat16_cm_0_0` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 65536, false>::kMinBlocksPerSm) +fna2_32x128x65536_sm80_bfloat16_cm_0_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_32x128x65536_sm80_bfloat16_cm_0_0` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 65536, false>::kMinBlocksPerSm) +fna2_64x128x65536_sm80_bfloat16_cm_0_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x128x65536_sm80_bfloat16_cm_0_0` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 65536, false>::kMinBlocksPerSm) +fna2_64x64x65536_sm80_bfloat16_cm_0_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x64x65536_sm80_bfloat16_cm_0_0` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +/////////////////////////////////////////////////////////////////// +// FNA-2D / bfloat16 / SM80 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 32, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 32, true>::kMinBlocksPerSm) +fna2_32x128x32_sm80_bfloat16_cm_0_0_rpb(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 32, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 32, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_32x128x32_sm80_bfloat16_cm_0_0_rpb` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 32, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 32, true>::kMinBlocksPerSm) +fna2_64x64x32_sm80_bfloat16_cm_0_0_rpb(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 32, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 32, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x64x32_sm80_bfloat16_cm_0_0_rpb` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 32, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 32, true>::kMinBlocksPerSm) +fna2_64x128x32_sm80_bfloat16_cm_0_0_rpb(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 32, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 32, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x128x32_sm80_bfloat16_cm_0_0_rpb` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 64, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 64, true>::kMinBlocksPerSm) +fna2_32x128x64_sm80_bfloat16_cm_0_0_rpb(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 64, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 64, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_32x128x64_sm80_bfloat16_cm_0_0_rpb` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 64, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 64, true>::kMinBlocksPerSm) +fna2_64x64x64_sm80_bfloat16_cm_0_0_rpb(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 64, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 64, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x64x64_sm80_bfloat16_cm_0_0_rpb` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 64, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 64, true>::kMinBlocksPerSm) +fna2_64x128x64_sm80_bfloat16_cm_0_0_rpb(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 64, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 64, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x128x64_sm80_bfloat16_cm_0_0_rpb` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 128, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 128, true>::kMinBlocksPerSm) +fna2_32x128x128_sm80_bfloat16_cm_0_0_rpb(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 128, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 128, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_32x128x128_sm80_bfloat16_cm_0_0_rpb` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 128, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 128, true>::kMinBlocksPerSm) +fna2_64x64x128_sm80_bfloat16_cm_0_0_rpb(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 128, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 128, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x64x128_sm80_bfloat16_cm_0_0_rpb` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 128, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 128, true>::kMinBlocksPerSm) +fna2_64x128x128_sm80_bfloat16_cm_0_0_rpb(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 128, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 128, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x128x128_sm80_bfloat16_cm_0_0_rpb` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 65536, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 65536, true>::kMinBlocksPerSm) +fna2_32x128x65536_sm80_bfloat16_cm_0_0_rpb(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 65536, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 65536, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_32x128x65536_sm80_bfloat16_cm_0_0_rpb` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 65536, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 65536, true>::kMinBlocksPerSm) +fna2_64x128x65536_sm80_bfloat16_cm_0_0_rpb(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 65536, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 65536, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x128x65536_sm80_bfloat16_cm_0_0_rpb` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 65536, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 65536, true>::kMinBlocksPerSm) +fna2_64x64x65536_sm80_bfloat16_cm_0_0_rpb(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 65536, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 65536, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x64x65536_sm80_bfloat16_cm_0_0_rpb` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +/////////////////////////////////////////////////////////////////// +// FNA-2D / bfloat16 / SM80 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 32, false>::kMinBlocksPerSm) +fna2_32x128x32_sm80_bfloat16_cm_0_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_32x128x32_sm80_bfloat16_cm_0_1` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 32, false>::kMinBlocksPerSm) +fna2_64x64x32_sm80_bfloat16_cm_0_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x64x32_sm80_bfloat16_cm_0_1` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 32, false>::kMinBlocksPerSm) +fna2_64x128x32_sm80_bfloat16_cm_0_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x128x32_sm80_bfloat16_cm_0_1` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 64, false>::kMinBlocksPerSm) +fna2_32x128x64_sm80_bfloat16_cm_0_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_32x128x64_sm80_bfloat16_cm_0_1` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 64, false>::kMinBlocksPerSm) +fna2_64x64x64_sm80_bfloat16_cm_0_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x64x64_sm80_bfloat16_cm_0_1` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 64, false>::kMinBlocksPerSm) +fna2_64x128x64_sm80_bfloat16_cm_0_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x128x64_sm80_bfloat16_cm_0_1` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 128, false>::kMinBlocksPerSm) +fna2_32x128x128_sm80_bfloat16_cm_0_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_32x128x128_sm80_bfloat16_cm_0_1` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 128, false>::kMinBlocksPerSm) +fna2_64x64x128_sm80_bfloat16_cm_0_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x64x128_sm80_bfloat16_cm_0_1` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 128, false>::kMinBlocksPerSm) +fna2_64x128x128_sm80_bfloat16_cm_0_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x128x128_sm80_bfloat16_cm_0_1` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 65536, false>::kMinBlocksPerSm) +fna2_32x128x65536_sm80_bfloat16_cm_0_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_32x128x65536_sm80_bfloat16_cm_0_1` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 65536, false>::kMinBlocksPerSm) +fna2_64x128x65536_sm80_bfloat16_cm_0_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x128x65536_sm80_bfloat16_cm_0_1` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 65536, false>::kMinBlocksPerSm) +fna2_64x64x65536_sm80_bfloat16_cm_0_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x64x65536_sm80_bfloat16_cm_0_1` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} +} +} +} + diff --git a/csrc/autogen/src/cuda/fna/source_7.cu b/csrc/autogen/src/cuda/fna/source_7.cu new file mode 100644 index 0000000..68e5596 --- /dev/null +++ b/csrc/autogen/src/cuda/fna/source_7.cu @@ -0,0 +1,2573 @@ +#include +#include +#include +#include +#include +namespace natten { +namespace cuda { +namespace fna { + + + +/////////////////////////////////////////////////////////////////// +// FNA-2D / bfloat16 / SM80 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 32, false>::kMinBlocksPerSm) +fna2_32x128x32_sm80_bfloat16_cm_1_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_32x128x32_sm80_bfloat16_cm_1_0` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 32, false>::kMinBlocksPerSm) +fna2_64x64x32_sm80_bfloat16_cm_1_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x64x32_sm80_bfloat16_cm_1_0` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 32, false>::kMinBlocksPerSm) +fna2_64x128x32_sm80_bfloat16_cm_1_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x128x32_sm80_bfloat16_cm_1_0` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 64, false>::kMinBlocksPerSm) +fna2_32x128x64_sm80_bfloat16_cm_1_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_32x128x64_sm80_bfloat16_cm_1_0` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 64, false>::kMinBlocksPerSm) +fna2_64x64x64_sm80_bfloat16_cm_1_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x64x64_sm80_bfloat16_cm_1_0` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 64, false>::kMinBlocksPerSm) +fna2_64x128x64_sm80_bfloat16_cm_1_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x128x64_sm80_bfloat16_cm_1_0` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 128, false>::kMinBlocksPerSm) +fna2_32x128x128_sm80_bfloat16_cm_1_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_32x128x128_sm80_bfloat16_cm_1_0` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 128, false>::kMinBlocksPerSm) +fna2_64x64x128_sm80_bfloat16_cm_1_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x64x128_sm80_bfloat16_cm_1_0` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 128, false>::kMinBlocksPerSm) +fna2_64x128x128_sm80_bfloat16_cm_1_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x128x128_sm80_bfloat16_cm_1_0` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 65536, false>::kMinBlocksPerSm) +fna2_32x128x65536_sm80_bfloat16_cm_1_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_32x128x65536_sm80_bfloat16_cm_1_0` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 65536, false>::kMinBlocksPerSm) +fna2_64x128x65536_sm80_bfloat16_cm_1_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x128x65536_sm80_bfloat16_cm_1_0` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 65536, false>::kMinBlocksPerSm) +fna2_64x64x65536_sm80_bfloat16_cm_1_0(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x64x65536_sm80_bfloat16_cm_1_0` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +/////////////////////////////////////////////////////////////////// +// FNA-2D / bfloat16 / SM80 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 32, false>::kMinBlocksPerSm) +fna2_32x128x32_sm80_bfloat16_cm_1_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_32x128x32_sm80_bfloat16_cm_1_1` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 32, false>::kMinBlocksPerSm) +fna2_64x64x32_sm80_bfloat16_cm_1_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x64x32_sm80_bfloat16_cm_1_1` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 32, false>::kMinBlocksPerSm) +fna2_64x128x32_sm80_bfloat16_cm_1_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x128x32_sm80_bfloat16_cm_1_1` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 64, false>::kMinBlocksPerSm) +fna2_32x128x64_sm80_bfloat16_cm_1_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_32x128x64_sm80_bfloat16_cm_1_1` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 64, false>::kMinBlocksPerSm) +fna2_64x64x64_sm80_bfloat16_cm_1_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x64x64_sm80_bfloat16_cm_1_1` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 64, false>::kMinBlocksPerSm) +fna2_64x128x64_sm80_bfloat16_cm_1_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x128x64_sm80_bfloat16_cm_1_1` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 128, false>::kMinBlocksPerSm) +fna2_32x128x128_sm80_bfloat16_cm_1_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_32x128x128_sm80_bfloat16_cm_1_1` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 128, false>::kMinBlocksPerSm) +fna2_64x64x128_sm80_bfloat16_cm_1_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x64x128_sm80_bfloat16_cm_1_1` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 128, false>::kMinBlocksPerSm) +fna2_64x128x128_sm80_bfloat16_cm_1_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x128x128_sm80_bfloat16_cm_1_1` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 65536, false>::kMinBlocksPerSm) +fna2_32x128x65536_sm80_bfloat16_cm_1_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 32, 128, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_32x128x65536_sm80_bfloat16_cm_1_1` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 65536, false>::kMinBlocksPerSm) +fna2_64x128x65536_sm80_bfloat16_cm_1_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 128, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x128x65536_sm80_bfloat16_cm_1_1` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 65536, false>::kMinBlocksPerSm) +fna2_64x64x65536_sm80_bfloat16_cm_1_1(typename FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 800 +#if __CUDA_ARCH__ < 1000 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<2, CausalMask, cutlass::bfloat16_t, cutlass::arch::Sm80, true, 64, 64, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna2_64x64x65536_sm80_bfloat16_cm_1_1` was built for SM80, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +/////////////////////////////////////////////////////////////////// +// FNA-3D / float32 / SM50 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 32, false>::kMinBlocksPerSm) +fna3_32x128x32_sm50_float32_cm_0_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x32_sm50_float32_cm_0_0_0` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 32, false>::kMinBlocksPerSm) +fna3_64x64x32_sm50_float32_cm_0_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x32_sm50_float32_cm_0_0_0` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 32, false>::kMinBlocksPerSm) +fna3_64x128x32_sm50_float32_cm_0_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x32_sm50_float32_cm_0_0_0` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 64, false>::kMinBlocksPerSm) +fna3_32x128x64_sm50_float32_cm_0_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x64_sm50_float32_cm_0_0_0` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 64, false>::kMinBlocksPerSm) +fna3_64x64x64_sm50_float32_cm_0_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x64_sm50_float32_cm_0_0_0` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 64, false>::kMinBlocksPerSm) +fna3_64x128x64_sm50_float32_cm_0_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x64_sm50_float32_cm_0_0_0` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 128, false>::kMinBlocksPerSm) +fna3_32x128x128_sm50_float32_cm_0_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x128_sm50_float32_cm_0_0_0` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 128, false>::kMinBlocksPerSm) +fna3_64x64x128_sm50_float32_cm_0_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x128_sm50_float32_cm_0_0_0` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 128, false>::kMinBlocksPerSm) +fna3_64x128x128_sm50_float32_cm_0_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x128_sm50_float32_cm_0_0_0` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 65536, false>::kMinBlocksPerSm) +fna3_32x128x65536_sm50_float32_cm_0_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x65536_sm50_float32_cm_0_0_0` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 65536, false>::kMinBlocksPerSm) +fna3_64x128x65536_sm50_float32_cm_0_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x65536_sm50_float32_cm_0_0_0` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 65536, false>::kMinBlocksPerSm) +fna3_64x64x65536_sm50_float32_cm_0_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x65536_sm50_float32_cm_0_0_0` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +/////////////////////////////////////////////////////////////////// +// FNA-3D / float32 / SM50 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 32, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 32, true>::kMinBlocksPerSm) +fna3_32x128x32_sm50_float32_cm_0_0_0_rpb(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 32, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 32, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x32_sm50_float32_cm_0_0_0_rpb` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 32, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 32, true>::kMinBlocksPerSm) +fna3_64x64x32_sm50_float32_cm_0_0_0_rpb(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 32, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 32, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x32_sm50_float32_cm_0_0_0_rpb` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 32, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 32, true>::kMinBlocksPerSm) +fna3_64x128x32_sm50_float32_cm_0_0_0_rpb(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 32, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 32, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x32_sm50_float32_cm_0_0_0_rpb` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 64, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 64, true>::kMinBlocksPerSm) +fna3_32x128x64_sm50_float32_cm_0_0_0_rpb(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 64, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 64, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x64_sm50_float32_cm_0_0_0_rpb` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 64, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 64, true>::kMinBlocksPerSm) +fna3_64x64x64_sm50_float32_cm_0_0_0_rpb(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 64, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 64, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x64_sm50_float32_cm_0_0_0_rpb` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 64, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 64, true>::kMinBlocksPerSm) +fna3_64x128x64_sm50_float32_cm_0_0_0_rpb(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 64, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 64, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x64_sm50_float32_cm_0_0_0_rpb` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 128, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 128, true>::kMinBlocksPerSm) +fna3_32x128x128_sm50_float32_cm_0_0_0_rpb(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 128, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 128, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x128_sm50_float32_cm_0_0_0_rpb` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 128, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 128, true>::kMinBlocksPerSm) +fna3_64x64x128_sm50_float32_cm_0_0_0_rpb(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 128, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 128, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x128_sm50_float32_cm_0_0_0_rpb` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 128, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 128, true>::kMinBlocksPerSm) +fna3_64x128x128_sm50_float32_cm_0_0_0_rpb(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 128, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 128, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x128_sm50_float32_cm_0_0_0_rpb` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 65536, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 65536, true>::kMinBlocksPerSm) +fna3_32x128x65536_sm50_float32_cm_0_0_0_rpb(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 65536, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 65536, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x65536_sm50_float32_cm_0_0_0_rpb` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 65536, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 65536, true>::kMinBlocksPerSm) +fna3_64x128x65536_sm50_float32_cm_0_0_0_rpb(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 65536, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 65536, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x65536_sm50_float32_cm_0_0_0_rpb` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 65536, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 65536, true>::kMinBlocksPerSm) +fna3_64x64x65536_sm50_float32_cm_0_0_0_rpb(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 65536, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 65536, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x65536_sm50_float32_cm_0_0_0_rpb` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +/////////////////////////////////////////////////////////////////// +// FNA-3D / float32 / SM50 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 32, false>::kMinBlocksPerSm) +fna3_32x128x32_sm50_float32_cm_0_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x32_sm50_float32_cm_0_0_1` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 32, false>::kMinBlocksPerSm) +fna3_64x64x32_sm50_float32_cm_0_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x32_sm50_float32_cm_0_0_1` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 32, false>::kMinBlocksPerSm) +fna3_64x128x32_sm50_float32_cm_0_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x32_sm50_float32_cm_0_0_1` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 64, false>::kMinBlocksPerSm) +fna3_32x128x64_sm50_float32_cm_0_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x64_sm50_float32_cm_0_0_1` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 64, false>::kMinBlocksPerSm) +fna3_64x64x64_sm50_float32_cm_0_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x64_sm50_float32_cm_0_0_1` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 64, false>::kMinBlocksPerSm) +fna3_64x128x64_sm50_float32_cm_0_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x64_sm50_float32_cm_0_0_1` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 128, false>::kMinBlocksPerSm) +fna3_32x128x128_sm50_float32_cm_0_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x128_sm50_float32_cm_0_0_1` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 128, false>::kMinBlocksPerSm) +fna3_64x64x128_sm50_float32_cm_0_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x128_sm50_float32_cm_0_0_1` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 128, false>::kMinBlocksPerSm) +fna3_64x128x128_sm50_float32_cm_0_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x128_sm50_float32_cm_0_0_1` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 65536, false>::kMinBlocksPerSm) +fna3_32x128x65536_sm50_float32_cm_0_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x65536_sm50_float32_cm_0_0_1` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 65536, false>::kMinBlocksPerSm) +fna3_64x128x65536_sm50_float32_cm_0_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x65536_sm50_float32_cm_0_0_1` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 65536, false>::kMinBlocksPerSm) +fna3_64x64x65536_sm50_float32_cm_0_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x65536_sm50_float32_cm_0_0_1` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +/////////////////////////////////////////////////////////////////// +// FNA-3D / float32 / SM50 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 32, false>::kMinBlocksPerSm) +fna3_32x128x32_sm50_float32_cm_0_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x32_sm50_float32_cm_0_1_0` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 32, false>::kMinBlocksPerSm) +fna3_64x64x32_sm50_float32_cm_0_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x32_sm50_float32_cm_0_1_0` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 32, false>::kMinBlocksPerSm) +fna3_64x128x32_sm50_float32_cm_0_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x32_sm50_float32_cm_0_1_0` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 64, false>::kMinBlocksPerSm) +fna3_32x128x64_sm50_float32_cm_0_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x64_sm50_float32_cm_0_1_0` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 64, false>::kMinBlocksPerSm) +fna3_64x64x64_sm50_float32_cm_0_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x64_sm50_float32_cm_0_1_0` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 64, false>::kMinBlocksPerSm) +fna3_64x128x64_sm50_float32_cm_0_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x64_sm50_float32_cm_0_1_0` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 128, false>::kMinBlocksPerSm) +fna3_32x128x128_sm50_float32_cm_0_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x128_sm50_float32_cm_0_1_0` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 128, false>::kMinBlocksPerSm) +fna3_64x64x128_sm50_float32_cm_0_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x128_sm50_float32_cm_0_1_0` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 128, false>::kMinBlocksPerSm) +fna3_64x128x128_sm50_float32_cm_0_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x128_sm50_float32_cm_0_1_0` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 65536, false>::kMinBlocksPerSm) +fna3_32x128x65536_sm50_float32_cm_0_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x65536_sm50_float32_cm_0_1_0` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 65536, false>::kMinBlocksPerSm) +fna3_64x128x65536_sm50_float32_cm_0_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x65536_sm50_float32_cm_0_1_0` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 65536, false>::kMinBlocksPerSm) +fna3_64x64x65536_sm50_float32_cm_0_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x65536_sm50_float32_cm_0_1_0` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +/////////////////////////////////////////////////////////////////// +// FNA-3D / float32 / SM50 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 32, false>::kMinBlocksPerSm) +fna3_32x128x32_sm50_float32_cm_0_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x32_sm50_float32_cm_0_1_1` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 32, false>::kMinBlocksPerSm) +fna3_64x64x32_sm50_float32_cm_0_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x32_sm50_float32_cm_0_1_1` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 32, false>::kMinBlocksPerSm) +fna3_64x128x32_sm50_float32_cm_0_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x32_sm50_float32_cm_0_1_1` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 64, false>::kMinBlocksPerSm) +fna3_32x128x64_sm50_float32_cm_0_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x64_sm50_float32_cm_0_1_1` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 64, false>::kMinBlocksPerSm) +fna3_64x64x64_sm50_float32_cm_0_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x64_sm50_float32_cm_0_1_1` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 64, false>::kMinBlocksPerSm) +fna3_64x128x64_sm50_float32_cm_0_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x64_sm50_float32_cm_0_1_1` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 128, false>::kMinBlocksPerSm) +fna3_32x128x128_sm50_float32_cm_0_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x128_sm50_float32_cm_0_1_1` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 128, false>::kMinBlocksPerSm) +fna3_64x64x128_sm50_float32_cm_0_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x128_sm50_float32_cm_0_1_1` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 128, false>::kMinBlocksPerSm) +fna3_64x128x128_sm50_float32_cm_0_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x128_sm50_float32_cm_0_1_1` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 65536, false>::kMinBlocksPerSm) +fna3_32x128x65536_sm50_float32_cm_0_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x65536_sm50_float32_cm_0_1_1` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 65536, false>::kMinBlocksPerSm) +fna3_64x128x65536_sm50_float32_cm_0_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x65536_sm50_float32_cm_0_1_1` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 65536, false>::kMinBlocksPerSm) +fna3_64x64x65536_sm50_float32_cm_0_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x65536_sm50_float32_cm_0_1_1` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +/////////////////////////////////////////////////////////////////// +// FNA-3D / float32 / SM50 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 32, false>::kMinBlocksPerSm) +fna3_32x128x32_sm50_float32_cm_1_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x32_sm50_float32_cm_1_0_0` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 32, false>::kMinBlocksPerSm) +fna3_64x64x32_sm50_float32_cm_1_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x32_sm50_float32_cm_1_0_0` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 32, false>::kMinBlocksPerSm) +fna3_64x128x32_sm50_float32_cm_1_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x32_sm50_float32_cm_1_0_0` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 64, false>::kMinBlocksPerSm) +fna3_32x128x64_sm50_float32_cm_1_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x64_sm50_float32_cm_1_0_0` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 64, false>::kMinBlocksPerSm) +fna3_64x64x64_sm50_float32_cm_1_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x64_sm50_float32_cm_1_0_0` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 64, false>::kMinBlocksPerSm) +fna3_64x128x64_sm50_float32_cm_1_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x64_sm50_float32_cm_1_0_0` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 128, false>::kMinBlocksPerSm) +fna3_32x128x128_sm50_float32_cm_1_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x128_sm50_float32_cm_1_0_0` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 128, false>::kMinBlocksPerSm) +fna3_64x64x128_sm50_float32_cm_1_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x128_sm50_float32_cm_1_0_0` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 128, false>::kMinBlocksPerSm) +fna3_64x128x128_sm50_float32_cm_1_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x128_sm50_float32_cm_1_0_0` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 65536, false>::kMinBlocksPerSm) +fna3_32x128x65536_sm50_float32_cm_1_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x65536_sm50_float32_cm_1_0_0` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 65536, false>::kMinBlocksPerSm) +fna3_64x128x65536_sm50_float32_cm_1_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x65536_sm50_float32_cm_1_0_0` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 65536, false>::kMinBlocksPerSm) +fna3_64x64x65536_sm50_float32_cm_1_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x65536_sm50_float32_cm_1_0_0` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +/////////////////////////////////////////////////////////////////// +// FNA-3D / float32 / SM50 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 32, false>::kMinBlocksPerSm) +fna3_32x128x32_sm50_float32_cm_1_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x32_sm50_float32_cm_1_0_1` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 32, false>::kMinBlocksPerSm) +fna3_64x64x32_sm50_float32_cm_1_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x32_sm50_float32_cm_1_0_1` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 32, false>::kMinBlocksPerSm) +fna3_64x128x32_sm50_float32_cm_1_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x32_sm50_float32_cm_1_0_1` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 64, false>::kMinBlocksPerSm) +fna3_32x128x64_sm50_float32_cm_1_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x64_sm50_float32_cm_1_0_1` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 64, false>::kMinBlocksPerSm) +fna3_64x64x64_sm50_float32_cm_1_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x64_sm50_float32_cm_1_0_1` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 64, false>::kMinBlocksPerSm) +fna3_64x128x64_sm50_float32_cm_1_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x64_sm50_float32_cm_1_0_1` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 128, false>::kMinBlocksPerSm) +fna3_32x128x128_sm50_float32_cm_1_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x128_sm50_float32_cm_1_0_1` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 128, false>::kMinBlocksPerSm) +fna3_64x64x128_sm50_float32_cm_1_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x128_sm50_float32_cm_1_0_1` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 128, false>::kMinBlocksPerSm) +fna3_64x128x128_sm50_float32_cm_1_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x128_sm50_float32_cm_1_0_1` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 65536, false>::kMinBlocksPerSm) +fna3_32x128x65536_sm50_float32_cm_1_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x65536_sm50_float32_cm_1_0_1` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 65536, false>::kMinBlocksPerSm) +fna3_64x128x65536_sm50_float32_cm_1_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x65536_sm50_float32_cm_1_0_1` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 65536, false>::kMinBlocksPerSm) +fna3_64x64x65536_sm50_float32_cm_1_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x65536_sm50_float32_cm_1_0_1` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +/////////////////////////////////////////////////////////////////// +// FNA-3D / float32 / SM50 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 32, false>::kMinBlocksPerSm) +fna3_32x128x32_sm50_float32_cm_1_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x32_sm50_float32_cm_1_1_0` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 32, false>::kMinBlocksPerSm) +fna3_64x64x32_sm50_float32_cm_1_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x32_sm50_float32_cm_1_1_0` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 32, false>::kMinBlocksPerSm) +fna3_64x128x32_sm50_float32_cm_1_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x32_sm50_float32_cm_1_1_0` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 64, false>::kMinBlocksPerSm) +fna3_32x128x64_sm50_float32_cm_1_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x64_sm50_float32_cm_1_1_0` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 64, false>::kMinBlocksPerSm) +fna3_64x64x64_sm50_float32_cm_1_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x64_sm50_float32_cm_1_1_0` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 64, false>::kMinBlocksPerSm) +fna3_64x128x64_sm50_float32_cm_1_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x64_sm50_float32_cm_1_1_0` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 128, false>::kMinBlocksPerSm) +fna3_32x128x128_sm50_float32_cm_1_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x128_sm50_float32_cm_1_1_0` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 128, false>::kMinBlocksPerSm) +fna3_64x64x128_sm50_float32_cm_1_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x128_sm50_float32_cm_1_1_0` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 128, false>::kMinBlocksPerSm) +fna3_64x128x128_sm50_float32_cm_1_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x128_sm50_float32_cm_1_1_0` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 65536, false>::kMinBlocksPerSm) +fna3_32x128x65536_sm50_float32_cm_1_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x65536_sm50_float32_cm_1_1_0` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 65536, false>::kMinBlocksPerSm) +fna3_64x128x65536_sm50_float32_cm_1_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x65536_sm50_float32_cm_1_1_0` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 65536, false>::kMinBlocksPerSm) +fna3_64x64x65536_sm50_float32_cm_1_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x65536_sm50_float32_cm_1_1_0` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} +} +} +} + diff --git a/csrc/autogen/src/cuda/fna/source_8.cu b/csrc/autogen/src/cuda/fna/source_8.cu new file mode 100644 index 0000000..39fa083 --- /dev/null +++ b/csrc/autogen/src/cuda/fna/source_8.cu @@ -0,0 +1,2573 @@ +#include +#include +#include +#include +#include +namespace natten { +namespace cuda { +namespace fna { + + + +/////////////////////////////////////////////////////////////////// +// FNA-3D / float32 / SM50 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 32, false>::kMinBlocksPerSm) +fna3_32x128x32_sm50_float32_cm_1_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x32_sm50_float32_cm_1_1_1` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 32, false>::kMinBlocksPerSm) +fna3_64x64x32_sm50_float32_cm_1_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x32_sm50_float32_cm_1_1_1` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 32, false>::kMinBlocksPerSm) +fna3_64x128x32_sm50_float32_cm_1_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x32_sm50_float32_cm_1_1_1` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 64, false>::kMinBlocksPerSm) +fna3_32x128x64_sm50_float32_cm_1_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x64_sm50_float32_cm_1_1_1` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 64, false>::kMinBlocksPerSm) +fna3_64x64x64_sm50_float32_cm_1_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x64_sm50_float32_cm_1_1_1` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 64, false>::kMinBlocksPerSm) +fna3_64x128x64_sm50_float32_cm_1_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x64_sm50_float32_cm_1_1_1` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 128, false>::kMinBlocksPerSm) +fna3_32x128x128_sm50_float32_cm_1_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x128_sm50_float32_cm_1_1_1` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 128, false>::kMinBlocksPerSm) +fna3_64x64x128_sm50_float32_cm_1_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x128_sm50_float32_cm_1_1_1` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 128, false>::kMinBlocksPerSm) +fna3_64x128x128_sm50_float32_cm_1_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x128_sm50_float32_cm_1_1_1` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 65536, false>::kMinBlocksPerSm) +fna3_32x128x65536_sm50_float32_cm_1_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 32, 128, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x65536_sm50_float32_cm_1_1_1` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 65536, false>::kMinBlocksPerSm) +fna3_64x128x65536_sm50_float32_cm_1_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 128, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x65536_sm50_float32_cm_1_1_1` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 65536, false>::kMinBlocksPerSm) +fna3_64x64x65536_sm50_float32_cm_1_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm50, true, 64, 64, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x65536_sm50_float32_cm_1_1_1` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +/////////////////////////////////////////////////////////////////// +// FNA-3D / float16 / SM50 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 32, false>::kMinBlocksPerSm) +fna3_32x128x32_sm50_float16_cm_0_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x32_sm50_float16_cm_0_0_0` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 32, false>::kMinBlocksPerSm) +fna3_64x64x32_sm50_float16_cm_0_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x32_sm50_float16_cm_0_0_0` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 32, false>::kMinBlocksPerSm) +fna3_64x128x32_sm50_float16_cm_0_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x32_sm50_float16_cm_0_0_0` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 64, false>::kMinBlocksPerSm) +fna3_32x128x64_sm50_float16_cm_0_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x64_sm50_float16_cm_0_0_0` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 64, false>::kMinBlocksPerSm) +fna3_64x64x64_sm50_float16_cm_0_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x64_sm50_float16_cm_0_0_0` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 64, false>::kMinBlocksPerSm) +fna3_64x128x64_sm50_float16_cm_0_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x64_sm50_float16_cm_0_0_0` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 128, false>::kMinBlocksPerSm) +fna3_32x128x128_sm50_float16_cm_0_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x128_sm50_float16_cm_0_0_0` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 128, false>::kMinBlocksPerSm) +fna3_64x64x128_sm50_float16_cm_0_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x128_sm50_float16_cm_0_0_0` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 128, false>::kMinBlocksPerSm) +fna3_64x128x128_sm50_float16_cm_0_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x128_sm50_float16_cm_0_0_0` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 65536, false>::kMinBlocksPerSm) +fna3_32x128x65536_sm50_float16_cm_0_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x65536_sm50_float16_cm_0_0_0` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 65536, false>::kMinBlocksPerSm) +fna3_64x128x65536_sm50_float16_cm_0_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x65536_sm50_float16_cm_0_0_0` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 65536, false>::kMinBlocksPerSm) +fna3_64x64x65536_sm50_float16_cm_0_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x65536_sm50_float16_cm_0_0_0` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +/////////////////////////////////////////////////////////////////// +// FNA-3D / float16 / SM50 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 32, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 32, true>::kMinBlocksPerSm) +fna3_32x128x32_sm50_float16_cm_0_0_0_rpb(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 32, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 32, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x32_sm50_float16_cm_0_0_0_rpb` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 32, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 32, true>::kMinBlocksPerSm) +fna3_64x64x32_sm50_float16_cm_0_0_0_rpb(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 32, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 32, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x32_sm50_float16_cm_0_0_0_rpb` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 32, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 32, true>::kMinBlocksPerSm) +fna3_64x128x32_sm50_float16_cm_0_0_0_rpb(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 32, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 32, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x32_sm50_float16_cm_0_0_0_rpb` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 64, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 64, true>::kMinBlocksPerSm) +fna3_32x128x64_sm50_float16_cm_0_0_0_rpb(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 64, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 64, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x64_sm50_float16_cm_0_0_0_rpb` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 64, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 64, true>::kMinBlocksPerSm) +fna3_64x64x64_sm50_float16_cm_0_0_0_rpb(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 64, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 64, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x64_sm50_float16_cm_0_0_0_rpb` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 64, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 64, true>::kMinBlocksPerSm) +fna3_64x128x64_sm50_float16_cm_0_0_0_rpb(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 64, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 64, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x64_sm50_float16_cm_0_0_0_rpb` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 128, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 128, true>::kMinBlocksPerSm) +fna3_32x128x128_sm50_float16_cm_0_0_0_rpb(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 128, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 128, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x128_sm50_float16_cm_0_0_0_rpb` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 128, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 128, true>::kMinBlocksPerSm) +fna3_64x64x128_sm50_float16_cm_0_0_0_rpb(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 128, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 128, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x128_sm50_float16_cm_0_0_0_rpb` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 128, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 128, true>::kMinBlocksPerSm) +fna3_64x128x128_sm50_float16_cm_0_0_0_rpb(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 128, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 128, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x128_sm50_float16_cm_0_0_0_rpb` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 65536, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 65536, true>::kMinBlocksPerSm) +fna3_32x128x65536_sm50_float16_cm_0_0_0_rpb(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 65536, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 65536, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x65536_sm50_float16_cm_0_0_0_rpb` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 65536, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 65536, true>::kMinBlocksPerSm) +fna3_64x128x65536_sm50_float16_cm_0_0_0_rpb(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 65536, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 65536, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x65536_sm50_float16_cm_0_0_0_rpb` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 65536, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 65536, true>::kMinBlocksPerSm) +fna3_64x64x65536_sm50_float16_cm_0_0_0_rpb(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 65536, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 65536, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x65536_sm50_float16_cm_0_0_0_rpb` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +/////////////////////////////////////////////////////////////////// +// FNA-3D / float16 / SM50 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 32, false>::kMinBlocksPerSm) +fna3_32x128x32_sm50_float16_cm_0_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x32_sm50_float16_cm_0_0_1` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 32, false>::kMinBlocksPerSm) +fna3_64x64x32_sm50_float16_cm_0_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x32_sm50_float16_cm_0_0_1` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 32, false>::kMinBlocksPerSm) +fna3_64x128x32_sm50_float16_cm_0_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x32_sm50_float16_cm_0_0_1` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 64, false>::kMinBlocksPerSm) +fna3_32x128x64_sm50_float16_cm_0_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x64_sm50_float16_cm_0_0_1` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 64, false>::kMinBlocksPerSm) +fna3_64x64x64_sm50_float16_cm_0_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x64_sm50_float16_cm_0_0_1` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 64, false>::kMinBlocksPerSm) +fna3_64x128x64_sm50_float16_cm_0_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x64_sm50_float16_cm_0_0_1` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 128, false>::kMinBlocksPerSm) +fna3_32x128x128_sm50_float16_cm_0_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x128_sm50_float16_cm_0_0_1` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 128, false>::kMinBlocksPerSm) +fna3_64x64x128_sm50_float16_cm_0_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x128_sm50_float16_cm_0_0_1` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 128, false>::kMinBlocksPerSm) +fna3_64x128x128_sm50_float16_cm_0_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x128_sm50_float16_cm_0_0_1` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 65536, false>::kMinBlocksPerSm) +fna3_32x128x65536_sm50_float16_cm_0_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x65536_sm50_float16_cm_0_0_1` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 65536, false>::kMinBlocksPerSm) +fna3_64x128x65536_sm50_float16_cm_0_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x65536_sm50_float16_cm_0_0_1` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 65536, false>::kMinBlocksPerSm) +fna3_64x64x65536_sm50_float16_cm_0_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x65536_sm50_float16_cm_0_0_1` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +/////////////////////////////////////////////////////////////////// +// FNA-3D / float16 / SM50 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 32, false>::kMinBlocksPerSm) +fna3_32x128x32_sm50_float16_cm_0_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x32_sm50_float16_cm_0_1_0` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 32, false>::kMinBlocksPerSm) +fna3_64x64x32_sm50_float16_cm_0_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x32_sm50_float16_cm_0_1_0` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 32, false>::kMinBlocksPerSm) +fna3_64x128x32_sm50_float16_cm_0_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x32_sm50_float16_cm_0_1_0` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 64, false>::kMinBlocksPerSm) +fna3_32x128x64_sm50_float16_cm_0_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x64_sm50_float16_cm_0_1_0` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 64, false>::kMinBlocksPerSm) +fna3_64x64x64_sm50_float16_cm_0_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x64_sm50_float16_cm_0_1_0` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 64, false>::kMinBlocksPerSm) +fna3_64x128x64_sm50_float16_cm_0_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x64_sm50_float16_cm_0_1_0` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 128, false>::kMinBlocksPerSm) +fna3_32x128x128_sm50_float16_cm_0_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x128_sm50_float16_cm_0_1_0` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 128, false>::kMinBlocksPerSm) +fna3_64x64x128_sm50_float16_cm_0_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x128_sm50_float16_cm_0_1_0` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 128, false>::kMinBlocksPerSm) +fna3_64x128x128_sm50_float16_cm_0_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x128_sm50_float16_cm_0_1_0` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 65536, false>::kMinBlocksPerSm) +fna3_32x128x65536_sm50_float16_cm_0_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x65536_sm50_float16_cm_0_1_0` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 65536, false>::kMinBlocksPerSm) +fna3_64x128x65536_sm50_float16_cm_0_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x65536_sm50_float16_cm_0_1_0` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 65536, false>::kMinBlocksPerSm) +fna3_64x64x65536_sm50_float16_cm_0_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x65536_sm50_float16_cm_0_1_0` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +/////////////////////////////////////////////////////////////////// +// FNA-3D / float16 / SM50 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 32, false>::kMinBlocksPerSm) +fna3_32x128x32_sm50_float16_cm_0_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x32_sm50_float16_cm_0_1_1` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 32, false>::kMinBlocksPerSm) +fna3_64x64x32_sm50_float16_cm_0_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x32_sm50_float16_cm_0_1_1` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 32, false>::kMinBlocksPerSm) +fna3_64x128x32_sm50_float16_cm_0_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x32_sm50_float16_cm_0_1_1` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 64, false>::kMinBlocksPerSm) +fna3_32x128x64_sm50_float16_cm_0_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x64_sm50_float16_cm_0_1_1` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 64, false>::kMinBlocksPerSm) +fna3_64x64x64_sm50_float16_cm_0_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x64_sm50_float16_cm_0_1_1` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 64, false>::kMinBlocksPerSm) +fna3_64x128x64_sm50_float16_cm_0_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x64_sm50_float16_cm_0_1_1` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 128, false>::kMinBlocksPerSm) +fna3_32x128x128_sm50_float16_cm_0_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x128_sm50_float16_cm_0_1_1` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 128, false>::kMinBlocksPerSm) +fna3_64x64x128_sm50_float16_cm_0_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x128_sm50_float16_cm_0_1_1` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 128, false>::kMinBlocksPerSm) +fna3_64x128x128_sm50_float16_cm_0_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x128_sm50_float16_cm_0_1_1` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 65536, false>::kMinBlocksPerSm) +fna3_32x128x65536_sm50_float16_cm_0_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x65536_sm50_float16_cm_0_1_1` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 65536, false>::kMinBlocksPerSm) +fna3_64x128x65536_sm50_float16_cm_0_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x65536_sm50_float16_cm_0_1_1` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 65536, false>::kMinBlocksPerSm) +fna3_64x64x65536_sm50_float16_cm_0_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x65536_sm50_float16_cm_0_1_1` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +/////////////////////////////////////////////////////////////////// +// FNA-3D / float16 / SM50 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 32, false>::kMinBlocksPerSm) +fna3_32x128x32_sm50_float16_cm_1_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x32_sm50_float16_cm_1_0_0` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 32, false>::kMinBlocksPerSm) +fna3_64x64x32_sm50_float16_cm_1_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x32_sm50_float16_cm_1_0_0` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 32, false>::kMinBlocksPerSm) +fna3_64x128x32_sm50_float16_cm_1_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x32_sm50_float16_cm_1_0_0` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 64, false>::kMinBlocksPerSm) +fna3_32x128x64_sm50_float16_cm_1_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x64_sm50_float16_cm_1_0_0` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 64, false>::kMinBlocksPerSm) +fna3_64x64x64_sm50_float16_cm_1_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x64_sm50_float16_cm_1_0_0` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 64, false>::kMinBlocksPerSm) +fna3_64x128x64_sm50_float16_cm_1_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x64_sm50_float16_cm_1_0_0` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 128, false>::kMinBlocksPerSm) +fna3_32x128x128_sm50_float16_cm_1_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x128_sm50_float16_cm_1_0_0` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 128, false>::kMinBlocksPerSm) +fna3_64x64x128_sm50_float16_cm_1_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x128_sm50_float16_cm_1_0_0` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 128, false>::kMinBlocksPerSm) +fna3_64x128x128_sm50_float16_cm_1_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x128_sm50_float16_cm_1_0_0` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 65536, false>::kMinBlocksPerSm) +fna3_32x128x65536_sm50_float16_cm_1_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x65536_sm50_float16_cm_1_0_0` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 65536, false>::kMinBlocksPerSm) +fna3_64x128x65536_sm50_float16_cm_1_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x65536_sm50_float16_cm_1_0_0` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 65536, false>::kMinBlocksPerSm) +fna3_64x64x65536_sm50_float16_cm_1_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x65536_sm50_float16_cm_1_0_0` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +/////////////////////////////////////////////////////////////////// +// FNA-3D / float16 / SM50 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 32, false>::kMinBlocksPerSm) +fna3_32x128x32_sm50_float16_cm_1_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x32_sm50_float16_cm_1_0_1` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 32, false>::kMinBlocksPerSm) +fna3_64x64x32_sm50_float16_cm_1_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x32_sm50_float16_cm_1_0_1` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 32, false>::kMinBlocksPerSm) +fna3_64x128x32_sm50_float16_cm_1_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x32_sm50_float16_cm_1_0_1` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 64, false>::kMinBlocksPerSm) +fna3_32x128x64_sm50_float16_cm_1_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x64_sm50_float16_cm_1_0_1` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 64, false>::kMinBlocksPerSm) +fna3_64x64x64_sm50_float16_cm_1_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x64_sm50_float16_cm_1_0_1` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 64, false>::kMinBlocksPerSm) +fna3_64x128x64_sm50_float16_cm_1_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x64_sm50_float16_cm_1_0_1` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 128, false>::kMinBlocksPerSm) +fna3_32x128x128_sm50_float16_cm_1_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x128_sm50_float16_cm_1_0_1` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 128, false>::kMinBlocksPerSm) +fna3_64x64x128_sm50_float16_cm_1_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x128_sm50_float16_cm_1_0_1` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 128, false>::kMinBlocksPerSm) +fna3_64x128x128_sm50_float16_cm_1_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x128_sm50_float16_cm_1_0_1` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 65536, false>::kMinBlocksPerSm) +fna3_32x128x65536_sm50_float16_cm_1_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x65536_sm50_float16_cm_1_0_1` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 65536, false>::kMinBlocksPerSm) +fna3_64x128x65536_sm50_float16_cm_1_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x65536_sm50_float16_cm_1_0_1` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 65536, false>::kMinBlocksPerSm) +fna3_64x64x65536_sm50_float16_cm_1_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x65536_sm50_float16_cm_1_0_1` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +/////////////////////////////////////////////////////////////////// +// FNA-3D / float16 / SM50 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 32, false>::kMinBlocksPerSm) +fna3_32x128x32_sm50_float16_cm_1_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x32_sm50_float16_cm_1_1_0` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 32, false>::kMinBlocksPerSm) +fna3_64x64x32_sm50_float16_cm_1_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x32_sm50_float16_cm_1_1_0` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 32, false>::kMinBlocksPerSm) +fna3_64x128x32_sm50_float16_cm_1_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x32_sm50_float16_cm_1_1_0` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 64, false>::kMinBlocksPerSm) +fna3_32x128x64_sm50_float16_cm_1_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x64_sm50_float16_cm_1_1_0` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 64, false>::kMinBlocksPerSm) +fna3_64x64x64_sm50_float16_cm_1_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x64_sm50_float16_cm_1_1_0` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 64, false>::kMinBlocksPerSm) +fna3_64x128x64_sm50_float16_cm_1_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x64_sm50_float16_cm_1_1_0` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 128, false>::kMinBlocksPerSm) +fna3_32x128x128_sm50_float16_cm_1_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x128_sm50_float16_cm_1_1_0` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 128, false>::kMinBlocksPerSm) +fna3_64x64x128_sm50_float16_cm_1_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x128_sm50_float16_cm_1_1_0` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 128, false>::kMinBlocksPerSm) +fna3_64x128x128_sm50_float16_cm_1_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x128_sm50_float16_cm_1_1_0` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 65536, false>::kMinBlocksPerSm) +fna3_32x128x65536_sm50_float16_cm_1_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x65536_sm50_float16_cm_1_1_0` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 65536, false>::kMinBlocksPerSm) +fna3_64x128x65536_sm50_float16_cm_1_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x65536_sm50_float16_cm_1_1_0` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 65536, false>::kMinBlocksPerSm) +fna3_64x64x65536_sm50_float16_cm_1_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x65536_sm50_float16_cm_1_1_0` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +/////////////////////////////////////////////////////////////////// +// FNA-3D / float16 / SM50 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 32, false>::kMinBlocksPerSm) +fna3_32x128x32_sm50_float16_cm_1_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x32_sm50_float16_cm_1_1_1` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 32, false>::kMinBlocksPerSm) +fna3_64x64x32_sm50_float16_cm_1_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x32_sm50_float16_cm_1_1_1` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 32, false>::kMinBlocksPerSm) +fna3_64x128x32_sm50_float16_cm_1_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x32_sm50_float16_cm_1_1_1` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 64, false>::kMinBlocksPerSm) +fna3_32x128x64_sm50_float16_cm_1_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x64_sm50_float16_cm_1_1_1` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 64, false>::kMinBlocksPerSm) +fna3_64x64x64_sm50_float16_cm_1_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x64_sm50_float16_cm_1_1_1` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 64, false>::kMinBlocksPerSm) +fna3_64x128x64_sm50_float16_cm_1_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x64_sm50_float16_cm_1_1_1` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 128, false>::kMinBlocksPerSm) +fna3_32x128x128_sm50_float16_cm_1_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x128_sm50_float16_cm_1_1_1` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 128, false>::kMinBlocksPerSm) +fna3_64x64x128_sm50_float16_cm_1_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x128_sm50_float16_cm_1_1_1` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 128, false>::kMinBlocksPerSm) +fna3_64x128x128_sm50_float16_cm_1_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x128_sm50_float16_cm_1_1_1` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 65536, false>::kMinBlocksPerSm) +fna3_32x128x65536_sm50_float16_cm_1_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 32, 128, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x65536_sm50_float16_cm_1_1_1` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 65536, false>::kMinBlocksPerSm) +fna3_64x128x65536_sm50_float16_cm_1_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 128, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x65536_sm50_float16_cm_1_1_1` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 65536, false>::kMinBlocksPerSm) +fna3_64x64x65536_sm50_float16_cm_1_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 500 +#if __CUDA_ARCH__ < 700 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm50, true, 64, 64, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x65536_sm50_float16_cm_1_1_1` was built for SM50, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} +} +} +} + diff --git a/csrc/autogen/src/cuda/fna/source_9.cu b/csrc/autogen/src/cuda/fna/source_9.cu new file mode 100644 index 0000000..84107fc --- /dev/null +++ b/csrc/autogen/src/cuda/fna/source_9.cu @@ -0,0 +1,2573 @@ +#include +#include +#include +#include +#include +namespace natten { +namespace cuda { +namespace fna { + + + +/////////////////////////////////////////////////////////////////// +// FNA-3D / float32 / SM70 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 32, false>::kMinBlocksPerSm) +fna3_32x128x32_sm70_float32_cm_0_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x32_sm70_float32_cm_0_0_0` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 32, false>::kMinBlocksPerSm) +fna3_64x64x32_sm70_float32_cm_0_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x32_sm70_float32_cm_0_0_0` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 32, false>::kMinBlocksPerSm) +fna3_64x128x32_sm70_float32_cm_0_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x32_sm70_float32_cm_0_0_0` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 64, false>::kMinBlocksPerSm) +fna3_32x128x64_sm70_float32_cm_0_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x64_sm70_float32_cm_0_0_0` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 64, false>::kMinBlocksPerSm) +fna3_64x64x64_sm70_float32_cm_0_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x64_sm70_float32_cm_0_0_0` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 64, false>::kMinBlocksPerSm) +fna3_64x128x64_sm70_float32_cm_0_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x64_sm70_float32_cm_0_0_0` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 128, false>::kMinBlocksPerSm) +fna3_32x128x128_sm70_float32_cm_0_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x128_sm70_float32_cm_0_0_0` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 128, false>::kMinBlocksPerSm) +fna3_64x64x128_sm70_float32_cm_0_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x128_sm70_float32_cm_0_0_0` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 128, false>::kMinBlocksPerSm) +fna3_64x128x128_sm70_float32_cm_0_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x128_sm70_float32_cm_0_0_0` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 65536, false>::kMinBlocksPerSm) +fna3_32x128x65536_sm70_float32_cm_0_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x65536_sm70_float32_cm_0_0_0` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 65536, false>::kMinBlocksPerSm) +fna3_64x128x65536_sm70_float32_cm_0_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x65536_sm70_float32_cm_0_0_0` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 65536, false>::kMinBlocksPerSm) +fna3_64x64x65536_sm70_float32_cm_0_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x65536_sm70_float32_cm_0_0_0` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +/////////////////////////////////////////////////////////////////// +// FNA-3D / float32 / SM70 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 32, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 32, true>::kMinBlocksPerSm) +fna3_32x128x32_sm70_float32_cm_0_0_0_rpb(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 32, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 32, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x32_sm70_float32_cm_0_0_0_rpb` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 32, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 32, true>::kMinBlocksPerSm) +fna3_64x64x32_sm70_float32_cm_0_0_0_rpb(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 32, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 32, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x32_sm70_float32_cm_0_0_0_rpb` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 32, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 32, true>::kMinBlocksPerSm) +fna3_64x128x32_sm70_float32_cm_0_0_0_rpb(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 32, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 32, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x32_sm70_float32_cm_0_0_0_rpb` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 64, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 64, true>::kMinBlocksPerSm) +fna3_32x128x64_sm70_float32_cm_0_0_0_rpb(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 64, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 64, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x64_sm70_float32_cm_0_0_0_rpb` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 64, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 64, true>::kMinBlocksPerSm) +fna3_64x64x64_sm70_float32_cm_0_0_0_rpb(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 64, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 64, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x64_sm70_float32_cm_0_0_0_rpb` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 64, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 64, true>::kMinBlocksPerSm) +fna3_64x128x64_sm70_float32_cm_0_0_0_rpb(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 64, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 64, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x64_sm70_float32_cm_0_0_0_rpb` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 128, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 128, true>::kMinBlocksPerSm) +fna3_32x128x128_sm70_float32_cm_0_0_0_rpb(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 128, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 128, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x128_sm70_float32_cm_0_0_0_rpb` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 128, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 128, true>::kMinBlocksPerSm) +fna3_64x64x128_sm70_float32_cm_0_0_0_rpb(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 128, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 128, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x128_sm70_float32_cm_0_0_0_rpb` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 128, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 128, true>::kMinBlocksPerSm) +fna3_64x128x128_sm70_float32_cm_0_0_0_rpb(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 128, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 128, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x128_sm70_float32_cm_0_0_0_rpb` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 65536, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 65536, true>::kMinBlocksPerSm) +fna3_32x128x65536_sm70_float32_cm_0_0_0_rpb(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 65536, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 65536, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x65536_sm70_float32_cm_0_0_0_rpb` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 65536, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 65536, true>::kMinBlocksPerSm) +fna3_64x128x65536_sm70_float32_cm_0_0_0_rpb(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 65536, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 65536, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x65536_sm70_float32_cm_0_0_0_rpb` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 65536, true>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 65536, true>::kMinBlocksPerSm) +fna3_64x64x65536_sm70_float32_cm_0_0_0_rpb(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 65536, true>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 65536, true>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x65536_sm70_float32_cm_0_0_0_rpb` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +/////////////////////////////////////////////////////////////////// +// FNA-3D / float32 / SM70 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 32, false>::kMinBlocksPerSm) +fna3_32x128x32_sm70_float32_cm_0_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x32_sm70_float32_cm_0_0_1` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 32, false>::kMinBlocksPerSm) +fna3_64x64x32_sm70_float32_cm_0_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x32_sm70_float32_cm_0_0_1` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 32, false>::kMinBlocksPerSm) +fna3_64x128x32_sm70_float32_cm_0_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x32_sm70_float32_cm_0_0_1` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 64, false>::kMinBlocksPerSm) +fna3_32x128x64_sm70_float32_cm_0_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x64_sm70_float32_cm_0_0_1` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 64, false>::kMinBlocksPerSm) +fna3_64x64x64_sm70_float32_cm_0_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x64_sm70_float32_cm_0_0_1` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 64, false>::kMinBlocksPerSm) +fna3_64x128x64_sm70_float32_cm_0_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x64_sm70_float32_cm_0_0_1` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 128, false>::kMinBlocksPerSm) +fna3_32x128x128_sm70_float32_cm_0_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x128_sm70_float32_cm_0_0_1` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 128, false>::kMinBlocksPerSm) +fna3_64x64x128_sm70_float32_cm_0_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x128_sm70_float32_cm_0_0_1` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 128, false>::kMinBlocksPerSm) +fna3_64x128x128_sm70_float32_cm_0_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x128_sm70_float32_cm_0_0_1` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 65536, false>::kMinBlocksPerSm) +fna3_32x128x65536_sm70_float32_cm_0_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x65536_sm70_float32_cm_0_0_1` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 65536, false>::kMinBlocksPerSm) +fna3_64x128x65536_sm70_float32_cm_0_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x65536_sm70_float32_cm_0_0_1` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 65536, false>::kMinBlocksPerSm) +fna3_64x64x65536_sm70_float32_cm_0_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x65536_sm70_float32_cm_0_0_1` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +/////////////////////////////////////////////////////////////////// +// FNA-3D / float32 / SM70 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 32, false>::kMinBlocksPerSm) +fna3_32x128x32_sm70_float32_cm_0_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x32_sm70_float32_cm_0_1_0` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 32, false>::kMinBlocksPerSm) +fna3_64x64x32_sm70_float32_cm_0_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x32_sm70_float32_cm_0_1_0` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 32, false>::kMinBlocksPerSm) +fna3_64x128x32_sm70_float32_cm_0_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x32_sm70_float32_cm_0_1_0` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 64, false>::kMinBlocksPerSm) +fna3_32x128x64_sm70_float32_cm_0_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x64_sm70_float32_cm_0_1_0` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 64, false>::kMinBlocksPerSm) +fna3_64x64x64_sm70_float32_cm_0_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x64_sm70_float32_cm_0_1_0` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 64, false>::kMinBlocksPerSm) +fna3_64x128x64_sm70_float32_cm_0_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x64_sm70_float32_cm_0_1_0` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 128, false>::kMinBlocksPerSm) +fna3_32x128x128_sm70_float32_cm_0_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x128_sm70_float32_cm_0_1_0` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 128, false>::kMinBlocksPerSm) +fna3_64x64x128_sm70_float32_cm_0_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x128_sm70_float32_cm_0_1_0` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 128, false>::kMinBlocksPerSm) +fna3_64x128x128_sm70_float32_cm_0_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x128_sm70_float32_cm_0_1_0` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 65536, false>::kMinBlocksPerSm) +fna3_32x128x65536_sm70_float32_cm_0_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x65536_sm70_float32_cm_0_1_0` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 65536, false>::kMinBlocksPerSm) +fna3_64x128x65536_sm70_float32_cm_0_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x65536_sm70_float32_cm_0_1_0` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 65536, false>::kMinBlocksPerSm) +fna3_64x64x65536_sm70_float32_cm_0_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x65536_sm70_float32_cm_0_1_0` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +/////////////////////////////////////////////////////////////////// +// FNA-3D / float32 / SM70 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 32, false>::kMinBlocksPerSm) +fna3_32x128x32_sm70_float32_cm_0_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x32_sm70_float32_cm_0_1_1` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 32, false>::kMinBlocksPerSm) +fna3_64x64x32_sm70_float32_cm_0_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x32_sm70_float32_cm_0_1_1` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 32, false>::kMinBlocksPerSm) +fna3_64x128x32_sm70_float32_cm_0_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x32_sm70_float32_cm_0_1_1` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 64, false>::kMinBlocksPerSm) +fna3_32x128x64_sm70_float32_cm_0_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x64_sm70_float32_cm_0_1_1` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 64, false>::kMinBlocksPerSm) +fna3_64x64x64_sm70_float32_cm_0_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x64_sm70_float32_cm_0_1_1` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 64, false>::kMinBlocksPerSm) +fna3_64x128x64_sm70_float32_cm_0_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x64_sm70_float32_cm_0_1_1` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 128, false>::kMinBlocksPerSm) +fna3_32x128x128_sm70_float32_cm_0_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x128_sm70_float32_cm_0_1_1` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 128, false>::kMinBlocksPerSm) +fna3_64x64x128_sm70_float32_cm_0_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x128_sm70_float32_cm_0_1_1` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 128, false>::kMinBlocksPerSm) +fna3_64x128x128_sm70_float32_cm_0_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x128_sm70_float32_cm_0_1_1` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 65536, false>::kMinBlocksPerSm) +fna3_32x128x65536_sm70_float32_cm_0_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x65536_sm70_float32_cm_0_1_1` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 65536, false>::kMinBlocksPerSm) +fna3_64x128x65536_sm70_float32_cm_0_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x65536_sm70_float32_cm_0_1_1` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 65536, false>::kMinBlocksPerSm) +fna3_64x64x65536_sm70_float32_cm_0_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x65536_sm70_float32_cm_0_1_1` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +/////////////////////////////////////////////////////////////////// +// FNA-3D / float32 / SM70 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 32, false>::kMinBlocksPerSm) +fna3_32x128x32_sm70_float32_cm_1_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x32_sm70_float32_cm_1_0_0` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 32, false>::kMinBlocksPerSm) +fna3_64x64x32_sm70_float32_cm_1_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x32_sm70_float32_cm_1_0_0` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 32, false>::kMinBlocksPerSm) +fna3_64x128x32_sm70_float32_cm_1_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x32_sm70_float32_cm_1_0_0` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 64, false>::kMinBlocksPerSm) +fna3_32x128x64_sm70_float32_cm_1_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x64_sm70_float32_cm_1_0_0` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 64, false>::kMinBlocksPerSm) +fna3_64x64x64_sm70_float32_cm_1_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x64_sm70_float32_cm_1_0_0` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 64, false>::kMinBlocksPerSm) +fna3_64x128x64_sm70_float32_cm_1_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x64_sm70_float32_cm_1_0_0` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 128, false>::kMinBlocksPerSm) +fna3_32x128x128_sm70_float32_cm_1_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x128_sm70_float32_cm_1_0_0` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 128, false>::kMinBlocksPerSm) +fna3_64x64x128_sm70_float32_cm_1_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x128_sm70_float32_cm_1_0_0` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 128, false>::kMinBlocksPerSm) +fna3_64x128x128_sm70_float32_cm_1_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x128_sm70_float32_cm_1_0_0` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 65536, false>::kMinBlocksPerSm) +fna3_32x128x65536_sm70_float32_cm_1_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x65536_sm70_float32_cm_1_0_0` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 65536, false>::kMinBlocksPerSm) +fna3_64x128x65536_sm70_float32_cm_1_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x65536_sm70_float32_cm_1_0_0` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 65536, false>::kMinBlocksPerSm) +fna3_64x64x65536_sm70_float32_cm_1_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x65536_sm70_float32_cm_1_0_0` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +/////////////////////////////////////////////////////////////////// +// FNA-3D / float32 / SM70 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 32, false>::kMinBlocksPerSm) +fna3_32x128x32_sm70_float32_cm_1_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x32_sm70_float32_cm_1_0_1` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 32, false>::kMinBlocksPerSm) +fna3_64x64x32_sm70_float32_cm_1_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x32_sm70_float32_cm_1_0_1` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 32, false>::kMinBlocksPerSm) +fna3_64x128x32_sm70_float32_cm_1_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x32_sm70_float32_cm_1_0_1` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 64, false>::kMinBlocksPerSm) +fna3_32x128x64_sm70_float32_cm_1_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x64_sm70_float32_cm_1_0_1` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 64, false>::kMinBlocksPerSm) +fna3_64x64x64_sm70_float32_cm_1_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x64_sm70_float32_cm_1_0_1` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 64, false>::kMinBlocksPerSm) +fna3_64x128x64_sm70_float32_cm_1_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x64_sm70_float32_cm_1_0_1` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 128, false>::kMinBlocksPerSm) +fna3_32x128x128_sm70_float32_cm_1_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x128_sm70_float32_cm_1_0_1` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 128, false>::kMinBlocksPerSm) +fna3_64x64x128_sm70_float32_cm_1_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x128_sm70_float32_cm_1_0_1` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 128, false>::kMinBlocksPerSm) +fna3_64x128x128_sm70_float32_cm_1_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x128_sm70_float32_cm_1_0_1` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 65536, false>::kMinBlocksPerSm) +fna3_32x128x65536_sm70_float32_cm_1_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x65536_sm70_float32_cm_1_0_1` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 65536, false>::kMinBlocksPerSm) +fna3_64x128x65536_sm70_float32_cm_1_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x65536_sm70_float32_cm_1_0_1` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 65536, false>::kMinBlocksPerSm) +fna3_64x64x65536_sm70_float32_cm_1_0_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x65536_sm70_float32_cm_1_0_1` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +/////////////////////////////////////////////////////////////////// +// FNA-3D / float32 / SM70 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 32, false>::kMinBlocksPerSm) +fna3_32x128x32_sm70_float32_cm_1_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x32_sm70_float32_cm_1_1_0` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 32, false>::kMinBlocksPerSm) +fna3_64x64x32_sm70_float32_cm_1_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x32_sm70_float32_cm_1_1_0` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 32, false>::kMinBlocksPerSm) +fna3_64x128x32_sm70_float32_cm_1_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x32_sm70_float32_cm_1_1_0` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 64, false>::kMinBlocksPerSm) +fna3_32x128x64_sm70_float32_cm_1_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x64_sm70_float32_cm_1_1_0` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 64, false>::kMinBlocksPerSm) +fna3_64x64x64_sm70_float32_cm_1_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x64_sm70_float32_cm_1_1_0` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 64, false>::kMinBlocksPerSm) +fna3_64x128x64_sm70_float32_cm_1_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x64_sm70_float32_cm_1_1_0` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 128, false>::kMinBlocksPerSm) +fna3_32x128x128_sm70_float32_cm_1_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x128_sm70_float32_cm_1_1_0` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 128, false>::kMinBlocksPerSm) +fna3_64x64x128_sm70_float32_cm_1_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x128_sm70_float32_cm_1_1_0` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 128, false>::kMinBlocksPerSm) +fna3_64x128x128_sm70_float32_cm_1_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x128_sm70_float32_cm_1_1_0` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 65536, false>::kMinBlocksPerSm) +fna3_32x128x65536_sm70_float32_cm_1_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x65536_sm70_float32_cm_1_1_0` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 65536, false>::kMinBlocksPerSm) +fna3_64x128x65536_sm70_float32_cm_1_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x65536_sm70_float32_cm_1_1_0` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 65536, false>::kMinBlocksPerSm) +fna3_64x64x65536_sm70_float32_cm_1_1_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x65536_sm70_float32_cm_1_1_0` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +/////////////////////////////////////////////////////////////////// +// FNA-3D / float32 / SM70 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 32, false>::kMinBlocksPerSm) +fna3_32x128x32_sm70_float32_cm_1_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x32_sm70_float32_cm_1_1_1` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 32, false>::kMinBlocksPerSm) +fna3_64x64x32_sm70_float32_cm_1_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x32_sm70_float32_cm_1_1_1` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 32, false>::kMinBlocksPerSm) +fna3_64x128x32_sm70_float32_cm_1_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x32_sm70_float32_cm_1_1_1` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 64, false>::kMinBlocksPerSm) +fna3_32x128x64_sm70_float32_cm_1_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x64_sm70_float32_cm_1_1_1` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 64, false>::kMinBlocksPerSm) +fna3_64x64x64_sm70_float32_cm_1_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x64_sm70_float32_cm_1_1_1` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 64, false>::kMinBlocksPerSm) +fna3_64x128x64_sm70_float32_cm_1_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x64_sm70_float32_cm_1_1_1` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 128, false>::kMinBlocksPerSm) +fna3_32x128x128_sm70_float32_cm_1_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x128_sm70_float32_cm_1_1_1` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 128, false>::kMinBlocksPerSm) +fna3_64x64x128_sm70_float32_cm_1_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x128_sm70_float32_cm_1_1_1` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 128, false>::kMinBlocksPerSm) +fna3_64x128x128_sm70_float32_cm_1_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x128_sm70_float32_cm_1_1_1` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 65536, false>::kMinBlocksPerSm) +fna3_32x128x65536_sm70_float32_cm_1_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 32, 128, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x65536_sm70_float32_cm_1_1_1` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 65536, false>::kMinBlocksPerSm) +fna3_64x128x65536_sm70_float32_cm_1_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 128, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x65536_sm70_float32_cm_1_1_1` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 65536, false>::kMinBlocksPerSm) +fna3_64x64x65536_sm70_float32_cm_1_1_1(typename FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, float, cutlass::arch::Sm70, true, 64, 64, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x65536_sm70_float32_cm_1_1_1` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +/////////////////////////////////////////////////////////////////// +// FNA-3D / float16 / SM70 +/////////////////////////////////////////////////////////////////// + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 32, false>::kMinBlocksPerSm) +fna3_32x128x32_sm70_float16_cm_0_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x32_sm70_float16_cm_0_0_0` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 32, false>::kMinBlocksPerSm) +fna3_64x64x32_sm70_float16_cm_0_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x32_sm70_float16_cm_0_0_0` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 32, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 32, false>::kMinBlocksPerSm) +fna3_64x128x32_sm70_float16_cm_0_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 32, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 32, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x32_sm70_float16_cm_0_0_0` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 64, false>::kMinBlocksPerSm) +fna3_32x128x64_sm70_float16_cm_0_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x64_sm70_float16_cm_0_0_0` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 64, false>::kMinBlocksPerSm) +fna3_64x64x64_sm70_float16_cm_0_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x64_sm70_float16_cm_0_0_0` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 64, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 64, false>::kMinBlocksPerSm) +fna3_64x128x64_sm70_float16_cm_0_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 64, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 64, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x64_sm70_float16_cm_0_0_0` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 128, false>::kMinBlocksPerSm) +fna3_32x128x128_sm70_float16_cm_0_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x128_sm70_float16_cm_0_0_0` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 128, false>::kMinBlocksPerSm) +fna3_64x64x128_sm70_float16_cm_0_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x128_sm70_float16_cm_0_0_0` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 128, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 128, false>::kMinBlocksPerSm) +fna3_64x128x128_sm70_float16_cm_0_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 128, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 128, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x128_sm70_float16_cm_0_0_0` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 65536, false>::kMinBlocksPerSm) +fna3_32x128x65536_sm70_float16_cm_0_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 32, 128, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_32x128x65536_sm70_float16_cm_0_0_0` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 65536, false>::kMinBlocksPerSm) +fna3_64x128x65536_sm70_float16_cm_0_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 128, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x128x65536_sm70_float16_cm_0_0_0` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} + + +__global__ void __launch_bounds__( + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 65536, false>::kNumThreads, + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 65536, false>::kMinBlocksPerSm) +fna3_64x64x65536_sm70_float16_cm_0_0_0(typename FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 65536, false>::Params p) { +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ >= 700 +#if __CUDA_ARCH__ < 750 + if (!p.advance_to_block()) { + return; + } + FusedNeighborhoodAttentionKernel<3, CausalMask, cutlass::half_t, cutlass::arch::Sm70, true, 64, 64, 65536, false>::attention_kernel(p); + return; +#endif +#endif + printf( + "FATAL: FNA kernel `fna3_64x64x65536_sm70_float16_cm_0_0_0` was built for SM70, but attempted to launch from SM%d\n", + int(__CUDA_ARCH__ + 0) / 10); +#endif +} +} +} +} + diff --git a/csrc/autogen/src/cuda/gemm/2d/sm70/source_0.cu b/csrc/autogen/src/cuda/gemm/2d/sm70/source_0.cu index 350736b..c789064 100644 --- a/csrc/autogen/src/cuda/gemm/2d/sm70/source_0.cu +++ b/csrc/autogen/src/cuda/gemm/2d/sm70/source_0.cu @@ -1,9 +1,9 @@ #include #include -#include #include -#include #include +#include +#include namespace natten { namespace cuda { namespace gemm { @@ -1016,6 +1016,958 @@ void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks25_align2( query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks27_align8( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 8, 8, 4, 2, 14, 1, 13>; + using ArchConfig = natten::gemm::detail::ArchArgs<70, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<8, 8, 1>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks27_align4( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 8, 8, 4, 2, 14, 1, 13>; + using ArchConfig = natten::gemm::detail::ArchArgs<70, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<4, 4, 1>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks27_align2( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 8, 8, 4, 2, 14, 1, 13>; + using ArchConfig = natten::gemm::detail::ArchArgs<70, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<2, 2, 1>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks29_align8( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 8, 8, 4, 2, 17, 1, 14>; + using ArchConfig = natten::gemm::detail::ArchArgs<70, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<8, 8, 1>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks29_align4( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 8, 8, 4, 2, 17, 1, 14>; + using ArchConfig = natten::gemm::detail::ArchArgs<70, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<4, 4, 1>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks29_align2( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 8, 8, 4, 2, 17, 1, 14>; + using ArchConfig = natten::gemm::detail::ArchArgs<70, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<2, 2, 1>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks31_align8( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 8, 8, 4, 2, 17, 1, 15>; + using ArchConfig = natten::gemm::detail::ArchArgs<70, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<8, 8, 1>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks31_align4( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 8, 8, 4, 2, 17, 1, 15>; + using ArchConfig = natten::gemm::detail::ArchArgs<70, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<4, 4, 1>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks31_align2( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 8, 8, 4, 2, 17, 1, 15>; + using ArchConfig = natten::gemm::detail::ArchArgs<70, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<2, 2, 1>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks33_align8( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 8, 8, 4, 2, 17, 1, 16>; + using ArchConfig = natten::gemm::detail::ArchArgs<70, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<8, 8, 1>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks33_align4( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 8, 8, 4, 2, 17, 1, 16>; + using ArchConfig = natten::gemm::detail::ArchArgs<70, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<4, 4, 1>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks33_align2( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 8, 8, 4, 2, 17, 1, 16>; + using ArchConfig = natten::gemm::detail::ArchArgs<70, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<2, 2, 1>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks35_align8( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 8, 8, 4, 2, 22, 1, 17>; + using ArchConfig = natten::gemm::detail::ArchArgs<70, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<8, 8, 1>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks35_align4( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 8, 8, 4, 2, 22, 1, 17>; + using ArchConfig = natten::gemm::detail::ArchArgs<70, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<4, 4, 1>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks35_align2( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 8, 8, 4, 2, 22, 1, 17>; + using ArchConfig = natten::gemm::detail::ArchArgs<70, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<2, 2, 1>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks37_align8( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 8, 8, 4, 2, 22, 1, 18>; + using ArchConfig = natten::gemm::detail::ArchArgs<70, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<8, 8, 1>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks37_align4( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 8, 8, 4, 2, 22, 1, 18>; + using ArchConfig = natten::gemm::detail::ArchArgs<70, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<4, 4, 1>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks37_align2( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 8, 8, 4, 2, 22, 1, 18>; + using ArchConfig = natten::gemm::detail::ArchArgs<70, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<2, 2, 1>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks39_align8( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 8, 8, 4, 2, 23, 1, 19>; + using ArchConfig = natten::gemm::detail::ArchArgs<70, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<8, 8, 1>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks39_align4( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 8, 8, 4, 2, 23, 1, 19>; + using ArchConfig = natten::gemm::detail::ArchArgs<70, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<4, 4, 1>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks39_align2( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 8, 8, 4, 2, 23, 1, 19>; + using ArchConfig = natten::gemm::detail::ArchArgs<70, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<2, 2, 1>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks41_align8( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 8, 8, 4, 2, 22, 1, 20>; + using ArchConfig = natten::gemm::detail::ArchArgs<70, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<8, 8, 1>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks41_align4( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 8, 8, 4, 2, 22, 1, 20>; + using ArchConfig = natten::gemm::detail::ArchArgs<70, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<4, 4, 1>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks41_align2( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 8, 8, 4, 2, 22, 1, 20>; + using ArchConfig = natten::gemm::detail::ArchArgs<70, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<2, 2, 1>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks43_align8( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 8, 8, 4, 2, 22, 1, 21>; + using ArchConfig = natten::gemm::detail::ArchArgs<70, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<8, 8, 1>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks43_align4( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 8, 8, 4, 2, 22, 1, 21>; + using ArchConfig = natten::gemm::detail::ArchArgs<70, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<4, 4, 1>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks43_align2( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 8, 8, 4, 2, 22, 1, 21>; + using ArchConfig = natten::gemm::detail::ArchArgs<70, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<2, 2, 1>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks45_align8( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 8, 8, 4, 2, 23, 1, 22>; + using ArchConfig = natten::gemm::detail::ArchArgs<70, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<8, 8, 1>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks45_align4( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 8, 8, 4, 2, 23, 1, 22>; + using ArchConfig = natten::gemm::detail::ArchArgs<70, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<4, 4, 1>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks45_align2( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 8, 8, 4, 2, 23, 1, 22>; + using ArchConfig = natten::gemm::detail::ArchArgs<70, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<2, 2, 1>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks47_align8( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 8, 8, 4, 2, 33, 1, 23>; + using ArchConfig = natten::gemm::detail::ArchArgs<70, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<8, 8, 1>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks47_align4( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 8, 8, 4, 2, 33, 1, 23>; + using ArchConfig = natten::gemm::detail::ArchArgs<70, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<4, 4, 1>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks47_align2( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 8, 8, 4, 2, 33, 1, 23>; + using ArchConfig = natten::gemm::detail::ArchArgs<70, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<2, 2, 1>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks49_align8( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 8, 8, 4, 2, 33, 1, 24>; + using ArchConfig = natten::gemm::detail::ArchArgs<70, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<8, 8, 1>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + } } } diff --git a/csrc/autogen/src/cuda/gemm/2d/sm70/source_1.cu b/csrc/autogen/src/cuda/gemm/2d/sm70/source_1.cu index e5763bb..2ca6dbe 100644 --- a/csrc/autogen/src/cuda/gemm/2d/sm70/source_1.cu +++ b/csrc/autogen/src/cuda/gemm/2d/sm70/source_1.cu @@ -1,14 +1,70 @@ #include #include -#include #include -#include #include +#include +#include namespace natten { namespace cuda { namespace gemm { -void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks27_align8( +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks49_align4( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 8, 8, 4, 2, 33, 1, 24>; + using ArchConfig = natten::gemm::detail::ArchArgs<70, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<4, 4, 1>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks49_align2( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 8, 8, 4, 2, 33, 1, 24>; + using ArchConfig = natten::gemm::detail::ArchArgs<70, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<2, 2, 1>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks51_align8( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -26,7 +82,7 @@ void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks27_align8( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 8, 8, 4, 2, 14, 1, 13>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 8, 8, 4, 2, 33, 1, 25>; using ArchConfig = natten::gemm::detail::ArchArgs<70, half>; using AConfig = natten::gemm::detail::AlignmentConfig<8, 8, 1>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -36,7 +92,7 @@ void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks27_align8( query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks27_align4( +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks51_align4( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -54,7 +110,7 @@ void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks27_align4( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 8, 8, 4, 2, 14, 1, 13>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 8, 8, 4, 2, 33, 1, 25>; using ArchConfig = natten::gemm::detail::ArchArgs<70, half>; using AConfig = natten::gemm::detail::AlignmentConfig<4, 4, 1>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -64,7 +120,7 @@ void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks27_align4( query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks27_align2( +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks51_align2( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -82,7 +138,7 @@ void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks27_align2( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 8, 8, 4, 2, 14, 1, 13>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 8, 8, 4, 2, 33, 1, 25>; using ArchConfig = natten::gemm::detail::ArchArgs<70, half>; using AConfig = natten::gemm::detail::AlignmentConfig<2, 2, 1>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -92,7 +148,7 @@ void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks27_align2( query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks29_align8( +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks53_align8( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -110,7 +166,7 @@ void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks29_align8( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 8, 8, 4, 2, 17, 1, 14>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 8, 8, 4, 2, 33, 1, 26>; using ArchConfig = natten::gemm::detail::ArchArgs<70, half>; using AConfig = natten::gemm::detail::AlignmentConfig<8, 8, 1>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -120,7 +176,7 @@ void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks29_align8( query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks29_align4( +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks53_align4( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -138,7 +194,7 @@ void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks29_align4( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 8, 8, 4, 2, 17, 1, 14>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 8, 8, 4, 2, 33, 1, 26>; using ArchConfig = natten::gemm::detail::ArchArgs<70, half>; using AConfig = natten::gemm::detail::AlignmentConfig<4, 4, 1>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -148,7 +204,7 @@ void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks29_align4( query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks29_align2( +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks53_align2( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -166,7 +222,7 @@ void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks29_align2( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 8, 8, 4, 2, 17, 1, 14>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 8, 8, 4, 2, 33, 1, 26>; using ArchConfig = natten::gemm::detail::ArchArgs<70, half>; using AConfig = natten::gemm::detail::AlignmentConfig<2, 2, 1>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -176,7 +232,7 @@ void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks29_align2( query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks31_align8( +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks55_align8( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -194,7 +250,7 @@ void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks31_align8( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 8, 8, 4, 2, 17, 1, 15>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 8, 8, 4, 2, 33, 1, 27>; using ArchConfig = natten::gemm::detail::ArchArgs<70, half>; using AConfig = natten::gemm::detail::AlignmentConfig<8, 8, 1>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -204,7 +260,7 @@ void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks31_align8( query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks31_align4( +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks55_align4( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -222,7 +278,7 @@ void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks31_align4( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 8, 8, 4, 2, 17, 1, 15>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 8, 8, 4, 2, 33, 1, 27>; using ArchConfig = natten::gemm::detail::ArchArgs<70, half>; using AConfig = natten::gemm::detail::AlignmentConfig<4, 4, 1>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -232,7 +288,7 @@ void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks31_align4( query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks31_align2( +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks55_align2( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -250,7 +306,7 @@ void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks31_align2( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 8, 8, 4, 2, 17, 1, 15>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 8, 8, 4, 2, 33, 1, 27>; using ArchConfig = natten::gemm::detail::ArchArgs<70, half>; using AConfig = natten::gemm::detail::AlignmentConfig<2, 2, 1>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -260,7 +316,7 @@ void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks31_align2( query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks33_align8( +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks57_align8( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -278,7 +334,7 @@ void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks33_align8( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 8, 8, 4, 2, 17, 1, 16>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 8, 8, 4, 2, 33, 1, 28>; using ArchConfig = natten::gemm::detail::ArchArgs<70, half>; using AConfig = natten::gemm::detail::AlignmentConfig<8, 8, 1>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -288,7 +344,7 @@ void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks33_align8( query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks33_align4( +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks57_align4( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -306,7 +362,7 @@ void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks33_align4( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 8, 8, 4, 2, 17, 1, 16>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 8, 8, 4, 2, 33, 1, 28>; using ArchConfig = natten::gemm::detail::ArchArgs<70, half>; using AConfig = natten::gemm::detail::AlignmentConfig<4, 4, 1>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -316,7 +372,7 @@ void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks33_align4( query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks33_align2( +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks57_align2( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -334,7 +390,7 @@ void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks33_align2( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 8, 8, 4, 2, 17, 1, 16>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 8, 8, 4, 2, 33, 1, 28>; using ArchConfig = natten::gemm::detail::ArchArgs<70, half>; using AConfig = natten::gemm::detail::AlignmentConfig<2, 2, 1>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -344,10 +400,11 @@ void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks33_align2( query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks3_align8( +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks59_align8( + void * query_ptr, + void * key_ptr, void * attn_ptr, - void * value_ptr, - void * output_ptr, + void * bias_ptr, int batch_size, int heads, int height, @@ -361,20 +418,21 @@ void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks3_align8( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 8, 8, 4, 2, 8, 2, 1>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 8, 8, 4, 2, 33, 1, 29>; using ArchConfig = natten::gemm::detail::ArchArgs<70, half>; - using AConfig = natten::gemm::detail::AlignmentConfig<1, 8, 8>; + using AConfig = natten::gemm::detail::AlignmentConfig<8, 8, 1>; using DConfig = natten::gemm::detail::DTypeConfig; - using Kernel = NeighborhoodNeighborhood2D; + using Kernel = PointwiseNeighborhood2D; Kernel kernel; kernel( -attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks3_align4( +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks59_align4( + void * query_ptr, + void * key_ptr, void * attn_ptr, - void * value_ptr, - void * output_ptr, + void * bias_ptr, int batch_size, int heads, int height, @@ -388,20 +446,21 @@ void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks3_align4( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 8, 8, 4, 2, 8, 2, 1>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 8, 8, 4, 2, 33, 1, 29>; using ArchConfig = natten::gemm::detail::ArchArgs<70, half>; - using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; + using AConfig = natten::gemm::detail::AlignmentConfig<4, 4, 1>; using DConfig = natten::gemm::detail::DTypeConfig; - using Kernel = NeighborhoodNeighborhood2D; + using Kernel = PointwiseNeighborhood2D; Kernel kernel; kernel( -attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks3_align2( +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks59_align2( + void * query_ptr, + void * key_ptr, void * attn_ptr, - void * value_ptr, - void * output_ptr, + void * bias_ptr, int batch_size, int heads, int height, @@ -415,20 +474,21 @@ void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks3_align2( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 8, 8, 4, 2, 8, 2, 1>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 8, 8, 4, 2, 33, 1, 29>; using ArchConfig = natten::gemm::detail::ArchArgs<70, half>; - using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; + using AConfig = natten::gemm::detail::AlignmentConfig<2, 2, 1>; using DConfig = natten::gemm::detail::DTypeConfig; - using Kernel = NeighborhoodNeighborhood2D; + using Kernel = PointwiseNeighborhood2D; Kernel kernel; kernel( -attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks5_align8( +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks61_align8( + void * query_ptr, + void * key_ptr, void * attn_ptr, - void * value_ptr, - void * output_ptr, + void * bias_ptr, int batch_size, int heads, int height, @@ -442,20 +502,21 @@ void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks5_align8( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 8, 8, 4, 2, 8, 2, 2>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 8, 8, 4, 2, 33, 1, 30>; using ArchConfig = natten::gemm::detail::ArchArgs<70, half>; - using AConfig = natten::gemm::detail::AlignmentConfig<1, 8, 8>; + using AConfig = natten::gemm::detail::AlignmentConfig<8, 8, 1>; using DConfig = natten::gemm::detail::DTypeConfig; - using Kernel = NeighborhoodNeighborhood2D; + using Kernel = PointwiseNeighborhood2D; Kernel kernel; kernel( -attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks5_align4( +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks61_align4( + void * query_ptr, + void * key_ptr, void * attn_ptr, - void * value_ptr, - void * output_ptr, + void * bias_ptr, int batch_size, int heads, int height, @@ -469,20 +530,21 @@ void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks5_align4( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 8, 8, 4, 2, 8, 2, 2>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 8, 8, 4, 2, 33, 1, 30>; using ArchConfig = natten::gemm::detail::ArchArgs<70, half>; - using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; + using AConfig = natten::gemm::detail::AlignmentConfig<4, 4, 1>; using DConfig = natten::gemm::detail::DTypeConfig; - using Kernel = NeighborhoodNeighborhood2D; + using Kernel = PointwiseNeighborhood2D; Kernel kernel; kernel( -attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks5_align2( +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks61_align2( + void * query_ptr, + void * key_ptr, void * attn_ptr, - void * value_ptr, - void * output_ptr, + void * bias_ptr, int batch_size, int heads, int height, @@ -496,20 +558,21 @@ void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks5_align2( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 8, 8, 4, 2, 8, 2, 2>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 8, 8, 4, 2, 33, 1, 30>; using ArchConfig = natten::gemm::detail::ArchArgs<70, half>; - using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; + using AConfig = natten::gemm::detail::AlignmentConfig<2, 2, 1>; using DConfig = natten::gemm::detail::DTypeConfig; - using Kernel = NeighborhoodNeighborhood2D; + using Kernel = PointwiseNeighborhood2D; Kernel kernel; kernel( -attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks7_align8( +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks63_align8( + void * query_ptr, + void * key_ptr, void * attn_ptr, - void * value_ptr, - void * output_ptr, + void * bias_ptr, int batch_size, int heads, int height, @@ -523,20 +586,21 @@ void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks7_align8( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 8, 8, 4, 2, 8, 2, 3>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 8, 8, 4, 2, 33, 1, 31>; using ArchConfig = natten::gemm::detail::ArchArgs<70, half>; - using AConfig = natten::gemm::detail::AlignmentConfig<1, 8, 8>; + using AConfig = natten::gemm::detail::AlignmentConfig<8, 8, 1>; using DConfig = natten::gemm::detail::DTypeConfig; - using Kernel = NeighborhoodNeighborhood2D; + using Kernel = PointwiseNeighborhood2D; Kernel kernel; kernel( -attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks7_align4( +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks63_align4( + void * query_ptr, + void * key_ptr, void * attn_ptr, - void * value_ptr, - void * output_ptr, + void * bias_ptr, int batch_size, int heads, int height, @@ -550,20 +614,21 @@ void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks7_align4( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 8, 8, 4, 2, 8, 2, 3>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 8, 8, 4, 2, 33, 1, 31>; using ArchConfig = natten::gemm::detail::ArchArgs<70, half>; - using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; + using AConfig = natten::gemm::detail::AlignmentConfig<4, 4, 1>; using DConfig = natten::gemm::detail::DTypeConfig; - using Kernel = NeighborhoodNeighborhood2D; + using Kernel = PointwiseNeighborhood2D; Kernel kernel; kernel( -attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks7_align2( +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks63_align2( + void * query_ptr, + void * key_ptr, void * attn_ptr, - void * value_ptr, - void * output_ptr, + void * bias_ptr, int batch_size, int heads, int height, @@ -577,17 +642,17 @@ void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks7_align2( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 8, 8, 4, 2, 8, 2, 3>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 8, 8, 4, 2, 33, 1, 31>; using ArchConfig = natten::gemm::detail::ArchArgs<70, half>; - using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; + using AConfig = natten::gemm::detail::AlignmentConfig<2, 2, 1>; using DConfig = natten::gemm::detail::DTypeConfig; - using Kernel = NeighborhoodNeighborhood2D; + using Kernel = PointwiseNeighborhood2D; Kernel kernel; kernel( -attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks9_align8( +void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks3_align8( void * attn_ptr, void * value_ptr, void * output_ptr, @@ -604,7 +669,7 @@ void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks9_align8( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 8, 8, 4, 2, 8, 2, 4>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 8, 8, 4, 2, 8, 2, 1>; using ArchConfig = natten::gemm::detail::ArchArgs<70, half>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 8, 8>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -614,7 +679,7 @@ void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks9_align8( attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks9_align4( +void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks3_align4( void * attn_ptr, void * value_ptr, void * output_ptr, @@ -631,7 +696,7 @@ void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks9_align4( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 8, 8, 4, 2, 8, 2, 4>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 8, 8, 4, 2, 8, 2, 1>; using ArchConfig = natten::gemm::detail::ArchArgs<70, half>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -641,7 +706,7 @@ void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks9_align4( attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks9_align2( +void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks3_align2( void * attn_ptr, void * value_ptr, void * output_ptr, @@ -658,7 +723,7 @@ void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks9_align2( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 8, 8, 4, 2, 8, 2, 4>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 8, 8, 4, 2, 8, 2, 1>; using ArchConfig = natten::gemm::detail::ArchArgs<70, half>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -668,7 +733,7 @@ void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks9_align2( attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks11_align8( +void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks5_align8( void * attn_ptr, void * value_ptr, void * output_ptr, @@ -685,7 +750,7 @@ void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks11_align8( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 8, 8, 4, 2, 8, 2, 5>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 8, 8, 4, 2, 8, 2, 2>; using ArchConfig = natten::gemm::detail::ArchArgs<70, half>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 8, 8>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -695,7 +760,7 @@ void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks11_align8( attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks11_align4( +void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks5_align4( void * attn_ptr, void * value_ptr, void * output_ptr, @@ -712,7 +777,7 @@ void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks11_align4( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 8, 8, 4, 2, 8, 2, 5>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 8, 8, 4, 2, 8, 2, 2>; using ArchConfig = natten::gemm::detail::ArchArgs<70, half>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -722,7 +787,7 @@ void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks11_align4( attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks11_align2( +void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks5_align2( void * attn_ptr, void * value_ptr, void * output_ptr, @@ -739,7 +804,7 @@ void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks11_align2( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 8, 8, 4, 2, 8, 2, 5>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 8, 8, 4, 2, 8, 2, 2>; using ArchConfig = natten::gemm::detail::ArchArgs<70, half>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -749,7 +814,7 @@ void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks11_align2( attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks13_align8( +void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks7_align8( void * attn_ptr, void * value_ptr, void * output_ptr, @@ -766,7 +831,7 @@ void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks13_align8( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 8, 8, 4, 2, 8, 2, 6>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 8, 8, 4, 2, 8, 2, 3>; using ArchConfig = natten::gemm::detail::ArchArgs<70, half>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 8, 8>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -776,7 +841,7 @@ void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks13_align8( attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks13_align4( +void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks7_align4( void * attn_ptr, void * value_ptr, void * output_ptr, @@ -793,7 +858,7 @@ void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks13_align4( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 8, 8, 4, 2, 8, 2, 6>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 8, 8, 4, 2, 8, 2, 3>; using ArchConfig = natten::gemm::detail::ArchArgs<70, half>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -803,7 +868,7 @@ void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks13_align4( attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks13_align2( +void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks7_align2( void * attn_ptr, void * value_ptr, void * output_ptr, @@ -820,7 +885,7 @@ void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks13_align2( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 8, 8, 4, 2, 8, 2, 6>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 8, 8, 4, 2, 8, 2, 3>; using ArchConfig = natten::gemm::detail::ArchArgs<70, half>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -830,7 +895,7 @@ void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks13_align2( attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks15_align8( +void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks9_align8( void * attn_ptr, void * value_ptr, void * output_ptr, @@ -847,7 +912,7 @@ void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks15_align8( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 8, 8, 4, 2, 8, 2, 7>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 8, 8, 4, 2, 8, 2, 4>; using ArchConfig = natten::gemm::detail::ArchArgs<70, half>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 8, 8>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -857,7 +922,7 @@ void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks15_align8( attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks15_align4( +void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks9_align4( void * attn_ptr, void * value_ptr, void * output_ptr, @@ -874,7 +939,7 @@ void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks15_align4( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 8, 8, 4, 2, 8, 2, 7>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 8, 8, 4, 2, 8, 2, 4>; using ArchConfig = natten::gemm::detail::ArchArgs<70, half>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -884,7 +949,7 @@ void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks15_align4( attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks15_align2( +void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks9_align2( void * attn_ptr, void * value_ptr, void * output_ptr, @@ -901,7 +966,7 @@ void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks15_align2( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 8, 8, 4, 2, 8, 2, 7>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 8, 8, 4, 2, 8, 2, 4>; using ArchConfig = natten::gemm::detail::ArchArgs<70, half>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -911,7 +976,7 @@ void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks15_align2( attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks17_align8( +void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks11_align8( void * attn_ptr, void * value_ptr, void * output_ptr, @@ -928,7 +993,7 @@ void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks17_align8( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 8, 8, 4, 2, 8, 2, 8>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 8, 8, 4, 2, 8, 2, 5>; using ArchConfig = natten::gemm::detail::ArchArgs<70, half>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 8, 8>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -938,7 +1003,7 @@ void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks17_align8( attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks17_align4( +void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks11_align4( void * attn_ptr, void * value_ptr, void * output_ptr, @@ -955,7 +1020,7 @@ void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks17_align4( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 8, 8, 4, 2, 8, 2, 8>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 8, 8, 4, 2, 8, 2, 5>; using ArchConfig = natten::gemm::detail::ArchArgs<70, half>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -965,7 +1030,7 @@ void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks17_align4( attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks17_align2( +void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks11_align2( void * attn_ptr, void * value_ptr, void * output_ptr, @@ -982,7 +1047,7 @@ void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks17_align2( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 8, 8, 4, 2, 8, 2, 8>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 8, 8, 4, 2, 8, 2, 5>; using ArchConfig = natten::gemm::detail::ArchArgs<70, half>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -992,6 +1057,870 @@ void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks17_align2( attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } +void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks13_align8( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 8, 8, 4, 2, 8, 2, 6>; + using ArchConfig = natten::gemm::detail::ArchArgs<70, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 8, 8>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks13_align4( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 8, 8, 4, 2, 8, 2, 6>; + using ArchConfig = natten::gemm::detail::ArchArgs<70, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks13_align2( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 8, 8, 4, 2, 8, 2, 6>; + using ArchConfig = natten::gemm::detail::ArchArgs<70, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks15_align8( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 8, 8, 4, 2, 8, 2, 7>; + using ArchConfig = natten::gemm::detail::ArchArgs<70, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 8, 8>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks15_align4( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 8, 8, 4, 2, 8, 2, 7>; + using ArchConfig = natten::gemm::detail::ArchArgs<70, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks15_align2( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 8, 8, 4, 2, 8, 2, 7>; + using ArchConfig = natten::gemm::detail::ArchArgs<70, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks17_align8( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 8, 8, 4, 2, 8, 2, 8>; + using ArchConfig = natten::gemm::detail::ArchArgs<70, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 8, 8>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks17_align4( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 8, 8, 4, 2, 8, 2, 8>; + using ArchConfig = natten::gemm::detail::ArchArgs<70, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks17_align2( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 8, 8, 4, 2, 8, 2, 8>; + using ArchConfig = natten::gemm::detail::ArchArgs<70, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks19_align8( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 8, 8, 4, 2, 8, 2, 9>; + using ArchConfig = natten::gemm::detail::ArchArgs<70, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 8, 8>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks19_align4( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 8, 8, 4, 2, 8, 2, 9>; + using ArchConfig = natten::gemm::detail::ArchArgs<70, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks19_align2( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 8, 8, 4, 2, 8, 2, 9>; + using ArchConfig = natten::gemm::detail::ArchArgs<70, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks21_align8( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 8, 8, 4, 2, 11, 2, 10>; + using ArchConfig = natten::gemm::detail::ArchArgs<70, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 8, 8>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks21_align4( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 8, 8, 4, 2, 11, 2, 10>; + using ArchConfig = natten::gemm::detail::ArchArgs<70, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks21_align2( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 8, 8, 4, 2, 11, 2, 10>; + using ArchConfig = natten::gemm::detail::ArchArgs<70, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks23_align8( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 8, 8, 4, 2, 11, 2, 11>; + using ArchConfig = natten::gemm::detail::ArchArgs<70, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 8, 8>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks23_align4( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 8, 8, 4, 2, 11, 2, 11>; + using ArchConfig = natten::gemm::detail::ArchArgs<70, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks23_align2( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 8, 8, 4, 2, 11, 2, 11>; + using ArchConfig = natten::gemm::detail::ArchArgs<70, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks25_align8( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 8, 8, 4, 2, 11, 2, 12>; + using ArchConfig = natten::gemm::detail::ArchArgs<70, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 8, 8>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks25_align4( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 8, 8, 4, 2, 11, 2, 12>; + using ArchConfig = natten::gemm::detail::ArchArgs<70, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks25_align2( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 8, 8, 4, 2, 11, 2, 12>; + using ArchConfig = natten::gemm::detail::ArchArgs<70, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks27_align8( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 8, 8, 4, 2, 11, 2, 13>; + using ArchConfig = natten::gemm::detail::ArchArgs<70, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 8, 8>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks27_align4( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 8, 8, 4, 2, 11, 2, 13>; + using ArchConfig = natten::gemm::detail::ArchArgs<70, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks27_align2( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 8, 8, 4, 2, 11, 2, 13>; + using ArchConfig = natten::gemm::detail::ArchArgs<70, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks29_align8( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 8, 8, 4, 2, 11, 2, 14>; + using ArchConfig = natten::gemm::detail::ArchArgs<70, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 8, 8>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks29_align4( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 8, 8, 4, 2, 11, 2, 14>; + using ArchConfig = natten::gemm::detail::ArchArgs<70, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks29_align2( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 8, 8, 4, 2, 11, 2, 14>; + using ArchConfig = natten::gemm::detail::ArchArgs<70, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks31_align8( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 8, 8, 4, 2, 11, 2, 15>; + using ArchConfig = natten::gemm::detail::ArchArgs<70, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 8, 8>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks31_align4( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 8, 8, 4, 2, 11, 2, 15>; + using ArchConfig = natten::gemm::detail::ArchArgs<70, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks31_align2( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 8, 8, 4, 2, 11, 2, 15>; + using ArchConfig = natten::gemm::detail::ArchArgs<70, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks33_align8( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 8, 8, 4, 2, 11, 2, 16>; + using ArchConfig = natten::gemm::detail::ArchArgs<70, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 8, 8>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks33_align4( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 8, 8, 4, 2, 11, 2, 16>; + using ArchConfig = natten::gemm::detail::ArchArgs<70, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + } } } diff --git a/csrc/autogen/src/cuda/gemm/2d/sm70/source_2.cu b/csrc/autogen/src/cuda/gemm/2d/sm70/source_2.cu index 99b7c53..0fd20b2 100644 --- a/csrc/autogen/src/cuda/gemm/2d/sm70/source_2.cu +++ b/csrc/autogen/src/cuda/gemm/2d/sm70/source_2.cu @@ -1,14 +1,770 @@ #include #include -#include #include -#include #include +#include +#include namespace natten { namespace cuda { namespace gemm { -void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks19_align8( +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks33_align2( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 8, 8, 4, 2, 11, 2, 16>; + using ArchConfig = natten::gemm::detail::ArchArgs<70, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks35_align8( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 8, 8, 4, 2, 11, 2, 17>; + using ArchConfig = natten::gemm::detail::ArchArgs<70, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 8, 8>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks35_align4( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 8, 8, 4, 2, 11, 2, 17>; + using ArchConfig = natten::gemm::detail::ArchArgs<70, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks35_align2( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 8, 8, 4, 2, 11, 2, 17>; + using ArchConfig = natten::gemm::detail::ArchArgs<70, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks37_align8( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 8, 8, 4, 2, 11, 2, 18>; + using ArchConfig = natten::gemm::detail::ArchArgs<70, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 8, 8>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks37_align4( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 8, 8, 4, 2, 11, 2, 18>; + using ArchConfig = natten::gemm::detail::ArchArgs<70, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks37_align2( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 8, 8, 4, 2, 11, 2, 18>; + using ArchConfig = natten::gemm::detail::ArchArgs<70, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks39_align8( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 8, 8, 4, 2, 11, 2, 19>; + using ArchConfig = natten::gemm::detail::ArchArgs<70, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 8, 8>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks39_align4( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 8, 8, 4, 2, 11, 2, 19>; + using ArchConfig = natten::gemm::detail::ArchArgs<70, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks39_align2( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 8, 8, 4, 2, 11, 2, 19>; + using ArchConfig = natten::gemm::detail::ArchArgs<70, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks41_align8( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 8, 8, 4, 2, 11, 2, 20>; + using ArchConfig = natten::gemm::detail::ArchArgs<70, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 8, 8>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks41_align4( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 8, 8, 4, 2, 11, 2, 20>; + using ArchConfig = natten::gemm::detail::ArchArgs<70, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks41_align2( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 8, 8, 4, 2, 11, 2, 20>; + using ArchConfig = natten::gemm::detail::ArchArgs<70, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks43_align8( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 8, 8, 4, 2, 11, 2, 21>; + using ArchConfig = natten::gemm::detail::ArchArgs<70, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 8, 8>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks43_align4( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 8, 8, 4, 2, 11, 2, 21>; + using ArchConfig = natten::gemm::detail::ArchArgs<70, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks43_align2( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 8, 8, 4, 2, 11, 2, 21>; + using ArchConfig = natten::gemm::detail::ArchArgs<70, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks45_align8( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 8, 8, 4, 2, 11, 2, 22>; + using ArchConfig = natten::gemm::detail::ArchArgs<70, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 8, 8>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks45_align4( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 8, 8, 4, 2, 11, 2, 22>; + using ArchConfig = natten::gemm::detail::ArchArgs<70, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks45_align2( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 8, 8, 4, 2, 11, 2, 22>; + using ArchConfig = natten::gemm::detail::ArchArgs<70, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks47_align8( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 8, 8, 4, 2, 11, 2, 23>; + using ArchConfig = natten::gemm::detail::ArchArgs<70, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 8, 8>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks47_align4( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 8, 8, 4, 2, 11, 2, 23>; + using ArchConfig = natten::gemm::detail::ArchArgs<70, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks47_align2( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 8, 8, 4, 2, 11, 2, 23>; + using ArchConfig = natten::gemm::detail::ArchArgs<70, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks49_align8( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 8, 8, 4, 2, 11, 2, 24>; + using ArchConfig = natten::gemm::detail::ArchArgs<70, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 8, 8>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks49_align4( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 8, 8, 4, 2, 11, 2, 24>; + using ArchConfig = natten::gemm::detail::ArchArgs<70, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks49_align2( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 8, 8, 4, 2, 11, 2, 24>; + using ArchConfig = natten::gemm::detail::ArchArgs<70, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks51_align8( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 8, 8, 4, 2, 11, 2, 25>; + using ArchConfig = natten::gemm::detail::ArchArgs<70, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 8, 8>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks51_align4( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 8, 8, 4, 2, 11, 2, 25>; + using ArchConfig = natten::gemm::detail::ArchArgs<70, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks51_align2( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 8, 8, 4, 2, 11, 2, 25>; + using ArchConfig = natten::gemm::detail::ArchArgs<70, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks53_align8( void * attn_ptr, void * value_ptr, void * output_ptr, @@ -25,7 +781,7 @@ void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks19_align8( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 8, 8, 4, 2, 8, 2, 9>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 8, 8, 4, 2, 11, 2, 26>; using ArchConfig = natten::gemm::detail::ArchArgs<70, half>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 8, 8>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -35,7 +791,7 @@ void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks19_align8( attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks19_align4( +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks53_align4( void * attn_ptr, void * value_ptr, void * output_ptr, @@ -52,7 +808,7 @@ void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks19_align4( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 8, 8, 4, 2, 8, 2, 9>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 8, 8, 4, 2, 11, 2, 26>; using ArchConfig = natten::gemm::detail::ArchArgs<70, half>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -62,7 +818,7 @@ void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks19_align4( attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks19_align2( +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks53_align2( void * attn_ptr, void * value_ptr, void * output_ptr, @@ -79,7 +835,7 @@ void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks19_align2( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 8, 8, 4, 2, 8, 2, 9>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 8, 8, 4, 2, 11, 2, 26>; using ArchConfig = natten::gemm::detail::ArchArgs<70, half>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -89,7 +845,7 @@ void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks19_align2( attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks21_align8( +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks55_align8( void * attn_ptr, void * value_ptr, void * output_ptr, @@ -106,7 +862,7 @@ void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks21_align8( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 8, 8, 4, 2, 11, 2, 10>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 8, 8, 4, 2, 11, 2, 27>; using ArchConfig = natten::gemm::detail::ArchArgs<70, half>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 8, 8>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -116,7 +872,7 @@ void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks21_align8( attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks21_align4( +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks55_align4( void * attn_ptr, void * value_ptr, void * output_ptr, @@ -133,7 +889,7 @@ void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks21_align4( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 8, 8, 4, 2, 11, 2, 10>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 8, 8, 4, 2, 11, 2, 27>; using ArchConfig = natten::gemm::detail::ArchArgs<70, half>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -143,7 +899,7 @@ void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks21_align4( attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks21_align2( +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks55_align2( void * attn_ptr, void * value_ptr, void * output_ptr, @@ -160,7 +916,7 @@ void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks21_align2( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 8, 8, 4, 2, 11, 2, 10>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 8, 8, 4, 2, 11, 2, 27>; using ArchConfig = natten::gemm::detail::ArchArgs<70, half>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -170,7 +926,7 @@ void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks21_align2( attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks23_align8( +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks57_align8( void * attn_ptr, void * value_ptr, void * output_ptr, @@ -187,7 +943,7 @@ void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks23_align8( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 8, 8, 4, 2, 11, 2, 11>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 8, 8, 4, 2, 11, 2, 28>; using ArchConfig = natten::gemm::detail::ArchArgs<70, half>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 8, 8>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -197,7 +953,7 @@ void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks23_align8( attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks23_align4( +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks57_align4( void * attn_ptr, void * value_ptr, void * output_ptr, @@ -214,7 +970,7 @@ void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks23_align4( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 8, 8, 4, 2, 11, 2, 11>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 8, 8, 4, 2, 11, 2, 28>; using ArchConfig = natten::gemm::detail::ArchArgs<70, half>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -224,7 +980,7 @@ void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks23_align4( attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks23_align2( +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks57_align2( void * attn_ptr, void * value_ptr, void * output_ptr, @@ -241,7 +997,7 @@ void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks23_align2( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 8, 8, 4, 2, 11, 2, 11>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 8, 8, 4, 2, 11, 2, 28>; using ArchConfig = natten::gemm::detail::ArchArgs<70, half>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -251,7 +1007,7 @@ void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks23_align2( attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks25_align8( +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks59_align8( void * attn_ptr, void * value_ptr, void * output_ptr, @@ -268,7 +1024,7 @@ void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks25_align8( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 8, 8, 4, 2, 11, 2, 12>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 8, 8, 4, 2, 11, 2, 29>; using ArchConfig = natten::gemm::detail::ArchArgs<70, half>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 8, 8>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -278,7 +1034,7 @@ void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks25_align8( attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks25_align4( +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks59_align4( void * attn_ptr, void * value_ptr, void * output_ptr, @@ -295,7 +1051,7 @@ void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks25_align4( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 8, 8, 4, 2, 11, 2, 12>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 8, 8, 4, 2, 11, 2, 29>; using ArchConfig = natten::gemm::detail::ArchArgs<70, half>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -305,7 +1061,7 @@ void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks25_align4( attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks25_align2( +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks59_align2( void * attn_ptr, void * value_ptr, void * output_ptr, @@ -322,7 +1078,7 @@ void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks25_align2( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 8, 8, 4, 2, 11, 2, 12>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 8, 8, 4, 2, 11, 2, 29>; using ArchConfig = natten::gemm::detail::ArchArgs<70, half>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -332,7 +1088,7 @@ void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks25_align2( attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks27_align8( +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks61_align8( void * attn_ptr, void * value_ptr, void * output_ptr, @@ -349,7 +1105,7 @@ void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks27_align8( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 8, 8, 4, 2, 11, 2, 13>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 8, 8, 4, 2, 11, 2, 30>; using ArchConfig = natten::gemm::detail::ArchArgs<70, half>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 8, 8>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -359,7 +1115,7 @@ void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks27_align8( attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks27_align4( +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks61_align4( void * attn_ptr, void * value_ptr, void * output_ptr, @@ -376,7 +1132,7 @@ void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks27_align4( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 8, 8, 4, 2, 11, 2, 13>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 8, 8, 4, 2, 11, 2, 30>; using ArchConfig = natten::gemm::detail::ArchArgs<70, half>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -386,7 +1142,7 @@ void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks27_align4( attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks27_align2( +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks61_align2( void * attn_ptr, void * value_ptr, void * output_ptr, @@ -403,7 +1159,7 @@ void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks27_align2( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 8, 8, 4, 2, 11, 2, 13>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 8, 8, 4, 2, 11, 2, 30>; using ArchConfig = natten::gemm::detail::ArchArgs<70, half>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -413,7 +1169,7 @@ void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks27_align2( attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks29_align8( +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks63_align8( void * attn_ptr, void * value_ptr, void * output_ptr, @@ -430,7 +1186,7 @@ void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks29_align8( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 8, 8, 4, 2, 11, 2, 14>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 8, 8, 4, 2, 11, 2, 31>; using ArchConfig = natten::gemm::detail::ArchArgs<70, half>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 8, 8>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -440,7 +1196,7 @@ void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks29_align8( attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks29_align4( +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks63_align4( void * attn_ptr, void * value_ptr, void * output_ptr, @@ -457,7 +1213,7 @@ void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks29_align4( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 8, 8, 4, 2, 11, 2, 14>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 8, 8, 4, 2, 11, 2, 31>; using ArchConfig = natten::gemm::detail::ArchArgs<70, half>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -467,7 +1223,7 @@ void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks29_align4( attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks29_align2( +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks63_align2( void * attn_ptr, void * value_ptr, void * output_ptr, @@ -484,7 +1240,7 @@ void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks29_align2( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 8, 8, 4, 2, 11, 2, 14>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 8, 8, 4, 2, 11, 2, 31>; using ArchConfig = natten::gemm::detail::ArchArgs<70, half>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -494,10 +1250,10 @@ void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks29_align2( attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks31_align8( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks3_align8( void * attn_ptr, - void * value_ptr, - void * output_ptr, + void * d_output_ptr, + void * d_value_ptr, int batch_size, int heads, int height, @@ -511,20 +1267,20 @@ void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks31_align8( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 8, 8, 4, 2, 11, 2, 15>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 8, 8, 4, 2, 8, 4, 1>; using ArchConfig = natten::gemm::detail::ArchArgs<70, half>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 8, 8>; using DConfig = natten::gemm::detail::DTypeConfig; - using Kernel = NeighborhoodNeighborhood2D; + using Kernel = InverseNeighborhood2D; Kernel kernel; kernel( -attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks31_align4( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks3_align4( void * attn_ptr, - void * value_ptr, - void * output_ptr, + void * d_output_ptr, + void * d_value_ptr, int batch_size, int heads, int height, @@ -538,20 +1294,20 @@ void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks31_align4( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 8, 8, 4, 2, 11, 2, 15>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 8, 8, 4, 2, 8, 4, 1>; using ArchConfig = natten::gemm::detail::ArchArgs<70, half>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; using DConfig = natten::gemm::detail::DTypeConfig; - using Kernel = NeighborhoodNeighborhood2D; + using Kernel = InverseNeighborhood2D; Kernel kernel; kernel( -attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks31_align2( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks3_align2( void * attn_ptr, - void * value_ptr, - void * output_ptr, + void * d_output_ptr, + void * d_value_ptr, int batch_size, int heads, int height, @@ -565,20 +1321,20 @@ void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks31_align2( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 8, 8, 4, 2, 11, 2, 15>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 8, 8, 4, 2, 8, 4, 1>; using ArchConfig = natten::gemm::detail::ArchArgs<70, half>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; using DConfig = natten::gemm::detail::DTypeConfig; - using Kernel = NeighborhoodNeighborhood2D; + using Kernel = InverseNeighborhood2D; Kernel kernel; kernel( -attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks33_align8( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks5_align8( void * attn_ptr, - void * value_ptr, - void * output_ptr, + void * d_output_ptr, + void * d_value_ptr, int batch_size, int heads, int height, @@ -592,20 +1348,20 @@ void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks33_align8( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 8, 8, 4, 2, 11, 2, 16>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 8, 8, 4, 2, 8, 4, 2>; using ArchConfig = natten::gemm::detail::ArchArgs<70, half>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 8, 8>; using DConfig = natten::gemm::detail::DTypeConfig; - using Kernel = NeighborhoodNeighborhood2D; + using Kernel = InverseNeighborhood2D; Kernel kernel; kernel( -attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks33_align4( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks5_align4( void * attn_ptr, - void * value_ptr, - void * output_ptr, + void * d_output_ptr, + void * d_value_ptr, int batch_size, int heads, int height, @@ -619,20 +1375,20 @@ void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks33_align4( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 8, 8, 4, 2, 11, 2, 16>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 8, 8, 4, 2, 8, 4, 2>; using ArchConfig = natten::gemm::detail::ArchArgs<70, half>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; using DConfig = natten::gemm::detail::DTypeConfig; - using Kernel = NeighborhoodNeighborhood2D; + using Kernel = InverseNeighborhood2D; Kernel kernel; kernel( -attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks33_align2( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks5_align2( void * attn_ptr, - void * value_ptr, - void * output_ptr, + void * d_output_ptr, + void * d_value_ptr, int batch_size, int heads, int height, @@ -646,17 +1402,17 @@ void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_8x8x4_2_sm70_ks33_align2( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 8, 8, 4, 2, 11, 2, 16>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 8, 8, 4, 2, 8, 4, 2>; using ArchConfig = natten::gemm::detail::ArchArgs<70, half>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; using DConfig = natten::gemm::detail::DTypeConfig; - using Kernel = NeighborhoodNeighborhood2D; + using Kernel = InverseNeighborhood2D; Kernel kernel; kernel( -attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks3_align8( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks7_align8( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -673,7 +1429,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks3_align8( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 8, 8, 4, 2, 8, 4, 1>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 8, 8, 4, 2, 8, 4, 3>; using ArchConfig = natten::gemm::detail::ArchArgs<70, half>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 8, 8>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -683,7 +1439,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks3_align8( attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks3_align4( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks7_align4( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -700,7 +1456,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks3_align4( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 8, 8, 4, 2, 8, 4, 1>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 8, 8, 4, 2, 8, 4, 3>; using ArchConfig = natten::gemm::detail::ArchArgs<70, half>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -710,7 +1466,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks3_align4( attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks3_align2( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks7_align2( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -727,7 +1483,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks3_align2( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 8, 8, 4, 2, 8, 4, 1>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 8, 8, 4, 2, 8, 4, 3>; using ArchConfig = natten::gemm::detail::ArchArgs<70, half>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -737,7 +1493,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks3_align2( attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks5_align8( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks9_align8( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -754,7 +1510,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks5_align8( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 8, 8, 4, 2, 8, 4, 2>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 8, 8, 4, 2, 8, 4, 4>; using ArchConfig = natten::gemm::detail::ArchArgs<70, half>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 8, 8>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -764,7 +1520,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks5_align8( attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks5_align4( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks9_align4( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -781,7 +1537,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks5_align4( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 8, 8, 4, 2, 8, 4, 2>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 8, 8, 4, 2, 8, 4, 4>; using ArchConfig = natten::gemm::detail::ArchArgs<70, half>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -791,7 +1547,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks5_align4( attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks5_align2( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks9_align2( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -808,7 +1564,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks5_align2( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 8, 8, 4, 2, 8, 4, 2>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 8, 8, 4, 2, 8, 4, 4>; using ArchConfig = natten::gemm::detail::ArchArgs<70, half>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -818,7 +1574,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks5_align2( attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks7_align8( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks11_align8( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -835,7 +1591,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks7_align8( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 8, 8, 4, 2, 8, 4, 3>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 8, 8, 4, 2, 8, 4, 5>; using ArchConfig = natten::gemm::detail::ArchArgs<70, half>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 8, 8>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -845,7 +1601,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks7_align8( attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks7_align4( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks11_align4( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -862,7 +1618,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks7_align4( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 8, 8, 4, 2, 8, 4, 3>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 8, 8, 4, 2, 8, 4, 5>; using ArchConfig = natten::gemm::detail::ArchArgs<70, half>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -872,7 +1628,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks7_align4( attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks7_align2( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks11_align2( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -889,7 +1645,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks7_align2( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 8, 8, 4, 2, 8, 4, 3>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 8, 8, 4, 2, 8, 4, 5>; using ArchConfig = natten::gemm::detail::ArchArgs<70, half>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -899,7 +1655,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks7_align2( attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks9_align8( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks13_align8( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -916,7 +1672,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks9_align8( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 8, 8, 4, 2, 8, 4, 4>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 8, 8, 4, 2, 8, 4, 6>; using ArchConfig = natten::gemm::detail::ArchArgs<70, half>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 8, 8>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -926,7 +1682,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks9_align8( attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks9_align4( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks13_align4( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -943,7 +1699,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks9_align4( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 8, 8, 4, 2, 8, 4, 4>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 8, 8, 4, 2, 8, 4, 6>; using ArchConfig = natten::gemm::detail::ArchArgs<70, half>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -953,7 +1709,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks9_align4( attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks9_align2( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks13_align2( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -970,7 +1726,169 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks9_align2( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 8, 8, 4, 2, 8, 4, 4>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 8, 8, 4, 2, 8, 4, 6>; + using ArchConfig = natten::gemm::detail::ArchArgs<70, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks15_align8( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 8, 8, 4, 2, 8, 4, 7>; + using ArchConfig = natten::gemm::detail::ArchArgs<70, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 8, 8>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks15_align4( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 8, 8, 4, 2, 8, 4, 7>; + using ArchConfig = natten::gemm::detail::ArchArgs<70, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks15_align2( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 8, 8, 4, 2, 8, 4, 7>; + using ArchConfig = natten::gemm::detail::ArchArgs<70, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks17_align8( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 8, 8, 4, 2, 8, 4, 8>; + using ArchConfig = natten::gemm::detail::ArchArgs<70, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 8, 8>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks17_align4( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 8, 8, 4, 2, 8, 4, 8>; + using ArchConfig = natten::gemm::detail::ArchArgs<70, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks17_align2( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 8, 8, 4, 2, 8, 4, 8>; using ArchConfig = natten::gemm::detail::ArchArgs<70, half>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; using DConfig = natten::gemm::detail::DTypeConfig; diff --git a/csrc/autogen/src/cuda/gemm/2d/sm70/source_3.cu b/csrc/autogen/src/cuda/gemm/2d/sm70/source_3.cu index 4ec67ab..27f1cef 100644 --- a/csrc/autogen/src/cuda/gemm/2d/sm70/source_3.cu +++ b/csrc/autogen/src/cuda/gemm/2d/sm70/source_3.cu @@ -1,14 +1,14 @@ #include #include -#include #include -#include #include +#include +#include namespace natten { namespace cuda { namespace gemm { -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks11_align8( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks19_align8( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -25,7 +25,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks11_align8( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 8, 8, 4, 2, 8, 4, 5>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 8, 8, 4, 2, 8, 4, 9>; using ArchConfig = natten::gemm::detail::ArchArgs<70, half>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 8, 8>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -35,7 +35,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks11_align8( attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks11_align4( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks19_align4( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -52,7 +52,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks11_align4( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 8, 8, 4, 2, 8, 4, 5>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 8, 8, 4, 2, 8, 4, 9>; using ArchConfig = natten::gemm::detail::ArchArgs<70, half>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -62,7 +62,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks11_align4( attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks11_align2( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks19_align2( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -79,7 +79,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks11_align2( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 8, 8, 4, 2, 8, 4, 5>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 8, 8, 4, 2, 8, 4, 9>; using ArchConfig = natten::gemm::detail::ArchArgs<70, half>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -89,7 +89,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks11_align2( attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks13_align8( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks21_align8( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -106,7 +106,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks13_align8( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 8, 8, 4, 2, 8, 4, 6>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 8, 8, 4, 2, 8, 4, 10>; using ArchConfig = natten::gemm::detail::ArchArgs<70, half>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 8, 8>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -116,7 +116,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks13_align8( attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks13_align4( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks21_align4( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -133,7 +133,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks13_align4( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 8, 8, 4, 2, 8, 4, 6>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 8, 8, 4, 2, 8, 4, 10>; using ArchConfig = natten::gemm::detail::ArchArgs<70, half>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -143,7 +143,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks13_align4( attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks13_align2( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks21_align2( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -160,7 +160,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks13_align2( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 8, 8, 4, 2, 8, 4, 6>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 8, 8, 4, 2, 8, 4, 10>; using ArchConfig = natten::gemm::detail::ArchArgs<70, half>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -170,7 +170,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks13_align2( attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks15_align8( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks23_align8( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -187,7 +187,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks15_align8( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 8, 8, 4, 2, 8, 4, 7>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 8, 8, 4, 2, 8, 4, 11>; using ArchConfig = natten::gemm::detail::ArchArgs<70, half>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 8, 8>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -197,7 +197,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks15_align8( attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks15_align4( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks23_align4( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -214,7 +214,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks15_align4( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 8, 8, 4, 2, 8, 4, 7>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 8, 8, 4, 2, 8, 4, 11>; using ArchConfig = natten::gemm::detail::ArchArgs<70, half>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -224,7 +224,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks15_align4( attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks15_align2( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks23_align2( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -241,7 +241,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks15_align2( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 8, 8, 4, 2, 8, 4, 7>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 8, 8, 4, 2, 8, 4, 11>; using ArchConfig = natten::gemm::detail::ArchArgs<70, half>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -251,7 +251,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks15_align2( attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks17_align8( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks25_align8( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -268,7 +268,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks17_align8( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 8, 8, 4, 2, 8, 4, 8>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 8, 8, 4, 2, 8, 4, 12>; using ArchConfig = natten::gemm::detail::ArchArgs<70, half>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 8, 8>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -278,7 +278,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks17_align8( attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks17_align4( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks25_align4( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -295,7 +295,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks17_align4( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 8, 8, 4, 2, 8, 4, 8>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 8, 8, 4, 2, 8, 4, 12>; using ArchConfig = natten::gemm::detail::ArchArgs<70, half>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -305,7 +305,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks17_align4( attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks17_align2( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks25_align2( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -322,7 +322,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks17_align2( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 8, 8, 4, 2, 8, 4, 8>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 8, 8, 4, 2, 8, 4, 12>; using ArchConfig = natten::gemm::detail::ArchArgs<70, half>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -332,7 +332,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks17_align2( attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks19_align8( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks27_align8( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -349,7 +349,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks19_align8( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 8, 8, 4, 2, 8, 4, 9>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 8, 8, 4, 2, 8, 4, 13>; using ArchConfig = natten::gemm::detail::ArchArgs<70, half>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 8, 8>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -359,7 +359,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks19_align8( attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks19_align4( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks27_align4( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -376,7 +376,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks19_align4( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 8, 8, 4, 2, 8, 4, 9>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 8, 8, 4, 2, 8, 4, 13>; using ArchConfig = natten::gemm::detail::ArchArgs<70, half>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -386,7 +386,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks19_align4( attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks19_align2( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks27_align2( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -403,7 +403,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks19_align2( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 8, 8, 4, 2, 8, 4, 9>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 8, 8, 4, 2, 8, 4, 13>; using ArchConfig = natten::gemm::detail::ArchArgs<70, half>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -413,7 +413,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks19_align2( attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks21_align8( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks29_align8( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -430,7 +430,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks21_align8( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 8, 8, 4, 2, 8, 4, 10>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 8, 8, 4, 2, 8, 4, 14>; using ArchConfig = natten::gemm::detail::ArchArgs<70, half>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 8, 8>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -440,7 +440,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks21_align8( attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks21_align4( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks29_align4( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -457,7 +457,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks21_align4( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 8, 8, 4, 2, 8, 4, 10>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 8, 8, 4, 2, 8, 4, 14>; using ArchConfig = natten::gemm::detail::ArchArgs<70, half>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -467,7 +467,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks21_align4( attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks21_align2( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks29_align2( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -484,7 +484,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks21_align2( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 8, 8, 4, 2, 8, 4, 10>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 8, 8, 4, 2, 8, 4, 14>; using ArchConfig = natten::gemm::detail::ArchArgs<70, half>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -494,7 +494,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks21_align2( attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks23_align8( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks31_align8( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -511,7 +511,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks23_align8( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 8, 8, 4, 2, 8, 4, 11>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 8, 8, 4, 2, 8, 4, 15>; using ArchConfig = natten::gemm::detail::ArchArgs<70, half>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 8, 8>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -521,7 +521,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks23_align8( attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks23_align4( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks31_align4( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -538,7 +538,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks23_align4( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 8, 8, 4, 2, 8, 4, 11>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 8, 8, 4, 2, 8, 4, 15>; using ArchConfig = natten::gemm::detail::ArchArgs<70, half>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -548,7 +548,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks23_align4( attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks23_align2( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks31_align2( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -565,7 +565,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks23_align2( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 8, 8, 4, 2, 8, 4, 11>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 8, 8, 4, 2, 8, 4, 15>; using ArchConfig = natten::gemm::detail::ArchArgs<70, half>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -575,7 +575,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks23_align2( attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks25_align8( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks33_align8( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -592,7 +592,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks25_align8( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 8, 8, 4, 2, 8, 4, 12>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 8, 8, 4, 2, 8, 4, 16>; using ArchConfig = natten::gemm::detail::ArchArgs<70, half>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 8, 8>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -602,7 +602,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks25_align8( attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks25_align4( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks33_align4( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -619,7 +619,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks25_align4( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 8, 8, 4, 2, 8, 4, 12>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 8, 8, 4, 2, 8, 4, 16>; using ArchConfig = natten::gemm::detail::ArchArgs<70, half>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -629,7 +629,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks25_align4( attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks25_align2( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks33_align2( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -646,7 +646,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks25_align2( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 8, 8, 4, 2, 8, 4, 12>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 8, 8, 4, 2, 8, 4, 16>; using ArchConfig = natten::gemm::detail::ArchArgs<70, half>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -656,7 +656,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks25_align2( attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks27_align8( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks35_align8( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -673,7 +673,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks27_align8( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 8, 8, 4, 2, 8, 4, 13>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 8, 8, 4, 2, 8, 4, 17>; using ArchConfig = natten::gemm::detail::ArchArgs<70, half>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 8, 8>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -683,7 +683,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks27_align8( attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks27_align4( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks35_align4( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -700,7 +700,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks27_align4( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 8, 8, 4, 2, 8, 4, 13>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 8, 8, 4, 2, 8, 4, 17>; using ArchConfig = natten::gemm::detail::ArchArgs<70, half>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -710,7 +710,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks27_align4( attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks27_align2( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks35_align2( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -727,7 +727,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks27_align2( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 8, 8, 4, 2, 8, 4, 13>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 8, 8, 4, 2, 8, 4, 17>; using ArchConfig = natten::gemm::detail::ArchArgs<70, half>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -737,7 +737,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks27_align2( attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks29_align8( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks37_align8( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -754,7 +754,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks29_align8( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 8, 8, 4, 2, 8, 4, 14>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 8, 8, 4, 2, 8, 4, 18>; using ArchConfig = natten::gemm::detail::ArchArgs<70, half>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 8, 8>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -764,7 +764,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks29_align8( attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks29_align4( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks37_align4( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -781,7 +781,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks29_align4( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 8, 8, 4, 2, 8, 4, 14>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 8, 8, 4, 2, 8, 4, 18>; using ArchConfig = natten::gemm::detail::ArchArgs<70, half>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -791,7 +791,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks29_align4( attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks29_align2( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks37_align2( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -808,7 +808,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks29_align2( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 8, 8, 4, 2, 8, 4, 14>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 8, 8, 4, 2, 8, 4, 18>; using ArchConfig = natten::gemm::detail::ArchArgs<70, half>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -818,7 +818,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks29_align2( attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks31_align8( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks39_align8( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -835,7 +835,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks31_align8( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 8, 8, 4, 2, 8, 4, 15>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 8, 8, 4, 2, 8, 4, 19>; using ArchConfig = natten::gemm::detail::ArchArgs<70, half>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 8, 8>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -845,7 +845,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks31_align8( attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks31_align4( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks39_align4( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -862,7 +862,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks31_align4( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 8, 8, 4, 2, 8, 4, 15>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 8, 8, 4, 2, 8, 4, 19>; using ArchConfig = natten::gemm::detail::ArchArgs<70, half>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -872,7 +872,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks31_align4( attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks31_align2( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks39_align2( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -889,7 +889,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks31_align2( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 8, 8, 4, 2, 8, 4, 15>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 8, 8, 4, 2, 8, 4, 19>; using ArchConfig = natten::gemm::detail::ArchArgs<70, half>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -899,7 +899,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks31_align2( attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks33_align8( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks41_align8( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -916,7 +916,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks33_align8( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 8, 8, 4, 2, 8, 4, 16>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 8, 8, 4, 2, 8, 4, 20>; using ArchConfig = natten::gemm::detail::ArchArgs<70, half>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 8, 8>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -926,7 +926,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks33_align8( attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks33_align4( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks41_align4( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -943,7 +943,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks33_align4( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 8, 8, 4, 2, 8, 4, 16>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 8, 8, 4, 2, 8, 4, 20>; using ArchConfig = natten::gemm::detail::ArchArgs<70, half>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -953,7 +953,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks33_align4( attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks33_align2( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks41_align2( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -970,7 +970,898 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks33_align2( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 8, 8, 4, 2, 8, 4, 16>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 8, 8, 4, 2, 8, 4, 20>; + using ArchConfig = natten::gemm::detail::ArchArgs<70, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks43_align8( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 8, 8, 4, 2, 8, 4, 21>; + using ArchConfig = natten::gemm::detail::ArchArgs<70, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 8, 8>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks43_align4( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 8, 8, 4, 2, 8, 4, 21>; + using ArchConfig = natten::gemm::detail::ArchArgs<70, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks43_align2( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 8, 8, 4, 2, 8, 4, 21>; + using ArchConfig = natten::gemm::detail::ArchArgs<70, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks45_align8( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 8, 8, 4, 2, 8, 4, 22>; + using ArchConfig = natten::gemm::detail::ArchArgs<70, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 8, 8>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks45_align4( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 8, 8, 4, 2, 8, 4, 22>; + using ArchConfig = natten::gemm::detail::ArchArgs<70, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks45_align2( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 8, 8, 4, 2, 8, 4, 22>; + using ArchConfig = natten::gemm::detail::ArchArgs<70, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks47_align8( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 8, 8, 4, 2, 8, 4, 23>; + using ArchConfig = natten::gemm::detail::ArchArgs<70, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 8, 8>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks47_align4( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 8, 8, 4, 2, 8, 4, 23>; + using ArchConfig = natten::gemm::detail::ArchArgs<70, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks47_align2( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 8, 8, 4, 2, 8, 4, 23>; + using ArchConfig = natten::gemm::detail::ArchArgs<70, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks49_align8( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 8, 8, 4, 2, 8, 4, 24>; + using ArchConfig = natten::gemm::detail::ArchArgs<70, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 8, 8>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks49_align4( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 8, 8, 4, 2, 8, 4, 24>; + using ArchConfig = natten::gemm::detail::ArchArgs<70, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks49_align2( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 8, 8, 4, 2, 8, 4, 24>; + using ArchConfig = natten::gemm::detail::ArchArgs<70, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks51_align8( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 8, 8, 4, 2, 8, 4, 25>; + using ArchConfig = natten::gemm::detail::ArchArgs<70, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 8, 8>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks51_align4( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 8, 8, 4, 2, 8, 4, 25>; + using ArchConfig = natten::gemm::detail::ArchArgs<70, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks51_align2( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 8, 8, 4, 2, 8, 4, 25>; + using ArchConfig = natten::gemm::detail::ArchArgs<70, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks53_align8( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 8, 8, 4, 2, 8, 4, 26>; + using ArchConfig = natten::gemm::detail::ArchArgs<70, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 8, 8>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks53_align4( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 8, 8, 4, 2, 8, 4, 26>; + using ArchConfig = natten::gemm::detail::ArchArgs<70, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks53_align2( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 8, 8, 4, 2, 8, 4, 26>; + using ArchConfig = natten::gemm::detail::ArchArgs<70, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks55_align8( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 8, 8, 4, 2, 8, 4, 27>; + using ArchConfig = natten::gemm::detail::ArchArgs<70, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 8, 8>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks55_align4( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 8, 8, 4, 2, 8, 4, 27>; + using ArchConfig = natten::gemm::detail::ArchArgs<70, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks55_align2( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 8, 8, 4, 2, 8, 4, 27>; + using ArchConfig = natten::gemm::detail::ArchArgs<70, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks57_align8( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 8, 8, 4, 2, 8, 4, 28>; + using ArchConfig = natten::gemm::detail::ArchArgs<70, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 8, 8>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks57_align4( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 8, 8, 4, 2, 8, 4, 28>; + using ArchConfig = natten::gemm::detail::ArchArgs<70, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks57_align2( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 8, 8, 4, 2, 8, 4, 28>; + using ArchConfig = natten::gemm::detail::ArchArgs<70, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks59_align8( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 8, 8, 4, 2, 8, 4, 29>; + using ArchConfig = natten::gemm::detail::ArchArgs<70, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 8, 8>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks59_align4( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 8, 8, 4, 2, 8, 4, 29>; + using ArchConfig = natten::gemm::detail::ArchArgs<70, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks59_align2( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 8, 8, 4, 2, 8, 4, 29>; + using ArchConfig = natten::gemm::detail::ArchArgs<70, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks61_align8( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 8, 8, 4, 2, 8, 4, 30>; + using ArchConfig = natten::gemm::detail::ArchArgs<70, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 8, 8>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks61_align4( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 8, 8, 4, 2, 8, 4, 30>; + using ArchConfig = natten::gemm::detail::ArchArgs<70, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks61_align2( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 8, 8, 4, 2, 8, 4, 30>; + using ArchConfig = natten::gemm::detail::ArchArgs<70, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks63_align8( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 8, 8, 4, 2, 8, 4, 31>; + using ArchConfig = natten::gemm::detail::ArchArgs<70, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 8, 8>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks63_align4( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 8, 8, 4, 2, 8, 4, 31>; + using ArchConfig = natten::gemm::detail::ArchArgs<70, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_8x8x4_2_sm70_ks63_align2( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 8, 8, 4, 2, 8, 4, 31>; using ArchConfig = natten::gemm::detail::ArchArgs<70, half>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; using DConfig = natten::gemm::detail::DTypeConfig; diff --git a/csrc/autogen/src/cuda/gemm/2d/sm75/source_0.cu b/csrc/autogen/src/cuda/gemm/2d/sm75/source_0.cu index 44b1217..7f8aeb0 100644 --- a/csrc/autogen/src/cuda/gemm/2d/sm75/source_0.cu +++ b/csrc/autogen/src/cuda/gemm/2d/sm75/source_0.cu @@ -1,9 +1,9 @@ #include #include -#include #include -#include #include +#include +#include namespace natten { namespace cuda { namespace gemm { @@ -1016,6 +1016,958 @@ void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks25_align2( query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks27_align8( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 8, 2, 14, 1, 13>; + using ArchConfig = natten::gemm::detail::ArchArgs<75, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<8, 8, 1>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks27_align4( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 8, 2, 14, 1, 13>; + using ArchConfig = natten::gemm::detail::ArchArgs<75, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<4, 4, 1>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks27_align2( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 8, 2, 14, 1, 13>; + using ArchConfig = natten::gemm::detail::ArchArgs<75, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<2, 2, 1>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks29_align8( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 8, 2, 17, 1, 14>; + using ArchConfig = natten::gemm::detail::ArchArgs<75, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<8, 8, 1>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks29_align4( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 8, 2, 17, 1, 14>; + using ArchConfig = natten::gemm::detail::ArchArgs<75, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<4, 4, 1>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks29_align2( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 8, 2, 17, 1, 14>; + using ArchConfig = natten::gemm::detail::ArchArgs<75, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<2, 2, 1>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks31_align8( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 8, 2, 17, 1, 15>; + using ArchConfig = natten::gemm::detail::ArchArgs<75, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<8, 8, 1>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks31_align4( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 8, 2, 17, 1, 15>; + using ArchConfig = natten::gemm::detail::ArchArgs<75, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<4, 4, 1>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks31_align2( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 8, 2, 17, 1, 15>; + using ArchConfig = natten::gemm::detail::ArchArgs<75, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<2, 2, 1>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks33_align8( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 8, 2, 17, 1, 16>; + using ArchConfig = natten::gemm::detail::ArchArgs<75, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<8, 8, 1>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks33_align4( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 8, 2, 17, 1, 16>; + using ArchConfig = natten::gemm::detail::ArchArgs<75, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<4, 4, 1>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks33_align2( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 8, 2, 17, 1, 16>; + using ArchConfig = natten::gemm::detail::ArchArgs<75, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<2, 2, 1>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks35_align8( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 8, 2, 22, 1, 17>; + using ArchConfig = natten::gemm::detail::ArchArgs<75, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<8, 8, 1>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks35_align4( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 8, 2, 22, 1, 17>; + using ArchConfig = natten::gemm::detail::ArchArgs<75, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<4, 4, 1>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks35_align2( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 8, 2, 22, 1, 17>; + using ArchConfig = natten::gemm::detail::ArchArgs<75, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<2, 2, 1>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks37_align8( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 8, 2, 22, 1, 18>; + using ArchConfig = natten::gemm::detail::ArchArgs<75, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<8, 8, 1>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks37_align4( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 8, 2, 22, 1, 18>; + using ArchConfig = natten::gemm::detail::ArchArgs<75, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<4, 4, 1>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks37_align2( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 8, 2, 22, 1, 18>; + using ArchConfig = natten::gemm::detail::ArchArgs<75, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<2, 2, 1>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks39_align8( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 8, 2, 23, 1, 19>; + using ArchConfig = natten::gemm::detail::ArchArgs<75, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<8, 8, 1>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks39_align4( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 8, 2, 23, 1, 19>; + using ArchConfig = natten::gemm::detail::ArchArgs<75, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<4, 4, 1>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks39_align2( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 8, 2, 23, 1, 19>; + using ArchConfig = natten::gemm::detail::ArchArgs<75, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<2, 2, 1>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks41_align8( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 8, 2, 22, 1, 20>; + using ArchConfig = natten::gemm::detail::ArchArgs<75, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<8, 8, 1>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks41_align4( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 8, 2, 22, 1, 20>; + using ArchConfig = natten::gemm::detail::ArchArgs<75, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<4, 4, 1>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks41_align2( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 8, 2, 22, 1, 20>; + using ArchConfig = natten::gemm::detail::ArchArgs<75, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<2, 2, 1>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks43_align8( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 8, 2, 22, 1, 21>; + using ArchConfig = natten::gemm::detail::ArchArgs<75, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<8, 8, 1>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks43_align4( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 8, 2, 22, 1, 21>; + using ArchConfig = natten::gemm::detail::ArchArgs<75, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<4, 4, 1>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks43_align2( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 8, 2, 22, 1, 21>; + using ArchConfig = natten::gemm::detail::ArchArgs<75, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<2, 2, 1>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks45_align8( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 8, 2, 23, 1, 22>; + using ArchConfig = natten::gemm::detail::ArchArgs<75, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<8, 8, 1>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks45_align4( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 8, 2, 23, 1, 22>; + using ArchConfig = natten::gemm::detail::ArchArgs<75, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<4, 4, 1>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks45_align2( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 8, 2, 23, 1, 22>; + using ArchConfig = natten::gemm::detail::ArchArgs<75, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<2, 2, 1>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks47_align8( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 8, 2, 33, 1, 23>; + using ArchConfig = natten::gemm::detail::ArchArgs<75, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<8, 8, 1>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks47_align4( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 8, 2, 33, 1, 23>; + using ArchConfig = natten::gemm::detail::ArchArgs<75, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<4, 4, 1>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks47_align2( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 8, 2, 33, 1, 23>; + using ArchConfig = natten::gemm::detail::ArchArgs<75, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<2, 2, 1>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks49_align8( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 8, 2, 33, 1, 24>; + using ArchConfig = natten::gemm::detail::ArchArgs<75, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<8, 8, 1>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + } } } diff --git a/csrc/autogen/src/cuda/gemm/2d/sm75/source_1.cu b/csrc/autogen/src/cuda/gemm/2d/sm75/source_1.cu index b545714..9dd7263 100644 --- a/csrc/autogen/src/cuda/gemm/2d/sm75/source_1.cu +++ b/csrc/autogen/src/cuda/gemm/2d/sm75/source_1.cu @@ -1,14 +1,70 @@ #include #include -#include #include -#include #include +#include +#include namespace natten { namespace cuda { namespace gemm { -void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks27_align8( +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks49_align4( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 8, 2, 33, 1, 24>; + using ArchConfig = natten::gemm::detail::ArchArgs<75, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<4, 4, 1>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks49_align2( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 8, 2, 33, 1, 24>; + using ArchConfig = natten::gemm::detail::ArchArgs<75, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<2, 2, 1>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks51_align8( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -26,7 +82,7 @@ void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks27_align8( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 8, 2, 14, 1, 13>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 8, 2, 33, 1, 25>; using ArchConfig = natten::gemm::detail::ArchArgs<75, half>; using AConfig = natten::gemm::detail::AlignmentConfig<8, 8, 1>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -36,7 +92,7 @@ void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks27_align8( query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks27_align4( +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks51_align4( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -54,7 +110,7 @@ void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks27_align4( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 8, 2, 14, 1, 13>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 8, 2, 33, 1, 25>; using ArchConfig = natten::gemm::detail::ArchArgs<75, half>; using AConfig = natten::gemm::detail::AlignmentConfig<4, 4, 1>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -64,7 +120,7 @@ void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks27_align4( query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks27_align2( +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks51_align2( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -82,7 +138,7 @@ void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks27_align2( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 8, 2, 14, 1, 13>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 8, 2, 33, 1, 25>; using ArchConfig = natten::gemm::detail::ArchArgs<75, half>; using AConfig = natten::gemm::detail::AlignmentConfig<2, 2, 1>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -92,7 +148,7 @@ void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks27_align2( query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks29_align8( +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks53_align8( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -110,7 +166,7 @@ void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks29_align8( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 8, 2, 17, 1, 14>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 8, 2, 33, 1, 26>; using ArchConfig = natten::gemm::detail::ArchArgs<75, half>; using AConfig = natten::gemm::detail::AlignmentConfig<8, 8, 1>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -120,7 +176,7 @@ void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks29_align8( query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks29_align4( +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks53_align4( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -138,7 +194,7 @@ void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks29_align4( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 8, 2, 17, 1, 14>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 8, 2, 33, 1, 26>; using ArchConfig = natten::gemm::detail::ArchArgs<75, half>; using AConfig = natten::gemm::detail::AlignmentConfig<4, 4, 1>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -148,7 +204,7 @@ void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks29_align4( query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks29_align2( +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks53_align2( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -166,7 +222,7 @@ void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks29_align2( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 8, 2, 17, 1, 14>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 8, 2, 33, 1, 26>; using ArchConfig = natten::gemm::detail::ArchArgs<75, half>; using AConfig = natten::gemm::detail::AlignmentConfig<2, 2, 1>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -176,7 +232,7 @@ void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks29_align2( query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks31_align8( +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks55_align8( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -194,7 +250,7 @@ void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks31_align8( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 8, 2, 17, 1, 15>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 8, 2, 33, 1, 27>; using ArchConfig = natten::gemm::detail::ArchArgs<75, half>; using AConfig = natten::gemm::detail::AlignmentConfig<8, 8, 1>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -204,7 +260,7 @@ void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks31_align8( query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks31_align4( +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks55_align4( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -222,7 +278,7 @@ void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks31_align4( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 8, 2, 17, 1, 15>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 8, 2, 33, 1, 27>; using ArchConfig = natten::gemm::detail::ArchArgs<75, half>; using AConfig = natten::gemm::detail::AlignmentConfig<4, 4, 1>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -232,7 +288,7 @@ void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks31_align4( query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks31_align2( +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks55_align2( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -250,7 +306,7 @@ void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks31_align2( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 8, 2, 17, 1, 15>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 8, 2, 33, 1, 27>; using ArchConfig = natten::gemm::detail::ArchArgs<75, half>; using AConfig = natten::gemm::detail::AlignmentConfig<2, 2, 1>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -260,7 +316,7 @@ void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks31_align2( query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks33_align8( +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks57_align8( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -278,7 +334,7 @@ void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks33_align8( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 8, 2, 17, 1, 16>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 8, 2, 33, 1, 28>; using ArchConfig = natten::gemm::detail::ArchArgs<75, half>; using AConfig = natten::gemm::detail::AlignmentConfig<8, 8, 1>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -288,7 +344,7 @@ void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks33_align8( query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks33_align4( +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks57_align4( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -306,7 +362,7 @@ void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks33_align4( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 8, 2, 17, 1, 16>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 8, 2, 33, 1, 28>; using ArchConfig = natten::gemm::detail::ArchArgs<75, half>; using AConfig = natten::gemm::detail::AlignmentConfig<4, 4, 1>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -316,7 +372,7 @@ void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks33_align4( query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks33_align2( +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks57_align2( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -334,7 +390,7 @@ void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks33_align2( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 8, 2, 17, 1, 16>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 8, 2, 33, 1, 28>; using ArchConfig = natten::gemm::detail::ArchArgs<75, half>; using AConfig = natten::gemm::detail::AlignmentConfig<2, 2, 1>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -344,10 +400,11 @@ void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks33_align2( query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks3_align8( +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks59_align8( + void * query_ptr, + void * key_ptr, void * attn_ptr, - void * value_ptr, - void * output_ptr, + void * bias_ptr, int batch_size, int heads, int height, @@ -361,20 +418,21 @@ void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks3_align8( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 8, 2, 8, 2, 1>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 8, 2, 33, 1, 29>; using ArchConfig = natten::gemm::detail::ArchArgs<75, half>; - using AConfig = natten::gemm::detail::AlignmentConfig<1, 8, 8>; + using AConfig = natten::gemm::detail::AlignmentConfig<8, 8, 1>; using DConfig = natten::gemm::detail::DTypeConfig; - using Kernel = NeighborhoodNeighborhood2D; + using Kernel = PointwiseNeighborhood2D; Kernel kernel; kernel( -attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks3_align4( +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks59_align4( + void * query_ptr, + void * key_ptr, void * attn_ptr, - void * value_ptr, - void * output_ptr, + void * bias_ptr, int batch_size, int heads, int height, @@ -388,20 +446,21 @@ void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks3_align4( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 8, 2, 8, 2, 1>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 8, 2, 33, 1, 29>; using ArchConfig = natten::gemm::detail::ArchArgs<75, half>; - using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; + using AConfig = natten::gemm::detail::AlignmentConfig<4, 4, 1>; using DConfig = natten::gemm::detail::DTypeConfig; - using Kernel = NeighborhoodNeighborhood2D; + using Kernel = PointwiseNeighborhood2D; Kernel kernel; kernel( -attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks3_align2( +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks59_align2( + void * query_ptr, + void * key_ptr, void * attn_ptr, - void * value_ptr, - void * output_ptr, + void * bias_ptr, int batch_size, int heads, int height, @@ -415,20 +474,21 @@ void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks3_align2( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 8, 2, 8, 2, 1>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 8, 2, 33, 1, 29>; using ArchConfig = natten::gemm::detail::ArchArgs<75, half>; - using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; + using AConfig = natten::gemm::detail::AlignmentConfig<2, 2, 1>; using DConfig = natten::gemm::detail::DTypeConfig; - using Kernel = NeighborhoodNeighborhood2D; + using Kernel = PointwiseNeighborhood2D; Kernel kernel; kernel( -attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks5_align8( +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks61_align8( + void * query_ptr, + void * key_ptr, void * attn_ptr, - void * value_ptr, - void * output_ptr, + void * bias_ptr, int batch_size, int heads, int height, @@ -442,20 +502,21 @@ void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks5_align8( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 8, 2, 8, 2, 2>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 8, 2, 33, 1, 30>; using ArchConfig = natten::gemm::detail::ArchArgs<75, half>; - using AConfig = natten::gemm::detail::AlignmentConfig<1, 8, 8>; + using AConfig = natten::gemm::detail::AlignmentConfig<8, 8, 1>; using DConfig = natten::gemm::detail::DTypeConfig; - using Kernel = NeighborhoodNeighborhood2D; + using Kernel = PointwiseNeighborhood2D; Kernel kernel; kernel( -attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks5_align4( +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks61_align4( + void * query_ptr, + void * key_ptr, void * attn_ptr, - void * value_ptr, - void * output_ptr, + void * bias_ptr, int batch_size, int heads, int height, @@ -469,20 +530,21 @@ void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks5_align4( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 8, 2, 8, 2, 2>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 8, 2, 33, 1, 30>; using ArchConfig = natten::gemm::detail::ArchArgs<75, half>; - using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; + using AConfig = natten::gemm::detail::AlignmentConfig<4, 4, 1>; using DConfig = natten::gemm::detail::DTypeConfig; - using Kernel = NeighborhoodNeighborhood2D; + using Kernel = PointwiseNeighborhood2D; Kernel kernel; kernel( -attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks5_align2( +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks61_align2( + void * query_ptr, + void * key_ptr, void * attn_ptr, - void * value_ptr, - void * output_ptr, + void * bias_ptr, int batch_size, int heads, int height, @@ -496,20 +558,21 @@ void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks5_align2( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 8, 2, 8, 2, 2>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 8, 2, 33, 1, 30>; using ArchConfig = natten::gemm::detail::ArchArgs<75, half>; - using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; + using AConfig = natten::gemm::detail::AlignmentConfig<2, 2, 1>; using DConfig = natten::gemm::detail::DTypeConfig; - using Kernel = NeighborhoodNeighborhood2D; + using Kernel = PointwiseNeighborhood2D; Kernel kernel; kernel( -attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks7_align8( +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks63_align8( + void * query_ptr, + void * key_ptr, void * attn_ptr, - void * value_ptr, - void * output_ptr, + void * bias_ptr, int batch_size, int heads, int height, @@ -523,20 +586,21 @@ void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks7_align8( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 8, 2, 8, 2, 3>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 8, 2, 33, 1, 31>; using ArchConfig = natten::gemm::detail::ArchArgs<75, half>; - using AConfig = natten::gemm::detail::AlignmentConfig<1, 8, 8>; + using AConfig = natten::gemm::detail::AlignmentConfig<8, 8, 1>; using DConfig = natten::gemm::detail::DTypeConfig; - using Kernel = NeighborhoodNeighborhood2D; + using Kernel = PointwiseNeighborhood2D; Kernel kernel; kernel( -attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks7_align4( +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks63_align4( + void * query_ptr, + void * key_ptr, void * attn_ptr, - void * value_ptr, - void * output_ptr, + void * bias_ptr, int batch_size, int heads, int height, @@ -550,20 +614,21 @@ void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks7_align4( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 8, 2, 8, 2, 3>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 8, 2, 33, 1, 31>; using ArchConfig = natten::gemm::detail::ArchArgs<75, half>; - using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; + using AConfig = natten::gemm::detail::AlignmentConfig<4, 4, 1>; using DConfig = natten::gemm::detail::DTypeConfig; - using Kernel = NeighborhoodNeighborhood2D; + using Kernel = PointwiseNeighborhood2D; Kernel kernel; kernel( -attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks7_align2( +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks63_align2( + void * query_ptr, + void * key_ptr, void * attn_ptr, - void * value_ptr, - void * output_ptr, + void * bias_ptr, int batch_size, int heads, int height, @@ -577,17 +642,17 @@ void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks7_align2( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 8, 2, 8, 2, 3>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 8, 2, 33, 1, 31>; using ArchConfig = natten::gemm::detail::ArchArgs<75, half>; - using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; + using AConfig = natten::gemm::detail::AlignmentConfig<2, 2, 1>; using DConfig = natten::gemm::detail::DTypeConfig; - using Kernel = NeighborhoodNeighborhood2D; + using Kernel = PointwiseNeighborhood2D; Kernel kernel; kernel( -attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks9_align8( +void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks3_align8( void * attn_ptr, void * value_ptr, void * output_ptr, @@ -604,7 +669,7 @@ void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks9_align8( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 8, 2, 8, 2, 4>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 8, 2, 8, 2, 1>; using ArchConfig = natten::gemm::detail::ArchArgs<75, half>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 8, 8>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -614,7 +679,7 @@ void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks9_align8( attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks9_align4( +void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks3_align4( void * attn_ptr, void * value_ptr, void * output_ptr, @@ -631,7 +696,7 @@ void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks9_align4( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 8, 2, 8, 2, 4>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 8, 2, 8, 2, 1>; using ArchConfig = natten::gemm::detail::ArchArgs<75, half>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -641,7 +706,7 @@ void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks9_align4( attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks9_align2( +void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks3_align2( void * attn_ptr, void * value_ptr, void * output_ptr, @@ -658,7 +723,7 @@ void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks9_align2( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 8, 2, 8, 2, 4>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 8, 2, 8, 2, 1>; using ArchConfig = natten::gemm::detail::ArchArgs<75, half>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -668,7 +733,7 @@ void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks9_align2( attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks11_align8( +void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks5_align8( void * attn_ptr, void * value_ptr, void * output_ptr, @@ -685,7 +750,7 @@ void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks11_align8( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 8, 2, 8, 2, 5>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 8, 2, 8, 2, 2>; using ArchConfig = natten::gemm::detail::ArchArgs<75, half>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 8, 8>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -695,7 +760,7 @@ void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks11_align8( attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks11_align4( +void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks5_align4( void * attn_ptr, void * value_ptr, void * output_ptr, @@ -712,7 +777,7 @@ void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks11_align4( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 8, 2, 8, 2, 5>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 8, 2, 8, 2, 2>; using ArchConfig = natten::gemm::detail::ArchArgs<75, half>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -722,7 +787,7 @@ void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks11_align4( attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks11_align2( +void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks5_align2( void * attn_ptr, void * value_ptr, void * output_ptr, @@ -739,7 +804,7 @@ void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks11_align2( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 8, 2, 8, 2, 5>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 8, 2, 8, 2, 2>; using ArchConfig = natten::gemm::detail::ArchArgs<75, half>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -749,7 +814,7 @@ void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks11_align2( attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks13_align8( +void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks7_align8( void * attn_ptr, void * value_ptr, void * output_ptr, @@ -766,7 +831,7 @@ void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks13_align8( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 8, 2, 8, 2, 6>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 8, 2, 8, 2, 3>; using ArchConfig = natten::gemm::detail::ArchArgs<75, half>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 8, 8>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -776,7 +841,7 @@ void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks13_align8( attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks13_align4( +void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks7_align4( void * attn_ptr, void * value_ptr, void * output_ptr, @@ -793,7 +858,7 @@ void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks13_align4( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 8, 2, 8, 2, 6>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 8, 2, 8, 2, 3>; using ArchConfig = natten::gemm::detail::ArchArgs<75, half>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -803,7 +868,7 @@ void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks13_align4( attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks13_align2( +void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks7_align2( void * attn_ptr, void * value_ptr, void * output_ptr, @@ -820,7 +885,7 @@ void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks13_align2( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 8, 2, 8, 2, 6>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 8, 2, 8, 2, 3>; using ArchConfig = natten::gemm::detail::ArchArgs<75, half>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -830,7 +895,7 @@ void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks13_align2( attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks15_align8( +void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks9_align8( void * attn_ptr, void * value_ptr, void * output_ptr, @@ -847,7 +912,7 @@ void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks15_align8( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 8, 2, 8, 2, 7>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 8, 2, 8, 2, 4>; using ArchConfig = natten::gemm::detail::ArchArgs<75, half>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 8, 8>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -857,7 +922,7 @@ void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks15_align8( attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks15_align4( +void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks9_align4( void * attn_ptr, void * value_ptr, void * output_ptr, @@ -874,7 +939,7 @@ void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks15_align4( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 8, 2, 8, 2, 7>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 8, 2, 8, 2, 4>; using ArchConfig = natten::gemm::detail::ArchArgs<75, half>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -884,7 +949,7 @@ void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks15_align4( attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks15_align2( +void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks9_align2( void * attn_ptr, void * value_ptr, void * output_ptr, @@ -901,7 +966,7 @@ void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks15_align2( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 8, 2, 8, 2, 7>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 8, 2, 8, 2, 4>; using ArchConfig = natten::gemm::detail::ArchArgs<75, half>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -911,7 +976,7 @@ void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks15_align2( attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks17_align8( +void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks11_align8( void * attn_ptr, void * value_ptr, void * output_ptr, @@ -928,7 +993,7 @@ void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks17_align8( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 8, 2, 8, 2, 8>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 8, 2, 8, 2, 5>; using ArchConfig = natten::gemm::detail::ArchArgs<75, half>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 8, 8>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -938,7 +1003,7 @@ void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks17_align8( attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks17_align4( +void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks11_align4( void * attn_ptr, void * value_ptr, void * output_ptr, @@ -955,7 +1020,7 @@ void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks17_align4( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 8, 2, 8, 2, 8>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 8, 2, 8, 2, 5>; using ArchConfig = natten::gemm::detail::ArchArgs<75, half>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -965,7 +1030,7 @@ void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks17_align4( attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks17_align2( +void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks11_align2( void * attn_ptr, void * value_ptr, void * output_ptr, @@ -982,7 +1047,7 @@ void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks17_align2( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 8, 2, 8, 2, 8>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 8, 2, 8, 2, 5>; using ArchConfig = natten::gemm::detail::ArchArgs<75, half>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -992,6 +1057,870 @@ void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks17_align2( attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } +void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks13_align8( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 8, 2, 8, 2, 6>; + using ArchConfig = natten::gemm::detail::ArchArgs<75, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 8, 8>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks13_align4( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 8, 2, 8, 2, 6>; + using ArchConfig = natten::gemm::detail::ArchArgs<75, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks13_align2( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 8, 2, 8, 2, 6>; + using ArchConfig = natten::gemm::detail::ArchArgs<75, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks15_align8( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 8, 2, 8, 2, 7>; + using ArchConfig = natten::gemm::detail::ArchArgs<75, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 8, 8>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks15_align4( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 8, 2, 8, 2, 7>; + using ArchConfig = natten::gemm::detail::ArchArgs<75, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks15_align2( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 8, 2, 8, 2, 7>; + using ArchConfig = natten::gemm::detail::ArchArgs<75, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks17_align8( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 8, 2, 8, 2, 8>; + using ArchConfig = natten::gemm::detail::ArchArgs<75, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 8, 8>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks17_align4( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 8, 2, 8, 2, 8>; + using ArchConfig = natten::gemm::detail::ArchArgs<75, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks17_align2( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 8, 2, 8, 2, 8>; + using ArchConfig = natten::gemm::detail::ArchArgs<75, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks19_align8( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 8, 2, 8, 2, 9>; + using ArchConfig = natten::gemm::detail::ArchArgs<75, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 8, 8>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks19_align4( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 8, 2, 8, 2, 9>; + using ArchConfig = natten::gemm::detail::ArchArgs<75, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks19_align2( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 8, 2, 8, 2, 9>; + using ArchConfig = natten::gemm::detail::ArchArgs<75, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks21_align8( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 8, 2, 11, 2, 10>; + using ArchConfig = natten::gemm::detail::ArchArgs<75, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 8, 8>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks21_align4( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 8, 2, 11, 2, 10>; + using ArchConfig = natten::gemm::detail::ArchArgs<75, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks21_align2( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 8, 2, 11, 2, 10>; + using ArchConfig = natten::gemm::detail::ArchArgs<75, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks23_align8( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 8, 2, 11, 2, 11>; + using ArchConfig = natten::gemm::detail::ArchArgs<75, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 8, 8>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks23_align4( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 8, 2, 11, 2, 11>; + using ArchConfig = natten::gemm::detail::ArchArgs<75, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks23_align2( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 8, 2, 11, 2, 11>; + using ArchConfig = natten::gemm::detail::ArchArgs<75, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks25_align8( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 8, 2, 11, 2, 12>; + using ArchConfig = natten::gemm::detail::ArchArgs<75, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 8, 8>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks25_align4( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 8, 2, 11, 2, 12>; + using ArchConfig = natten::gemm::detail::ArchArgs<75, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks25_align2( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 8, 2, 11, 2, 12>; + using ArchConfig = natten::gemm::detail::ArchArgs<75, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks27_align8( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 8, 2, 11, 2, 13>; + using ArchConfig = natten::gemm::detail::ArchArgs<75, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 8, 8>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks27_align4( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 8, 2, 11, 2, 13>; + using ArchConfig = natten::gemm::detail::ArchArgs<75, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks27_align2( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 8, 2, 11, 2, 13>; + using ArchConfig = natten::gemm::detail::ArchArgs<75, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks29_align8( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 8, 2, 11, 2, 14>; + using ArchConfig = natten::gemm::detail::ArchArgs<75, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 8, 8>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks29_align4( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 8, 2, 11, 2, 14>; + using ArchConfig = natten::gemm::detail::ArchArgs<75, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks29_align2( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 8, 2, 11, 2, 14>; + using ArchConfig = natten::gemm::detail::ArchArgs<75, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks31_align8( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 8, 2, 11, 2, 15>; + using ArchConfig = natten::gemm::detail::ArchArgs<75, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 8, 8>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks31_align4( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 8, 2, 11, 2, 15>; + using ArchConfig = natten::gemm::detail::ArchArgs<75, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks31_align2( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 8, 2, 11, 2, 15>; + using ArchConfig = natten::gemm::detail::ArchArgs<75, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks33_align8( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 8, 2, 11, 2, 16>; + using ArchConfig = natten::gemm::detail::ArchArgs<75, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 8, 8>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks33_align4( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 8, 2, 11, 2, 16>; + using ArchConfig = natten::gemm::detail::ArchArgs<75, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + } } } diff --git a/csrc/autogen/src/cuda/gemm/2d/sm75/source_2.cu b/csrc/autogen/src/cuda/gemm/2d/sm75/source_2.cu index d825571..f7786b5 100644 --- a/csrc/autogen/src/cuda/gemm/2d/sm75/source_2.cu +++ b/csrc/autogen/src/cuda/gemm/2d/sm75/source_2.cu @@ -1,14 +1,770 @@ #include #include -#include #include -#include #include +#include +#include namespace natten { namespace cuda { namespace gemm { -void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks19_align8( +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks33_align2( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 8, 2, 11, 2, 16>; + using ArchConfig = natten::gemm::detail::ArchArgs<75, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks35_align8( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 8, 2, 11, 2, 17>; + using ArchConfig = natten::gemm::detail::ArchArgs<75, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 8, 8>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks35_align4( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 8, 2, 11, 2, 17>; + using ArchConfig = natten::gemm::detail::ArchArgs<75, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks35_align2( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 8, 2, 11, 2, 17>; + using ArchConfig = natten::gemm::detail::ArchArgs<75, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks37_align8( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 8, 2, 11, 2, 18>; + using ArchConfig = natten::gemm::detail::ArchArgs<75, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 8, 8>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks37_align4( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 8, 2, 11, 2, 18>; + using ArchConfig = natten::gemm::detail::ArchArgs<75, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks37_align2( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 8, 2, 11, 2, 18>; + using ArchConfig = natten::gemm::detail::ArchArgs<75, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks39_align8( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 8, 2, 11, 2, 19>; + using ArchConfig = natten::gemm::detail::ArchArgs<75, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 8, 8>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks39_align4( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 8, 2, 11, 2, 19>; + using ArchConfig = natten::gemm::detail::ArchArgs<75, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks39_align2( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 8, 2, 11, 2, 19>; + using ArchConfig = natten::gemm::detail::ArchArgs<75, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks41_align8( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 8, 2, 11, 2, 20>; + using ArchConfig = natten::gemm::detail::ArchArgs<75, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 8, 8>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks41_align4( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 8, 2, 11, 2, 20>; + using ArchConfig = natten::gemm::detail::ArchArgs<75, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks41_align2( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 8, 2, 11, 2, 20>; + using ArchConfig = natten::gemm::detail::ArchArgs<75, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks43_align8( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 8, 2, 11, 2, 21>; + using ArchConfig = natten::gemm::detail::ArchArgs<75, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 8, 8>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks43_align4( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 8, 2, 11, 2, 21>; + using ArchConfig = natten::gemm::detail::ArchArgs<75, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks43_align2( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 8, 2, 11, 2, 21>; + using ArchConfig = natten::gemm::detail::ArchArgs<75, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks45_align8( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 8, 2, 11, 2, 22>; + using ArchConfig = natten::gemm::detail::ArchArgs<75, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 8, 8>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks45_align4( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 8, 2, 11, 2, 22>; + using ArchConfig = natten::gemm::detail::ArchArgs<75, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks45_align2( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 8, 2, 11, 2, 22>; + using ArchConfig = natten::gemm::detail::ArchArgs<75, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks47_align8( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 8, 2, 11, 2, 23>; + using ArchConfig = natten::gemm::detail::ArchArgs<75, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 8, 8>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks47_align4( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 8, 2, 11, 2, 23>; + using ArchConfig = natten::gemm::detail::ArchArgs<75, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks47_align2( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 8, 2, 11, 2, 23>; + using ArchConfig = natten::gemm::detail::ArchArgs<75, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks49_align8( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 8, 2, 11, 2, 24>; + using ArchConfig = natten::gemm::detail::ArchArgs<75, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 8, 8>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks49_align4( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 8, 2, 11, 2, 24>; + using ArchConfig = natten::gemm::detail::ArchArgs<75, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks49_align2( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 8, 2, 11, 2, 24>; + using ArchConfig = natten::gemm::detail::ArchArgs<75, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks51_align8( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 8, 2, 11, 2, 25>; + using ArchConfig = natten::gemm::detail::ArchArgs<75, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 8, 8>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks51_align4( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 8, 2, 11, 2, 25>; + using ArchConfig = natten::gemm::detail::ArchArgs<75, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks51_align2( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 8, 2, 11, 2, 25>; + using ArchConfig = natten::gemm::detail::ArchArgs<75, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks53_align8( void * attn_ptr, void * value_ptr, void * output_ptr, @@ -25,7 +781,7 @@ void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks19_align8( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 8, 2, 8, 2, 9>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 8, 2, 11, 2, 26>; using ArchConfig = natten::gemm::detail::ArchArgs<75, half>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 8, 8>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -35,7 +791,7 @@ void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks19_align8( attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks19_align4( +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks53_align4( void * attn_ptr, void * value_ptr, void * output_ptr, @@ -52,7 +808,7 @@ void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks19_align4( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 8, 2, 8, 2, 9>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 8, 2, 11, 2, 26>; using ArchConfig = natten::gemm::detail::ArchArgs<75, half>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -62,7 +818,7 @@ void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks19_align4( attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks19_align2( +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks53_align2( void * attn_ptr, void * value_ptr, void * output_ptr, @@ -79,7 +835,7 @@ void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks19_align2( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 8, 2, 8, 2, 9>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 8, 2, 11, 2, 26>; using ArchConfig = natten::gemm::detail::ArchArgs<75, half>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -89,7 +845,7 @@ void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks19_align2( attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks21_align8( +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks55_align8( void * attn_ptr, void * value_ptr, void * output_ptr, @@ -106,7 +862,7 @@ void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks21_align8( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 8, 2, 11, 2, 10>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 8, 2, 11, 2, 27>; using ArchConfig = natten::gemm::detail::ArchArgs<75, half>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 8, 8>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -116,7 +872,7 @@ void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks21_align8( attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks21_align4( +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks55_align4( void * attn_ptr, void * value_ptr, void * output_ptr, @@ -133,7 +889,7 @@ void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks21_align4( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 8, 2, 11, 2, 10>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 8, 2, 11, 2, 27>; using ArchConfig = natten::gemm::detail::ArchArgs<75, half>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -143,7 +899,7 @@ void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks21_align4( attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks21_align2( +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks55_align2( void * attn_ptr, void * value_ptr, void * output_ptr, @@ -160,7 +916,7 @@ void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks21_align2( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 8, 2, 11, 2, 10>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 8, 2, 11, 2, 27>; using ArchConfig = natten::gemm::detail::ArchArgs<75, half>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -170,7 +926,7 @@ void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks21_align2( attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks23_align8( +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks57_align8( void * attn_ptr, void * value_ptr, void * output_ptr, @@ -187,7 +943,7 @@ void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks23_align8( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 8, 2, 11, 2, 11>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 8, 2, 11, 2, 28>; using ArchConfig = natten::gemm::detail::ArchArgs<75, half>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 8, 8>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -197,7 +953,7 @@ void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks23_align8( attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks23_align4( +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks57_align4( void * attn_ptr, void * value_ptr, void * output_ptr, @@ -214,7 +970,7 @@ void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks23_align4( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 8, 2, 11, 2, 11>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 8, 2, 11, 2, 28>; using ArchConfig = natten::gemm::detail::ArchArgs<75, half>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -224,7 +980,7 @@ void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks23_align4( attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks23_align2( +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks57_align2( void * attn_ptr, void * value_ptr, void * output_ptr, @@ -241,7 +997,7 @@ void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks23_align2( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 8, 2, 11, 2, 11>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 8, 2, 11, 2, 28>; using ArchConfig = natten::gemm::detail::ArchArgs<75, half>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -251,7 +1007,7 @@ void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks23_align2( attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks25_align8( +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks59_align8( void * attn_ptr, void * value_ptr, void * output_ptr, @@ -268,7 +1024,7 @@ void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks25_align8( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 8, 2, 11, 2, 12>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 8, 2, 11, 2, 29>; using ArchConfig = natten::gemm::detail::ArchArgs<75, half>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 8, 8>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -278,7 +1034,7 @@ void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks25_align8( attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks25_align4( +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks59_align4( void * attn_ptr, void * value_ptr, void * output_ptr, @@ -295,7 +1051,7 @@ void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks25_align4( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 8, 2, 11, 2, 12>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 8, 2, 11, 2, 29>; using ArchConfig = natten::gemm::detail::ArchArgs<75, half>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -305,7 +1061,7 @@ void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks25_align4( attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks25_align2( +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks59_align2( void * attn_ptr, void * value_ptr, void * output_ptr, @@ -322,7 +1078,7 @@ void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks25_align2( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 8, 2, 11, 2, 12>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 8, 2, 11, 2, 29>; using ArchConfig = natten::gemm::detail::ArchArgs<75, half>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -332,7 +1088,7 @@ void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks25_align2( attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks27_align8( +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks61_align8( void * attn_ptr, void * value_ptr, void * output_ptr, @@ -349,7 +1105,7 @@ void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks27_align8( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 8, 2, 11, 2, 13>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 8, 2, 11, 2, 30>; using ArchConfig = natten::gemm::detail::ArchArgs<75, half>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 8, 8>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -359,7 +1115,7 @@ void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks27_align8( attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks27_align4( +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks61_align4( void * attn_ptr, void * value_ptr, void * output_ptr, @@ -376,7 +1132,7 @@ void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks27_align4( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 8, 2, 11, 2, 13>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 8, 2, 11, 2, 30>; using ArchConfig = natten::gemm::detail::ArchArgs<75, half>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -386,7 +1142,7 @@ void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks27_align4( attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks27_align2( +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks61_align2( void * attn_ptr, void * value_ptr, void * output_ptr, @@ -403,7 +1159,7 @@ void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks27_align2( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 8, 2, 11, 2, 13>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 8, 2, 11, 2, 30>; using ArchConfig = natten::gemm::detail::ArchArgs<75, half>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -413,7 +1169,7 @@ void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks27_align2( attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks29_align8( +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks63_align8( void * attn_ptr, void * value_ptr, void * output_ptr, @@ -430,7 +1186,7 @@ void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks29_align8( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 8, 2, 11, 2, 14>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 8, 2, 11, 2, 31>; using ArchConfig = natten::gemm::detail::ArchArgs<75, half>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 8, 8>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -440,7 +1196,7 @@ void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks29_align8( attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks29_align4( +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks63_align4( void * attn_ptr, void * value_ptr, void * output_ptr, @@ -457,7 +1213,7 @@ void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks29_align4( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 8, 2, 11, 2, 14>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 8, 2, 11, 2, 31>; using ArchConfig = natten::gemm::detail::ArchArgs<75, half>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -467,7 +1223,7 @@ void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks29_align4( attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks29_align2( +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks63_align2( void * attn_ptr, void * value_ptr, void * output_ptr, @@ -484,7 +1240,7 @@ void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks29_align2( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 8, 2, 11, 2, 14>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 8, 2, 11, 2, 31>; using ArchConfig = natten::gemm::detail::ArchArgs<75, half>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -494,10 +1250,10 @@ void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks29_align2( attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks31_align8( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks3_align8( void * attn_ptr, - void * value_ptr, - void * output_ptr, + void * d_output_ptr, + void * d_value_ptr, int batch_size, int heads, int height, @@ -511,20 +1267,20 @@ void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks31_align8( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 8, 2, 11, 2, 15>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 8, 2, 8, 4, 1>; using ArchConfig = natten::gemm::detail::ArchArgs<75, half>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 8, 8>; using DConfig = natten::gemm::detail::DTypeConfig; - using Kernel = NeighborhoodNeighborhood2D; + using Kernel = InverseNeighborhood2D; Kernel kernel; kernel( -attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks31_align4( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks3_align4( void * attn_ptr, - void * value_ptr, - void * output_ptr, + void * d_output_ptr, + void * d_value_ptr, int batch_size, int heads, int height, @@ -538,20 +1294,20 @@ void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks31_align4( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 8, 2, 11, 2, 15>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 8, 2, 8, 4, 1>; using ArchConfig = natten::gemm::detail::ArchArgs<75, half>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; using DConfig = natten::gemm::detail::DTypeConfig; - using Kernel = NeighborhoodNeighborhood2D; + using Kernel = InverseNeighborhood2D; Kernel kernel; kernel( -attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks31_align2( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks3_align2( void * attn_ptr, - void * value_ptr, - void * output_ptr, + void * d_output_ptr, + void * d_value_ptr, int batch_size, int heads, int height, @@ -565,20 +1321,20 @@ void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks31_align2( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 8, 2, 11, 2, 15>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 8, 2, 8, 4, 1>; using ArchConfig = natten::gemm::detail::ArchArgs<75, half>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; using DConfig = natten::gemm::detail::DTypeConfig; - using Kernel = NeighborhoodNeighborhood2D; + using Kernel = InverseNeighborhood2D; Kernel kernel; kernel( -attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks33_align8( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks5_align8( void * attn_ptr, - void * value_ptr, - void * output_ptr, + void * d_output_ptr, + void * d_value_ptr, int batch_size, int heads, int height, @@ -592,20 +1348,20 @@ void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks33_align8( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 8, 2, 11, 2, 16>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 8, 2, 8, 4, 2>; using ArchConfig = natten::gemm::detail::ArchArgs<75, half>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 8, 8>; using DConfig = natten::gemm::detail::DTypeConfig; - using Kernel = NeighborhoodNeighborhood2D; + using Kernel = InverseNeighborhood2D; Kernel kernel; kernel( -attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks33_align4( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks5_align4( void * attn_ptr, - void * value_ptr, - void * output_ptr, + void * d_output_ptr, + void * d_value_ptr, int batch_size, int heads, int height, @@ -619,20 +1375,20 @@ void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks33_align4( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 8, 2, 11, 2, 16>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 8, 2, 8, 4, 2>; using ArchConfig = natten::gemm::detail::ArchArgs<75, half>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; using DConfig = natten::gemm::detail::DTypeConfig; - using Kernel = NeighborhoodNeighborhood2D; + using Kernel = InverseNeighborhood2D; Kernel kernel; kernel( -attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks33_align2( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks5_align2( void * attn_ptr, - void * value_ptr, - void * output_ptr, + void * d_output_ptr, + void * d_value_ptr, int batch_size, int heads, int height, @@ -646,17 +1402,17 @@ void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x8_2_sm75_ks33_align2( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 8, 2, 11, 2, 16>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 8, 2, 8, 4, 2>; using ArchConfig = natten::gemm::detail::ArchArgs<75, half>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; using DConfig = natten::gemm::detail::DTypeConfig; - using Kernel = NeighborhoodNeighborhood2D; + using Kernel = InverseNeighborhood2D; Kernel kernel; kernel( -attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks3_align8( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks7_align8( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -673,7 +1429,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks3_align8( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 8, 2, 8, 4, 1>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 8, 2, 8, 4, 3>; using ArchConfig = natten::gemm::detail::ArchArgs<75, half>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 8, 8>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -683,7 +1439,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks3_align8( attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks3_align4( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks7_align4( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -700,7 +1456,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks3_align4( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 8, 2, 8, 4, 1>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 8, 2, 8, 4, 3>; using ArchConfig = natten::gemm::detail::ArchArgs<75, half>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -710,7 +1466,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks3_align4( attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks3_align2( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks7_align2( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -727,7 +1483,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks3_align2( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 8, 2, 8, 4, 1>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 8, 2, 8, 4, 3>; using ArchConfig = natten::gemm::detail::ArchArgs<75, half>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -737,7 +1493,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks3_align2( attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks5_align8( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks9_align8( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -754,7 +1510,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks5_align8( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 8, 2, 8, 4, 2>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 8, 2, 8, 4, 4>; using ArchConfig = natten::gemm::detail::ArchArgs<75, half>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 8, 8>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -764,7 +1520,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks5_align8( attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks5_align4( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks9_align4( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -781,7 +1537,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks5_align4( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 8, 2, 8, 4, 2>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 8, 2, 8, 4, 4>; using ArchConfig = natten::gemm::detail::ArchArgs<75, half>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -791,7 +1547,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks5_align4( attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks5_align2( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks9_align2( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -808,7 +1564,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks5_align2( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 8, 2, 8, 4, 2>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 8, 2, 8, 4, 4>; using ArchConfig = natten::gemm::detail::ArchArgs<75, half>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -818,7 +1574,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks5_align2( attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks7_align8( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks11_align8( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -835,7 +1591,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks7_align8( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 8, 2, 8, 4, 3>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 8, 2, 8, 4, 5>; using ArchConfig = natten::gemm::detail::ArchArgs<75, half>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 8, 8>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -845,7 +1601,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks7_align8( attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks7_align4( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks11_align4( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -862,7 +1618,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks7_align4( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 8, 2, 8, 4, 3>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 8, 2, 8, 4, 5>; using ArchConfig = natten::gemm::detail::ArchArgs<75, half>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -872,7 +1628,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks7_align4( attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks7_align2( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks11_align2( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -889,7 +1645,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks7_align2( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 8, 2, 8, 4, 3>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 8, 2, 8, 4, 5>; using ArchConfig = natten::gemm::detail::ArchArgs<75, half>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -899,7 +1655,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks7_align2( attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks9_align8( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks13_align8( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -916,7 +1672,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks9_align8( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 8, 2, 8, 4, 4>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 8, 2, 8, 4, 6>; using ArchConfig = natten::gemm::detail::ArchArgs<75, half>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 8, 8>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -926,7 +1682,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks9_align8( attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks9_align4( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks13_align4( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -943,7 +1699,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks9_align4( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 8, 2, 8, 4, 4>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 8, 2, 8, 4, 6>; using ArchConfig = natten::gemm::detail::ArchArgs<75, half>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -953,7 +1709,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks9_align4( attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks9_align2( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks13_align2( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -970,7 +1726,169 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks9_align2( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 8, 2, 8, 4, 4>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 8, 2, 8, 4, 6>; + using ArchConfig = natten::gemm::detail::ArchArgs<75, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks15_align8( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 8, 2, 8, 4, 7>; + using ArchConfig = natten::gemm::detail::ArchArgs<75, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 8, 8>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks15_align4( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 8, 2, 8, 4, 7>; + using ArchConfig = natten::gemm::detail::ArchArgs<75, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks15_align2( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 8, 2, 8, 4, 7>; + using ArchConfig = natten::gemm::detail::ArchArgs<75, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks17_align8( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 8, 2, 8, 4, 8>; + using ArchConfig = natten::gemm::detail::ArchArgs<75, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 8, 8>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks17_align4( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 8, 2, 8, 4, 8>; + using ArchConfig = natten::gemm::detail::ArchArgs<75, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks17_align2( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 8, 2, 8, 4, 8>; using ArchConfig = natten::gemm::detail::ArchArgs<75, half>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; using DConfig = natten::gemm::detail::DTypeConfig; diff --git a/csrc/autogen/src/cuda/gemm/2d/sm75/source_3.cu b/csrc/autogen/src/cuda/gemm/2d/sm75/source_3.cu index 301486f..fb17f39 100644 --- a/csrc/autogen/src/cuda/gemm/2d/sm75/source_3.cu +++ b/csrc/autogen/src/cuda/gemm/2d/sm75/source_3.cu @@ -1,14 +1,14 @@ #include #include -#include #include -#include #include +#include +#include namespace natten { namespace cuda { namespace gemm { -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks11_align8( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks19_align8( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -25,7 +25,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks11_align8( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 8, 2, 8, 4, 5>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 8, 2, 8, 4, 9>; using ArchConfig = natten::gemm::detail::ArchArgs<75, half>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 8, 8>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -35,7 +35,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks11_align8( attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks11_align4( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks19_align4( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -52,7 +52,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks11_align4( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 8, 2, 8, 4, 5>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 8, 2, 8, 4, 9>; using ArchConfig = natten::gemm::detail::ArchArgs<75, half>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -62,7 +62,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks11_align4( attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks11_align2( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks19_align2( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -79,7 +79,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks11_align2( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 8, 2, 8, 4, 5>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 8, 2, 8, 4, 9>; using ArchConfig = natten::gemm::detail::ArchArgs<75, half>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -89,7 +89,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks11_align2( attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks13_align8( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks21_align8( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -106,7 +106,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks13_align8( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 8, 2, 8, 4, 6>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 8, 2, 8, 4, 10>; using ArchConfig = natten::gemm::detail::ArchArgs<75, half>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 8, 8>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -116,7 +116,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks13_align8( attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks13_align4( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks21_align4( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -133,7 +133,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks13_align4( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 8, 2, 8, 4, 6>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 8, 2, 8, 4, 10>; using ArchConfig = natten::gemm::detail::ArchArgs<75, half>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -143,7 +143,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks13_align4( attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks13_align2( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks21_align2( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -160,7 +160,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks13_align2( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 8, 2, 8, 4, 6>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 8, 2, 8, 4, 10>; using ArchConfig = natten::gemm::detail::ArchArgs<75, half>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -170,7 +170,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks13_align2( attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks15_align8( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks23_align8( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -187,7 +187,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks15_align8( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 8, 2, 8, 4, 7>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 8, 2, 8, 4, 11>; using ArchConfig = natten::gemm::detail::ArchArgs<75, half>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 8, 8>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -197,7 +197,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks15_align8( attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks15_align4( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks23_align4( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -214,7 +214,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks15_align4( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 8, 2, 8, 4, 7>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 8, 2, 8, 4, 11>; using ArchConfig = natten::gemm::detail::ArchArgs<75, half>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -224,7 +224,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks15_align4( attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks15_align2( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks23_align2( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -241,7 +241,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks15_align2( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 8, 2, 8, 4, 7>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 8, 2, 8, 4, 11>; using ArchConfig = natten::gemm::detail::ArchArgs<75, half>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -251,7 +251,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks15_align2( attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks17_align8( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks25_align8( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -268,7 +268,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks17_align8( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 8, 2, 8, 4, 8>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 8, 2, 8, 4, 12>; using ArchConfig = natten::gemm::detail::ArchArgs<75, half>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 8, 8>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -278,7 +278,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks17_align8( attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks17_align4( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks25_align4( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -295,7 +295,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks17_align4( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 8, 2, 8, 4, 8>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 8, 2, 8, 4, 12>; using ArchConfig = natten::gemm::detail::ArchArgs<75, half>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -305,7 +305,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks17_align4( attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks17_align2( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks25_align2( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -322,7 +322,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks17_align2( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 8, 2, 8, 4, 8>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 8, 2, 8, 4, 12>; using ArchConfig = natten::gemm::detail::ArchArgs<75, half>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -332,7 +332,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks17_align2( attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks19_align8( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks27_align8( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -349,7 +349,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks19_align8( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 8, 2, 8, 4, 9>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 8, 2, 8, 4, 13>; using ArchConfig = natten::gemm::detail::ArchArgs<75, half>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 8, 8>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -359,7 +359,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks19_align8( attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks19_align4( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks27_align4( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -376,7 +376,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks19_align4( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 8, 2, 8, 4, 9>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 8, 2, 8, 4, 13>; using ArchConfig = natten::gemm::detail::ArchArgs<75, half>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -386,7 +386,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks19_align4( attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks19_align2( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks27_align2( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -403,7 +403,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks19_align2( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 8, 2, 8, 4, 9>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 8, 2, 8, 4, 13>; using ArchConfig = natten::gemm::detail::ArchArgs<75, half>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -413,7 +413,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks19_align2( attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks21_align8( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks29_align8( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -430,7 +430,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks21_align8( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 8, 2, 8, 4, 10>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 8, 2, 8, 4, 14>; using ArchConfig = natten::gemm::detail::ArchArgs<75, half>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 8, 8>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -440,7 +440,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks21_align8( attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks21_align4( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks29_align4( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -457,7 +457,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks21_align4( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 8, 2, 8, 4, 10>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 8, 2, 8, 4, 14>; using ArchConfig = natten::gemm::detail::ArchArgs<75, half>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -467,7 +467,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks21_align4( attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks21_align2( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks29_align2( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -484,7 +484,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks21_align2( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 8, 2, 8, 4, 10>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 8, 2, 8, 4, 14>; using ArchConfig = natten::gemm::detail::ArchArgs<75, half>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -494,7 +494,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks21_align2( attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks23_align8( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks31_align8( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -511,7 +511,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks23_align8( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 8, 2, 8, 4, 11>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 8, 2, 8, 4, 15>; using ArchConfig = natten::gemm::detail::ArchArgs<75, half>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 8, 8>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -521,7 +521,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks23_align8( attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks23_align4( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks31_align4( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -538,7 +538,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks23_align4( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 8, 2, 8, 4, 11>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 8, 2, 8, 4, 15>; using ArchConfig = natten::gemm::detail::ArchArgs<75, half>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -548,7 +548,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks23_align4( attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks23_align2( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks31_align2( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -565,7 +565,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks23_align2( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 8, 2, 8, 4, 11>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 8, 2, 8, 4, 15>; using ArchConfig = natten::gemm::detail::ArchArgs<75, half>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -575,7 +575,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks23_align2( attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks25_align8( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks33_align8( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -592,7 +592,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks25_align8( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 8, 2, 8, 4, 12>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 8, 2, 8, 4, 16>; using ArchConfig = natten::gemm::detail::ArchArgs<75, half>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 8, 8>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -602,7 +602,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks25_align8( attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks25_align4( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks33_align4( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -619,7 +619,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks25_align4( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 8, 2, 8, 4, 12>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 8, 2, 8, 4, 16>; using ArchConfig = natten::gemm::detail::ArchArgs<75, half>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -629,7 +629,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks25_align4( attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks25_align2( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks33_align2( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -646,7 +646,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks25_align2( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 8, 2, 8, 4, 12>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 8, 2, 8, 4, 16>; using ArchConfig = natten::gemm::detail::ArchArgs<75, half>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -656,7 +656,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks25_align2( attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks27_align8( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks35_align8( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -673,7 +673,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks27_align8( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 8, 2, 8, 4, 13>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 8, 2, 8, 4, 17>; using ArchConfig = natten::gemm::detail::ArchArgs<75, half>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 8, 8>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -683,7 +683,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks27_align8( attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks27_align4( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks35_align4( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -700,7 +700,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks27_align4( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 8, 2, 8, 4, 13>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 8, 2, 8, 4, 17>; using ArchConfig = natten::gemm::detail::ArchArgs<75, half>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -710,7 +710,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks27_align4( attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks27_align2( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks35_align2( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -727,7 +727,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks27_align2( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 8, 2, 8, 4, 13>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 8, 2, 8, 4, 17>; using ArchConfig = natten::gemm::detail::ArchArgs<75, half>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -737,7 +737,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks27_align2( attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks29_align8( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks37_align8( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -754,7 +754,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks29_align8( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 8, 2, 8, 4, 14>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 8, 2, 8, 4, 18>; using ArchConfig = natten::gemm::detail::ArchArgs<75, half>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 8, 8>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -764,7 +764,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks29_align8( attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks29_align4( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks37_align4( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -781,7 +781,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks29_align4( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 8, 2, 8, 4, 14>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 8, 2, 8, 4, 18>; using ArchConfig = natten::gemm::detail::ArchArgs<75, half>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -791,7 +791,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks29_align4( attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks29_align2( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks37_align2( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -808,7 +808,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks29_align2( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 8, 2, 8, 4, 14>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 8, 2, 8, 4, 18>; using ArchConfig = natten::gemm::detail::ArchArgs<75, half>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -818,7 +818,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks29_align2( attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks31_align8( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks39_align8( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -835,7 +835,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks31_align8( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 8, 2, 8, 4, 15>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 8, 2, 8, 4, 19>; using ArchConfig = natten::gemm::detail::ArchArgs<75, half>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 8, 8>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -845,7 +845,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks31_align8( attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks31_align4( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks39_align4( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -862,7 +862,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks31_align4( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 8, 2, 8, 4, 15>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 8, 2, 8, 4, 19>; using ArchConfig = natten::gemm::detail::ArchArgs<75, half>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -872,7 +872,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks31_align4( attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks31_align2( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks39_align2( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -889,7 +889,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks31_align2( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 8, 2, 8, 4, 15>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 8, 2, 8, 4, 19>; using ArchConfig = natten::gemm::detail::ArchArgs<75, half>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -899,7 +899,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks31_align2( attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks33_align8( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks41_align8( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -916,7 +916,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks33_align8( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 8, 2, 8, 4, 16>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 8, 2, 8, 4, 20>; using ArchConfig = natten::gemm::detail::ArchArgs<75, half>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 8, 8>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -926,7 +926,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks33_align8( attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks33_align4( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks41_align4( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -943,7 +943,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks33_align4( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 8, 2, 8, 4, 16>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 8, 2, 8, 4, 20>; using ArchConfig = natten::gemm::detail::ArchArgs<75, half>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -953,7 +953,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks33_align4( attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks33_align2( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks41_align2( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -970,7 +970,898 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks33_align2( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 8, 2, 8, 4, 16>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 8, 2, 8, 4, 20>; + using ArchConfig = natten::gemm::detail::ArchArgs<75, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks43_align8( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 8, 2, 8, 4, 21>; + using ArchConfig = natten::gemm::detail::ArchArgs<75, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 8, 8>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks43_align4( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 8, 2, 8, 4, 21>; + using ArchConfig = natten::gemm::detail::ArchArgs<75, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks43_align2( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 8, 2, 8, 4, 21>; + using ArchConfig = natten::gemm::detail::ArchArgs<75, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks45_align8( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 8, 2, 8, 4, 22>; + using ArchConfig = natten::gemm::detail::ArchArgs<75, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 8, 8>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks45_align4( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 8, 2, 8, 4, 22>; + using ArchConfig = natten::gemm::detail::ArchArgs<75, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks45_align2( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 8, 2, 8, 4, 22>; + using ArchConfig = natten::gemm::detail::ArchArgs<75, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks47_align8( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 8, 2, 8, 4, 23>; + using ArchConfig = natten::gemm::detail::ArchArgs<75, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 8, 8>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks47_align4( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 8, 2, 8, 4, 23>; + using ArchConfig = natten::gemm::detail::ArchArgs<75, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks47_align2( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 8, 2, 8, 4, 23>; + using ArchConfig = natten::gemm::detail::ArchArgs<75, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks49_align8( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 8, 2, 8, 4, 24>; + using ArchConfig = natten::gemm::detail::ArchArgs<75, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 8, 8>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks49_align4( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 8, 2, 8, 4, 24>; + using ArchConfig = natten::gemm::detail::ArchArgs<75, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks49_align2( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 8, 2, 8, 4, 24>; + using ArchConfig = natten::gemm::detail::ArchArgs<75, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks51_align8( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 8, 2, 8, 4, 25>; + using ArchConfig = natten::gemm::detail::ArchArgs<75, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 8, 8>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks51_align4( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 8, 2, 8, 4, 25>; + using ArchConfig = natten::gemm::detail::ArchArgs<75, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks51_align2( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 8, 2, 8, 4, 25>; + using ArchConfig = natten::gemm::detail::ArchArgs<75, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks53_align8( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 8, 2, 8, 4, 26>; + using ArchConfig = natten::gemm::detail::ArchArgs<75, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 8, 8>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks53_align4( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 8, 2, 8, 4, 26>; + using ArchConfig = natten::gemm::detail::ArchArgs<75, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks53_align2( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 8, 2, 8, 4, 26>; + using ArchConfig = natten::gemm::detail::ArchArgs<75, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks55_align8( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 8, 2, 8, 4, 27>; + using ArchConfig = natten::gemm::detail::ArchArgs<75, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 8, 8>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks55_align4( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 8, 2, 8, 4, 27>; + using ArchConfig = natten::gemm::detail::ArchArgs<75, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks55_align2( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 8, 2, 8, 4, 27>; + using ArchConfig = natten::gemm::detail::ArchArgs<75, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks57_align8( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 8, 2, 8, 4, 28>; + using ArchConfig = natten::gemm::detail::ArchArgs<75, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 8, 8>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks57_align4( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 8, 2, 8, 4, 28>; + using ArchConfig = natten::gemm::detail::ArchArgs<75, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks57_align2( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 8, 2, 8, 4, 28>; + using ArchConfig = natten::gemm::detail::ArchArgs<75, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks59_align8( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 8, 2, 8, 4, 29>; + using ArchConfig = natten::gemm::detail::ArchArgs<75, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 8, 8>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks59_align4( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 8, 2, 8, 4, 29>; + using ArchConfig = natten::gemm::detail::ArchArgs<75, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks59_align2( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 8, 2, 8, 4, 29>; + using ArchConfig = natten::gemm::detail::ArchArgs<75, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks61_align8( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 8, 2, 8, 4, 30>; + using ArchConfig = natten::gemm::detail::ArchArgs<75, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 8, 8>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks61_align4( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 8, 2, 8, 4, 30>; + using ArchConfig = natten::gemm::detail::ArchArgs<75, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks61_align2( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 8, 2, 8, 4, 30>; + using ArchConfig = natten::gemm::detail::ArchArgs<75, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks63_align8( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 8, 2, 8, 4, 31>; + using ArchConfig = natten::gemm::detail::ArchArgs<75, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 8, 8>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks63_align4( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 8, 2, 8, 4, 31>; + using ArchConfig = natten::gemm::detail::ArchArgs<75, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x8_2_sm75_ks63_align2( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 8, 2, 8, 4, 31>; using ArchConfig = natten::gemm::detail::ArchArgs<75, half>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; using DConfig = natten::gemm::detail::DTypeConfig; diff --git a/csrc/autogen/src/cuda/gemm/2d/sm80/source_0.cu b/csrc/autogen/src/cuda/gemm/2d/sm80/source_0.cu index bf32e17..748e41f 100644 --- a/csrc/autogen/src/cuda/gemm/2d/sm80/source_0.cu +++ b/csrc/autogen/src/cuda/gemm/2d/sm80/source_0.cu @@ -1,9 +1,9 @@ #include #include -#include #include -#include #include +#include +#include namespace natten { namespace cuda { namespace gemm { @@ -428,6 +428,426 @@ void na2d_pn_cuda_gemm_double_128x128x16_64x64x16_8x8x4_3_sm80_ks31_align1( query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } +void na2d_pn_cuda_gemm_double_128x128x16_64x64x16_8x8x4_3_sm80_ks33_align1( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 16, 64, 64, 16, 8, 8, 4, 3, 17, 1, 16>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, double>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 1, 1>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_pn_cuda_gemm_double_128x128x16_64x64x16_8x8x4_3_sm80_ks35_align1( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 16, 64, 64, 16, 8, 8, 4, 3, 22, 1, 17>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, double>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 1, 1>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_pn_cuda_gemm_double_128x128x16_64x64x16_8x8x4_3_sm80_ks37_align1( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 16, 64, 64, 16, 8, 8, 4, 3, 22, 1, 18>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, double>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 1, 1>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_pn_cuda_gemm_double_128x128x16_64x64x16_8x8x4_3_sm80_ks39_align1( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 16, 64, 64, 16, 8, 8, 4, 3, 23, 1, 19>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, double>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 1, 1>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_pn_cuda_gemm_double_128x128x16_64x64x16_8x8x4_3_sm80_ks41_align1( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 16, 64, 64, 16, 8, 8, 4, 3, 22, 1, 20>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, double>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 1, 1>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_pn_cuda_gemm_double_128x128x16_64x64x16_8x8x4_3_sm80_ks43_align1( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 16, 64, 64, 16, 8, 8, 4, 3, 22, 1, 21>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, double>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 1, 1>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_pn_cuda_gemm_double_128x128x16_64x64x16_8x8x4_3_sm80_ks45_align1( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 16, 64, 64, 16, 8, 8, 4, 3, 23, 1, 22>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, double>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 1, 1>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_pn_cuda_gemm_double_128x128x16_64x64x16_8x8x4_3_sm80_ks47_align1( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 16, 64, 64, 16, 8, 8, 4, 3, 33, 1, 23>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, double>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 1, 1>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_pn_cuda_gemm_double_128x128x16_64x64x16_8x8x4_3_sm80_ks49_align1( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 16, 64, 64, 16, 8, 8, 4, 3, 33, 1, 24>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, double>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 1, 1>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_pn_cuda_gemm_double_128x128x16_64x64x16_8x8x4_3_sm80_ks51_align1( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 16, 64, 64, 16, 8, 8, 4, 3, 33, 1, 25>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, double>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 1, 1>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_pn_cuda_gemm_double_128x128x16_64x64x16_8x8x4_3_sm80_ks53_align1( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 16, 64, 64, 16, 8, 8, 4, 3, 33, 1, 26>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, double>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 1, 1>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_pn_cuda_gemm_double_128x128x16_64x64x16_8x8x4_3_sm80_ks55_align1( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 16, 64, 64, 16, 8, 8, 4, 3, 33, 1, 27>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, double>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 1, 1>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_pn_cuda_gemm_double_128x128x16_64x64x16_8x8x4_3_sm80_ks57_align1( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 16, 64, 64, 16, 8, 8, 4, 3, 33, 1, 28>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, double>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 1, 1>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_pn_cuda_gemm_double_128x128x16_64x64x16_8x8x4_3_sm80_ks59_align1( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 16, 64, 64, 16, 8, 8, 4, 3, 33, 1, 29>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, double>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 1, 1>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_pn_cuda_gemm_double_128x128x16_64x64x16_8x8x4_3_sm80_ks61_align1( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 16, 64, 64, 16, 8, 8, 4, 3, 33, 1, 30>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, double>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 1, 1>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + } } } diff --git a/csrc/autogen/src/cuda/gemm/2d/sm80/source_1.cu b/csrc/autogen/src/cuda/gemm/2d/sm80/source_1.cu index 22ee6f9..5c1fc0a 100644 --- a/csrc/autogen/src/cuda/gemm/2d/sm80/source_1.cu +++ b/csrc/autogen/src/cuda/gemm/2d/sm80/source_1.cu @@ -1,14 +1,14 @@ #include #include -#include #include -#include #include +#include +#include namespace natten { namespace cuda { namespace gemm { -void na2d_pn_cuda_gemm_double_128x128x16_64x64x16_8x8x4_3_sm80_ks33_align1( +void na2d_pn_cuda_gemm_double_128x128x16_64x64x16_8x8x4_3_sm80_ks63_align1( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -26,7 +26,7 @@ void na2d_pn_cuda_gemm_double_128x128x16_64x64x16_8x8x4_3_sm80_ks33_align1( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 16, 64, 64, 16, 8, 8, 4, 3, 17, 1, 16>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 16, 64, 64, 16, 8, 8, 4, 3, 33, 1, 31>; using ArchConfig = natten::gemm::detail::ArchArgs<80, double>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 1, 1>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -568,6 +568,576 @@ query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, a } } +void na2d_pn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks11_align1( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 16, 32, 16, 16, 16, 8, 8, 3, 6, 1, 5>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 1, 1>; + if (natten::kEnableGemmTF32) { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } else { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } +} + +void na2d_pn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks13_align4( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 16, 32, 16, 16, 16, 8, 8, 3, 10, 1, 6>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; + using AConfig = natten::gemm::detail::AlignmentConfig<4, 4, 1>; + if (natten::kEnableGemmTF32) { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } else { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } +} + +void na2d_pn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks13_align2( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 16, 32, 16, 16, 16, 8, 8, 3, 10, 1, 6>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; + using AConfig = natten::gemm::detail::AlignmentConfig<2, 2, 1>; + if (natten::kEnableGemmTF32) { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } else { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } +} + +void na2d_pn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks13_align1( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 16, 32, 16, 16, 16, 8, 8, 3, 10, 1, 6>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 1, 1>; + if (natten::kEnableGemmTF32) { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } else { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } +} + +void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks15_align4( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 16, 64, 64, 16, 16, 8, 8, 3, 9, 1, 7>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; + using AConfig = natten::gemm::detail::AlignmentConfig<4, 4, 1>; + if (natten::kEnableGemmTF32) { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } else { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } +} + +void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks15_align2( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 16, 64, 64, 16, 16, 8, 8, 3, 9, 1, 7>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; + using AConfig = natten::gemm::detail::AlignmentConfig<2, 2, 1>; + if (natten::kEnableGemmTF32) { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } else { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } +} + +void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks15_align1( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 16, 64, 64, 16, 16, 8, 8, 3, 9, 1, 7>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 1, 1>; + if (natten::kEnableGemmTF32) { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } else { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } +} + +void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks17_align4( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 16, 64, 64, 16, 16, 8, 8, 3, 11, 1, 8>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; + using AConfig = natten::gemm::detail::AlignmentConfig<4, 4, 1>; + if (natten::kEnableGemmTF32) { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } else { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } +} + +void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks17_align2( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 16, 64, 64, 16, 16, 8, 8, 3, 11, 1, 8>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; + using AConfig = natten::gemm::detail::AlignmentConfig<2, 2, 1>; + if (natten::kEnableGemmTF32) { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } else { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } +} + +void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks17_align1( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 16, 64, 64, 16, 16, 8, 8, 3, 11, 1, 8>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 1, 1>; + if (natten::kEnableGemmTF32) { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } else { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } +} + +void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks19_align4( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 16, 64, 64, 16, 16, 8, 8, 3, 13, 1, 9>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; + using AConfig = natten::gemm::detail::AlignmentConfig<4, 4, 1>; + if (natten::kEnableGemmTF32) { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } else { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } +} + +void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks19_align2( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 16, 64, 64, 16, 16, 8, 8, 3, 13, 1, 9>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; + using AConfig = natten::gemm::detail::AlignmentConfig<2, 2, 1>; + if (natten::kEnableGemmTF32) { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } else { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } +} + +void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks19_align1( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 16, 64, 64, 16, 16, 8, 8, 3, 13, 1, 9>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 1, 1>; + if (natten::kEnableGemmTF32) { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } else { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } +} + +void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks21_align4( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 16, 64, 64, 16, 16, 8, 8, 3, 12, 1, 10>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; + using AConfig = natten::gemm::detail::AlignmentConfig<4, 4, 1>; + if (natten::kEnableGemmTF32) { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } else { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } +} + +void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks21_align2( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 16, 64, 64, 16, 16, 8, 8, 3, 12, 1, 10>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; + using AConfig = natten::gemm::detail::AlignmentConfig<2, 2, 1>; + if (natten::kEnableGemmTF32) { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } else { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } +} + } } } diff --git a/csrc/autogen/src/cuda/gemm/2d/sm80/source_10.cu b/csrc/autogen/src/cuda/gemm/2d/sm80/source_10.cu index 667c8bd..9f10851 100644 --- a/csrc/autogen/src/cuda/gemm/2d/sm80/source_10.cu +++ b/csrc/autogen/src/cuda/gemm/2d/sm80/source_10.cu @@ -1,14 +1,14 @@ #include #include -#include #include -#include #include +#include +#include namespace natten { namespace cuda { namespace gemm { -void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks27_align2( +void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks57_align2( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -26,7 +26,7 @@ void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks27_align2( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 14, 1, 13>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 33, 1, 28>; using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; using AConfig = natten::gemm::detail::AlignmentConfig<2, 2, 1>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -36,7 +36,7 @@ void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks27_align2( query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks29_align8( +void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks59_align8( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -54,7 +54,7 @@ void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks29_align8( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 17, 1, 14>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 33, 1, 29>; using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; using AConfig = natten::gemm::detail::AlignmentConfig<8, 8, 1>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -64,7 +64,7 @@ void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks29_align8( query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks29_align4( +void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks59_align4( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -82,7 +82,7 @@ void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks29_align4( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 17, 1, 14>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 33, 1, 29>; using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; using AConfig = natten::gemm::detail::AlignmentConfig<4, 4, 1>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -92,7 +92,7 @@ void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks29_align4( query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks29_align2( +void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks59_align2( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -110,7 +110,7 @@ void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks29_align2( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 17, 1, 14>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 33, 1, 29>; using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; using AConfig = natten::gemm::detail::AlignmentConfig<2, 2, 1>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -120,7 +120,7 @@ void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks29_align2( query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks31_align8( +void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks61_align8( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -138,7 +138,7 @@ void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks31_align8( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 17, 1, 15>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 33, 1, 30>; using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; using AConfig = natten::gemm::detail::AlignmentConfig<8, 8, 1>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -148,7 +148,7 @@ void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks31_align8( query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks31_align4( +void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks61_align4( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -166,7 +166,7 @@ void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks31_align4( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 17, 1, 15>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 33, 1, 30>; using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; using AConfig = natten::gemm::detail::AlignmentConfig<4, 4, 1>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -176,7 +176,7 @@ void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks31_align4( query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks31_align2( +void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks61_align2( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -194,7 +194,7 @@ void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks31_align2( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 17, 1, 15>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 33, 1, 30>; using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; using AConfig = natten::gemm::detail::AlignmentConfig<2, 2, 1>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -204,7 +204,7 @@ void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks31_align2( query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks33_align8( +void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks63_align8( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -222,7 +222,7 @@ void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks33_align8( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 17, 1, 16>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 33, 1, 31>; using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; using AConfig = natten::gemm::detail::AlignmentConfig<8, 8, 1>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -232,7 +232,7 @@ void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks33_align8( query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks33_align4( +void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks63_align4( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -250,7 +250,7 @@ void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks33_align4( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 17, 1, 16>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 33, 1, 31>; using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; using AConfig = natten::gemm::detail::AlignmentConfig<4, 4, 1>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -260,7 +260,7 @@ void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks33_align4( query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks33_align2( +void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks63_align2( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -278,7 +278,7 @@ void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks33_align2( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 17, 1, 16>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 33, 1, 31>; using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; using AConfig = natten::gemm::detail::AlignmentConfig<2, 2, 1>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -423,6 +423,411 @@ void na2d_nn_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks11_align1( attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } +void na2d_nn_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks13_align1( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 16, 32, 32, 16, 8, 8, 4, 3, 8, 2, 6>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, double>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 1, 1>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_nn_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks15_align1( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 16, 32, 32, 16, 8, 8, 4, 3, 8, 2, 7>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, double>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 1, 1>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_nn_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks17_align1( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 16, 32, 32, 16, 8, 8, 4, 3, 8, 2, 8>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, double>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 1, 1>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_nn_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks19_align1( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 16, 32, 32, 16, 8, 8, 4, 3, 8, 2, 9>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, double>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 1, 1>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_nn_cuda_gemm_double_128x128x16_64x64x16_8x8x4_3_sm80_ks21_align1( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 16, 64, 64, 16, 8, 8, 4, 3, 11, 2, 10>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, double>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 1, 1>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_nn_cuda_gemm_double_128x128x16_64x64x16_8x8x4_3_sm80_ks23_align1( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 16, 64, 64, 16, 8, 8, 4, 3, 11, 2, 11>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, double>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 1, 1>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_nn_cuda_gemm_double_128x128x16_64x64x16_8x8x4_3_sm80_ks25_align1( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 16, 64, 64, 16, 8, 8, 4, 3, 11, 2, 12>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, double>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 1, 1>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_nn_cuda_gemm_double_128x128x16_64x64x16_8x8x4_3_sm80_ks27_align1( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 16, 64, 64, 16, 8, 8, 4, 3, 11, 2, 13>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, double>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 1, 1>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_nn_cuda_gemm_double_128x128x16_64x64x16_8x8x4_3_sm80_ks29_align1( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 16, 64, 64, 16, 8, 8, 4, 3, 11, 2, 14>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, double>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 1, 1>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_nn_cuda_gemm_double_128x128x16_64x64x16_8x8x4_3_sm80_ks31_align1( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 16, 64, 64, 16, 8, 8, 4, 3, 11, 2, 15>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, double>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 1, 1>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_nn_cuda_gemm_double_128x128x16_64x64x16_8x8x4_3_sm80_ks33_align1( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 16, 64, 64, 16, 8, 8, 4, 3, 11, 2, 16>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, double>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 1, 1>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_nn_cuda_gemm_double_128x128x16_64x64x16_8x8x4_3_sm80_ks35_align1( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 16, 64, 64, 16, 8, 8, 4, 3, 11, 2, 17>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, double>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 1, 1>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_nn_cuda_gemm_double_128x128x16_64x64x16_8x8x4_3_sm80_ks37_align1( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 16, 64, 64, 16, 8, 8, 4, 3, 11, 2, 18>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, double>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 1, 1>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_nn_cuda_gemm_double_128x128x16_64x64x16_8x8x4_3_sm80_ks39_align1( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 16, 64, 64, 16, 8, 8, 4, 3, 11, 2, 19>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, double>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 1, 1>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_nn_cuda_gemm_double_128x128x16_64x64x16_8x8x4_3_sm80_ks41_align1( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 16, 64, 64, 16, 8, 8, 4, 3, 11, 2, 20>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, double>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 1, 1>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + } } } diff --git a/csrc/autogen/src/cuda/gemm/2d/sm80/source_11.cu b/csrc/autogen/src/cuda/gemm/2d/sm80/source_11.cu index 3211741..998ce59 100644 --- a/csrc/autogen/src/cuda/gemm/2d/sm80/source_11.cu +++ b/csrc/autogen/src/cuda/gemm/2d/sm80/source_11.cu @@ -1,14 +1,14 @@ #include #include -#include #include -#include #include +#include +#include namespace natten { namespace cuda { namespace gemm { -void na2d_nn_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks13_align1( +void na2d_nn_cuda_gemm_double_128x128x16_64x64x16_8x8x4_3_sm80_ks43_align1( void * attn_ptr, void * value_ptr, void * output_ptr, @@ -25,7 +25,7 @@ void na2d_nn_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks13_align1( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 16, 32, 32, 16, 8, 8, 4, 3, 8, 2, 6>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 16, 64, 64, 16, 8, 8, 4, 3, 11, 2, 21>; using ArchConfig = natten::gemm::detail::ArchArgs<80, double>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 1, 1>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -35,7 +35,7 @@ void na2d_nn_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks13_align1( attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_nn_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks15_align1( +void na2d_nn_cuda_gemm_double_128x128x16_64x64x16_8x8x4_3_sm80_ks45_align1( void * attn_ptr, void * value_ptr, void * output_ptr, @@ -52,7 +52,7 @@ void na2d_nn_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks15_align1( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 16, 32, 32, 16, 8, 8, 4, 3, 8, 2, 7>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 16, 64, 64, 16, 8, 8, 4, 3, 11, 2, 22>; using ArchConfig = natten::gemm::detail::ArchArgs<80, double>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 1, 1>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -62,7 +62,7 @@ void na2d_nn_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks15_align1( attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_nn_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks17_align1( +void na2d_nn_cuda_gemm_double_128x128x16_64x64x16_8x8x4_3_sm80_ks47_align1( void * attn_ptr, void * value_ptr, void * output_ptr, @@ -79,7 +79,7 @@ void na2d_nn_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks17_align1( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 16, 32, 32, 16, 8, 8, 4, 3, 8, 2, 8>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 16, 64, 64, 16, 8, 8, 4, 3, 11, 2, 23>; using ArchConfig = natten::gemm::detail::ArchArgs<80, double>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 1, 1>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -89,7 +89,7 @@ void na2d_nn_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks17_align1( attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_nn_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks19_align1( +void na2d_nn_cuda_gemm_double_128x128x16_64x64x16_8x8x4_3_sm80_ks49_align1( void * attn_ptr, void * value_ptr, void * output_ptr, @@ -106,7 +106,7 @@ void na2d_nn_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks19_align1( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 16, 32, 32, 16, 8, 8, 4, 3, 8, 2, 9>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 16, 64, 64, 16, 8, 8, 4, 3, 11, 2, 24>; using ArchConfig = natten::gemm::detail::ArchArgs<80, double>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 1, 1>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -116,7 +116,7 @@ void na2d_nn_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks19_align1( attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_nn_cuda_gemm_double_128x128x16_64x64x16_8x8x4_3_sm80_ks21_align1( +void na2d_nn_cuda_gemm_double_128x128x16_64x64x16_8x8x4_3_sm80_ks51_align1( void * attn_ptr, void * value_ptr, void * output_ptr, @@ -133,7 +133,7 @@ void na2d_nn_cuda_gemm_double_128x128x16_64x64x16_8x8x4_3_sm80_ks21_align1( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 16, 64, 64, 16, 8, 8, 4, 3, 11, 2, 10>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 16, 64, 64, 16, 8, 8, 4, 3, 11, 2, 25>; using ArchConfig = natten::gemm::detail::ArchArgs<80, double>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 1, 1>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -143,7 +143,7 @@ void na2d_nn_cuda_gemm_double_128x128x16_64x64x16_8x8x4_3_sm80_ks21_align1( attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_nn_cuda_gemm_double_128x128x16_64x64x16_8x8x4_3_sm80_ks23_align1( +void na2d_nn_cuda_gemm_double_128x128x16_64x64x16_8x8x4_3_sm80_ks53_align1( void * attn_ptr, void * value_ptr, void * output_ptr, @@ -160,7 +160,7 @@ void na2d_nn_cuda_gemm_double_128x128x16_64x64x16_8x8x4_3_sm80_ks23_align1( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 16, 64, 64, 16, 8, 8, 4, 3, 11, 2, 11>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 16, 64, 64, 16, 8, 8, 4, 3, 11, 2, 26>; using ArchConfig = natten::gemm::detail::ArchArgs<80, double>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 1, 1>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -170,7 +170,7 @@ void na2d_nn_cuda_gemm_double_128x128x16_64x64x16_8x8x4_3_sm80_ks23_align1( attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_nn_cuda_gemm_double_128x128x16_64x64x16_8x8x4_3_sm80_ks25_align1( +void na2d_nn_cuda_gemm_double_128x128x16_64x64x16_8x8x4_3_sm80_ks55_align1( void * attn_ptr, void * value_ptr, void * output_ptr, @@ -187,7 +187,7 @@ void na2d_nn_cuda_gemm_double_128x128x16_64x64x16_8x8x4_3_sm80_ks25_align1( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 16, 64, 64, 16, 8, 8, 4, 3, 11, 2, 12>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 16, 64, 64, 16, 8, 8, 4, 3, 11, 2, 27>; using ArchConfig = natten::gemm::detail::ArchArgs<80, double>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 1, 1>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -197,7 +197,7 @@ void na2d_nn_cuda_gemm_double_128x128x16_64x64x16_8x8x4_3_sm80_ks25_align1( attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_nn_cuda_gemm_double_128x128x16_64x64x16_8x8x4_3_sm80_ks27_align1( +void na2d_nn_cuda_gemm_double_128x128x16_64x64x16_8x8x4_3_sm80_ks57_align1( void * attn_ptr, void * value_ptr, void * output_ptr, @@ -214,7 +214,7 @@ void na2d_nn_cuda_gemm_double_128x128x16_64x64x16_8x8x4_3_sm80_ks27_align1( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 16, 64, 64, 16, 8, 8, 4, 3, 11, 2, 13>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 16, 64, 64, 16, 8, 8, 4, 3, 11, 2, 28>; using ArchConfig = natten::gemm::detail::ArchArgs<80, double>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 1, 1>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -224,7 +224,7 @@ void na2d_nn_cuda_gemm_double_128x128x16_64x64x16_8x8x4_3_sm80_ks27_align1( attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_nn_cuda_gemm_double_128x128x16_64x64x16_8x8x4_3_sm80_ks29_align1( +void na2d_nn_cuda_gemm_double_128x128x16_64x64x16_8x8x4_3_sm80_ks59_align1( void * attn_ptr, void * value_ptr, void * output_ptr, @@ -241,7 +241,7 @@ void na2d_nn_cuda_gemm_double_128x128x16_64x64x16_8x8x4_3_sm80_ks29_align1( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 16, 64, 64, 16, 8, 8, 4, 3, 11, 2, 14>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 16, 64, 64, 16, 8, 8, 4, 3, 11, 2, 29>; using ArchConfig = natten::gemm::detail::ArchArgs<80, double>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 1, 1>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -251,7 +251,7 @@ void na2d_nn_cuda_gemm_double_128x128x16_64x64x16_8x8x4_3_sm80_ks29_align1( attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_nn_cuda_gemm_double_128x128x16_64x64x16_8x8x4_3_sm80_ks31_align1( +void na2d_nn_cuda_gemm_double_128x128x16_64x64x16_8x8x4_3_sm80_ks61_align1( void * attn_ptr, void * value_ptr, void * output_ptr, @@ -268,7 +268,7 @@ void na2d_nn_cuda_gemm_double_128x128x16_64x64x16_8x8x4_3_sm80_ks31_align1( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 16, 64, 64, 16, 8, 8, 4, 3, 11, 2, 15>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 16, 64, 64, 16, 8, 8, 4, 3, 11, 2, 30>; using ArchConfig = natten::gemm::detail::ArchArgs<80, double>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 1, 1>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -278,7 +278,7 @@ void na2d_nn_cuda_gemm_double_128x128x16_64x64x16_8x8x4_3_sm80_ks31_align1( attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_nn_cuda_gemm_double_128x128x16_64x64x16_8x8x4_3_sm80_ks33_align1( +void na2d_nn_cuda_gemm_double_128x128x16_64x64x16_8x8x4_3_sm80_ks63_align1( void * attn_ptr, void * value_ptr, void * output_ptr, @@ -295,7 +295,7 @@ void na2d_nn_cuda_gemm_double_128x128x16_64x64x16_8x8x4_3_sm80_ks33_align1( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 16, 64, 64, 16, 8, 8, 4, 3, 11, 2, 16>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 16, 64, 64, 16, 8, 8, 4, 3, 11, 2, 31>; using ArchConfig = natten::gemm::detail::ArchArgs<80, double>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 1, 1>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -453,6 +453,561 @@ attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_str } } +void na2d_nn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks5_align2( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 16, 32, 16, 16, 16, 8, 8, 3, 8, 2, 2>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; + if (natten::kEnableGemmTF32) { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } else { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } +} + +void na2d_nn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks5_align1( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 16, 32, 16, 16, 16, 8, 8, 3, 8, 2, 2>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 1, 1>; + if (natten::kEnableGemmTF32) { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } else { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } +} + +void na2d_nn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks7_align4( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 16, 32, 16, 16, 16, 8, 8, 3, 8, 2, 3>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; + if (natten::kEnableGemmTF32) { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } else { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } +} + +void na2d_nn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks7_align2( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 16, 32, 16, 16, 16, 8, 8, 3, 8, 2, 3>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; + if (natten::kEnableGemmTF32) { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } else { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } +} + +void na2d_nn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks7_align1( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 16, 32, 16, 16, 16, 8, 8, 3, 8, 2, 3>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 1, 1>; + if (natten::kEnableGemmTF32) { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } else { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } +} + +void na2d_nn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks9_align4( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 16, 32, 16, 16, 16, 8, 8, 3, 8, 2, 4>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; + if (natten::kEnableGemmTF32) { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } else { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } +} + +void na2d_nn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks9_align2( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 16, 32, 16, 16, 16, 8, 8, 3, 8, 2, 4>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; + if (natten::kEnableGemmTF32) { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } else { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } +} + +void na2d_nn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks9_align1( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 16, 32, 16, 16, 16, 8, 8, 3, 8, 2, 4>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 1, 1>; + if (natten::kEnableGemmTF32) { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } else { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } +} + +void na2d_nn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks11_align4( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 16, 32, 16, 16, 16, 8, 8, 3, 8, 2, 5>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; + if (natten::kEnableGemmTF32) { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } else { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } +} + +void na2d_nn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks11_align2( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 16, 32, 16, 16, 16, 8, 8, 3, 8, 2, 5>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; + if (natten::kEnableGemmTF32) { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } else { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } +} + +void na2d_nn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks11_align1( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 16, 32, 16, 16, 16, 8, 8, 3, 8, 2, 5>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 1, 1>; + if (natten::kEnableGemmTF32) { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } else { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } +} + +void na2d_nn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks13_align4( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 16, 32, 16, 16, 16, 8, 8, 3, 8, 2, 6>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; + if (natten::kEnableGemmTF32) { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } else { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } +} + +void na2d_nn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks13_align2( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 16, 32, 16, 16, 16, 8, 8, 3, 8, 2, 6>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; + if (natten::kEnableGemmTF32) { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } else { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } +} + +void na2d_nn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks13_align1( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 16, 32, 16, 16, 16, 8, 8, 3, 8, 2, 6>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 1, 1>; + if (natten::kEnableGemmTF32) { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } else { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } +} + +void na2d_nn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks15_align4( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 16, 32, 16, 16, 16, 8, 8, 3, 8, 2, 7>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; + if (natten::kEnableGemmTF32) { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } else { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } +} + } } } diff --git a/csrc/autogen/src/cuda/gemm/2d/sm80/source_12.cu b/csrc/autogen/src/cuda/gemm/2d/sm80/source_12.cu index e353a82..db5b24c 100644 --- a/csrc/autogen/src/cuda/gemm/2d/sm80/source_12.cu +++ b/csrc/autogen/src/cuda/gemm/2d/sm80/source_12.cu @@ -1,14 +1,14 @@ #include #include -#include #include -#include #include +#include +#include namespace natten { namespace cuda { namespace gemm { -void na2d_nn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks5_align2( +void na2d_nn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks15_align2( void * attn_ptr, void * value_ptr, void * output_ptr, @@ -25,7 +25,7 @@ void na2d_nn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks5_align2( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 16, 32, 16, 16, 16, 8, 8, 3, 8, 2, 2>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 16, 32, 16, 16, 16, 8, 8, 3, 8, 2, 7>; using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; if (natten::kEnableGemmTF32) { @@ -45,7 +45,7 @@ attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_str } } -void na2d_nn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks5_align1( +void na2d_nn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks15_align1( void * attn_ptr, void * value_ptr, void * output_ptr, @@ -62,7 +62,7 @@ void na2d_nn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks5_align1( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 16, 32, 16, 16, 16, 8, 8, 3, 8, 2, 2>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 16, 32, 16, 16, 16, 8, 8, 3, 8, 2, 7>; using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 1, 1>; if (natten::kEnableGemmTF32) { @@ -82,7 +82,7 @@ attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_str } } -void na2d_nn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks7_align4( +void na2d_nn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks17_align4( void * attn_ptr, void * value_ptr, void * output_ptr, @@ -99,7 +99,7 @@ void na2d_nn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks7_align4( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 16, 32, 16, 16, 16, 8, 8, 3, 8, 2, 3>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 16, 32, 16, 16, 16, 8, 8, 3, 8, 2, 8>; using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; if (natten::kEnableGemmTF32) { @@ -119,7 +119,7 @@ attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_str } } -void na2d_nn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks7_align2( +void na2d_nn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks17_align2( void * attn_ptr, void * value_ptr, void * output_ptr, @@ -136,7 +136,7 @@ void na2d_nn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks7_align2( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 16, 32, 16, 16, 16, 8, 8, 3, 8, 2, 3>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 16, 32, 16, 16, 16, 8, 8, 3, 8, 2, 8>; using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; if (natten::kEnableGemmTF32) { @@ -156,7 +156,7 @@ attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_str } } -void na2d_nn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks7_align1( +void na2d_nn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks17_align1( void * attn_ptr, void * value_ptr, void * output_ptr, @@ -173,7 +173,7 @@ void na2d_nn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks7_align1( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 16, 32, 16, 16, 16, 8, 8, 3, 8, 2, 3>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 16, 32, 16, 16, 16, 8, 8, 3, 8, 2, 8>; using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 1, 1>; if (natten::kEnableGemmTF32) { @@ -193,7 +193,7 @@ attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_str } } -void na2d_nn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks9_align4( +void na2d_nn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks19_align4( void * attn_ptr, void * value_ptr, void * output_ptr, @@ -210,7 +210,7 @@ void na2d_nn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks9_align4( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 16, 32, 16, 16, 16, 8, 8, 3, 8, 2, 4>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 16, 32, 16, 16, 16, 8, 8, 3, 8, 2, 9>; using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; if (natten::kEnableGemmTF32) { @@ -230,7 +230,7 @@ attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_str } } -void na2d_nn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks9_align2( +void na2d_nn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks19_align2( void * attn_ptr, void * value_ptr, void * output_ptr, @@ -247,7 +247,7 @@ void na2d_nn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks9_align2( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 16, 32, 16, 16, 16, 8, 8, 3, 8, 2, 4>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 16, 32, 16, 16, 16, 8, 8, 3, 8, 2, 9>; using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; if (natten::kEnableGemmTF32) { @@ -267,7 +267,7 @@ attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_str } } -void na2d_nn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks9_align1( +void na2d_nn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks19_align1( void * attn_ptr, void * value_ptr, void * output_ptr, @@ -284,7 +284,7 @@ void na2d_nn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks9_align1( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 16, 32, 16, 16, 16, 8, 8, 3, 8, 2, 4>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 16, 32, 16, 16, 16, 8, 8, 3, 8, 2, 9>; using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 1, 1>; if (natten::kEnableGemmTF32) { @@ -304,7 +304,7 @@ attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_str } } -void na2d_nn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks11_align4( +void na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks21_align4( void * attn_ptr, void * value_ptr, void * output_ptr, @@ -321,7 +321,7 @@ void na2d_nn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks11_align4( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 16, 32, 16, 16, 16, 8, 8, 3, 8, 2, 5>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 16, 64, 64, 16, 16, 8, 8, 3, 11, 2, 10>; using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; if (natten::kEnableGemmTF32) { @@ -341,7 +341,7 @@ attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_str } } -void na2d_nn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks11_align2( +void na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks21_align2( void * attn_ptr, void * value_ptr, void * output_ptr, @@ -358,7 +358,7 @@ void na2d_nn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks11_align2( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 16, 32, 16, 16, 16, 8, 8, 3, 8, 2, 5>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 16, 64, 64, 16, 16, 8, 8, 3, 11, 2, 10>; using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; if (natten::kEnableGemmTF32) { @@ -378,7 +378,7 @@ attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_str } } -void na2d_nn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks11_align1( +void na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks21_align1( void * attn_ptr, void * value_ptr, void * output_ptr, @@ -395,7 +395,7 @@ void na2d_nn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks11_align1( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 16, 32, 16, 16, 16, 8, 8, 3, 8, 2, 5>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 16, 64, 64, 16, 16, 8, 8, 3, 11, 2, 10>; using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 1, 1>; if (natten::kEnableGemmTF32) { @@ -415,7 +415,7 @@ attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_str } } -void na2d_nn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks13_align4( +void na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks23_align4( void * attn_ptr, void * value_ptr, void * output_ptr, @@ -432,7 +432,7 @@ void na2d_nn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks13_align4( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 16, 32, 16, 16, 16, 8, 8, 3, 8, 2, 6>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 16, 64, 64, 16, 16, 8, 8, 3, 11, 2, 11>; using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; if (natten::kEnableGemmTF32) { @@ -452,7 +452,7 @@ attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_str } } -void na2d_nn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks13_align2( +void na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks23_align2( void * attn_ptr, void * value_ptr, void * output_ptr, @@ -469,7 +469,7 @@ void na2d_nn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks13_align2( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 16, 32, 16, 16, 16, 8, 8, 3, 8, 2, 6>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 16, 64, 64, 16, 16, 8, 8, 3, 11, 2, 11>; using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; if (natten::kEnableGemmTF32) { @@ -489,7 +489,7 @@ attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_str } } -void na2d_nn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks13_align1( +void na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks23_align1( void * attn_ptr, void * value_ptr, void * output_ptr, @@ -506,7 +506,7 @@ void na2d_nn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks13_align1( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 16, 32, 16, 16, 16, 8, 8, 3, 8, 2, 6>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 16, 64, 64, 16, 16, 8, 8, 3, 11, 2, 11>; using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 1, 1>; if (natten::kEnableGemmTF32) { @@ -526,7 +526,7 @@ attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_str } } -void na2d_nn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks15_align4( +void na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks25_align4( void * attn_ptr, void * value_ptr, void * output_ptr, @@ -543,7 +543,562 @@ void na2d_nn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks15_align4( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 16, 32, 16, 16, 16, 8, 8, 3, 8, 2, 7>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 16, 64, 64, 16, 16, 8, 8, 3, 11, 2, 12>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; + if (natten::kEnableGemmTF32) { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } else { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } +} + +void na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks25_align2( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 16, 64, 64, 16, 16, 8, 8, 3, 11, 2, 12>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; + if (natten::kEnableGemmTF32) { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } else { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } +} + +void na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks25_align1( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 16, 64, 64, 16, 16, 8, 8, 3, 11, 2, 12>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 1, 1>; + if (natten::kEnableGemmTF32) { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } else { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } +} + +void na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks27_align4( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 16, 64, 64, 16, 16, 8, 8, 3, 11, 2, 13>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; + if (natten::kEnableGemmTF32) { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } else { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } +} + +void na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks27_align2( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 16, 64, 64, 16, 16, 8, 8, 3, 11, 2, 13>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; + if (natten::kEnableGemmTF32) { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } else { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } +} + +void na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks27_align1( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 16, 64, 64, 16, 16, 8, 8, 3, 11, 2, 13>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 1, 1>; + if (natten::kEnableGemmTF32) { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } else { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } +} + +void na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks29_align4( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 16, 64, 64, 16, 16, 8, 8, 3, 11, 2, 14>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; + if (natten::kEnableGemmTF32) { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } else { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } +} + +void na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks29_align2( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 16, 64, 64, 16, 16, 8, 8, 3, 11, 2, 14>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; + if (natten::kEnableGemmTF32) { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } else { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } +} + +void na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks29_align1( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 16, 64, 64, 16, 16, 8, 8, 3, 11, 2, 14>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 1, 1>; + if (natten::kEnableGemmTF32) { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } else { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } +} + +void na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks31_align4( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 16, 64, 64, 16, 16, 8, 8, 3, 11, 2, 15>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; + if (natten::kEnableGemmTF32) { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } else { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } +} + +void na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks31_align2( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 16, 64, 64, 16, 16, 8, 8, 3, 11, 2, 15>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; + if (natten::kEnableGemmTF32) { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } else { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } +} + +void na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks31_align1( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 16, 64, 64, 16, 16, 8, 8, 3, 11, 2, 15>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 1, 1>; + if (natten::kEnableGemmTF32) { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } else { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } +} + +void na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks33_align4( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 16, 64, 64, 16, 16, 8, 8, 3, 11, 2, 16>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; + if (natten::kEnableGemmTF32) { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } else { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } +} + +void na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks33_align2( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 16, 64, 64, 16, 16, 8, 8, 3, 11, 2, 16>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; + if (natten::kEnableGemmTF32) { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } else { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } +} + +void na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks33_align1( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 16, 64, 64, 16, 16, 8, 8, 3, 11, 2, 16>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 1, 1>; + if (natten::kEnableGemmTF32) { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } else { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } +} + +void na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks35_align4( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 16, 64, 64, 16, 16, 8, 8, 3, 11, 2, 17>; using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; if (natten::kEnableGemmTF32) { diff --git a/csrc/autogen/src/cuda/gemm/2d/sm80/source_13.cu b/csrc/autogen/src/cuda/gemm/2d/sm80/source_13.cu index 7787a45..b8fd31c 100644 --- a/csrc/autogen/src/cuda/gemm/2d/sm80/source_13.cu +++ b/csrc/autogen/src/cuda/gemm/2d/sm80/source_13.cu @@ -1,14 +1,569 @@ #include #include -#include #include -#include #include +#include +#include namespace natten { namespace cuda { namespace gemm { -void na2d_nn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks15_align2( +void na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks35_align2( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 16, 64, 64, 16, 16, 8, 8, 3, 11, 2, 17>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; + if (natten::kEnableGemmTF32) { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } else { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } +} + +void na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks35_align1( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 16, 64, 64, 16, 16, 8, 8, 3, 11, 2, 17>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 1, 1>; + if (natten::kEnableGemmTF32) { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } else { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } +} + +void na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks37_align4( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 16, 64, 64, 16, 16, 8, 8, 3, 11, 2, 18>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; + if (natten::kEnableGemmTF32) { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } else { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } +} + +void na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks37_align2( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 16, 64, 64, 16, 16, 8, 8, 3, 11, 2, 18>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; + if (natten::kEnableGemmTF32) { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } else { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } +} + +void na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks37_align1( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 16, 64, 64, 16, 16, 8, 8, 3, 11, 2, 18>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 1, 1>; + if (natten::kEnableGemmTF32) { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } else { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } +} + +void na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks39_align4( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 16, 64, 64, 16, 16, 8, 8, 3, 11, 2, 19>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; + if (natten::kEnableGemmTF32) { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } else { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } +} + +void na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks39_align2( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 16, 64, 64, 16, 16, 8, 8, 3, 11, 2, 19>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; + if (natten::kEnableGemmTF32) { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } else { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } +} + +void na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks39_align1( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 16, 64, 64, 16, 16, 8, 8, 3, 11, 2, 19>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 1, 1>; + if (natten::kEnableGemmTF32) { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } else { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } +} + +void na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks41_align4( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 16, 64, 64, 16, 16, 8, 8, 3, 11, 2, 20>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; + if (natten::kEnableGemmTF32) { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } else { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } +} + +void na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks41_align2( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 16, 64, 64, 16, 16, 8, 8, 3, 11, 2, 20>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; + if (natten::kEnableGemmTF32) { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } else { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } +} + +void na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks41_align1( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 16, 64, 64, 16, 16, 8, 8, 3, 11, 2, 20>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 1, 1>; + if (natten::kEnableGemmTF32) { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } else { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } +} + +void na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks43_align4( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 16, 64, 64, 16, 16, 8, 8, 3, 11, 2, 21>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; + if (natten::kEnableGemmTF32) { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } else { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } +} + +void na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks43_align2( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 16, 64, 64, 16, 16, 8, 8, 3, 11, 2, 21>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; + if (natten::kEnableGemmTF32) { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } else { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } +} + +void na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks43_align1( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 16, 64, 64, 16, 16, 8, 8, 3, 11, 2, 21>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 1, 1>; + if (natten::kEnableGemmTF32) { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } else { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } +} + +void na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks45_align4( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 16, 64, 64, 16, 16, 8, 8, 3, 11, 2, 22>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; + if (natten::kEnableGemmTF32) { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } else { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } +} + +void na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks45_align2( void * attn_ptr, void * value_ptr, void * output_ptr, @@ -25,7 +580,7 @@ void na2d_nn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks15_align2( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 16, 32, 16, 16, 16, 8, 8, 3, 8, 2, 7>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 16, 64, 64, 16, 16, 8, 8, 3, 11, 2, 22>; using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; if (natten::kEnableGemmTF32) { @@ -45,7 +600,7 @@ attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_str } } -void na2d_nn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks15_align1( +void na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks45_align1( void * attn_ptr, void * value_ptr, void * output_ptr, @@ -62,7 +617,7 @@ void na2d_nn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks15_align1( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 16, 32, 16, 16, 16, 8, 8, 3, 8, 2, 7>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 16, 64, 64, 16, 16, 8, 8, 3, 11, 2, 22>; using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 1, 1>; if (natten::kEnableGemmTF32) { @@ -82,7 +637,7 @@ attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_str } } -void na2d_nn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks17_align4( +void na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks47_align4( void * attn_ptr, void * value_ptr, void * output_ptr, @@ -99,7 +654,7 @@ void na2d_nn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks17_align4( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 16, 32, 16, 16, 16, 8, 8, 3, 8, 2, 8>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 16, 64, 64, 16, 16, 8, 8, 3, 11, 2, 23>; using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; if (natten::kEnableGemmTF32) { @@ -119,7 +674,7 @@ attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_str } } -void na2d_nn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks17_align2( +void na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks47_align2( void * attn_ptr, void * value_ptr, void * output_ptr, @@ -136,7 +691,7 @@ void na2d_nn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks17_align2( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 16, 32, 16, 16, 16, 8, 8, 3, 8, 2, 8>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 16, 64, 64, 16, 16, 8, 8, 3, 11, 2, 23>; using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; if (natten::kEnableGemmTF32) { @@ -156,7 +711,7 @@ attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_str } } -void na2d_nn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks17_align1( +void na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks47_align1( void * attn_ptr, void * value_ptr, void * output_ptr, @@ -173,7 +728,7 @@ void na2d_nn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks17_align1( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 16, 32, 16, 16, 16, 8, 8, 3, 8, 2, 8>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 16, 64, 64, 16, 16, 8, 8, 3, 11, 2, 23>; using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 1, 1>; if (natten::kEnableGemmTF32) { @@ -193,7 +748,7 @@ attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_str } } -void na2d_nn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks19_align4( +void na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks49_align4( void * attn_ptr, void * value_ptr, void * output_ptr, @@ -210,7 +765,7 @@ void na2d_nn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks19_align4( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 16, 32, 16, 16, 16, 8, 8, 3, 8, 2, 9>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 16, 64, 64, 16, 16, 8, 8, 3, 11, 2, 24>; using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; if (natten::kEnableGemmTF32) { @@ -230,7 +785,7 @@ attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_str } } -void na2d_nn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks19_align2( +void na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks49_align2( void * attn_ptr, void * value_ptr, void * output_ptr, @@ -247,7 +802,7 @@ void na2d_nn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks19_align2( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 16, 32, 16, 16, 16, 8, 8, 3, 8, 2, 9>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 16, 64, 64, 16, 16, 8, 8, 3, 11, 2, 24>; using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; if (natten::kEnableGemmTF32) { @@ -267,7 +822,7 @@ attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_str } } -void na2d_nn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks19_align1( +void na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks49_align1( void * attn_ptr, void * value_ptr, void * output_ptr, @@ -284,7 +839,7 @@ void na2d_nn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks19_align1( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 16, 32, 16, 16, 16, 8, 8, 3, 8, 2, 9>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 16, 64, 64, 16, 16, 8, 8, 3, 11, 2, 24>; using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 1, 1>; if (natten::kEnableGemmTF32) { @@ -304,7 +859,7 @@ attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_str } } -void na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks21_align4( +void na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks51_align4( void * attn_ptr, void * value_ptr, void * output_ptr, @@ -321,7 +876,7 @@ void na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks21_align4( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 16, 64, 64, 16, 16, 8, 8, 3, 11, 2, 10>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 16, 64, 64, 16, 16, 8, 8, 3, 11, 2, 25>; using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; if (natten::kEnableGemmTF32) { @@ -341,7 +896,7 @@ attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_str } } -void na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks21_align2( +void na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks51_align2( void * attn_ptr, void * value_ptr, void * output_ptr, @@ -358,7 +913,7 @@ void na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks21_align2( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 16, 64, 64, 16, 16, 8, 8, 3, 11, 2, 10>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 16, 64, 64, 16, 16, 8, 8, 3, 11, 2, 25>; using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; if (natten::kEnableGemmTF32) { @@ -378,7 +933,7 @@ attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_str } } -void na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks21_align1( +void na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks51_align1( void * attn_ptr, void * value_ptr, void * output_ptr, @@ -395,7 +950,7 @@ void na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks21_align1( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 16, 64, 64, 16, 16, 8, 8, 3, 11, 2, 10>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 16, 64, 64, 16, 16, 8, 8, 3, 11, 2, 25>; using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 1, 1>; if (natten::kEnableGemmTF32) { @@ -415,7 +970,7 @@ attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_str } } -void na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks23_align4( +void na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks53_align4( void * attn_ptr, void * value_ptr, void * output_ptr, @@ -432,7 +987,7 @@ void na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks23_align4( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 16, 64, 64, 16, 16, 8, 8, 3, 11, 2, 11>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 16, 64, 64, 16, 16, 8, 8, 3, 11, 2, 26>; using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; if (natten::kEnableGemmTF32) { @@ -452,7 +1007,7 @@ attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_str } } -void na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks23_align2( +void na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks53_align2( void * attn_ptr, void * value_ptr, void * output_ptr, @@ -469,7 +1024,7 @@ void na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks23_align2( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 16, 64, 64, 16, 16, 8, 8, 3, 11, 2, 11>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 16, 64, 64, 16, 16, 8, 8, 3, 11, 2, 26>; using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; if (natten::kEnableGemmTF32) { @@ -489,7 +1044,7 @@ attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_str } } -void na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks23_align1( +void na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks53_align1( void * attn_ptr, void * value_ptr, void * output_ptr, @@ -506,7 +1061,7 @@ void na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks23_align1( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 16, 64, 64, 16, 16, 8, 8, 3, 11, 2, 11>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 16, 64, 64, 16, 16, 8, 8, 3, 11, 2, 26>; using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 1, 1>; if (natten::kEnableGemmTF32) { @@ -526,7 +1081,7 @@ attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_str } } -void na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks25_align4( +void na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks55_align4( void * attn_ptr, void * value_ptr, void * output_ptr, @@ -543,7 +1098,7 @@ void na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks25_align4( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 16, 64, 64, 16, 16, 8, 8, 3, 11, 2, 12>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 16, 64, 64, 16, 16, 8, 8, 3, 11, 2, 27>; using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; if (natten::kEnableGemmTF32) { diff --git a/csrc/autogen/src/cuda/gemm/2d/sm80/source_14.cu b/csrc/autogen/src/cuda/gemm/2d/sm80/source_14.cu index adac443..3ac3b47 100644 --- a/csrc/autogen/src/cuda/gemm/2d/sm80/source_14.cu +++ b/csrc/autogen/src/cuda/gemm/2d/sm80/source_14.cu @@ -1,14 +1,14 @@ #include #include -#include #include -#include #include +#include +#include namespace natten { namespace cuda { namespace gemm { -void na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks25_align2( +void na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks55_align2( void * attn_ptr, void * value_ptr, void * output_ptr, @@ -25,7 +25,7 @@ void na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks25_align2( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 16, 64, 64, 16, 16, 8, 8, 3, 11, 2, 12>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 16, 64, 64, 16, 16, 8, 8, 3, 11, 2, 27>; using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; if (natten::kEnableGemmTF32) { @@ -45,7 +45,7 @@ attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_str } } -void na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks25_align1( +void na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks55_align1( void * attn_ptr, void * value_ptr, void * output_ptr, @@ -62,7 +62,7 @@ void na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks25_align1( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 16, 64, 64, 16, 16, 8, 8, 3, 11, 2, 12>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 16, 64, 64, 16, 16, 8, 8, 3, 11, 2, 27>; using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 1, 1>; if (natten::kEnableGemmTF32) { @@ -82,7 +82,7 @@ attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_str } } -void na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks27_align4( +void na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks57_align4( void * attn_ptr, void * value_ptr, void * output_ptr, @@ -99,7 +99,7 @@ void na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks27_align4( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 16, 64, 64, 16, 16, 8, 8, 3, 11, 2, 13>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 16, 64, 64, 16, 16, 8, 8, 3, 11, 2, 28>; using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; if (natten::kEnableGemmTF32) { @@ -119,7 +119,7 @@ attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_str } } -void na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks27_align2( +void na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks57_align2( void * attn_ptr, void * value_ptr, void * output_ptr, @@ -136,7 +136,7 @@ void na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks27_align2( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 16, 64, 64, 16, 16, 8, 8, 3, 11, 2, 13>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 16, 64, 64, 16, 16, 8, 8, 3, 11, 2, 28>; using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; if (natten::kEnableGemmTF32) { @@ -156,7 +156,7 @@ attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_str } } -void na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks27_align1( +void na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks57_align1( void * attn_ptr, void * value_ptr, void * output_ptr, @@ -173,7 +173,7 @@ void na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks27_align1( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 16, 64, 64, 16, 16, 8, 8, 3, 11, 2, 13>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 16, 64, 64, 16, 16, 8, 8, 3, 11, 2, 28>; using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 1, 1>; if (natten::kEnableGemmTF32) { @@ -193,7 +193,7 @@ attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_str } } -void na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks29_align4( +void na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks59_align4( void * attn_ptr, void * value_ptr, void * output_ptr, @@ -210,7 +210,7 @@ void na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks29_align4( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 16, 64, 64, 16, 16, 8, 8, 3, 11, 2, 14>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 16, 64, 64, 16, 16, 8, 8, 3, 11, 2, 29>; using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; if (natten::kEnableGemmTF32) { @@ -230,7 +230,7 @@ attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_str } } -void na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks29_align2( +void na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks59_align2( void * attn_ptr, void * value_ptr, void * output_ptr, @@ -247,7 +247,7 @@ void na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks29_align2( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 16, 64, 64, 16, 16, 8, 8, 3, 11, 2, 14>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 16, 64, 64, 16, 16, 8, 8, 3, 11, 2, 29>; using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; if (natten::kEnableGemmTF32) { @@ -267,7 +267,7 @@ attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_str } } -void na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks29_align1( +void na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks59_align1( void * attn_ptr, void * value_ptr, void * output_ptr, @@ -284,7 +284,7 @@ void na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks29_align1( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 16, 64, 64, 16, 16, 8, 8, 3, 11, 2, 14>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 16, 64, 64, 16, 16, 8, 8, 3, 11, 2, 29>; using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 1, 1>; if (natten::kEnableGemmTF32) { @@ -304,7 +304,7 @@ attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_str } } -void na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks31_align4( +void na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks61_align4( void * attn_ptr, void * value_ptr, void * output_ptr, @@ -321,7 +321,7 @@ void na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks31_align4( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 16, 64, 64, 16, 16, 8, 8, 3, 11, 2, 15>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 16, 64, 64, 16, 16, 8, 8, 3, 11, 2, 30>; using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; if (natten::kEnableGemmTF32) { @@ -341,7 +341,7 @@ attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_str } } -void na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks31_align2( +void na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks61_align2( void * attn_ptr, void * value_ptr, void * output_ptr, @@ -358,7 +358,7 @@ void na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks31_align2( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 16, 64, 64, 16, 16, 8, 8, 3, 11, 2, 15>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 16, 64, 64, 16, 16, 8, 8, 3, 11, 2, 30>; using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; if (natten::kEnableGemmTF32) { @@ -378,7 +378,7 @@ attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_str } } -void na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks31_align1( +void na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks61_align1( void * attn_ptr, void * value_ptr, void * output_ptr, @@ -395,7 +395,7 @@ void na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks31_align1( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 16, 64, 64, 16, 16, 8, 8, 3, 11, 2, 15>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 16, 64, 64, 16, 16, 8, 8, 3, 11, 2, 30>; using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 1, 1>; if (natten::kEnableGemmTF32) { @@ -415,7 +415,7 @@ attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_str } } -void na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks33_align4( +void na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks63_align4( void * attn_ptr, void * value_ptr, void * output_ptr, @@ -432,7 +432,7 @@ void na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks33_align4( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 16, 64, 64, 16, 16, 8, 8, 3, 11, 2, 16>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 16, 64, 64, 16, 16, 8, 8, 3, 11, 2, 31>; using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; if (natten::kEnableGemmTF32) { @@ -452,7 +452,7 @@ attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_str } } -void na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks33_align2( +void na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks63_align2( void * attn_ptr, void * value_ptr, void * output_ptr, @@ -469,7 +469,7 @@ void na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks33_align2( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 16, 64, 64, 16, 16, 8, 8, 3, 11, 2, 16>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 16, 64, 64, 16, 16, 8, 8, 3, 11, 2, 31>; using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; if (natten::kEnableGemmTF32) { @@ -489,7 +489,7 @@ attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_str } } -void na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks33_align1( +void na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks63_align1( void * attn_ptr, void * value_ptr, void * output_ptr, @@ -506,7 +506,7 @@ void na2d_nn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks33_align1( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 16, 64, 64, 16, 16, 8, 8, 3, 11, 2, 16>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 16, 64, 64, 16, 16, 8, 8, 3, 11, 2, 31>; using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 1, 1>; if (natten::kEnableGemmTF32) { @@ -553,6 +553,411 @@ void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks3_align8( attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } +void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks3_align4( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 2, 1>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks3_align2( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 2, 1>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks5_align8( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 2, 2>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 8, 8>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks5_align4( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 2, 2>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks5_align2( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 2, 2>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks7_align8( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 2, 3>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 8, 8>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks7_align4( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 2, 3>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks7_align2( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 2, 3>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks9_align8( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 2, 4>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 8, 8>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks9_align4( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 2, 4>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks9_align2( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 2, 4>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks11_align8( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 2, 5>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 8, 8>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks11_align4( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 2, 5>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks11_align2( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 2, 5>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks13_align8( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 2, 6>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 8, 8>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + } } } diff --git a/csrc/autogen/src/cuda/gemm/2d/sm80/source_15.cu b/csrc/autogen/src/cuda/gemm/2d/sm80/source_15.cu index a414d04..9127bd2 100644 --- a/csrc/autogen/src/cuda/gemm/2d/sm80/source_15.cu +++ b/csrc/autogen/src/cuda/gemm/2d/sm80/source_15.cu @@ -1,14 +1,14 @@ #include #include -#include #include -#include #include +#include +#include namespace natten { namespace cuda { namespace gemm { -void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks3_align4( +void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks13_align4( void * attn_ptr, void * value_ptr, void * output_ptr, @@ -25,7 +25,7 @@ void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks3_align4( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 2, 1>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 2, 6>; using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -35,7 +35,7 @@ void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks3_align4( attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks3_align2( +void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks13_align2( void * attn_ptr, void * value_ptr, void * output_ptr, @@ -52,7 +52,7 @@ void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks3_align2( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 2, 1>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 2, 6>; using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -62,7 +62,7 @@ void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks3_align2( attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks5_align8( +void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks15_align8( void * attn_ptr, void * value_ptr, void * output_ptr, @@ -79,7 +79,7 @@ void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks5_align8( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 2, 2>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 2, 7>; using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 8, 8>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -89,7 +89,7 @@ void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks5_align8( attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks5_align4( +void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks15_align4( void * attn_ptr, void * value_ptr, void * output_ptr, @@ -106,7 +106,7 @@ void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks5_align4( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 2, 2>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 2, 7>; using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -116,7 +116,7 @@ void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks5_align4( attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks5_align2( +void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks15_align2( void * attn_ptr, void * value_ptr, void * output_ptr, @@ -133,7 +133,7 @@ void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks5_align2( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 2, 2>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 2, 7>; using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -143,7 +143,7 @@ void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks5_align2( attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks7_align8( +void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks17_align8( void * attn_ptr, void * value_ptr, void * output_ptr, @@ -160,7 +160,7 @@ void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks7_align8( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 2, 3>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 2, 8>; using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 8, 8>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -170,7 +170,7 @@ void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks7_align8( attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks7_align4( +void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks17_align4( void * attn_ptr, void * value_ptr, void * output_ptr, @@ -187,7 +187,7 @@ void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks7_align4( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 2, 3>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 2, 8>; using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -197,7 +197,7 @@ void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks7_align4( attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks7_align2( +void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks17_align2( void * attn_ptr, void * value_ptr, void * output_ptr, @@ -214,7 +214,7 @@ void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks7_align2( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 2, 3>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 2, 8>; using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -224,7 +224,7 @@ void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks7_align2( attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks9_align8( +void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks19_align8( void * attn_ptr, void * value_ptr, void * output_ptr, @@ -241,7 +241,7 @@ void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks9_align8( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 2, 4>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 2, 9>; using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 8, 8>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -251,7 +251,7 @@ void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks9_align8( attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks9_align4( +void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks19_align4( void * attn_ptr, void * value_ptr, void * output_ptr, @@ -268,7 +268,7 @@ void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks9_align4( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 2, 4>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 2, 9>; using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -278,7 +278,7 @@ void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks9_align4( attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks9_align2( +void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks19_align2( void * attn_ptr, void * value_ptr, void * output_ptr, @@ -295,7 +295,7 @@ void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks9_align2( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 2, 4>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 2, 9>; using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -305,7 +305,7 @@ void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks9_align2( attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks11_align8( +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks21_align8( void * attn_ptr, void * value_ptr, void * output_ptr, @@ -322,7 +322,7 @@ void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks11_align8( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 2, 5>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 11, 2, 10>; using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 8, 8>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -332,7 +332,7 @@ void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks11_align8( attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks11_align4( +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks21_align4( void * attn_ptr, void * value_ptr, void * output_ptr, @@ -349,7 +349,7 @@ void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks11_align4( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 2, 5>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 11, 2, 10>; using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -359,7 +359,7 @@ void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks11_align4( attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks11_align2( +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks21_align2( void * attn_ptr, void * value_ptr, void * output_ptr, @@ -376,7 +376,7 @@ void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks11_align2( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 2, 5>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 11, 2, 10>; using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -386,7 +386,7 @@ void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks11_align2( attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks13_align8( +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks23_align8( void * attn_ptr, void * value_ptr, void * output_ptr, @@ -403,7 +403,412 @@ void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks13_align8( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 2, 6>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 11, 2, 11>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 8, 8>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks23_align4( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 11, 2, 11>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks23_align2( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 11, 2, 11>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks25_align8( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 11, 2, 12>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 8, 8>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks25_align4( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 11, 2, 12>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks25_align2( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 11, 2, 12>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks27_align8( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 11, 2, 13>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 8, 8>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks27_align4( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 11, 2, 13>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks27_align2( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 11, 2, 13>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks29_align8( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 11, 2, 14>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 8, 8>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks29_align4( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 11, 2, 14>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks29_align2( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 11, 2, 14>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks31_align8( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 11, 2, 15>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 8, 8>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks31_align4( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 11, 2, 15>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks31_align2( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 11, 2, 15>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks33_align8( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 11, 2, 16>; using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 8, 8>; using DConfig = natten::gemm::detail::DTypeConfig; diff --git a/csrc/autogen/src/cuda/gemm/2d/sm80/source_16.cu b/csrc/autogen/src/cuda/gemm/2d/sm80/source_16.cu index 49f6bcf..278c931 100644 --- a/csrc/autogen/src/cuda/gemm/2d/sm80/source_16.cu +++ b/csrc/autogen/src/cuda/gemm/2d/sm80/source_16.cu @@ -1,14 +1,419 @@ #include #include -#include #include -#include #include +#include +#include namespace natten { namespace cuda { namespace gemm { -void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks13_align4( +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks33_align4( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 11, 2, 16>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks33_align2( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 11, 2, 16>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks35_align8( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 11, 2, 17>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 8, 8>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks35_align4( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 11, 2, 17>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks35_align2( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 11, 2, 17>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks37_align8( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 11, 2, 18>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 8, 8>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks37_align4( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 11, 2, 18>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks37_align2( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 11, 2, 18>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks39_align8( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 11, 2, 19>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 8, 8>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks39_align4( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 11, 2, 19>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks39_align2( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 11, 2, 19>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks41_align8( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 11, 2, 20>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 8, 8>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks41_align4( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 11, 2, 20>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks41_align2( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 11, 2, 20>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks43_align8( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 11, 2, 21>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 8, 8>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks43_align4( void * attn_ptr, void * value_ptr, void * output_ptr, @@ -25,7 +430,7 @@ void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks13_align4( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 2, 6>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 11, 2, 21>; using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -35,7 +440,7 @@ void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks13_align4( attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks13_align2( +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks43_align2( void * attn_ptr, void * value_ptr, void * output_ptr, @@ -52,7 +457,7 @@ void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks13_align2( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 2, 6>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 11, 2, 21>; using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -62,7 +467,7 @@ void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks13_align2( attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks15_align8( +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks45_align8( void * attn_ptr, void * value_ptr, void * output_ptr, @@ -79,7 +484,7 @@ void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks15_align8( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 2, 7>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 11, 2, 22>; using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 8, 8>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -89,7 +494,7 @@ void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks15_align8( attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks15_align4( +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks45_align4( void * attn_ptr, void * value_ptr, void * output_ptr, @@ -106,7 +511,7 @@ void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks15_align4( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 2, 7>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 11, 2, 22>; using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -116,7 +521,7 @@ void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks15_align4( attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks15_align2( +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks45_align2( void * attn_ptr, void * value_ptr, void * output_ptr, @@ -133,7 +538,7 @@ void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks15_align2( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 2, 7>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 11, 2, 22>; using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -143,7 +548,7 @@ void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks15_align2( attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks17_align8( +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks47_align8( void * attn_ptr, void * value_ptr, void * output_ptr, @@ -160,7 +565,7 @@ void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks17_align8( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 2, 8>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 11, 2, 23>; using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 8, 8>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -170,7 +575,7 @@ void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks17_align8( attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks17_align4( +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks47_align4( void * attn_ptr, void * value_ptr, void * output_ptr, @@ -187,7 +592,7 @@ void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks17_align4( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 2, 8>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 11, 2, 23>; using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -197,7 +602,7 @@ void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks17_align4( attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks17_align2( +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks47_align2( void * attn_ptr, void * value_ptr, void * output_ptr, @@ -214,7 +619,7 @@ void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks17_align2( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 2, 8>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 11, 2, 23>; using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -224,7 +629,7 @@ void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks17_align2( attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks19_align8( +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks49_align8( void * attn_ptr, void * value_ptr, void * output_ptr, @@ -241,7 +646,7 @@ void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks19_align8( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 2, 9>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 11, 2, 24>; using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 8, 8>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -251,7 +656,7 @@ void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks19_align8( attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks19_align4( +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks49_align4( void * attn_ptr, void * value_ptr, void * output_ptr, @@ -268,7 +673,7 @@ void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks19_align4( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 2, 9>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 11, 2, 24>; using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -278,7 +683,7 @@ void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks19_align4( attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks19_align2( +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks49_align2( void * attn_ptr, void * value_ptr, void * output_ptr, @@ -295,7 +700,7 @@ void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks19_align2( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 2, 9>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 11, 2, 24>; using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -305,7 +710,7 @@ void na2d_nn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks19_align2( attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks21_align8( +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks51_align8( void * attn_ptr, void * value_ptr, void * output_ptr, @@ -322,7 +727,7 @@ void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks21_align8( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 11, 2, 10>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 11, 2, 25>; using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 8, 8>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -332,7 +737,7 @@ void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks21_align8( attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks21_align4( +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks51_align4( void * attn_ptr, void * value_ptr, void * output_ptr, @@ -349,7 +754,7 @@ void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks21_align4( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 11, 2, 10>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 11, 2, 25>; using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -359,7 +764,7 @@ void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks21_align4( attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks21_align2( +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks51_align2( void * attn_ptr, void * value_ptr, void * output_ptr, @@ -376,7 +781,7 @@ void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks21_align2( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 11, 2, 10>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 11, 2, 25>; using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -386,7 +791,7 @@ void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks21_align2( attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks23_align8( +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks53_align8( void * attn_ptr, void * value_ptr, void * output_ptr, @@ -403,7 +808,7 @@ void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks23_align8( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 11, 2, 11>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 11, 2, 26>; using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 8, 8>; using DConfig = natten::gemm::detail::DTypeConfig; diff --git a/csrc/autogen/src/cuda/gemm/2d/sm80/source_17.cu b/csrc/autogen/src/cuda/gemm/2d/sm80/source_17.cu index 3fac187..198d8b2 100644 --- a/csrc/autogen/src/cuda/gemm/2d/sm80/source_17.cu +++ b/csrc/autogen/src/cuda/gemm/2d/sm80/source_17.cu @@ -1,14 +1,14 @@ #include #include -#include #include -#include #include +#include +#include namespace natten { namespace cuda { namespace gemm { -void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks23_align4( +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks53_align4( void * attn_ptr, void * value_ptr, void * output_ptr, @@ -25,7 +25,7 @@ void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks23_align4( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 11, 2, 11>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 11, 2, 26>; using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -35,7 +35,7 @@ void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks23_align4( attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks23_align2( +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks53_align2( void * attn_ptr, void * value_ptr, void * output_ptr, @@ -52,7 +52,7 @@ void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks23_align2( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 11, 2, 11>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 11, 2, 26>; using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -62,7 +62,7 @@ void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks23_align2( attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks25_align8( +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks55_align8( void * attn_ptr, void * value_ptr, void * output_ptr, @@ -79,7 +79,7 @@ void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks25_align8( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 11, 2, 12>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 11, 2, 27>; using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 8, 8>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -89,7 +89,7 @@ void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks25_align8( attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks25_align4( +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks55_align4( void * attn_ptr, void * value_ptr, void * output_ptr, @@ -106,7 +106,7 @@ void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks25_align4( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 11, 2, 12>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 11, 2, 27>; using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -116,7 +116,7 @@ void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks25_align4( attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks25_align2( +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks55_align2( void * attn_ptr, void * value_ptr, void * output_ptr, @@ -133,7 +133,7 @@ void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks25_align2( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 11, 2, 12>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 11, 2, 27>; using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -143,7 +143,7 @@ void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks25_align2( attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks27_align8( +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks57_align8( void * attn_ptr, void * value_ptr, void * output_ptr, @@ -160,7 +160,7 @@ void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks27_align8( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 11, 2, 13>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 11, 2, 28>; using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 8, 8>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -170,7 +170,7 @@ void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks27_align8( attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks27_align4( +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks57_align4( void * attn_ptr, void * value_ptr, void * output_ptr, @@ -187,7 +187,7 @@ void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks27_align4( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 11, 2, 13>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 11, 2, 28>; using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -197,7 +197,7 @@ void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks27_align4( attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks27_align2( +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks57_align2( void * attn_ptr, void * value_ptr, void * output_ptr, @@ -214,7 +214,7 @@ void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks27_align2( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 11, 2, 13>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 11, 2, 28>; using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -224,7 +224,7 @@ void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks27_align2( attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks29_align8( +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks59_align8( void * attn_ptr, void * value_ptr, void * output_ptr, @@ -241,7 +241,7 @@ void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks29_align8( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 11, 2, 14>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 11, 2, 29>; using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 8, 8>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -251,7 +251,7 @@ void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks29_align8( attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks29_align4( +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks59_align4( void * attn_ptr, void * value_ptr, void * output_ptr, @@ -268,7 +268,7 @@ void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks29_align4( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 11, 2, 14>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 11, 2, 29>; using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -278,7 +278,7 @@ void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks29_align4( attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks29_align2( +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks59_align2( void * attn_ptr, void * value_ptr, void * output_ptr, @@ -295,7 +295,7 @@ void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks29_align2( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 11, 2, 14>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 11, 2, 29>; using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -305,7 +305,7 @@ void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks29_align2( attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks31_align8( +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks61_align8( void * attn_ptr, void * value_ptr, void * output_ptr, @@ -322,7 +322,7 @@ void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks31_align8( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 11, 2, 15>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 11, 2, 30>; using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 8, 8>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -332,7 +332,7 @@ void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks31_align8( attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks31_align4( +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks61_align4( void * attn_ptr, void * value_ptr, void * output_ptr, @@ -349,7 +349,7 @@ void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks31_align4( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 11, 2, 15>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 11, 2, 30>; using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -359,7 +359,7 @@ void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks31_align4( attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks31_align2( +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks61_align2( void * attn_ptr, void * value_ptr, void * output_ptr, @@ -376,7 +376,7 @@ void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks31_align2( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 11, 2, 15>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 11, 2, 30>; using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -386,7 +386,7 @@ void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks31_align2( attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks33_align8( +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks63_align8( void * attn_ptr, void * value_ptr, void * output_ptr, @@ -403,7 +403,7 @@ void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks33_align8( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 11, 2, 16>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 11, 2, 31>; using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 8, 8>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -413,6 +413,411 @@ void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks33_align8( attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks63_align4( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 11, 2, 31>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks63_align2( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 11, 2, 31>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks3_align8( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 2, 1>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 8, 8>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks3_align4( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 2, 1>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks3_align2( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 2, 1>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks5_align8( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 2, 2>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 8, 8>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks5_align4( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 2, 2>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks5_align2( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 2, 2>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks7_align8( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 2, 3>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 8, 8>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks7_align4( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 2, 3>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks7_align2( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 2, 3>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks9_align8( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 2, 4>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 8, 8>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks9_align4( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 2, 4>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks9_align2( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 2, 4>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks11_align8( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 2, 5>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 8, 8>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + } } } diff --git a/csrc/autogen/src/cuda/gemm/2d/sm80/source_18.cu b/csrc/autogen/src/cuda/gemm/2d/sm80/source_18.cu index d7475d1..4a0895f 100644 --- a/csrc/autogen/src/cuda/gemm/2d/sm80/source_18.cu +++ b/csrc/autogen/src/cuda/gemm/2d/sm80/source_18.cu @@ -1,14 +1,14 @@ #include #include -#include #include -#include #include +#include +#include namespace natten { namespace cuda { namespace gemm { -void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks33_align4( +void na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks11_align4( void * attn_ptr, void * value_ptr, void * output_ptr, @@ -25,17 +25,17 @@ void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks33_align4( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 11, 2, 16>; - using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 2, 5>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; - using DConfig = natten::gemm::detail::DTypeConfig; + using DConfig = natten::gemm::detail::DTypeConfig; using Kernel = NeighborhoodNeighborhood2D; Kernel kernel; kernel( attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks33_align2( +void na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks11_align2( void * attn_ptr, void * value_ptr, void * output_ptr, @@ -52,17 +52,17 @@ void na2d_nn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks33_align2( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 11, 2, 16>; - using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 2, 5>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; - using DConfig = natten::gemm::detail::DTypeConfig; + using DConfig = natten::gemm::detail::DTypeConfig; using Kernel = NeighborhoodNeighborhood2D; Kernel kernel; kernel( attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks3_align8( +void na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks13_align8( void * attn_ptr, void * value_ptr, void * output_ptr, @@ -79,7 +79,7 @@ void na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks3_align8( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 2, 1>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 2, 6>; using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 8, 8>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -89,7 +89,7 @@ void na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks3_align8( attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks3_align4( +void na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks13_align4( void * attn_ptr, void * value_ptr, void * output_ptr, @@ -106,7 +106,7 @@ void na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks3_align4( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 2, 1>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 2, 6>; using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -116,7 +116,7 @@ void na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks3_align4( attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks3_align2( +void na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks13_align2( void * attn_ptr, void * value_ptr, void * output_ptr, @@ -133,7 +133,7 @@ void na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks3_align2( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 2, 1>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 2, 6>; using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -143,7 +143,7 @@ void na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks3_align2( attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks5_align8( +void na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks15_align8( void * attn_ptr, void * value_ptr, void * output_ptr, @@ -160,7 +160,7 @@ void na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks5_align8( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 2, 2>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 2, 7>; using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 8, 8>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -170,7 +170,7 @@ void na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks5_align8( attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks5_align4( +void na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks15_align4( void * attn_ptr, void * value_ptr, void * output_ptr, @@ -187,7 +187,7 @@ void na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks5_align4( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 2, 2>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 2, 7>; using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -197,7 +197,7 @@ void na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks5_align4( attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks5_align2( +void na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks15_align2( void * attn_ptr, void * value_ptr, void * output_ptr, @@ -214,7 +214,7 @@ void na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks5_align2( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 2, 2>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 2, 7>; using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -224,7 +224,7 @@ void na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks5_align2( attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks7_align8( +void na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks17_align8( void * attn_ptr, void * value_ptr, void * output_ptr, @@ -241,7 +241,7 @@ void na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks7_align8( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 2, 3>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 2, 8>; using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 8, 8>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -251,7 +251,7 @@ void na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks7_align8( attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks7_align4( +void na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks17_align4( void * attn_ptr, void * value_ptr, void * output_ptr, @@ -268,7 +268,7 @@ void na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks7_align4( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 2, 3>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 2, 8>; using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -278,7 +278,7 @@ void na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks7_align4( attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks7_align2( +void na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks17_align2( void * attn_ptr, void * value_ptr, void * output_ptr, @@ -295,7 +295,7 @@ void na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks7_align2( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 2, 3>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 2, 8>; using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -305,7 +305,7 @@ void na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks7_align2( attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks9_align8( +void na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks19_align8( void * attn_ptr, void * value_ptr, void * output_ptr, @@ -322,7 +322,7 @@ void na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks9_align8( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 2, 4>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 2, 9>; using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 8, 8>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -332,7 +332,7 @@ void na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks9_align8( attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks9_align4( +void na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks19_align4( void * attn_ptr, void * value_ptr, void * output_ptr, @@ -349,7 +349,7 @@ void na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks9_align4( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 2, 4>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 2, 9>; using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -359,7 +359,7 @@ void na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks9_align4( attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks9_align2( +void na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks19_align2( void * attn_ptr, void * value_ptr, void * output_ptr, @@ -376,7 +376,7 @@ void na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks9_align2( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 2, 4>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 2, 9>; using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -386,7 +386,7 @@ void na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks9_align2( attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks11_align8( +void na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks21_align8( void * attn_ptr, void * value_ptr, void * output_ptr, @@ -403,7 +403,412 @@ void na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks11_align8( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 2, 5>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 11, 2, 10>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 8, 8>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks21_align4( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 11, 2, 10>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks21_align2( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 11, 2, 10>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks23_align8( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 11, 2, 11>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 8, 8>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks23_align4( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 11, 2, 11>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks23_align2( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 11, 2, 11>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks25_align8( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 11, 2, 12>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 8, 8>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks25_align4( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 11, 2, 12>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks25_align2( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 11, 2, 12>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks27_align8( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 11, 2, 13>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 8, 8>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks27_align4( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 11, 2, 13>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks27_align2( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 11, 2, 13>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks29_align8( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 11, 2, 14>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 8, 8>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks29_align4( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 11, 2, 14>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks29_align2( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 11, 2, 14>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks31_align8( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 11, 2, 15>; using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 8, 8>; using DConfig = natten::gemm::detail::DTypeConfig; diff --git a/csrc/autogen/src/cuda/gemm/2d/sm80/source_19.cu b/csrc/autogen/src/cuda/gemm/2d/sm80/source_19.cu index 14ac693..f3efc33 100644 --- a/csrc/autogen/src/cuda/gemm/2d/sm80/source_19.cu +++ b/csrc/autogen/src/cuda/gemm/2d/sm80/source_19.cu @@ -1,14 +1,419 @@ #include #include -#include #include -#include #include +#include +#include namespace natten { namespace cuda { namespace gemm { -void na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks11_align4( +void na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks31_align4( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 11, 2, 15>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks31_align2( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 11, 2, 15>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks33_align8( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 11, 2, 16>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 8, 8>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks33_align4( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 11, 2, 16>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks33_align2( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 11, 2, 16>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks35_align8( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 11, 2, 17>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 8, 8>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks35_align4( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 11, 2, 17>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks35_align2( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 11, 2, 17>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks37_align8( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 11, 2, 18>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 8, 8>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks37_align4( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 11, 2, 18>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks37_align2( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 11, 2, 18>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks39_align8( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 11, 2, 19>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 8, 8>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks39_align4( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 11, 2, 19>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks39_align2( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 11, 2, 19>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks41_align8( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 11, 2, 20>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 8, 8>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks41_align4( void * attn_ptr, void * value_ptr, void * output_ptr, @@ -25,7 +430,7 @@ void na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks11_align4( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 2, 5>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 11, 2, 20>; using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -35,7 +440,7 @@ void na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks11_align4( attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks11_align2( +void na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks41_align2( void * attn_ptr, void * value_ptr, void * output_ptr, @@ -52,7 +457,7 @@ void na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks11_align2( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 2, 5>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 11, 2, 20>; using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -62,7 +467,7 @@ void na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks11_align2( attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks13_align8( +void na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks43_align8( void * attn_ptr, void * value_ptr, void * output_ptr, @@ -79,7 +484,7 @@ void na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks13_align8( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 2, 6>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 11, 2, 21>; using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 8, 8>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -89,7 +494,7 @@ void na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks13_align8( attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks13_align4( +void na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks43_align4( void * attn_ptr, void * value_ptr, void * output_ptr, @@ -106,7 +511,7 @@ void na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks13_align4( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 2, 6>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 11, 2, 21>; using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -116,7 +521,7 @@ void na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks13_align4( attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks13_align2( +void na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks43_align2( void * attn_ptr, void * value_ptr, void * output_ptr, @@ -133,7 +538,7 @@ void na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks13_align2( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 2, 6>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 11, 2, 21>; using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -143,7 +548,7 @@ void na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks13_align2( attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks15_align8( +void na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks45_align8( void * attn_ptr, void * value_ptr, void * output_ptr, @@ -160,7 +565,7 @@ void na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks15_align8( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 2, 7>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 11, 2, 22>; using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 8, 8>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -170,7 +575,7 @@ void na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks15_align8( attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks15_align4( +void na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks45_align4( void * attn_ptr, void * value_ptr, void * output_ptr, @@ -187,7 +592,7 @@ void na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks15_align4( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 2, 7>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 11, 2, 22>; using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -197,7 +602,7 @@ void na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks15_align4( attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks15_align2( +void na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks45_align2( void * attn_ptr, void * value_ptr, void * output_ptr, @@ -214,7 +619,7 @@ void na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks15_align2( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 2, 7>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 11, 2, 22>; using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -224,7 +629,7 @@ void na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks15_align2( attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks17_align8( +void na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks47_align8( void * attn_ptr, void * value_ptr, void * output_ptr, @@ -241,7 +646,7 @@ void na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks17_align8( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 2, 8>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 11, 2, 23>; using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 8, 8>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -251,7 +656,7 @@ void na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks17_align8( attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks17_align4( +void na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks47_align4( void * attn_ptr, void * value_ptr, void * output_ptr, @@ -268,7 +673,7 @@ void na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks17_align4( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 2, 8>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 11, 2, 23>; using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -278,7 +683,7 @@ void na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks17_align4( attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks17_align2( +void na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks47_align2( void * attn_ptr, void * value_ptr, void * output_ptr, @@ -295,7 +700,7 @@ void na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks17_align2( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 2, 8>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 11, 2, 23>; using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -305,7 +710,7 @@ void na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks17_align2( attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks19_align8( +void na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks49_align8( void * attn_ptr, void * value_ptr, void * output_ptr, @@ -322,7 +727,7 @@ void na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks19_align8( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 2, 9>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 11, 2, 24>; using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 8, 8>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -332,7 +737,7 @@ void na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks19_align8( attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks19_align4( +void na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks49_align4( void * attn_ptr, void * value_ptr, void * output_ptr, @@ -349,7 +754,7 @@ void na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks19_align4( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 2, 9>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 11, 2, 24>; using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -359,7 +764,7 @@ void na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks19_align4( attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks19_align2( +void na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks49_align2( void * attn_ptr, void * value_ptr, void * output_ptr, @@ -376,7 +781,7 @@ void na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks19_align2( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 2, 9>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 11, 2, 24>; using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -386,7 +791,7 @@ void na2d_nn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks19_align2( attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks21_align8( +void na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks51_align8( void * attn_ptr, void * value_ptr, void * output_ptr, @@ -403,7 +808,7 @@ void na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks21_align8( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 11, 2, 10>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 11, 2, 25>; using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 8, 8>; using DConfig = natten::gemm::detail::DTypeConfig; diff --git a/csrc/autogen/src/cuda/gemm/2d/sm80/source_2.cu b/csrc/autogen/src/cuda/gemm/2d/sm80/source_2.cu index bd13d20..bd50395 100644 --- a/csrc/autogen/src/cuda/gemm/2d/sm80/source_2.cu +++ b/csrc/autogen/src/cuda/gemm/2d/sm80/source_2.cu @@ -1,14 +1,14 @@ #include #include -#include #include -#include #include +#include +#include namespace natten { namespace cuda { namespace gemm { -void na2d_pn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks11_align1( +void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks21_align1( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -26,7 +26,7 @@ void na2d_pn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks11_align1( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 16, 32, 16, 16, 16, 8, 8, 3, 6, 1, 5>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 16, 64, 64, 16, 16, 8, 8, 3, 12, 1, 10>; using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 1, 1>; if (natten::kEnableGemmTF32) { @@ -46,7 +46,7 @@ query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, a } } -void na2d_pn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks13_align4( +void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks23_align4( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -64,7 +64,7 @@ void na2d_pn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks13_align4( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 16, 32, 16, 16, 16, 8, 8, 3, 10, 1, 6>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 16, 64, 64, 16, 16, 8, 8, 3, 14, 1, 11>; using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; using AConfig = natten::gemm::detail::AlignmentConfig<4, 4, 1>; if (natten::kEnableGemmTF32) { @@ -84,7 +84,7 @@ query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, a } } -void na2d_pn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks13_align2( +void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks23_align2( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -102,7 +102,7 @@ void na2d_pn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks13_align2( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 16, 32, 16, 16, 16, 8, 8, 3, 10, 1, 6>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 16, 64, 64, 16, 16, 8, 8, 3, 14, 1, 11>; using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; using AConfig = natten::gemm::detail::AlignmentConfig<2, 2, 1>; if (natten::kEnableGemmTF32) { @@ -122,7 +122,7 @@ query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, a } } -void na2d_pn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks13_align1( +void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks23_align1( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -140,7 +140,7 @@ void na2d_pn_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks13_align1( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 16, 32, 16, 16, 16, 8, 8, 3, 10, 1, 6>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 16, 64, 64, 16, 16, 8, 8, 3, 14, 1, 11>; using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 1, 1>; if (natten::kEnableGemmTF32) { @@ -160,7 +160,7 @@ query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, a } } -void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks15_align4( +void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks25_align4( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -178,7 +178,7 @@ void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks15_align4( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 16, 64, 64, 16, 16, 8, 8, 3, 9, 1, 7>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 16, 64, 64, 16, 16, 8, 8, 3, 13, 1, 12>; using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; using AConfig = natten::gemm::detail::AlignmentConfig<4, 4, 1>; if (natten::kEnableGemmTF32) { @@ -198,7 +198,7 @@ query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, a } } -void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks15_align2( +void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks25_align2( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -216,7 +216,7 @@ void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks15_align2( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 16, 64, 64, 16, 16, 8, 8, 3, 9, 1, 7>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 16, 64, 64, 16, 16, 8, 8, 3, 13, 1, 12>; using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; using AConfig = natten::gemm::detail::AlignmentConfig<2, 2, 1>; if (natten::kEnableGemmTF32) { @@ -236,7 +236,7 @@ query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, a } } -void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks15_align1( +void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks25_align1( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -254,7 +254,7 @@ void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks15_align1( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 16, 64, 64, 16, 16, 8, 8, 3, 9, 1, 7>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 16, 64, 64, 16, 16, 8, 8, 3, 13, 1, 12>; using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 1, 1>; if (natten::kEnableGemmTF32) { @@ -274,7 +274,7 @@ query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, a } } -void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks17_align4( +void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks27_align4( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -292,7 +292,7 @@ void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks17_align4( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 16, 64, 64, 16, 16, 8, 8, 3, 11, 1, 8>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 16, 64, 64, 16, 16, 8, 8, 3, 14, 1, 13>; using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; using AConfig = natten::gemm::detail::AlignmentConfig<4, 4, 1>; if (natten::kEnableGemmTF32) { @@ -312,7 +312,7 @@ query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, a } } -void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks17_align2( +void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks27_align2( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -330,7 +330,7 @@ void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks17_align2( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 16, 64, 64, 16, 16, 8, 8, 3, 11, 1, 8>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 16, 64, 64, 16, 16, 8, 8, 3, 14, 1, 13>; using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; using AConfig = natten::gemm::detail::AlignmentConfig<2, 2, 1>; if (natten::kEnableGemmTF32) { @@ -350,7 +350,7 @@ query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, a } } -void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks17_align1( +void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks27_align1( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -368,7 +368,7 @@ void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks17_align1( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 16, 64, 64, 16, 16, 8, 8, 3, 11, 1, 8>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 16, 64, 64, 16, 16, 8, 8, 3, 14, 1, 13>; using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 1, 1>; if (natten::kEnableGemmTF32) { @@ -388,7 +388,7 @@ query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, a } } -void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks19_align4( +void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks29_align4( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -406,7 +406,7 @@ void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks19_align4( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 16, 64, 64, 16, 16, 8, 8, 3, 13, 1, 9>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 16, 64, 64, 16, 16, 8, 8, 3, 17, 1, 14>; using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; using AConfig = natten::gemm::detail::AlignmentConfig<4, 4, 1>; if (natten::kEnableGemmTF32) { @@ -426,7 +426,7 @@ query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, a } } -void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks19_align2( +void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks29_align2( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -444,7 +444,7 @@ void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks19_align2( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 16, 64, 64, 16, 16, 8, 8, 3, 13, 1, 9>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 16, 64, 64, 16, 16, 8, 8, 3, 17, 1, 14>; using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; using AConfig = natten::gemm::detail::AlignmentConfig<2, 2, 1>; if (natten::kEnableGemmTF32) { @@ -464,7 +464,7 @@ query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, a } } -void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks19_align1( +void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks29_align1( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -482,7 +482,7 @@ void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks19_align1( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 16, 64, 64, 16, 16, 8, 8, 3, 13, 1, 9>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 16, 64, 64, 16, 16, 8, 8, 3, 17, 1, 14>; using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 1, 1>; if (natten::kEnableGemmTF32) { @@ -502,7 +502,7 @@ query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, a } } -void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks21_align4( +void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks31_align4( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -520,7 +520,7 @@ void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks21_align4( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 16, 64, 64, 16, 16, 8, 8, 3, 12, 1, 10>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 16, 64, 64, 16, 16, 8, 8, 3, 17, 1, 15>; using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; using AConfig = natten::gemm::detail::AlignmentConfig<4, 4, 1>; if (natten::kEnableGemmTF32) { @@ -540,7 +540,7 @@ query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, a } } -void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks21_align2( +void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks31_align2( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -558,7 +558,577 @@ void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks21_align2( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 16, 64, 64, 16, 16, 8, 8, 3, 12, 1, 10>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 16, 64, 64, 16, 16, 8, 8, 3, 17, 1, 15>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; + using AConfig = natten::gemm::detail::AlignmentConfig<2, 2, 1>; + if (natten::kEnableGemmTF32) { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } else { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } +} + +void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks31_align1( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 16, 64, 64, 16, 16, 8, 8, 3, 17, 1, 15>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 1, 1>; + if (natten::kEnableGemmTF32) { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } else { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } +} + +void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks33_align4( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 16, 64, 64, 16, 16, 8, 8, 3, 17, 1, 16>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; + using AConfig = natten::gemm::detail::AlignmentConfig<4, 4, 1>; + if (natten::kEnableGemmTF32) { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } else { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } +} + +void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks33_align2( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 16, 64, 64, 16, 16, 8, 8, 3, 17, 1, 16>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; + using AConfig = natten::gemm::detail::AlignmentConfig<2, 2, 1>; + if (natten::kEnableGemmTF32) { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } else { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } +} + +void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks33_align1( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 16, 64, 64, 16, 16, 8, 8, 3, 17, 1, 16>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 1, 1>; + if (natten::kEnableGemmTF32) { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } else { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } +} + +void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks35_align4( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 16, 64, 64, 16, 16, 8, 8, 3, 22, 1, 17>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; + using AConfig = natten::gemm::detail::AlignmentConfig<4, 4, 1>; + if (natten::kEnableGemmTF32) { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } else { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } +} + +void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks35_align2( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 16, 64, 64, 16, 16, 8, 8, 3, 22, 1, 17>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; + using AConfig = natten::gemm::detail::AlignmentConfig<2, 2, 1>; + if (natten::kEnableGemmTF32) { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } else { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } +} + +void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks35_align1( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 16, 64, 64, 16, 16, 8, 8, 3, 22, 1, 17>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 1, 1>; + if (natten::kEnableGemmTF32) { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } else { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } +} + +void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks37_align4( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 16, 64, 64, 16, 16, 8, 8, 3, 22, 1, 18>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; + using AConfig = natten::gemm::detail::AlignmentConfig<4, 4, 1>; + if (natten::kEnableGemmTF32) { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } else { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } +} + +void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks37_align2( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 16, 64, 64, 16, 16, 8, 8, 3, 22, 1, 18>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; + using AConfig = natten::gemm::detail::AlignmentConfig<2, 2, 1>; + if (natten::kEnableGemmTF32) { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } else { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } +} + +void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks37_align1( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 16, 64, 64, 16, 16, 8, 8, 3, 22, 1, 18>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 1, 1>; + if (natten::kEnableGemmTF32) { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } else { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } +} + +void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks39_align4( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 16, 64, 64, 16, 16, 8, 8, 3, 23, 1, 19>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; + using AConfig = natten::gemm::detail::AlignmentConfig<4, 4, 1>; + if (natten::kEnableGemmTF32) { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } else { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } +} + +void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks39_align2( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 16, 64, 64, 16, 16, 8, 8, 3, 23, 1, 19>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; + using AConfig = natten::gemm::detail::AlignmentConfig<2, 2, 1>; + if (natten::kEnableGemmTF32) { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } else { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } +} + +void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks39_align1( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 16, 64, 64, 16, 16, 8, 8, 3, 23, 1, 19>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 1, 1>; + if (natten::kEnableGemmTF32) { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } else { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } +} + +void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks41_align4( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 16, 64, 64, 16, 16, 8, 8, 3, 22, 1, 20>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; + using AConfig = natten::gemm::detail::AlignmentConfig<4, 4, 1>; + if (natten::kEnableGemmTF32) { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } else { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } +} + +void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks41_align2( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 16, 64, 64, 16, 16, 8, 8, 3, 22, 1, 20>; using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; using AConfig = natten::gemm::detail::AlignmentConfig<2, 2, 1>; if (natten::kEnableGemmTF32) { diff --git a/csrc/autogen/src/cuda/gemm/2d/sm80/source_20.cu b/csrc/autogen/src/cuda/gemm/2d/sm80/source_20.cu index fe4e030..ac0caa4 100644 --- a/csrc/autogen/src/cuda/gemm/2d/sm80/source_20.cu +++ b/csrc/autogen/src/cuda/gemm/2d/sm80/source_20.cu @@ -1,14 +1,14 @@ #include #include -#include #include -#include #include +#include +#include namespace natten { namespace cuda { namespace gemm { -void na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks21_align4( +void na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks51_align4( void * attn_ptr, void * value_ptr, void * output_ptr, @@ -25,7 +25,7 @@ void na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks21_align4( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 11, 2, 10>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 11, 2, 25>; using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -35,7 +35,7 @@ void na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks21_align4( attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks21_align2( +void na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks51_align2( void * attn_ptr, void * value_ptr, void * output_ptr, @@ -52,7 +52,7 @@ void na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks21_align2( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 11, 2, 10>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 11, 2, 25>; using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -62,7 +62,7 @@ void na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks21_align2( attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks23_align8( +void na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks53_align8( void * attn_ptr, void * value_ptr, void * output_ptr, @@ -79,7 +79,7 @@ void na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks23_align8( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 11, 2, 11>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 11, 2, 26>; using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 8, 8>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -89,7 +89,7 @@ void na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks23_align8( attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks23_align4( +void na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks53_align4( void * attn_ptr, void * value_ptr, void * output_ptr, @@ -106,7 +106,7 @@ void na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks23_align4( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 11, 2, 11>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 11, 2, 26>; using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -116,7 +116,7 @@ void na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks23_align4( attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks23_align2( +void na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks53_align2( void * attn_ptr, void * value_ptr, void * output_ptr, @@ -133,7 +133,7 @@ void na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks23_align2( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 11, 2, 11>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 11, 2, 26>; using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -143,7 +143,7 @@ void na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks23_align2( attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks25_align8( +void na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks55_align8( void * attn_ptr, void * value_ptr, void * output_ptr, @@ -160,7 +160,7 @@ void na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks25_align8( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 11, 2, 12>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 11, 2, 27>; using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 8, 8>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -170,7 +170,7 @@ void na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks25_align8( attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks25_align4( +void na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks55_align4( void * attn_ptr, void * value_ptr, void * output_ptr, @@ -187,7 +187,7 @@ void na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks25_align4( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 11, 2, 12>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 11, 2, 27>; using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -197,7 +197,7 @@ void na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks25_align4( attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks25_align2( +void na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks55_align2( void * attn_ptr, void * value_ptr, void * output_ptr, @@ -214,7 +214,7 @@ void na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks25_align2( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 11, 2, 12>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 11, 2, 27>; using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -224,7 +224,7 @@ void na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks25_align2( attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks27_align8( +void na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks57_align8( void * attn_ptr, void * value_ptr, void * output_ptr, @@ -241,7 +241,7 @@ void na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks27_align8( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 11, 2, 13>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 11, 2, 28>; using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 8, 8>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -251,7 +251,7 @@ void na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks27_align8( attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks27_align4( +void na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks57_align4( void * attn_ptr, void * value_ptr, void * output_ptr, @@ -268,7 +268,7 @@ void na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks27_align4( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 11, 2, 13>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 11, 2, 28>; using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -278,7 +278,7 @@ void na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks27_align4( attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks27_align2( +void na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks57_align2( void * attn_ptr, void * value_ptr, void * output_ptr, @@ -295,7 +295,7 @@ void na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks27_align2( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 11, 2, 13>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 11, 2, 28>; using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -305,7 +305,7 @@ void na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks27_align2( attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks29_align8( +void na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks59_align8( void * attn_ptr, void * value_ptr, void * output_ptr, @@ -322,7 +322,7 @@ void na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks29_align8( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 11, 2, 14>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 11, 2, 29>; using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 8, 8>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -332,7 +332,7 @@ void na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks29_align8( attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks29_align4( +void na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks59_align4( void * attn_ptr, void * value_ptr, void * output_ptr, @@ -349,7 +349,7 @@ void na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks29_align4( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 11, 2, 14>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 11, 2, 29>; using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -359,7 +359,7 @@ void na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks29_align4( attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks29_align2( +void na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks59_align2( void * attn_ptr, void * value_ptr, void * output_ptr, @@ -376,7 +376,7 @@ void na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks29_align2( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 11, 2, 14>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 11, 2, 29>; using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -386,7 +386,7 @@ void na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks29_align2( attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks31_align8( +void na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks61_align8( void * attn_ptr, void * value_ptr, void * output_ptr, @@ -403,7 +403,7 @@ void na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks31_align8( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 11, 2, 15>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 11, 2, 30>; using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 8, 8>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -413,6 +413,411 @@ void na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks31_align8( attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } +void na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks61_align4( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 11, 2, 30>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks61_align2( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 11, 2, 30>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks63_align8( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 11, 2, 31>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 8, 8>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks63_align4( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 11, 2, 31>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks63_align2( + void * attn_ptr, + void * value_ptr, + void * output_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 11, 2, 31>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = NeighborhoodNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_in_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks3_align1( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 16, 32, 32, 16, 8, 8, 4, 3, 8, 4, 1>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, double>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 1, 1>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_in_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks5_align1( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 16, 32, 32, 16, 8, 8, 4, 3, 8, 4, 2>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, double>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 1, 1>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_in_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks7_align1( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 16, 32, 32, 16, 8, 8, 4, 3, 8, 4, 3>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, double>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 1, 1>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_in_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks9_align1( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 16, 32, 32, 16, 8, 8, 4, 3, 8, 4, 4>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, double>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 1, 1>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_in_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks11_align1( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 16, 32, 32, 16, 8, 8, 4, 3, 8, 4, 5>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, double>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 1, 1>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_in_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks13_align1( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 16, 32, 32, 16, 8, 8, 4, 3, 8, 4, 6>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, double>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 1, 1>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_in_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks15_align1( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 16, 32, 32, 16, 8, 8, 4, 3, 8, 4, 7>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, double>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 1, 1>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_in_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks17_align1( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 16, 32, 32, 16, 8, 8, 4, 3, 8, 4, 8>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, double>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 1, 1>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_in_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks19_align1( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 16, 32, 32, 16, 8, 8, 4, 3, 8, 4, 9>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, double>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 1, 1>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_in_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks21_align1( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 16, 32, 32, 16, 8, 8, 4, 3, 8, 4, 10>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, double>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 1, 1>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + } } } diff --git a/csrc/autogen/src/cuda/gemm/2d/sm80/source_21.cu b/csrc/autogen/src/cuda/gemm/2d/sm80/source_21.cu index 3245c07..1db2784 100644 --- a/csrc/autogen/src/cuda/gemm/2d/sm80/source_21.cu +++ b/csrc/autogen/src/cuda/gemm/2d/sm80/source_21.cu @@ -1,17 +1,17 @@ #include #include -#include #include -#include #include +#include +#include namespace natten { namespace cuda { namespace gemm { -void na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks31_align4( +void na2d_in_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks23_align1( void * attn_ptr, - void * value_ptr, - void * output_ptr, + void * d_output_ptr, + void * d_value_ptr, int batch_size, int heads, int height, @@ -25,20 +25,20 @@ void na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks31_align4( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 11, 2, 15>; - using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; - using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; - using DConfig = natten::gemm::detail::DTypeConfig; - using Kernel = NeighborhoodNeighborhood2D; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 16, 32, 32, 16, 8, 8, 4, 3, 8, 4, 11>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, double>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 1, 1>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; Kernel kernel; kernel( -attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks31_align2( +void na2d_in_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks25_align1( void * attn_ptr, - void * value_ptr, - void * output_ptr, + void * d_output_ptr, + void * d_value_ptr, int batch_size, int heads, int height, @@ -52,20 +52,20 @@ void na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks31_align2( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 11, 2, 15>; - using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; - using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; - using DConfig = natten::gemm::detail::DTypeConfig; - using Kernel = NeighborhoodNeighborhood2D; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 16, 32, 32, 16, 8, 8, 4, 3, 8, 4, 12>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, double>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 1, 1>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; Kernel kernel; kernel( -attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks33_align8( +void na2d_in_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks27_align1( void * attn_ptr, - void * value_ptr, - void * output_ptr, + void * d_output_ptr, + void * d_value_ptr, int batch_size, int heads, int height, @@ -79,20 +79,20 @@ void na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks33_align8( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 11, 2, 16>; - using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; - using AConfig = natten::gemm::detail::AlignmentConfig<1, 8, 8>; - using DConfig = natten::gemm::detail::DTypeConfig; - using Kernel = NeighborhoodNeighborhood2D; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 16, 32, 32, 16, 8, 8, 4, 3, 8, 4, 13>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, double>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 1, 1>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; Kernel kernel; kernel( -attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks33_align4( +void na2d_in_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks29_align1( void * attn_ptr, - void * value_ptr, - void * output_ptr, + void * d_output_ptr, + void * d_value_ptr, int batch_size, int heads, int height, @@ -106,20 +106,20 @@ void na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks33_align4( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 11, 2, 16>; - using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; - using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; - using DConfig = natten::gemm::detail::DTypeConfig; - using Kernel = NeighborhoodNeighborhood2D; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 16, 32, 32, 16, 8, 8, 4, 3, 8, 4, 14>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, double>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 1, 1>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; Kernel kernel; kernel( -attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks33_align2( +void na2d_in_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks31_align1( void * attn_ptr, - void * value_ptr, - void * output_ptr, + void * d_output_ptr, + void * d_value_ptr, int batch_size, int heads, int height, @@ -133,17 +133,44 @@ void na2d_nn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks33_align2( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 11, 2, 16>; - using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; - using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; - using DConfig = natten::gemm::detail::DTypeConfig; - using Kernel = NeighborhoodNeighborhood2D; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 16, 32, 32, 16, 8, 8, 4, 3, 8, 4, 15>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, double>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 1, 1>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_in_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks33_align1( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 16, 32, 32, 16, 8, 8, 4, 3, 8, 4, 16>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, double>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 1, 1>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; Kernel kernel; kernel( -attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_in_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks3_align1( +void na2d_in_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks35_align1( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -160,7 +187,7 @@ void na2d_in_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks3_align1( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 16, 32, 32, 16, 8, 8, 4, 3, 8, 4, 1>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 16, 32, 32, 16, 8, 8, 4, 3, 8, 4, 17>; using ArchConfig = natten::gemm::detail::ArchArgs<80, double>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 1, 1>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -170,7 +197,7 @@ void na2d_in_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks3_align1( attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_in_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks5_align1( +void na2d_in_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks37_align1( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -187,7 +214,7 @@ void na2d_in_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks5_align1( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 16, 32, 32, 16, 8, 8, 4, 3, 8, 4, 2>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 16, 32, 32, 16, 8, 8, 4, 3, 8, 4, 18>; using ArchConfig = natten::gemm::detail::ArchArgs<80, double>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 1, 1>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -197,7 +224,7 @@ void na2d_in_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks5_align1( attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_in_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks7_align1( +void na2d_in_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks39_align1( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -214,7 +241,7 @@ void na2d_in_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks7_align1( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 16, 32, 32, 16, 8, 8, 4, 3, 8, 4, 3>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 16, 32, 32, 16, 8, 8, 4, 3, 8, 4, 19>; using ArchConfig = natten::gemm::detail::ArchArgs<80, double>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 1, 1>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -224,7 +251,7 @@ void na2d_in_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks7_align1( attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_in_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks9_align1( +void na2d_in_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks41_align1( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -241,7 +268,7 @@ void na2d_in_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks9_align1( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 16, 32, 32, 16, 8, 8, 4, 3, 8, 4, 4>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 16, 32, 32, 16, 8, 8, 4, 3, 8, 4, 20>; using ArchConfig = natten::gemm::detail::ArchArgs<80, double>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 1, 1>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -251,7 +278,7 @@ void na2d_in_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks9_align1( attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_in_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks11_align1( +void na2d_in_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks43_align1( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -268,7 +295,7 @@ void na2d_in_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks11_align1( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 16, 32, 32, 16, 8, 8, 4, 3, 8, 4, 5>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 16, 32, 32, 16, 8, 8, 4, 3, 8, 4, 21>; using ArchConfig = natten::gemm::detail::ArchArgs<80, double>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 1, 1>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -278,7 +305,7 @@ void na2d_in_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks11_align1( attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_in_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks13_align1( +void na2d_in_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks45_align1( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -295,7 +322,7 @@ void na2d_in_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks13_align1( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 16, 32, 32, 16, 8, 8, 4, 3, 8, 4, 6>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 16, 32, 32, 16, 8, 8, 4, 3, 8, 4, 22>; using ArchConfig = natten::gemm::detail::ArchArgs<80, double>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 1, 1>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -305,7 +332,7 @@ void na2d_in_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks13_align1( attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_in_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks15_align1( +void na2d_in_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks47_align1( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -322,7 +349,7 @@ void na2d_in_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks15_align1( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 16, 32, 32, 16, 8, 8, 4, 3, 8, 4, 7>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 16, 32, 32, 16, 8, 8, 4, 3, 8, 4, 23>; using ArchConfig = natten::gemm::detail::ArchArgs<80, double>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 1, 1>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -332,7 +359,7 @@ void na2d_in_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks15_align1( attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_in_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks17_align1( +void na2d_in_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks49_align1( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -349,7 +376,7 @@ void na2d_in_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks17_align1( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 16, 32, 32, 16, 8, 8, 4, 3, 8, 4, 8>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 16, 32, 32, 16, 8, 8, 4, 3, 8, 4, 24>; using ArchConfig = natten::gemm::detail::ArchArgs<80, double>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 1, 1>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -359,7 +386,7 @@ void na2d_in_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks17_align1( attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_in_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks19_align1( +void na2d_in_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks51_align1( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -376,7 +403,7 @@ void na2d_in_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks19_align1( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 16, 32, 32, 16, 8, 8, 4, 3, 8, 4, 9>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 16, 32, 32, 16, 8, 8, 4, 3, 8, 4, 25>; using ArchConfig = natten::gemm::detail::ArchArgs<80, double>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 1, 1>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -386,7 +413,7 @@ void na2d_in_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks19_align1( attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_in_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks21_align1( +void na2d_in_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks53_align1( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -403,7 +430,7 @@ void na2d_in_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks21_align1( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 16, 32, 32, 16, 8, 8, 4, 3, 8, 4, 10>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 16, 32, 32, 16, 8, 8, 4, 3, 8, 4, 26>; using ArchConfig = natten::gemm::detail::ArchArgs<80, double>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 1, 1>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -413,6 +440,474 @@ void na2d_in_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks21_align1( attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } +void na2d_in_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks55_align1( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 16, 32, 32, 16, 8, 8, 4, 3, 8, 4, 27>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, double>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 1, 1>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_in_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks57_align1( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 16, 32, 32, 16, 8, 8, 4, 3, 8, 4, 28>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, double>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 1, 1>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_in_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks59_align1( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 16, 32, 32, 16, 8, 8, 4, 3, 8, 4, 29>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, double>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 1, 1>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_in_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks61_align1( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 16, 32, 32, 16, 8, 8, 4, 3, 8, 4, 30>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, double>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 1, 1>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_in_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks63_align1( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 16, 32, 32, 16, 8, 8, 4, 3, 8, 4, 31>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, double>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 1, 1>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks3_align4( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 16, 32, 16, 16, 16, 8, 8, 3, 8, 4, 1>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; + if (natten::kEnableGemmTF32) { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } else { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } +} + +void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks3_align2( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 16, 32, 16, 16, 16, 8, 8, 3, 8, 4, 1>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; + if (natten::kEnableGemmTF32) { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } else { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } +} + +void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks3_align1( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 16, 32, 16, 16, 16, 8, 8, 3, 8, 4, 1>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 1, 1>; + if (natten::kEnableGemmTF32) { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } else { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } +} + +void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks5_align4( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 16, 32, 16, 16, 16, 8, 8, 3, 8, 4, 2>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; + if (natten::kEnableGemmTF32) { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } else { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } +} + +void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks5_align2( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 16, 32, 16, 16, 16, 8, 8, 3, 8, 4, 2>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; + if (natten::kEnableGemmTF32) { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } else { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } +} + +void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks5_align1( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 16, 32, 16, 16, 16, 8, 8, 3, 8, 4, 2>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 1, 1>; + if (natten::kEnableGemmTF32) { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } else { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } +} + +void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks7_align4( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 16, 32, 16, 16, 16, 8, 8, 3, 8, 4, 3>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; + if (natten::kEnableGemmTF32) { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } else { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } +} + +void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks7_align2( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 16, 32, 16, 16, 16, 8, 8, 3, 8, 4, 3>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; + if (natten::kEnableGemmTF32) { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } else { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } +} + +void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks7_align1( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 16, 32, 16, 16, 16, 8, 8, 3, 8, 4, 3>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 1, 1>; + if (natten::kEnableGemmTF32) { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } else { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } +} + } } } diff --git a/csrc/autogen/src/cuda/gemm/2d/sm80/source_22.cu b/csrc/autogen/src/cuda/gemm/2d/sm80/source_22.cu index 5f7dc70..a9a3d86 100644 --- a/csrc/autogen/src/cuda/gemm/2d/sm80/source_22.cu +++ b/csrc/autogen/src/cuda/gemm/2d/sm80/source_22.cu @@ -1,14 +1,14 @@ #include #include -#include #include -#include #include +#include +#include namespace natten { namespace cuda { namespace gemm { -void na2d_in_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks23_align1( +void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks9_align4( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -25,17 +25,27 @@ void na2d_in_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks23_align1( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 16, 32, 32, 16, 8, 8, 4, 3, 8, 4, 11>; - using ArchConfig = natten::gemm::detail::ArchArgs<80, double>; - using AConfig = natten::gemm::detail::AlignmentConfig<1, 1, 1>; - using DConfig = natten::gemm::detail::DTypeConfig; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 16, 32, 16, 16, 16, 8, 8, 3, 8, 4, 4>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; + if (natten::kEnableGemmTF32) { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } else { + using DConfig = natten::gemm::detail::DTypeConfig; using Kernel = InverseNeighborhood2D; Kernel kernel; kernel( attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } } -void na2d_in_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks25_align1( +void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks9_align2( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -52,17 +62,27 @@ void na2d_in_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks25_align1( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 16, 32, 32, 16, 8, 8, 4, 3, 8, 4, 12>; - using ArchConfig = natten::gemm::detail::ArchArgs<80, double>; - using AConfig = natten::gemm::detail::AlignmentConfig<1, 1, 1>; - using DConfig = natten::gemm::detail::DTypeConfig; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 16, 32, 16, 16, 16, 8, 8, 3, 8, 4, 4>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; + if (natten::kEnableGemmTF32) { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } else { + using DConfig = natten::gemm::detail::DTypeConfig; using Kernel = InverseNeighborhood2D; Kernel kernel; kernel( attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } } -void na2d_in_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks27_align1( +void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks9_align1( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -79,17 +99,27 @@ void na2d_in_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks27_align1( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 16, 32, 32, 16, 8, 8, 4, 3, 8, 4, 13>; - using ArchConfig = natten::gemm::detail::ArchArgs<80, double>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 16, 32, 16, 16, 16, 8, 8, 3, 8, 4, 4>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 1, 1>; - using DConfig = natten::gemm::detail::DTypeConfig; + if (natten::kEnableGemmTF32) { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } else { + using DConfig = natten::gemm::detail::DTypeConfig; using Kernel = InverseNeighborhood2D; Kernel kernel; kernel( attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } } -void na2d_in_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks29_align1( +void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks11_align4( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -106,17 +136,64 @@ void na2d_in_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks29_align1( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 16, 32, 32, 16, 8, 8, 4, 3, 8, 4, 14>; - using ArchConfig = natten::gemm::detail::ArchArgs<80, double>; - using AConfig = natten::gemm::detail::AlignmentConfig<1, 1, 1>; - using DConfig = natten::gemm::detail::DTypeConfig; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 16, 32, 16, 16, 16, 8, 8, 3, 8, 4, 5>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; + if (natten::kEnableGemmTF32) { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } else { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } +} + +void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks11_align2( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 16, 32, 16, 16, 16, 8, 8, 3, 8, 4, 5>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; + if (natten::kEnableGemmTF32) { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } else { + using DConfig = natten::gemm::detail::DTypeConfig; using Kernel = InverseNeighborhood2D; Kernel kernel; kernel( attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } } -void na2d_in_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks31_align1( +void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks11_align1( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -133,17 +210,101 @@ void na2d_in_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks31_align1( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 16, 32, 32, 16, 8, 8, 4, 3, 8, 4, 15>; - using ArchConfig = natten::gemm::detail::ArchArgs<80, double>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 16, 32, 16, 16, 16, 8, 8, 3, 8, 4, 5>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 1, 1>; - using DConfig = natten::gemm::detail::DTypeConfig; + if (natten::kEnableGemmTF32) { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } else { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } +} + +void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks13_align4( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 16, 32, 16, 16, 16, 8, 8, 3, 8, 4, 6>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; + if (natten::kEnableGemmTF32) { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } else { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } +} + +void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks13_align2( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 16, 32, 16, 16, 16, 8, 8, 3, 8, 4, 6>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; + if (natten::kEnableGemmTF32) { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } else { + using DConfig = natten::gemm::detail::DTypeConfig; using Kernel = InverseNeighborhood2D; Kernel kernel; kernel( attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } } -void na2d_in_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks33_align1( +void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks13_align1( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -160,17 +321,471 @@ void na2d_in_cuda_gemm_double_64x64x16_32x32x16_8x8x4_3_sm80_ks33_align1( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 16, 32, 32, 16, 8, 8, 4, 3, 8, 4, 16>; - using ArchConfig = natten::gemm::detail::ArchArgs<80, double>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 16, 32, 16, 16, 16, 8, 8, 3, 8, 4, 6>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 1, 1>; - using DConfig = natten::gemm::detail::DTypeConfig; + if (natten::kEnableGemmTF32) { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } else { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } +} + +void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks15_align4( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 16, 32, 16, 16, 16, 8, 8, 3, 8, 4, 7>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; + if (natten::kEnableGemmTF32) { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } else { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } +} + +void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks15_align2( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 16, 32, 16, 16, 16, 8, 8, 3, 8, 4, 7>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; + if (natten::kEnableGemmTF32) { + using DConfig = natten::gemm::detail::DTypeConfig; using Kernel = InverseNeighborhood2D; Kernel kernel; kernel( attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } else { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } +} + +void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks15_align1( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 16, 32, 16, 16, 16, 8, 8, 3, 8, 4, 7>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 1, 1>; + if (natten::kEnableGemmTF32) { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } else { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } +} + +void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks17_align4( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 16, 32, 16, 16, 16, 8, 8, 3, 8, 4, 8>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; + if (natten::kEnableGemmTF32) { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } else { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } +} + +void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks17_align2( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 16, 32, 16, 16, 16, 8, 8, 3, 8, 4, 8>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; + if (natten::kEnableGemmTF32) { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } else { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } +} + +void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks17_align1( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 16, 32, 16, 16, 16, 8, 8, 3, 8, 4, 8>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 1, 1>; + if (natten::kEnableGemmTF32) { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } else { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } +} + +void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks19_align4( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 16, 32, 16, 16, 16, 8, 8, 3, 8, 4, 9>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; + if (natten::kEnableGemmTF32) { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } else { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } +} + +void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks19_align2( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 16, 32, 16, 16, 16, 8, 8, 3, 8, 4, 9>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; + if (natten::kEnableGemmTF32) { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } else { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } +} + +void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks19_align1( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 16, 32, 16, 16, 16, 8, 8, 3, 8, 4, 9>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 1, 1>; + if (natten::kEnableGemmTF32) { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } else { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } +} + +void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks21_align4( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 16, 32, 16, 16, 16, 8, 8, 3, 8, 4, 10>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; + if (natten::kEnableGemmTF32) { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } else { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } +} + +void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks21_align2( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 16, 32, 16, 16, 16, 8, 8, 3, 8, 4, 10>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; + if (natten::kEnableGemmTF32) { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } else { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } +} + +void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks21_align1( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 16, 32, 16, 16, 16, 8, 8, 3, 8, 4, 10>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 1, 1>; + if (natten::kEnableGemmTF32) { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } else { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } } -void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks3_align4( +void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks23_align4( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -187,7 +802,7 @@ void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks3_align4( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 16, 32, 16, 16, 16, 8, 8, 3, 8, 4, 1>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 16, 32, 16, 16, 16, 8, 8, 3, 8, 4, 11>; using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; if (natten::kEnableGemmTF32) { @@ -207,7 +822,7 @@ attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn } } -void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks3_align2( +void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks23_align2( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -224,7 +839,7 @@ void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks3_align2( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 16, 32, 16, 16, 16, 8, 8, 3, 8, 4, 1>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 16, 32, 16, 16, 16, 8, 8, 3, 8, 4, 11>; using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; if (natten::kEnableGemmTF32) { @@ -244,7 +859,7 @@ attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn } } -void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks3_align1( +void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks23_align1( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -261,7 +876,7 @@ void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks3_align1( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 16, 32, 16, 16, 16, 8, 8, 3, 8, 4, 1>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 16, 32, 16, 16, 16, 8, 8, 3, 8, 4, 11>; using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 1, 1>; if (natten::kEnableGemmTF32) { @@ -281,7 +896,7 @@ attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn } } -void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks5_align4( +void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks25_align4( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -298,7 +913,7 @@ void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks5_align4( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 16, 32, 16, 16, 16, 8, 8, 3, 8, 4, 2>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 16, 32, 16, 16, 16, 8, 8, 3, 8, 4, 12>; using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; if (natten::kEnableGemmTF32) { @@ -318,7 +933,7 @@ attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn } } -void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks5_align2( +void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks25_align2( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -335,7 +950,7 @@ void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks5_align2( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 16, 32, 16, 16, 16, 8, 8, 3, 8, 4, 2>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 16, 32, 16, 16, 16, 8, 8, 3, 8, 4, 12>; using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; if (natten::kEnableGemmTF32) { @@ -355,7 +970,7 @@ attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn } } -void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks5_align1( +void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks25_align1( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -372,7 +987,7 @@ void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks5_align1( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 16, 32, 16, 16, 16, 8, 8, 3, 8, 4, 2>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 16, 32, 16, 16, 16, 8, 8, 3, 8, 4, 12>; using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 1, 1>; if (natten::kEnableGemmTF32) { @@ -392,7 +1007,7 @@ attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn } } -void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks7_align4( +void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks27_align4( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -409,7 +1024,7 @@ void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks7_align4( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 16, 32, 16, 16, 16, 8, 8, 3, 8, 4, 3>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 16, 32, 16, 16, 16, 8, 8, 3, 8, 4, 13>; using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; if (natten::kEnableGemmTF32) { @@ -429,7 +1044,7 @@ attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn } } -void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks7_align2( +void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks27_align2( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -446,7 +1061,7 @@ void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks7_align2( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 16, 32, 16, 16, 16, 8, 8, 3, 8, 4, 3>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 16, 32, 16, 16, 16, 8, 8, 3, 8, 4, 13>; using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; if (natten::kEnableGemmTF32) { @@ -466,7 +1081,7 @@ attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn } } -void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks7_align1( +void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks27_align1( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -483,7 +1098,7 @@ void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks7_align1( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 16, 32, 16, 16, 16, 8, 8, 3, 8, 4, 3>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 16, 32, 16, 16, 16, 8, 8, 3, 8, 4, 13>; using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 1, 1>; if (natten::kEnableGemmTF32) { diff --git a/csrc/autogen/src/cuda/gemm/2d/sm80/source_23.cu b/csrc/autogen/src/cuda/gemm/2d/sm80/source_23.cu index 93604ed..cb1f449 100644 --- a/csrc/autogen/src/cuda/gemm/2d/sm80/source_23.cu +++ b/csrc/autogen/src/cuda/gemm/2d/sm80/source_23.cu @@ -1,14 +1,569 @@ #include #include -#include #include -#include #include +#include +#include namespace natten { namespace cuda { namespace gemm { -void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks9_align4( +void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks29_align4( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 16, 32, 16, 16, 16, 8, 8, 3, 8, 4, 14>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; + if (natten::kEnableGemmTF32) { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } else { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } +} + +void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks29_align2( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 16, 32, 16, 16, 16, 8, 8, 3, 8, 4, 14>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; + if (natten::kEnableGemmTF32) { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } else { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } +} + +void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks29_align1( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 16, 32, 16, 16, 16, 8, 8, 3, 8, 4, 14>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 1, 1>; + if (natten::kEnableGemmTF32) { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } else { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } +} + +void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks31_align4( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 16, 32, 16, 16, 16, 8, 8, 3, 8, 4, 15>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; + if (natten::kEnableGemmTF32) { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } else { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } +} + +void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks31_align2( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 16, 32, 16, 16, 16, 8, 8, 3, 8, 4, 15>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; + if (natten::kEnableGemmTF32) { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } else { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } +} + +void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks31_align1( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 16, 32, 16, 16, 16, 8, 8, 3, 8, 4, 15>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 1, 1>; + if (natten::kEnableGemmTF32) { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } else { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } +} + +void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks33_align4( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 16, 32, 16, 16, 16, 8, 8, 3, 8, 4, 16>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; + if (natten::kEnableGemmTF32) { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } else { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } +} + +void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks33_align2( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 16, 32, 16, 16, 16, 8, 8, 3, 8, 4, 16>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; + if (natten::kEnableGemmTF32) { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } else { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } +} + +void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks33_align1( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 16, 32, 16, 16, 16, 8, 8, 3, 8, 4, 16>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 1, 1>; + if (natten::kEnableGemmTF32) { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } else { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } +} + +void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks35_align4( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 16, 32, 16, 16, 16, 8, 8, 3, 8, 4, 17>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; + if (natten::kEnableGemmTF32) { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } else { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } +} + +void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks35_align2( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 16, 32, 16, 16, 16, 8, 8, 3, 8, 4, 17>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; + if (natten::kEnableGemmTF32) { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } else { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } +} + +void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks35_align1( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 16, 32, 16, 16, 16, 8, 8, 3, 8, 4, 17>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 1, 1>; + if (natten::kEnableGemmTF32) { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } else { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } +} + +void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks37_align4( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 16, 32, 16, 16, 16, 8, 8, 3, 8, 4, 18>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; + if (natten::kEnableGemmTF32) { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } else { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } +} + +void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks37_align2( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 16, 32, 16, 16, 16, 8, 8, 3, 8, 4, 18>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; + if (natten::kEnableGemmTF32) { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } else { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } +} + +void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks37_align1( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 16, 32, 16, 16, 16, 8, 8, 3, 8, 4, 18>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 1, 1>; + if (natten::kEnableGemmTF32) { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } else { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } +} + +void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks39_align4( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -25,7 +580,7 @@ void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks9_align4( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 16, 32, 16, 16, 16, 8, 8, 3, 8, 4, 4>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 16, 32, 16, 16, 16, 8, 8, 3, 8, 4, 19>; using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; if (natten::kEnableGemmTF32) { @@ -45,7 +600,7 @@ attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn } } -void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks9_align2( +void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks39_align2( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -62,7 +617,7 @@ void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks9_align2( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 16, 32, 16, 16, 16, 8, 8, 3, 8, 4, 4>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 16, 32, 16, 16, 16, 8, 8, 3, 8, 4, 19>; using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; if (natten::kEnableGemmTF32) { @@ -82,7 +637,7 @@ attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn } } -void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks9_align1( +void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks39_align1( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -99,7 +654,7 @@ void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks9_align1( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 16, 32, 16, 16, 16, 8, 8, 3, 8, 4, 4>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 16, 32, 16, 16, 16, 8, 8, 3, 8, 4, 19>; using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 1, 1>; if (natten::kEnableGemmTF32) { @@ -119,7 +674,7 @@ attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn } } -void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks11_align4( +void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks41_align4( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -136,7 +691,7 @@ void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks11_align4( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 16, 32, 16, 16, 16, 8, 8, 3, 8, 4, 5>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 16, 32, 16, 16, 16, 8, 8, 3, 8, 4, 20>; using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; if (natten::kEnableGemmTF32) { @@ -156,7 +711,7 @@ attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn } } -void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks11_align2( +void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks41_align2( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -173,7 +728,7 @@ void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks11_align2( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 16, 32, 16, 16, 16, 8, 8, 3, 8, 4, 5>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 16, 32, 16, 16, 16, 8, 8, 3, 8, 4, 20>; using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; if (natten::kEnableGemmTF32) { @@ -193,7 +748,7 @@ attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn } } -void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks11_align1( +void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks41_align1( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -210,7 +765,7 @@ void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks11_align1( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 16, 32, 16, 16, 16, 8, 8, 3, 8, 4, 5>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 16, 32, 16, 16, 16, 8, 8, 3, 8, 4, 20>; using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 1, 1>; if (natten::kEnableGemmTF32) { @@ -230,7 +785,7 @@ attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn } } -void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks13_align4( +void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks43_align4( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -247,7 +802,7 @@ void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks13_align4( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 16, 32, 16, 16, 16, 8, 8, 3, 8, 4, 6>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 16, 32, 16, 16, 16, 8, 8, 3, 8, 4, 21>; using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; if (natten::kEnableGemmTF32) { @@ -267,7 +822,7 @@ attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn } } -void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks13_align2( +void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks43_align2( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -284,7 +839,7 @@ void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks13_align2( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 16, 32, 16, 16, 16, 8, 8, 3, 8, 4, 6>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 16, 32, 16, 16, 16, 8, 8, 3, 8, 4, 21>; using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; if (natten::kEnableGemmTF32) { @@ -304,7 +859,7 @@ attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn } } -void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks13_align1( +void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks43_align1( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -321,7 +876,7 @@ void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks13_align1( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 16, 32, 16, 16, 16, 8, 8, 3, 8, 4, 6>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 16, 32, 16, 16, 16, 8, 8, 3, 8, 4, 21>; using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 1, 1>; if (natten::kEnableGemmTF32) { @@ -341,7 +896,7 @@ attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn } } -void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks15_align4( +void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks45_align4( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -358,7 +913,7 @@ void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks15_align4( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 16, 32, 16, 16, 16, 8, 8, 3, 8, 4, 7>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 16, 32, 16, 16, 16, 8, 8, 3, 8, 4, 22>; using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; if (natten::kEnableGemmTF32) { @@ -378,7 +933,7 @@ attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn } } -void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks15_align2( +void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks45_align2( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -395,7 +950,7 @@ void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks15_align2( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 16, 32, 16, 16, 16, 8, 8, 3, 8, 4, 7>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 16, 32, 16, 16, 16, 8, 8, 3, 8, 4, 22>; using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; if (natten::kEnableGemmTF32) { @@ -415,7 +970,7 @@ attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn } } -void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks15_align1( +void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks45_align1( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -432,7 +987,7 @@ void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks15_align1( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 16, 32, 16, 16, 16, 8, 8, 3, 8, 4, 7>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 16, 32, 16, 16, 16, 8, 8, 3, 8, 4, 22>; using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 1, 1>; if (natten::kEnableGemmTF32) { @@ -452,7 +1007,7 @@ attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn } } -void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks17_align4( +void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks47_align4( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -469,7 +1024,7 @@ void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks17_align4( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 16, 32, 16, 16, 16, 8, 8, 3, 8, 4, 8>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 16, 32, 16, 16, 16, 8, 8, 3, 8, 4, 23>; using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; if (natten::kEnableGemmTF32) { @@ -489,7 +1044,7 @@ attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn } } -void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks17_align2( +void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks47_align2( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -506,7 +1061,7 @@ void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks17_align2( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 16, 32, 16, 16, 16, 8, 8, 3, 8, 4, 8>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 16, 32, 16, 16, 16, 8, 8, 3, 8, 4, 23>; using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; if (natten::kEnableGemmTF32) { @@ -526,7 +1081,7 @@ attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn } } -void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks17_align1( +void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks47_align1( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -543,7 +1098,7 @@ void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks17_align1( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 16, 32, 16, 16, 16, 8, 8, 3, 8, 4, 8>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 16, 32, 16, 16, 16, 8, 8, 3, 8, 4, 23>; using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 1, 1>; if (natten::kEnableGemmTF32) { diff --git a/csrc/autogen/src/cuda/gemm/2d/sm80/source_24.cu b/csrc/autogen/src/cuda/gemm/2d/sm80/source_24.cu index 1235f3b..c691c23 100644 --- a/csrc/autogen/src/cuda/gemm/2d/sm80/source_24.cu +++ b/csrc/autogen/src/cuda/gemm/2d/sm80/source_24.cu @@ -1,14 +1,199 @@ #include #include -#include #include -#include #include +#include +#include namespace natten { namespace cuda { namespace gemm { -void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks19_align4( +void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks49_align4( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 16, 32, 16, 16, 16, 8, 8, 3, 8, 4, 24>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; + if (natten::kEnableGemmTF32) { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } else { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } +} + +void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks49_align2( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 16, 32, 16, 16, 16, 8, 8, 3, 8, 4, 24>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; + if (natten::kEnableGemmTF32) { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } else { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } +} + +void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks49_align1( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 16, 32, 16, 16, 16, 8, 8, 3, 8, 4, 24>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 1, 1>; + if (natten::kEnableGemmTF32) { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } else { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } +} + +void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks51_align4( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 16, 32, 16, 16, 16, 8, 8, 3, 8, 4, 25>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; + if (natten::kEnableGemmTF32) { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } else { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } +} + +void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks51_align2( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 16, 32, 16, 16, 16, 8, 8, 3, 8, 4, 25>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; + if (natten::kEnableGemmTF32) { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } else { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } +} + +void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks51_align1( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -25,7 +210,44 @@ void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks19_align4( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 16, 32, 16, 16, 16, 8, 8, 3, 8, 4, 9>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 16, 32, 16, 16, 16, 8, 8, 3, 8, 4, 25>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 1, 1>; + if (natten::kEnableGemmTF32) { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } else { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } +} + +void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks53_align4( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 16, 32, 16, 16, 16, 8, 8, 3, 8, 4, 26>; using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; if (natten::kEnableGemmTF32) { @@ -45,7 +267,7 @@ attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn } } -void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks19_align2( +void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks53_align2( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -62,7 +284,7 @@ void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks19_align2( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 16, 32, 16, 16, 16, 8, 8, 3, 8, 4, 9>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 16, 32, 16, 16, 16, 8, 8, 3, 8, 4, 26>; using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; if (natten::kEnableGemmTF32) { @@ -82,7 +304,7 @@ attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn } } -void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks19_align1( +void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks53_align1( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -99,7 +321,7 @@ void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks19_align1( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 16, 32, 16, 16, 16, 8, 8, 3, 8, 4, 9>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 16, 32, 16, 16, 16, 8, 8, 3, 8, 4, 26>; using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 1, 1>; if (natten::kEnableGemmTF32) { @@ -119,7 +341,7 @@ attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn } } -void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks21_align4( +void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks55_align4( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -136,7 +358,7 @@ void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks21_align4( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 16, 32, 16, 16, 16, 8, 8, 3, 8, 4, 10>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 16, 32, 16, 16, 16, 8, 8, 3, 8, 4, 27>; using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; if (natten::kEnableGemmTF32) { @@ -156,7 +378,7 @@ attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn } } -void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks21_align2( +void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks55_align2( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -173,7 +395,7 @@ void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks21_align2( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 16, 32, 16, 16, 16, 8, 8, 3, 8, 4, 10>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 16, 32, 16, 16, 16, 8, 8, 3, 8, 4, 27>; using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; if (natten::kEnableGemmTF32) { @@ -193,7 +415,7 @@ attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn } } -void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks21_align1( +void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks55_align1( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -210,7 +432,7 @@ void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks21_align1( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 16, 32, 16, 16, 16, 8, 8, 3, 8, 4, 10>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 16, 32, 16, 16, 16, 8, 8, 3, 8, 4, 27>; using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 1, 1>; if (natten::kEnableGemmTF32) { @@ -230,7 +452,7 @@ attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn } } -void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks23_align4( +void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks57_align4( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -247,7 +469,7 @@ void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks23_align4( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 16, 32, 16, 16, 16, 8, 8, 3, 8, 4, 11>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 16, 32, 16, 16, 16, 8, 8, 3, 8, 4, 28>; using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; if (natten::kEnableGemmTF32) { @@ -267,7 +489,7 @@ attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn } } -void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks23_align2( +void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks57_align2( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -284,7 +506,7 @@ void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks23_align2( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 16, 32, 16, 16, 16, 8, 8, 3, 8, 4, 11>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 16, 32, 16, 16, 16, 8, 8, 3, 8, 4, 28>; using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; if (natten::kEnableGemmTF32) { @@ -304,7 +526,7 @@ attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn } } -void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks23_align1( +void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks57_align1( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -321,7 +543,7 @@ void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks23_align1( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 16, 32, 16, 16, 16, 8, 8, 3, 8, 4, 11>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 16, 32, 16, 16, 16, 8, 8, 3, 8, 4, 28>; using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 1, 1>; if (natten::kEnableGemmTF32) { @@ -341,7 +563,7 @@ attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn } } -void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks25_align4( +void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks59_align4( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -358,7 +580,7 @@ void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks25_align4( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 16, 32, 16, 16, 16, 8, 8, 3, 8, 4, 12>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 16, 32, 16, 16, 16, 8, 8, 3, 8, 4, 29>; using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; if (natten::kEnableGemmTF32) { @@ -378,7 +600,7 @@ attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn } } -void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks25_align2( +void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks59_align2( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -395,7 +617,7 @@ void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks25_align2( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 16, 32, 16, 16, 16, 8, 8, 3, 8, 4, 12>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 16, 32, 16, 16, 16, 8, 8, 3, 8, 4, 29>; using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; if (natten::kEnableGemmTF32) { @@ -415,7 +637,7 @@ attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn } } -void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks25_align1( +void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks59_align1( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -432,7 +654,7 @@ void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks25_align1( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 16, 32, 16, 16, 16, 8, 8, 3, 8, 4, 12>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 16, 32, 16, 16, 16, 8, 8, 3, 8, 4, 29>; using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 1, 1>; if (natten::kEnableGemmTF32) { @@ -452,7 +674,7 @@ attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn } } -void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks27_align4( +void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks61_align4( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -469,7 +691,7 @@ void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks27_align4( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 16, 32, 16, 16, 16, 8, 8, 3, 8, 4, 13>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 16, 32, 16, 16, 16, 8, 8, 3, 8, 4, 30>; using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; if (natten::kEnableGemmTF32) { @@ -489,7 +711,7 @@ attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn } } -void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks27_align2( +void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks61_align2( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -506,7 +728,7 @@ void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks27_align2( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 16, 32, 16, 16, 16, 8, 8, 3, 8, 4, 13>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 16, 32, 16, 16, 16, 8, 8, 3, 8, 4, 30>; using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; if (natten::kEnableGemmTF32) { @@ -526,7 +748,7 @@ attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn } } -void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks27_align1( +void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks61_align1( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -543,7 +765,7 @@ void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks27_align1( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 16, 32, 16, 16, 16, 8, 8, 3, 8, 4, 13>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 16, 32, 16, 16, 16, 8, 8, 3, 8, 4, 30>; using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 1, 1>; if (natten::kEnableGemmTF32) { @@ -563,6 +785,279 @@ attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn } } +void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks63_align4( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 16, 32, 16, 16, 16, 8, 8, 3, 8, 4, 31>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; + if (natten::kEnableGemmTF32) { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } else { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } +} + +void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks63_align2( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 16, 32, 16, 16, 16, 8, 8, 3, 8, 4, 31>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; + if (natten::kEnableGemmTF32) { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } else { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } +} + +void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks63_align1( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 16, 32, 16, 16, 16, 8, 8, 3, 8, 4, 31>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 1, 1>; + if (natten::kEnableGemmTF32) { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } else { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } +} + +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks3_align8( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 1>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 8, 8>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks3_align4( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 1>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks3_align2( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 1>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks5_align8( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 2>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 8, 8>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks5_align4( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 2>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks5_align2( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 2>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + } } } diff --git a/csrc/autogen/src/cuda/gemm/2d/sm80/source_25.cu b/csrc/autogen/src/cuda/gemm/2d/sm80/source_25.cu index ac4abb3..77c2048 100644 --- a/csrc/autogen/src/cuda/gemm/2d/sm80/source_25.cu +++ b/csrc/autogen/src/cuda/gemm/2d/sm80/source_25.cu @@ -1,14 +1,14 @@ #include #include -#include #include -#include #include +#include +#include namespace natten { namespace cuda { namespace gemm { -void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks29_align4( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks7_align8( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -25,27 +25,44 @@ void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks29_align4( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 16, 32, 16, 16, 16, 8, 8, 3, 8, 4, 14>; - using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; - using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; - if (natten::kEnableGemmTF32) { - using DConfig = natten::gemm::detail::DTypeConfig; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 3>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 8, 8>; + using DConfig = natten::gemm::detail::DTypeConfig; using Kernel = InverseNeighborhood2D; Kernel kernel; kernel( attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} - } else { - using DConfig = natten::gemm::detail::DTypeConfig; +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks7_align4( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 3>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; + using DConfig = natten::gemm::detail::DTypeConfig; using Kernel = InverseNeighborhood2D; Kernel kernel; kernel( attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); - - } } -void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks29_align2( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks7_align2( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -62,27 +79,44 @@ void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks29_align2( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 16, 32, 16, 16, 16, 8, 8, 3, 8, 4, 14>; - using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 3>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; - if (natten::kEnableGemmTF32) { - using DConfig = natten::gemm::detail::DTypeConfig; + using DConfig = natten::gemm::detail::DTypeConfig; using Kernel = InverseNeighborhood2D; Kernel kernel; kernel( attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} - } else { - using DConfig = natten::gemm::detail::DTypeConfig; +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks9_align8( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 4>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 8, 8>; + using DConfig = natten::gemm::detail::DTypeConfig; using Kernel = InverseNeighborhood2D; Kernel kernel; kernel( attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); - - } } -void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks29_align1( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks9_align4( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -99,27 +133,71 @@ void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks29_align1( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 16, 32, 16, 16, 16, 8, 8, 3, 8, 4, 14>; - using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; - using AConfig = natten::gemm::detail::AlignmentConfig<1, 1, 1>; - if (natten::kEnableGemmTF32) { - using DConfig = natten::gemm::detail::DTypeConfig; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 4>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; + using DConfig = natten::gemm::detail::DTypeConfig; using Kernel = InverseNeighborhood2D; Kernel kernel; kernel( attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} - } else { - using DConfig = natten::gemm::detail::DTypeConfig; +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks9_align2( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 4>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; + using DConfig = natten::gemm::detail::DTypeConfig; using Kernel = InverseNeighborhood2D; Kernel kernel; kernel( attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} - } +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks11_align8( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 5>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 8, 8>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks31_align4( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks11_align4( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -136,27 +214,71 @@ void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks31_align4( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 16, 32, 16, 16, 16, 8, 8, 3, 8, 4, 15>; - using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 5>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; - if (natten::kEnableGemmTF32) { - using DConfig = natten::gemm::detail::DTypeConfig; + using DConfig = natten::gemm::detail::DTypeConfig; using Kernel = InverseNeighborhood2D; Kernel kernel; kernel( attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} - } else { - using DConfig = natten::gemm::detail::DTypeConfig; +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks11_align2( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 5>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; + using DConfig = natten::gemm::detail::DTypeConfig; using Kernel = InverseNeighborhood2D; Kernel kernel; kernel( attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} - } +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks13_align8( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 6>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 8, 8>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks31_align2( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks13_align4( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -173,27 +295,71 @@ void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks31_align2( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 16, 32, 16, 16, 16, 8, 8, 3, 8, 4, 15>; - using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; - using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; - if (natten::kEnableGemmTF32) { - using DConfig = natten::gemm::detail::DTypeConfig; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 6>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; + using DConfig = natten::gemm::detail::DTypeConfig; using Kernel = InverseNeighborhood2D; Kernel kernel; kernel( attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} - } else { - using DConfig = natten::gemm::detail::DTypeConfig; +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks13_align2( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 6>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; + using DConfig = natten::gemm::detail::DTypeConfig; using Kernel = InverseNeighborhood2D; Kernel kernel; kernel( attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} - } +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks15_align8( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 7>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 8, 8>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks31_align1( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks15_align4( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -210,27 +376,71 @@ void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks31_align1( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 16, 32, 16, 16, 16, 8, 8, 3, 8, 4, 15>; - using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; - using AConfig = natten::gemm::detail::AlignmentConfig<1, 1, 1>; - if (natten::kEnableGemmTF32) { - using DConfig = natten::gemm::detail::DTypeConfig; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 7>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; + using DConfig = natten::gemm::detail::DTypeConfig; using Kernel = InverseNeighborhood2D; Kernel kernel; kernel( attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} - } else { - using DConfig = natten::gemm::detail::DTypeConfig; +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks15_align2( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 7>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; + using DConfig = natten::gemm::detail::DTypeConfig; using Kernel = InverseNeighborhood2D; Kernel kernel; kernel( attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} - } +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks17_align8( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 8>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 8, 8>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks33_align4( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks17_align4( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -247,27 +457,44 @@ void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks33_align4( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 16, 32, 16, 16, 16, 8, 8, 3, 8, 4, 16>; - using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 8>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; - if (natten::kEnableGemmTF32) { - using DConfig = natten::gemm::detail::DTypeConfig; + using DConfig = natten::gemm::detail::DTypeConfig; using Kernel = InverseNeighborhood2D; Kernel kernel; kernel( attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} - } else { - using DConfig = natten::gemm::detail::DTypeConfig; +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks17_align2( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 8>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; + using DConfig = natten::gemm::detail::DTypeConfig; using Kernel = InverseNeighborhood2D; Kernel kernel; kernel( attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); - - } } -void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks33_align2( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks19_align8( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -284,27 +511,71 @@ void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks33_align2( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 16, 32, 16, 16, 16, 8, 8, 3, 8, 4, 16>; - using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; - using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; - if (natten::kEnableGemmTF32) { - using DConfig = natten::gemm::detail::DTypeConfig; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 9>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 8, 8>; + using DConfig = natten::gemm::detail::DTypeConfig; using Kernel = InverseNeighborhood2D; Kernel kernel; kernel( attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} - } else { - using DConfig = natten::gemm::detail::DTypeConfig; +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks19_align4( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 9>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; + using DConfig = natten::gemm::detail::DTypeConfig; using Kernel = InverseNeighborhood2D; Kernel kernel; kernel( attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} - } +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks19_align2( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 9>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks33_align1( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks21_align8( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -321,27 +592,71 @@ void na2d_in_cuda_gemm_float_64x64x16_32x16x16_16x8x8_3_sm80_ks33_align1( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 16, 32, 16, 16, 16, 8, 8, 3, 8, 4, 16>; - using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; - using AConfig = natten::gemm::detail::AlignmentConfig<1, 1, 1>; - if (natten::kEnableGemmTF32) { - using DConfig = natten::gemm::detail::DTypeConfig; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 10>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 8, 8>; + using DConfig = natten::gemm::detail::DTypeConfig; using Kernel = InverseNeighborhood2D; Kernel kernel; kernel( attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} - } else { - using DConfig = natten::gemm::detail::DTypeConfig; +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks21_align4( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 10>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; + using DConfig = natten::gemm::detail::DTypeConfig; using Kernel = InverseNeighborhood2D; Kernel kernel; kernel( attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} - } +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks21_align2( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 10>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks3_align8( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks23_align8( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -358,7 +673,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks3_align8( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 1>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 11>; using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 8, 8>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -368,7 +683,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks3_align8( attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks3_align4( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks23_align4( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -385,7 +700,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks3_align4( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 1>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 11>; using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -395,7 +710,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks3_align4( attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks3_align2( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks23_align2( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -412,7 +727,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks3_align2( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 1>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 11>; using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -422,7 +737,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks3_align2( attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks5_align8( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks25_align8( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -439,7 +754,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks5_align8( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 2>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 12>; using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 8, 8>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -449,7 +764,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks5_align8( attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks5_align4( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks25_align4( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -466,7 +781,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks5_align4( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 2>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 12>; using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -476,7 +791,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks5_align4( attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks5_align2( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks25_align2( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -493,7 +808,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks5_align2( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 2>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 12>; using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; using DConfig = natten::gemm::detail::DTypeConfig; diff --git a/csrc/autogen/src/cuda/gemm/2d/sm80/source_26.cu b/csrc/autogen/src/cuda/gemm/2d/sm80/source_26.cu index af2dc50..4344c8c 100644 --- a/csrc/autogen/src/cuda/gemm/2d/sm80/source_26.cu +++ b/csrc/autogen/src/cuda/gemm/2d/sm80/source_26.cu @@ -1,14 +1,419 @@ #include #include -#include #include -#include #include +#include +#include namespace natten { namespace cuda { namespace gemm { -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks7_align8( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks27_align8( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 13>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 8, 8>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks27_align4( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 13>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks27_align2( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 13>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks29_align8( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 14>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 8, 8>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks29_align4( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 14>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks29_align2( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 14>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks31_align8( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 15>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 8, 8>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks31_align4( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 15>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks31_align2( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 15>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks33_align8( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 16>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 8, 8>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks33_align4( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 16>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks33_align2( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 16>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks35_align8( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 17>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 8, 8>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks35_align4( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 17>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks35_align2( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 17>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks37_align8( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -25,7 +430,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks7_align8( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 3>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 18>; using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 8, 8>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -35,7 +440,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks7_align8( attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks7_align4( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks37_align4( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -52,7 +457,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks7_align4( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 3>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 18>; using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -62,7 +467,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks7_align4( attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks7_align2( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks37_align2( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -79,7 +484,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks7_align2( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 3>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 18>; using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -89,7 +494,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks7_align2( attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks9_align8( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks39_align8( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -106,7 +511,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks9_align8( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 4>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 19>; using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 8, 8>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -116,7 +521,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks9_align8( attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks9_align4( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks39_align4( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -133,7 +538,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks9_align4( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 4>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 19>; using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -143,7 +548,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks9_align4( attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks9_align2( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks39_align2( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -160,7 +565,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks9_align2( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 4>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 19>; using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -170,7 +575,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks9_align2( attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks11_align8( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks41_align8( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -187,7 +592,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks11_align8( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 5>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 20>; using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 8, 8>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -197,7 +602,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks11_align8( attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks11_align4( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks41_align4( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -214,7 +619,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks11_align4( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 5>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 20>; using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -224,7 +629,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks11_align4( attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks11_align2( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks41_align2( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -241,7 +646,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks11_align2( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 5>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 20>; using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -251,7 +656,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks11_align2( attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks13_align8( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks43_align8( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -268,7 +673,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks13_align8( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 6>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 21>; using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 8, 8>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -278,7 +683,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks13_align8( attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks13_align4( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks43_align4( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -295,7 +700,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks13_align4( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 6>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 21>; using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -305,7 +710,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks13_align4( attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks13_align2( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks43_align2( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -322,7 +727,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks13_align2( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 6>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 21>; using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -332,7 +737,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks13_align2( attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks15_align8( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks45_align8( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -349,7 +754,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks15_align8( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 7>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 22>; using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 8, 8>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -359,7 +764,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks15_align8( attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks15_align4( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks45_align4( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -376,7 +781,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks15_align4( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 7>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 22>; using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -386,7 +791,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks15_align4( attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks15_align2( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks45_align2( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -403,7 +808,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks15_align2( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 7>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 22>; using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; using DConfig = natten::gemm::detail::DTypeConfig; diff --git a/csrc/autogen/src/cuda/gemm/2d/sm80/source_27.cu b/csrc/autogen/src/cuda/gemm/2d/sm80/source_27.cu index 19a17f7..8620568 100644 --- a/csrc/autogen/src/cuda/gemm/2d/sm80/source_27.cu +++ b/csrc/autogen/src/cuda/gemm/2d/sm80/source_27.cu @@ -1,14 +1,68 @@ #include #include -#include #include -#include #include +#include +#include namespace natten { namespace cuda { namespace gemm { -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks17_align8( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks47_align8( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 23>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 8, 8>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks47_align4( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 23>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks47_align2( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -25,7 +79,34 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks17_align8( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 8>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 23>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks49_align8( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 24>; using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 8, 8>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -35,7 +116,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks17_align8( attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks17_align4( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks49_align4( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -52,7 +133,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks17_align4( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 8>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 24>; using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -62,7 +143,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks17_align4( attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks17_align2( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks49_align2( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -79,7 +160,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks17_align2( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 8>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 24>; using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -89,7 +170,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks17_align2( attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks19_align8( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks51_align8( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -106,7 +187,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks19_align8( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 9>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 25>; using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 8, 8>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -116,7 +197,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks19_align8( attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks19_align4( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks51_align4( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -133,7 +214,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks19_align4( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 9>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 25>; using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -143,7 +224,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks19_align4( attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks19_align2( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks51_align2( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -160,7 +241,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks19_align2( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 9>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 25>; using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -170,7 +251,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks19_align2( attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks21_align8( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks53_align8( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -187,7 +268,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks21_align8( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 10>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 26>; using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 8, 8>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -197,7 +278,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks21_align8( attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks21_align4( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks53_align4( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -214,7 +295,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks21_align4( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 10>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 26>; using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -224,7 +305,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks21_align4( attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks21_align2( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks53_align2( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -241,7 +322,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks21_align2( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 10>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 26>; using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -251,7 +332,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks21_align2( attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks23_align8( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks55_align8( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -268,7 +349,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks23_align8( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 11>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 27>; using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 8, 8>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -278,7 +359,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks23_align8( attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks23_align4( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks55_align4( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -295,7 +376,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks23_align4( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 11>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 27>; using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -305,7 +386,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks23_align4( attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks23_align2( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks55_align2( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -322,7 +403,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks23_align2( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 11>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 27>; using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -332,7 +413,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks23_align2( attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks25_align8( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks57_align8( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -349,7 +430,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks25_align8( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 12>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 28>; using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 8, 8>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -359,7 +440,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks25_align8( attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks25_align4( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks57_align4( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -376,7 +457,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks25_align4( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 12>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 28>; using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -386,7 +467,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks25_align4( attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks25_align2( +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks57_align2( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -403,7 +484,7 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks25_align2( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 12>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 28>; using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -413,6 +494,330 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks25_align2( attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks59_align8( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 29>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 8, 8>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks59_align4( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 29>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks59_align2( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 29>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks61_align8( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 30>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 8, 8>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks61_align4( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 30>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks61_align2( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 30>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks63_align8( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 31>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 8, 8>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks63_align4( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 31>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks63_align2( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 31>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks3_align8( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 1>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 8, 8>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks3_align4( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 1>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks3_align2( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 1>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + } } } diff --git a/csrc/autogen/src/cuda/gemm/2d/sm80/source_28.cu b/csrc/autogen/src/cuda/gemm/2d/sm80/source_28.cu index 500fc61..0ee4125 100644 --- a/csrc/autogen/src/cuda/gemm/2d/sm80/source_28.cu +++ b/csrc/autogen/src/cuda/gemm/2d/sm80/source_28.cu @@ -1,14 +1,122 @@ #include #include -#include #include -#include #include +#include +#include namespace natten { namespace cuda { namespace gemm { -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks27_align8( +void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks5_align8( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 2>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 8, 8>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks5_align4( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 2>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks5_align2( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 2>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks7_align8( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 3>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 8, 8>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks7_align4( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -25,17 +133,71 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks27_align8( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 13>; - using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 3>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks7_align2( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 3>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks9_align8( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 4>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 8, 8>; - using DConfig = natten::gemm::detail::DTypeConfig; + using DConfig = natten::gemm::detail::DTypeConfig; using Kernel = InverseNeighborhood2D; Kernel kernel; kernel( attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks27_align4( +void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks9_align4( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -52,17 +214,17 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks27_align4( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 13>; - using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 4>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; - using DConfig = natten::gemm::detail::DTypeConfig; + using DConfig = natten::gemm::detail::DTypeConfig; using Kernel = InverseNeighborhood2D; Kernel kernel; kernel( attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks27_align2( +void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks9_align2( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -79,17 +241,17 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks27_align2( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 13>; - using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 4>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; - using DConfig = natten::gemm::detail::DTypeConfig; + using DConfig = natten::gemm::detail::DTypeConfig; using Kernel = InverseNeighborhood2D; Kernel kernel; kernel( attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks29_align8( +void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks11_align8( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -106,17 +268,17 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks29_align8( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 14>; - using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 5>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 8, 8>; - using DConfig = natten::gemm::detail::DTypeConfig; + using DConfig = natten::gemm::detail::DTypeConfig; using Kernel = InverseNeighborhood2D; Kernel kernel; kernel( attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks29_align4( +void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks11_align4( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -133,17 +295,17 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks29_align4( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 14>; - using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 5>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; - using DConfig = natten::gemm::detail::DTypeConfig; + using DConfig = natten::gemm::detail::DTypeConfig; using Kernel = InverseNeighborhood2D; Kernel kernel; kernel( attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks29_align2( +void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks11_align2( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -160,17 +322,17 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks29_align2( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 14>; - using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 5>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; - using DConfig = natten::gemm::detail::DTypeConfig; + using DConfig = natten::gemm::detail::DTypeConfig; using Kernel = InverseNeighborhood2D; Kernel kernel; kernel( attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks31_align8( +void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks13_align8( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -187,17 +349,17 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks31_align8( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 15>; - using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 6>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 8, 8>; - using DConfig = natten::gemm::detail::DTypeConfig; + using DConfig = natten::gemm::detail::DTypeConfig; using Kernel = InverseNeighborhood2D; Kernel kernel; kernel( attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks31_align4( +void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks13_align4( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -214,17 +376,17 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks31_align4( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 15>; - using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 6>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; - using DConfig = natten::gemm::detail::DTypeConfig; + using DConfig = natten::gemm::detail::DTypeConfig; using Kernel = InverseNeighborhood2D; Kernel kernel; kernel( attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks31_align2( +void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks13_align2( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -241,17 +403,17 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks31_align2( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 15>; - using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 6>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; - using DConfig = natten::gemm::detail::DTypeConfig; + using DConfig = natten::gemm::detail::DTypeConfig; using Kernel = InverseNeighborhood2D; Kernel kernel; kernel( attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks33_align8( +void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks15_align8( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -268,17 +430,17 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks33_align8( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 16>; - using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 7>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 8, 8>; - using DConfig = natten::gemm::detail::DTypeConfig; + using DConfig = natten::gemm::detail::DTypeConfig; using Kernel = InverseNeighborhood2D; Kernel kernel; kernel( attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks33_align4( +void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks15_align4( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -295,17 +457,17 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks33_align4( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 16>; - using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 7>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; - using DConfig = natten::gemm::detail::DTypeConfig; + using DConfig = natten::gemm::detail::DTypeConfig; using Kernel = InverseNeighborhood2D; Kernel kernel; kernel( attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks33_align2( +void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks15_align2( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -322,17 +484,260 @@ void na2d_in_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks33_align2( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 16>; - using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 7>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; - using DConfig = natten::gemm::detail::DTypeConfig; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks17_align8( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 8>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 8, 8>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks17_align4( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 8>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks17_align2( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 8>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks19_align8( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 9>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 8, 8>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks19_align4( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 9>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks19_align2( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 9>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks21_align8( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 10>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 8, 8>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks21_align4( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 10>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks21_align2( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 10>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; + using DConfig = natten::gemm::detail::DTypeConfig; using Kernel = InverseNeighborhood2D; Kernel kernel; kernel( attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks3_align8( +void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks23_align8( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -349,7 +754,7 @@ void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks3_align8( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 1>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 11>; using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 8, 8>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -359,7 +764,7 @@ void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks3_align8( attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks3_align4( +void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks23_align4( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -376,7 +781,7 @@ void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks3_align4( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 1>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 11>; using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -386,7 +791,7 @@ void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks3_align4( attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks3_align2( +void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks23_align2( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -403,7 +808,7 @@ void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks3_align2( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 1>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 11>; using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; using DConfig = natten::gemm::detail::DTypeConfig; diff --git a/csrc/autogen/src/cuda/gemm/2d/sm80/source_29.cu b/csrc/autogen/src/cuda/gemm/2d/sm80/source_29.cu index 7fc0c10..02b5f1a 100644 --- a/csrc/autogen/src/cuda/gemm/2d/sm80/source_29.cu +++ b/csrc/autogen/src/cuda/gemm/2d/sm80/source_29.cu @@ -1,14 +1,419 @@ #include #include -#include #include -#include #include +#include +#include namespace natten { namespace cuda { namespace gemm { -void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks5_align8( +void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks25_align8( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 12>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 8, 8>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks25_align4( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 12>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks25_align2( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 12>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks27_align8( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 13>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 8, 8>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks27_align4( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 13>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks27_align2( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 13>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks29_align8( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 14>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 8, 8>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks29_align4( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 14>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks29_align2( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 14>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks31_align8( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 15>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 8, 8>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks31_align4( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 15>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks31_align2( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 15>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks33_align8( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 16>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 8, 8>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks33_align4( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 16>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks33_align2( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 16>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks35_align8( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -25,7 +430,7 @@ void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks5_align8( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 2>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 17>; using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 8, 8>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -35,7 +440,7 @@ void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks5_align8( attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks5_align4( +void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks35_align4( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -52,7 +457,7 @@ void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks5_align4( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 2>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 17>; using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -62,7 +467,7 @@ void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks5_align4( attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks5_align2( +void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks35_align2( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -79,7 +484,7 @@ void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks5_align2( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 2>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 17>; using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -89,7 +494,7 @@ void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks5_align2( attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks7_align8( +void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks37_align8( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -106,7 +511,7 @@ void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks7_align8( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 3>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 18>; using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 8, 8>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -116,7 +521,7 @@ void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks7_align8( attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks7_align4( +void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks37_align4( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -133,7 +538,7 @@ void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks7_align4( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 3>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 18>; using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -143,7 +548,7 @@ void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks7_align4( attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks7_align2( +void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks37_align2( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -160,7 +565,7 @@ void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks7_align2( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 3>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 18>; using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -170,7 +575,7 @@ void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks7_align2( attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks9_align8( +void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks39_align8( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -187,7 +592,7 @@ void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks9_align8( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 4>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 19>; using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 8, 8>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -197,7 +602,7 @@ void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks9_align8( attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks9_align4( +void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks39_align4( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -214,7 +619,7 @@ void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks9_align4( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 4>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 19>; using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -224,7 +629,7 @@ void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks9_align4( attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks9_align2( +void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks39_align2( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -241,7 +646,7 @@ void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks9_align2( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 4>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 19>; using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -251,7 +656,7 @@ void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks9_align2( attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks11_align8( +void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks41_align8( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -268,7 +673,7 @@ void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks11_align8( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 5>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 20>; using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 8, 8>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -278,7 +683,7 @@ void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks11_align8( attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks11_align4( +void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks41_align4( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -295,7 +700,7 @@ void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks11_align4( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 5>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 20>; using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -305,7 +710,7 @@ void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks11_align4( attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks11_align2( +void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks41_align2( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -322,7 +727,7 @@ void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks11_align2( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 5>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 20>; using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -332,7 +737,7 @@ void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks11_align2( attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks13_align8( +void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks43_align8( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -349,7 +754,7 @@ void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks13_align8( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 6>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 21>; using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 8, 8>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -359,7 +764,7 @@ void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks13_align8( attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks13_align4( +void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks43_align4( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -376,7 +781,7 @@ void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks13_align4( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 6>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 21>; using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -386,7 +791,7 @@ void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks13_align4( attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks13_align2( +void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks43_align2( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -403,7 +808,7 @@ void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks13_align2( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 6>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 21>; using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; using DConfig = natten::gemm::detail::DTypeConfig; diff --git a/csrc/autogen/src/cuda/gemm/2d/sm80/source_3.cu b/csrc/autogen/src/cuda/gemm/2d/sm80/source_3.cu index 2ee828d..5eeb52f 100644 --- a/csrc/autogen/src/cuda/gemm/2d/sm80/source_3.cu +++ b/csrc/autogen/src/cuda/gemm/2d/sm80/source_3.cu @@ -1,14 +1,584 @@ #include #include -#include #include -#include #include +#include +#include namespace natten { namespace cuda { namespace gemm { -void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks21_align1( +void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks41_align1( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 16, 64, 64, 16, 16, 8, 8, 3, 22, 1, 20>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 1, 1>; + if (natten::kEnableGemmTF32) { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } else { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } +} + +void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks43_align4( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 16, 64, 64, 16, 16, 8, 8, 3, 22, 1, 21>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; + using AConfig = natten::gemm::detail::AlignmentConfig<4, 4, 1>; + if (natten::kEnableGemmTF32) { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } else { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } +} + +void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks43_align2( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 16, 64, 64, 16, 16, 8, 8, 3, 22, 1, 21>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; + using AConfig = natten::gemm::detail::AlignmentConfig<2, 2, 1>; + if (natten::kEnableGemmTF32) { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } else { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } +} + +void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks43_align1( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 16, 64, 64, 16, 16, 8, 8, 3, 22, 1, 21>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 1, 1>; + if (natten::kEnableGemmTF32) { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } else { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } +} + +void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks45_align4( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 16, 64, 64, 16, 16, 8, 8, 3, 23, 1, 22>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; + using AConfig = natten::gemm::detail::AlignmentConfig<4, 4, 1>; + if (natten::kEnableGemmTF32) { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } else { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } +} + +void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks45_align2( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 16, 64, 64, 16, 16, 8, 8, 3, 23, 1, 22>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; + using AConfig = natten::gemm::detail::AlignmentConfig<2, 2, 1>; + if (natten::kEnableGemmTF32) { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } else { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } +} + +void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks45_align1( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 16, 64, 64, 16, 16, 8, 8, 3, 23, 1, 22>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 1, 1>; + if (natten::kEnableGemmTF32) { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } else { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } +} + +void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks47_align4( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 16, 64, 64, 16, 16, 8, 8, 3, 33, 1, 23>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; + using AConfig = natten::gemm::detail::AlignmentConfig<4, 4, 1>; + if (natten::kEnableGemmTF32) { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } else { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } +} + +void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks47_align2( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 16, 64, 64, 16, 16, 8, 8, 3, 33, 1, 23>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; + using AConfig = natten::gemm::detail::AlignmentConfig<2, 2, 1>; + if (natten::kEnableGemmTF32) { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } else { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } +} + +void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks47_align1( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 16, 64, 64, 16, 16, 8, 8, 3, 33, 1, 23>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 1, 1>; + if (natten::kEnableGemmTF32) { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } else { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } +} + +void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks49_align4( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 16, 64, 64, 16, 16, 8, 8, 3, 33, 1, 24>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; + using AConfig = natten::gemm::detail::AlignmentConfig<4, 4, 1>; + if (natten::kEnableGemmTF32) { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } else { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } +} + +void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks49_align2( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 16, 64, 64, 16, 16, 8, 8, 3, 33, 1, 24>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; + using AConfig = natten::gemm::detail::AlignmentConfig<2, 2, 1>; + if (natten::kEnableGemmTF32) { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } else { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } +} + +void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks49_align1( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 16, 64, 64, 16, 16, 8, 8, 3, 33, 1, 24>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 1, 1>; + if (natten::kEnableGemmTF32) { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } else { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } +} + +void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks51_align4( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 16, 64, 64, 16, 16, 8, 8, 3, 33, 1, 25>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; + using AConfig = natten::gemm::detail::AlignmentConfig<4, 4, 1>; + if (natten::kEnableGemmTF32) { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } else { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } +} + +void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks51_align2( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 16, 64, 64, 16, 16, 8, 8, 3, 33, 1, 25>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; + using AConfig = natten::gemm::detail::AlignmentConfig<2, 2, 1>; + if (natten::kEnableGemmTF32) { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } else { + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); + + } +} + +void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks51_align1( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -26,7 +596,7 @@ void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks21_align1( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 16, 64, 64, 16, 16, 8, 8, 3, 12, 1, 10>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 16, 64, 64, 16, 16, 8, 8, 3, 33, 1, 25>; using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 1, 1>; if (natten::kEnableGemmTF32) { @@ -46,7 +616,7 @@ query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, a } } -void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks23_align4( +void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks53_align4( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -64,7 +634,7 @@ void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks23_align4( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 16, 64, 64, 16, 16, 8, 8, 3, 14, 1, 11>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 16, 64, 64, 16, 16, 8, 8, 3, 33, 1, 26>; using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; using AConfig = natten::gemm::detail::AlignmentConfig<4, 4, 1>; if (natten::kEnableGemmTF32) { @@ -84,7 +654,7 @@ query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, a } } -void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks23_align2( +void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks53_align2( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -102,7 +672,7 @@ void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks23_align2( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 16, 64, 64, 16, 16, 8, 8, 3, 14, 1, 11>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 16, 64, 64, 16, 16, 8, 8, 3, 33, 1, 26>; using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; using AConfig = natten::gemm::detail::AlignmentConfig<2, 2, 1>; if (natten::kEnableGemmTF32) { @@ -122,7 +692,7 @@ query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, a } } -void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks23_align1( +void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks53_align1( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -140,7 +710,7 @@ void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks23_align1( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 16, 64, 64, 16, 16, 8, 8, 3, 14, 1, 11>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 16, 64, 64, 16, 16, 8, 8, 3, 33, 1, 26>; using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 1, 1>; if (natten::kEnableGemmTF32) { @@ -160,7 +730,7 @@ query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, a } } -void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks25_align4( +void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks55_align4( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -178,7 +748,7 @@ void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks25_align4( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 16, 64, 64, 16, 16, 8, 8, 3, 13, 1, 12>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 16, 64, 64, 16, 16, 8, 8, 3, 33, 1, 27>; using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; using AConfig = natten::gemm::detail::AlignmentConfig<4, 4, 1>; if (natten::kEnableGemmTF32) { @@ -198,7 +768,7 @@ query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, a } } -void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks25_align2( +void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks55_align2( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -216,7 +786,7 @@ void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks25_align2( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 16, 64, 64, 16, 16, 8, 8, 3, 13, 1, 12>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 16, 64, 64, 16, 16, 8, 8, 3, 33, 1, 27>; using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; using AConfig = natten::gemm::detail::AlignmentConfig<2, 2, 1>; if (natten::kEnableGemmTF32) { @@ -236,7 +806,7 @@ query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, a } } -void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks25_align1( +void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks55_align1( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -254,7 +824,7 @@ void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks25_align1( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 16, 64, 64, 16, 16, 8, 8, 3, 13, 1, 12>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 16, 64, 64, 16, 16, 8, 8, 3, 33, 1, 27>; using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 1, 1>; if (natten::kEnableGemmTF32) { @@ -274,7 +844,7 @@ query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, a } } -void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks27_align4( +void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks57_align4( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -292,7 +862,7 @@ void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks27_align4( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 16, 64, 64, 16, 16, 8, 8, 3, 14, 1, 13>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 16, 64, 64, 16, 16, 8, 8, 3, 33, 1, 28>; using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; using AConfig = natten::gemm::detail::AlignmentConfig<4, 4, 1>; if (natten::kEnableGemmTF32) { @@ -312,7 +882,7 @@ query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, a } } -void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks27_align2( +void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks57_align2( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -330,7 +900,7 @@ void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks27_align2( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 16, 64, 64, 16, 16, 8, 8, 3, 14, 1, 13>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 16, 64, 64, 16, 16, 8, 8, 3, 33, 1, 28>; using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; using AConfig = natten::gemm::detail::AlignmentConfig<2, 2, 1>; if (natten::kEnableGemmTF32) { @@ -350,7 +920,7 @@ query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, a } } -void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks27_align1( +void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks57_align1( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -368,7 +938,7 @@ void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks27_align1( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 16, 64, 64, 16, 16, 8, 8, 3, 14, 1, 13>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 16, 64, 64, 16, 16, 8, 8, 3, 33, 1, 28>; using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 1, 1>; if (natten::kEnableGemmTF32) { @@ -388,7 +958,7 @@ query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, a } } -void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks29_align4( +void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks59_align4( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -406,7 +976,7 @@ void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks29_align4( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 16, 64, 64, 16, 16, 8, 8, 3, 17, 1, 14>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 16, 64, 64, 16, 16, 8, 8, 3, 33, 1, 29>; using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; using AConfig = natten::gemm::detail::AlignmentConfig<4, 4, 1>; if (natten::kEnableGemmTF32) { @@ -426,7 +996,7 @@ query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, a } } -void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks29_align2( +void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks59_align2( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -444,7 +1014,7 @@ void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks29_align2( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 16, 64, 64, 16, 16, 8, 8, 3, 17, 1, 14>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 16, 64, 64, 16, 16, 8, 8, 3, 33, 1, 29>; using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; using AConfig = natten::gemm::detail::AlignmentConfig<2, 2, 1>; if (natten::kEnableGemmTF32) { @@ -464,7 +1034,7 @@ query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, a } } -void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks29_align1( +void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks59_align1( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -482,7 +1052,7 @@ void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks29_align1( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 16, 64, 64, 16, 16, 8, 8, 3, 17, 1, 14>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 16, 64, 64, 16, 16, 8, 8, 3, 33, 1, 29>; using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 1, 1>; if (natten::kEnableGemmTF32) { @@ -502,7 +1072,7 @@ query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, a } } -void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks31_align4( +void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks61_align4( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -520,7 +1090,7 @@ void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks31_align4( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 16, 64, 64, 16, 16, 8, 8, 3, 17, 1, 15>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 16, 64, 64, 16, 16, 8, 8, 3, 33, 1, 30>; using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; using AConfig = natten::gemm::detail::AlignmentConfig<4, 4, 1>; if (natten::kEnableGemmTF32) { @@ -540,7 +1110,7 @@ query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, a } } -void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks31_align2( +void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks61_align2( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -558,7 +1128,7 @@ void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks31_align2( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 16, 64, 64, 16, 16, 8, 8, 3, 17, 1, 15>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 16, 64, 64, 16, 16, 8, 8, 3, 33, 1, 30>; using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; using AConfig = natten::gemm::detail::AlignmentConfig<2, 2, 1>; if (natten::kEnableGemmTF32) { diff --git a/csrc/autogen/src/cuda/gemm/2d/sm80/source_30.cu b/csrc/autogen/src/cuda/gemm/2d/sm80/source_30.cu index d616a03..52e433c 100644 --- a/csrc/autogen/src/cuda/gemm/2d/sm80/source_30.cu +++ b/csrc/autogen/src/cuda/gemm/2d/sm80/source_30.cu @@ -1,14 +1,419 @@ #include #include -#include #include -#include #include +#include +#include namespace natten { namespace cuda { namespace gemm { -void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks15_align8( +void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks45_align8( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 22>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 8, 8>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks45_align4( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 22>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks45_align2( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 22>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks47_align8( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 23>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 8, 8>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks47_align4( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 23>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks47_align2( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 23>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks49_align8( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 24>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 8, 8>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks49_align4( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 24>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks49_align2( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 24>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks51_align8( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 25>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 8, 8>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks51_align4( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 25>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks51_align2( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 25>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks53_align8( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 26>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 8, 8>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks53_align4( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 26>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks53_align2( + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 26>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; + using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = InverseNeighborhood2D; + Kernel kernel; + kernel( +attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks55_align8( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -25,7 +430,7 @@ void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks15_align8( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 7>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 27>; using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 8, 8>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -35,7 +440,7 @@ void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks15_align8( attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks15_align4( +void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks55_align4( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -52,7 +457,7 @@ void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks15_align4( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 7>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 27>; using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -62,7 +467,7 @@ void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks15_align4( attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks15_align2( +void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks55_align2( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -79,7 +484,7 @@ void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks15_align2( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 7>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 27>; using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -89,7 +494,7 @@ void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks15_align2( attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks17_align8( +void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks57_align8( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -106,7 +511,7 @@ void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks17_align8( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 8>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 28>; using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 8, 8>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -116,7 +521,7 @@ void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks17_align8( attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks17_align4( +void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks57_align4( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -133,7 +538,7 @@ void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks17_align4( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 8>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 28>; using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -143,7 +548,7 @@ void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks17_align4( attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks17_align2( +void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks57_align2( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -160,7 +565,7 @@ void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks17_align2( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 8>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 28>; using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -170,7 +575,7 @@ void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks17_align2( attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks19_align8( +void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks59_align8( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -187,7 +592,7 @@ void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks19_align8( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 9>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 29>; using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 8, 8>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -197,7 +602,7 @@ void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks19_align8( attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks19_align4( +void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks59_align4( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -214,7 +619,7 @@ void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks19_align4( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 9>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 29>; using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -224,7 +629,7 @@ void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks19_align4( attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks19_align2( +void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks59_align2( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -241,7 +646,7 @@ void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks19_align2( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 9>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 29>; using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -251,7 +656,7 @@ void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks19_align2( attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks21_align8( +void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks61_align8( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -268,7 +673,7 @@ void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks21_align8( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 10>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 30>; using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 8, 8>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -278,7 +683,7 @@ void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks21_align8( attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks21_align4( +void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks61_align4( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -295,7 +700,7 @@ void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks21_align4( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 10>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 30>; using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -305,7 +710,7 @@ void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks21_align4( attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks21_align2( +void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks61_align2( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -322,7 +727,7 @@ void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks21_align2( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 10>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 30>; using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -332,7 +737,7 @@ void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks21_align2( attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks23_align8( +void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks63_align8( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -349,7 +754,7 @@ void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks23_align8( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 11>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 31>; using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 8, 8>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -359,7 +764,7 @@ void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks23_align8( attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks23_align4( +void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks63_align4( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -376,7 +781,7 @@ void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks23_align4( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 11>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 31>; using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -386,7 +791,7 @@ void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks23_align4( attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks23_align2( +void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks63_align2( void * attn_ptr, void * d_output_ptr, void * d_value_ptr, @@ -403,7 +808,7 @@ void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks23_align2( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 11>; + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 31>; using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; using DConfig = natten::gemm::detail::DTypeConfig; diff --git a/csrc/autogen/src/cuda/gemm/2d/sm80/source_31.cu b/csrc/autogen/src/cuda/gemm/2d/sm80/source_31.cu index 514bbdb..2e38804 100644 --- a/csrc/autogen/src/cuda/gemm/2d/sm80/source_31.cu +++ b/csrc/autogen/src/cuda/gemm/2d/sm80/source_31.cu @@ -1,418 +1,9 @@ #include #include -#include -#include -#include -#include namespace natten { namespace cuda { namespace gemm { -void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks25_align8( - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation, - float scale, - cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 12>; - using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; - using AConfig = natten::gemm::detail::AlignmentConfig<1, 8, 8>; - using DConfig = natten::gemm::detail::DTypeConfig; - using Kernel = InverseNeighborhood2D; - Kernel kernel; - kernel( -attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); -} - -void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks25_align4( - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation, - float scale, - cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 12>; - using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; - using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; - using DConfig = natten::gemm::detail::DTypeConfig; - using Kernel = InverseNeighborhood2D; - Kernel kernel; - kernel( -attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); -} - -void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks25_align2( - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation, - float scale, - cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 12>; - using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; - using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; - using DConfig = natten::gemm::detail::DTypeConfig; - using Kernel = InverseNeighborhood2D; - Kernel kernel; - kernel( -attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); -} - -void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks27_align8( - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation, - float scale, - cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 13>; - using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; - using AConfig = natten::gemm::detail::AlignmentConfig<1, 8, 8>; - using DConfig = natten::gemm::detail::DTypeConfig; - using Kernel = InverseNeighborhood2D; - Kernel kernel; - kernel( -attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); -} - -void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks27_align4( - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation, - float scale, - cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 13>; - using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; - using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; - using DConfig = natten::gemm::detail::DTypeConfig; - using Kernel = InverseNeighborhood2D; - Kernel kernel; - kernel( -attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); -} - -void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks27_align2( - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation, - float scale, - cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 13>; - using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; - using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; - using DConfig = natten::gemm::detail::DTypeConfig; - using Kernel = InverseNeighborhood2D; - Kernel kernel; - kernel( -attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); -} - -void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks29_align8( - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation, - float scale, - cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 14>; - using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; - using AConfig = natten::gemm::detail::AlignmentConfig<1, 8, 8>; - using DConfig = natten::gemm::detail::DTypeConfig; - using Kernel = InverseNeighborhood2D; - Kernel kernel; - kernel( -attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); -} - -void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks29_align4( - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation, - float scale, - cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 14>; - using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; - using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; - using DConfig = natten::gemm::detail::DTypeConfig; - using Kernel = InverseNeighborhood2D; - Kernel kernel; - kernel( -attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); -} - -void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks29_align2( - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation, - float scale, - cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 14>; - using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; - using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; - using DConfig = natten::gemm::detail::DTypeConfig; - using Kernel = InverseNeighborhood2D; - Kernel kernel; - kernel( -attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); -} - -void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks31_align8( - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation, - float scale, - cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 15>; - using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; - using AConfig = natten::gemm::detail::AlignmentConfig<1, 8, 8>; - using DConfig = natten::gemm::detail::DTypeConfig; - using Kernel = InverseNeighborhood2D; - Kernel kernel; - kernel( -attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); -} - -void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks31_align4( - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation, - float scale, - cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 15>; - using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; - using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; - using DConfig = natten::gemm::detail::DTypeConfig; - using Kernel = InverseNeighborhood2D; - Kernel kernel; - kernel( -attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); -} - -void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks31_align2( - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation, - float scale, - cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 15>; - using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; - using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; - using DConfig = natten::gemm::detail::DTypeConfig; - using Kernel = InverseNeighborhood2D; - Kernel kernel; - kernel( -attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); -} - -void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks33_align8( - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation, - float scale, - cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 16>; - using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; - using AConfig = natten::gemm::detail::AlignmentConfig<1, 8, 8>; - using DConfig = natten::gemm::detail::DTypeConfig; - using Kernel = InverseNeighborhood2D; - Kernel kernel; - kernel( -attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); -} - -void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks33_align4( - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation, - float scale, - cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 16>; - using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; - using AConfig = natten::gemm::detail::AlignmentConfig<1, 4, 4>; - using DConfig = natten::gemm::detail::DTypeConfig; - using Kernel = InverseNeighborhood2D; - Kernel kernel; - kernel( -attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); -} - -void na2d_in_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks33_align2( - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation, - float scale, - cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 8, 4, 16>; - using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; - using AConfig = natten::gemm::detail::AlignmentConfig<1, 2, 2>; - using DConfig = natten::gemm::detail::DTypeConfig; - using Kernel = InverseNeighborhood2D; - Kernel kernel; - kernel( -attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); -} - } } } diff --git a/csrc/autogen/src/cuda/gemm/2d/sm80/source_4.cu b/csrc/autogen/src/cuda/gemm/2d/sm80/source_4.cu index f2afa55..d880a78 100644 --- a/csrc/autogen/src/cuda/gemm/2d/sm80/source_4.cu +++ b/csrc/autogen/src/cuda/gemm/2d/sm80/source_4.cu @@ -1,14 +1,14 @@ #include #include -#include #include -#include #include +#include +#include namespace natten { namespace cuda { namespace gemm { -void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks31_align1( +void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks61_align1( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -26,7 +26,7 @@ void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks31_align1( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 16, 64, 64, 16, 16, 8, 8, 3, 17, 1, 15>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 16, 64, 64, 16, 16, 8, 8, 3, 33, 1, 30>; using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 1, 1>; if (natten::kEnableGemmTF32) { @@ -46,7 +46,7 @@ query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, a } } -void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks33_align4( +void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks63_align4( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -64,7 +64,7 @@ void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks33_align4( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 16, 64, 64, 16, 16, 8, 8, 3, 17, 1, 16>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 16, 64, 64, 16, 16, 8, 8, 3, 33, 1, 31>; using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; using AConfig = natten::gemm::detail::AlignmentConfig<4, 4, 1>; if (natten::kEnableGemmTF32) { @@ -84,7 +84,7 @@ query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, a } } -void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks33_align2( +void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks63_align2( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -102,7 +102,7 @@ void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks33_align2( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 16, 64, 64, 16, 16, 8, 8, 3, 17, 1, 16>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 16, 64, 64, 16, 16, 8, 8, 3, 33, 1, 31>; using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; using AConfig = natten::gemm::detail::AlignmentConfig<2, 2, 1>; if (natten::kEnableGemmTF32) { @@ -122,7 +122,7 @@ query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, a } } -void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks33_align1( +void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks63_align1( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -140,7 +140,7 @@ void na2d_pn_cuda_gemm_float_128x128x16_64x64x16_16x8x8_3_sm80_ks33_align1( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 16, 64, 64, 16, 16, 8, 8, 3, 17, 1, 16>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 16, 64, 64, 16, 16, 8, 8, 3, 33, 1, 31>; using ArchConfig = natten::gemm::detail::ArchArgs<80, float>; using AConfig = natten::gemm::detail::AlignmentConfig<1, 1, 1>; if (natten::kEnableGemmTF32) { @@ -468,6 +468,426 @@ void na2d_pn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks9_align4( query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } +void na2d_pn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks9_align2( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 7, 1, 4>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<2, 2, 1>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_pn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks11_align8( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 6, 1, 5>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<8, 8, 1>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_pn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks11_align4( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 6, 1, 5>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<4, 4, 1>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_pn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks11_align2( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 6, 1, 5>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<2, 2, 1>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_pn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks13_align8( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 10, 1, 6>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<8, 8, 1>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_pn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks13_align4( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 10, 1, 6>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<4, 4, 1>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_pn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks13_align2( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 10, 1, 6>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<2, 2, 1>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks15_align8( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 9, 1, 7>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<8, 8, 1>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks15_align4( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 9, 1, 7>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<4, 4, 1>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks15_align2( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 9, 1, 7>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<2, 2, 1>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks17_align8( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 11, 1, 8>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<8, 8, 1>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks17_align4( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 11, 1, 8>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<4, 4, 1>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks17_align2( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 11, 1, 8>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<2, 2, 1>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks19_align8( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 13, 1, 9>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<8, 8, 1>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks19_align4( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 13, 1, 9>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<4, 4, 1>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + } } } diff --git a/csrc/autogen/src/cuda/gemm/2d/sm80/source_5.cu b/csrc/autogen/src/cuda/gemm/2d/sm80/source_5.cu index dc28c1b..d4f2a77 100644 --- a/csrc/autogen/src/cuda/gemm/2d/sm80/source_5.cu +++ b/csrc/autogen/src/cuda/gemm/2d/sm80/source_5.cu @@ -1,14 +1,14 @@ #include #include -#include #include -#include #include +#include +#include namespace natten { namespace cuda { namespace gemm { -void na2d_pn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks9_align2( +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks19_align2( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -26,7 +26,7 @@ void na2d_pn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks9_align2( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 7, 1, 4>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 13, 1, 9>; using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; using AConfig = natten::gemm::detail::AlignmentConfig<2, 2, 1>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -36,7 +36,7 @@ void na2d_pn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks9_align2( query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_pn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks11_align8( +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks21_align8( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -54,7 +54,7 @@ void na2d_pn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks11_align8( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 6, 1, 5>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 12, 1, 10>; using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; using AConfig = natten::gemm::detail::AlignmentConfig<8, 8, 1>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -64,7 +64,7 @@ void na2d_pn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks11_align8( query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_pn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks11_align4( +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks21_align4( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -82,7 +82,7 @@ void na2d_pn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks11_align4( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 6, 1, 5>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 12, 1, 10>; using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; using AConfig = natten::gemm::detail::AlignmentConfig<4, 4, 1>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -92,7 +92,7 @@ void na2d_pn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks11_align4( query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_pn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks11_align2( +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks21_align2( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -110,7 +110,7 @@ void na2d_pn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks11_align2( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 6, 1, 5>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 12, 1, 10>; using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; using AConfig = natten::gemm::detail::AlignmentConfig<2, 2, 1>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -120,7 +120,7 @@ void na2d_pn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks11_align2( query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_pn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks13_align8( +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks23_align8( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -138,7 +138,7 @@ void na2d_pn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks13_align8( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 10, 1, 6>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 14, 1, 11>; using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; using AConfig = natten::gemm::detail::AlignmentConfig<8, 8, 1>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -148,7 +148,7 @@ void na2d_pn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks13_align8( query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_pn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks13_align4( +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks23_align4( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -166,7 +166,7 @@ void na2d_pn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks13_align4( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 10, 1, 6>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 14, 1, 11>; using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; using AConfig = natten::gemm::detail::AlignmentConfig<4, 4, 1>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -176,7 +176,7 @@ void na2d_pn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks13_align4( query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_pn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks13_align2( +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks23_align2( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -194,7 +194,7 @@ void na2d_pn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks13_align2( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 10, 1, 6>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 14, 1, 11>; using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; using AConfig = natten::gemm::detail::AlignmentConfig<2, 2, 1>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -204,7 +204,7 @@ void na2d_pn_cuda_gemm_half_64x64x32_32x32x32_16x8x16_3_sm80_ks13_align2( query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks15_align8( +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks25_align8( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -222,7 +222,7 @@ void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks15_align8( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 9, 1, 7>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 13, 1, 12>; using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; using AConfig = natten::gemm::detail::AlignmentConfig<8, 8, 1>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -232,7 +232,7 @@ void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks15_align8( query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks15_align4( +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks25_align4( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -250,7 +250,7 @@ void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks15_align4( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 9, 1, 7>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 13, 1, 12>; using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; using AConfig = natten::gemm::detail::AlignmentConfig<4, 4, 1>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -260,7 +260,7 @@ void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks15_align4( query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks15_align2( +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks25_align2( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -278,7 +278,7 @@ void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks15_align2( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 9, 1, 7>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 13, 1, 12>; using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; using AConfig = natten::gemm::detail::AlignmentConfig<2, 2, 1>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -288,7 +288,7 @@ void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks15_align2( query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks17_align8( +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks27_align8( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -306,7 +306,7 @@ void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks17_align8( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 11, 1, 8>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 14, 1, 13>; using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; using AConfig = natten::gemm::detail::AlignmentConfig<8, 8, 1>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -316,7 +316,7 @@ void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks17_align8( query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks17_align4( +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks27_align4( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -334,7 +334,7 @@ void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks17_align4( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 11, 1, 8>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 14, 1, 13>; using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; using AConfig = natten::gemm::detail::AlignmentConfig<4, 4, 1>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -344,7 +344,7 @@ void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks17_align4( query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks17_align2( +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks27_align2( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -362,7 +362,7 @@ void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks17_align2( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 11, 1, 8>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 14, 1, 13>; using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; using AConfig = natten::gemm::detail::AlignmentConfig<2, 2, 1>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -372,7 +372,7 @@ void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks17_align2( query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks19_align8( +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks29_align8( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -390,7 +390,7 @@ void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks19_align8( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 13, 1, 9>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 17, 1, 14>; using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; using AConfig = natten::gemm::detail::AlignmentConfig<8, 8, 1>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -400,7 +400,7 @@ void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks19_align8( query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks19_align4( +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks29_align4( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -418,7 +418,427 @@ void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks19_align4( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 13, 1, 9>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 17, 1, 14>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<4, 4, 1>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks29_align2( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 17, 1, 14>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<2, 2, 1>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks31_align8( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 17, 1, 15>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<8, 8, 1>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks31_align4( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 17, 1, 15>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<4, 4, 1>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks31_align2( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 17, 1, 15>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<2, 2, 1>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks33_align8( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 17, 1, 16>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<8, 8, 1>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks33_align4( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 17, 1, 16>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<4, 4, 1>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks33_align2( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 17, 1, 16>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<2, 2, 1>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks35_align8( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 22, 1, 17>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<8, 8, 1>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks35_align4( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 22, 1, 17>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<4, 4, 1>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks35_align2( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 22, 1, 17>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<2, 2, 1>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks37_align8( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 22, 1, 18>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<8, 8, 1>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks37_align4( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 22, 1, 18>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<4, 4, 1>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks37_align2( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 22, 1, 18>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<2, 2, 1>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks39_align8( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 23, 1, 19>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<8, 8, 1>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks39_align4( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 23, 1, 19>; using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; using AConfig = natten::gemm::detail::AlignmentConfig<4, 4, 1>; using DConfig = natten::gemm::detail::DTypeConfig; diff --git a/csrc/autogen/src/cuda/gemm/2d/sm80/source_6.cu b/csrc/autogen/src/cuda/gemm/2d/sm80/source_6.cu index bf8e9bc..f2df9a3 100644 --- a/csrc/autogen/src/cuda/gemm/2d/sm80/source_6.cu +++ b/csrc/autogen/src/cuda/gemm/2d/sm80/source_6.cu @@ -1,14 +1,434 @@ #include #include -#include #include -#include #include +#include +#include namespace natten { namespace cuda { namespace gemm { -void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks19_align2( +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks39_align2( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 23, 1, 19>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<2, 2, 1>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks41_align8( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 22, 1, 20>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<8, 8, 1>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks41_align4( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 22, 1, 20>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<4, 4, 1>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks41_align2( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 22, 1, 20>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<2, 2, 1>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks43_align8( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 22, 1, 21>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<8, 8, 1>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks43_align4( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 22, 1, 21>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<4, 4, 1>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks43_align2( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 22, 1, 21>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<2, 2, 1>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks45_align8( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 23, 1, 22>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<8, 8, 1>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks45_align4( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 23, 1, 22>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<4, 4, 1>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks45_align2( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 23, 1, 22>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<2, 2, 1>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks47_align8( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 33, 1, 23>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<8, 8, 1>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks47_align4( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 33, 1, 23>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<4, 4, 1>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks47_align2( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 33, 1, 23>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<2, 2, 1>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks49_align8( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 33, 1, 24>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<8, 8, 1>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks49_align4( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 33, 1, 24>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; + using AConfig = natten::gemm::detail::AlignmentConfig<4, 4, 1>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks49_align2( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -26,7 +446,7 @@ void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks19_align2( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 13, 1, 9>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 33, 1, 24>; using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; using AConfig = natten::gemm::detail::AlignmentConfig<2, 2, 1>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -36,7 +456,7 @@ void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks19_align2( query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks21_align8( +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks51_align8( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -54,7 +474,7 @@ void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks21_align8( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 12, 1, 10>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 33, 1, 25>; using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; using AConfig = natten::gemm::detail::AlignmentConfig<8, 8, 1>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -64,7 +484,7 @@ void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks21_align8( query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks21_align4( +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks51_align4( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -82,7 +502,7 @@ void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks21_align4( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 12, 1, 10>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 33, 1, 25>; using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; using AConfig = natten::gemm::detail::AlignmentConfig<4, 4, 1>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -92,7 +512,7 @@ void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks21_align4( query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks21_align2( +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks51_align2( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -110,7 +530,7 @@ void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks21_align2( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 12, 1, 10>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 33, 1, 25>; using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; using AConfig = natten::gemm::detail::AlignmentConfig<2, 2, 1>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -120,7 +540,7 @@ void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks21_align2( query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks23_align8( +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks53_align8( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -138,7 +558,7 @@ void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks23_align8( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 14, 1, 11>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 33, 1, 26>; using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; using AConfig = natten::gemm::detail::AlignmentConfig<8, 8, 1>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -148,7 +568,7 @@ void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks23_align8( query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks23_align4( +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks53_align4( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -166,7 +586,7 @@ void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks23_align4( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 14, 1, 11>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 33, 1, 26>; using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; using AConfig = natten::gemm::detail::AlignmentConfig<4, 4, 1>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -176,7 +596,7 @@ void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks23_align4( query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks23_align2( +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks53_align2( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -194,7 +614,7 @@ void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks23_align2( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 14, 1, 11>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 33, 1, 26>; using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; using AConfig = natten::gemm::detail::AlignmentConfig<2, 2, 1>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -204,7 +624,7 @@ void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks23_align2( query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks25_align8( +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks55_align8( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -222,7 +642,7 @@ void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks25_align8( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 13, 1, 12>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 33, 1, 27>; using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; using AConfig = natten::gemm::detail::AlignmentConfig<8, 8, 1>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -232,7 +652,7 @@ void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks25_align8( query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks25_align4( +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks55_align4( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -250,7 +670,7 @@ void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks25_align4( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 13, 1, 12>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 33, 1, 27>; using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; using AConfig = natten::gemm::detail::AlignmentConfig<4, 4, 1>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -260,7 +680,7 @@ void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks25_align4( query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks25_align2( +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks55_align2( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -278,7 +698,7 @@ void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks25_align2( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 13, 1, 12>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 33, 1, 27>; using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; using AConfig = natten::gemm::detail::AlignmentConfig<2, 2, 1>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -288,7 +708,7 @@ void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks25_align2( query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks27_align8( +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks57_align8( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -306,7 +726,7 @@ void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks27_align8( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 14, 1, 13>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 33, 1, 28>; using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; using AConfig = natten::gemm::detail::AlignmentConfig<8, 8, 1>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -316,7 +736,7 @@ void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks27_align8( query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks27_align4( +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks57_align4( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -334,7 +754,7 @@ void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks27_align4( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 14, 1, 13>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 33, 1, 28>; using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; using AConfig = natten::gemm::detail::AlignmentConfig<4, 4, 1>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -344,7 +764,7 @@ void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks27_align4( query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks27_align2( +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks57_align2( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -362,7 +782,7 @@ void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks27_align2( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 14, 1, 13>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 33, 1, 28>; using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; using AConfig = natten::gemm::detail::AlignmentConfig<2, 2, 1>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -372,7 +792,7 @@ void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks27_align2( query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks29_align8( +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks59_align8( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -390,7 +810,7 @@ void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks29_align8( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 17, 1, 14>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 33, 1, 29>; using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; using AConfig = natten::gemm::detail::AlignmentConfig<8, 8, 1>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -400,7 +820,7 @@ void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks29_align8( query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks29_align4( +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks59_align4( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -418,7 +838,7 @@ void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks29_align4( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 17, 1, 14>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 33, 1, 29>; using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; using AConfig = natten::gemm::detail::AlignmentConfig<4, 4, 1>; using DConfig = natten::gemm::detail::DTypeConfig; diff --git a/csrc/autogen/src/cuda/gemm/2d/sm80/source_7.cu b/csrc/autogen/src/cuda/gemm/2d/sm80/source_7.cu index 3442ad9..29316d2 100644 --- a/csrc/autogen/src/cuda/gemm/2d/sm80/source_7.cu +++ b/csrc/autogen/src/cuda/gemm/2d/sm80/source_7.cu @@ -1,14 +1,14 @@ #include #include -#include #include -#include #include +#include +#include namespace natten { namespace cuda { namespace gemm { -void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks29_align2( +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks59_align2( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -26,7 +26,7 @@ void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks29_align2( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 17, 1, 14>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 33, 1, 29>; using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; using AConfig = natten::gemm::detail::AlignmentConfig<2, 2, 1>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -36,7 +36,7 @@ void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks29_align2( query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks31_align8( +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks61_align8( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -54,7 +54,7 @@ void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks31_align8( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 17, 1, 15>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 33, 1, 30>; using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; using AConfig = natten::gemm::detail::AlignmentConfig<8, 8, 1>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -64,7 +64,7 @@ void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks31_align8( query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks31_align4( +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks61_align4( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -82,7 +82,7 @@ void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks31_align4( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 17, 1, 15>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 33, 1, 30>; using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; using AConfig = natten::gemm::detail::AlignmentConfig<4, 4, 1>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -92,7 +92,7 @@ void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks31_align4( query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks31_align2( +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks61_align2( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -110,7 +110,7 @@ void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks31_align2( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 17, 1, 15>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 33, 1, 30>; using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; using AConfig = natten::gemm::detail::AlignmentConfig<2, 2, 1>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -120,7 +120,7 @@ void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks31_align2( query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks33_align8( +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks63_align8( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -138,7 +138,7 @@ void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks33_align8( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 17, 1, 16>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 33, 1, 31>; using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; using AConfig = natten::gemm::detail::AlignmentConfig<8, 8, 1>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -148,7 +148,7 @@ void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks33_align8( query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks33_align4( +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks63_align4( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -166,7 +166,7 @@ void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks33_align4( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 17, 1, 16>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 33, 1, 31>; using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; using AConfig = natten::gemm::detail::AlignmentConfig<4, 4, 1>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -176,7 +176,7 @@ void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks33_align4( query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks33_align2( +void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks63_align2( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -194,7 +194,7 @@ void na2d_pn_cuda_gemm_half_128x128x32_64x64x32_16x8x16_3_sm80_ks33_align2( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 17, 1, 16>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 33, 1, 31>; using ArchConfig = natten::gemm::detail::ArchArgs<80, half>; using AConfig = natten::gemm::detail::AlignmentConfig<2, 2, 1>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -428,6 +428,426 @@ void na2d_pn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks7_align4( query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } +void na2d_pn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks7_align2( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 5, 1, 3>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; + using AConfig = natten::gemm::detail::AlignmentConfig<2, 2, 1>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_pn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks9_align8( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 7, 1, 4>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; + using AConfig = natten::gemm::detail::AlignmentConfig<8, 8, 1>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_pn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks9_align4( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 7, 1, 4>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; + using AConfig = natten::gemm::detail::AlignmentConfig<4, 4, 1>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_pn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks9_align2( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 7, 1, 4>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; + using AConfig = natten::gemm::detail::AlignmentConfig<2, 2, 1>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_pn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks11_align8( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 6, 1, 5>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; + using AConfig = natten::gemm::detail::AlignmentConfig<8, 8, 1>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_pn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks11_align4( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 6, 1, 5>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; + using AConfig = natten::gemm::detail::AlignmentConfig<4, 4, 1>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_pn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks11_align2( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 6, 1, 5>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; + using AConfig = natten::gemm::detail::AlignmentConfig<2, 2, 1>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_pn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks13_align8( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 10, 1, 6>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; + using AConfig = natten::gemm::detail::AlignmentConfig<8, 8, 1>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_pn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks13_align4( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 10, 1, 6>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; + using AConfig = natten::gemm::detail::AlignmentConfig<4, 4, 1>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_pn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks13_align2( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 10, 1, 6>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; + using AConfig = natten::gemm::detail::AlignmentConfig<2, 2, 1>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks15_align8( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 9, 1, 7>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; + using AConfig = natten::gemm::detail::AlignmentConfig<8, 8, 1>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks15_align4( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 9, 1, 7>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; + using AConfig = natten::gemm::detail::AlignmentConfig<4, 4, 1>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks15_align2( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 9, 1, 7>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; + using AConfig = natten::gemm::detail::AlignmentConfig<2, 2, 1>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks17_align8( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 11, 1, 8>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; + using AConfig = natten::gemm::detail::AlignmentConfig<8, 8, 1>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks17_align4( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 11, 1, 8>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; + using AConfig = natten::gemm::detail::AlignmentConfig<4, 4, 1>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + } } } diff --git a/csrc/autogen/src/cuda/gemm/2d/sm80/source_8.cu b/csrc/autogen/src/cuda/gemm/2d/sm80/source_8.cu index 2fc10a8..e0437f7 100644 --- a/csrc/autogen/src/cuda/gemm/2d/sm80/source_8.cu +++ b/csrc/autogen/src/cuda/gemm/2d/sm80/source_8.cu @@ -1,14 +1,14 @@ #include #include -#include #include -#include #include +#include +#include namespace natten { namespace cuda { namespace gemm { -void na2d_pn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks7_align2( +void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks17_align2( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -26,7 +26,7 @@ void na2d_pn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks7_align2( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 5, 1, 3>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 11, 1, 8>; using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; using AConfig = natten::gemm::detail::AlignmentConfig<2, 2, 1>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -36,7 +36,7 @@ void na2d_pn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks7_align2( query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_pn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks9_align8( +void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks19_align8( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -54,7 +54,7 @@ void na2d_pn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks9_align8( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 7, 1, 4>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 13, 1, 9>; using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; using AConfig = natten::gemm::detail::AlignmentConfig<8, 8, 1>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -64,7 +64,7 @@ void na2d_pn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks9_align8( query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_pn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks9_align4( +void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks19_align4( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -82,7 +82,7 @@ void na2d_pn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks9_align4( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 7, 1, 4>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 13, 1, 9>; using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; using AConfig = natten::gemm::detail::AlignmentConfig<4, 4, 1>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -92,7 +92,7 @@ void na2d_pn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks9_align4( query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_pn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks9_align2( +void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks19_align2( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -110,7 +110,7 @@ void na2d_pn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks9_align2( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 7, 1, 4>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 13, 1, 9>; using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; using AConfig = natten::gemm::detail::AlignmentConfig<2, 2, 1>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -120,7 +120,7 @@ void na2d_pn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks9_align2( query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_pn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks11_align8( +void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks21_align8( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -138,7 +138,7 @@ void na2d_pn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks11_align8( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 6, 1, 5>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 12, 1, 10>; using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; using AConfig = natten::gemm::detail::AlignmentConfig<8, 8, 1>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -148,7 +148,7 @@ void na2d_pn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks11_align8( query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_pn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks11_align4( +void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks21_align4( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -166,7 +166,7 @@ void na2d_pn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks11_align4( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 6, 1, 5>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 12, 1, 10>; using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; using AConfig = natten::gemm::detail::AlignmentConfig<4, 4, 1>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -176,7 +176,7 @@ void na2d_pn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks11_align4( query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_pn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks11_align2( +void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks21_align2( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -194,7 +194,7 @@ void na2d_pn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks11_align2( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 6, 1, 5>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 12, 1, 10>; using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; using AConfig = natten::gemm::detail::AlignmentConfig<2, 2, 1>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -204,7 +204,7 @@ void na2d_pn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks11_align2( query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_pn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks13_align8( +void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks23_align8( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -222,7 +222,7 @@ void na2d_pn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks13_align8( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 10, 1, 6>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 14, 1, 11>; using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; using AConfig = natten::gemm::detail::AlignmentConfig<8, 8, 1>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -232,7 +232,7 @@ void na2d_pn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks13_align8( query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_pn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks13_align4( +void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks23_align4( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -250,7 +250,7 @@ void na2d_pn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks13_align4( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 10, 1, 6>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 14, 1, 11>; using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; using AConfig = natten::gemm::detail::AlignmentConfig<4, 4, 1>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -260,7 +260,7 @@ void na2d_pn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks13_align4( query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_pn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks13_align2( +void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks23_align2( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -278,7 +278,7 @@ void na2d_pn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks13_align2( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<64, 64, 32, 32, 32, 32, 16, 8, 16, 3, 10, 1, 6>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 14, 1, 11>; using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; using AConfig = natten::gemm::detail::AlignmentConfig<2, 2, 1>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -288,7 +288,7 @@ void na2d_pn_cuda_gemm_bfloat16_64x64x32_32x32x32_16x8x16_3_sm80_ks13_align2( query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks15_align8( +void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks25_align8( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -306,7 +306,7 @@ void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks15_align8( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 9, 1, 7>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 13, 1, 12>; using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; using AConfig = natten::gemm::detail::AlignmentConfig<8, 8, 1>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -316,7 +316,7 @@ void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks15_align8( query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks15_align4( +void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks25_align4( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -334,7 +334,7 @@ void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks15_align4( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 9, 1, 7>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 13, 1, 12>; using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; using AConfig = natten::gemm::detail::AlignmentConfig<4, 4, 1>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -344,7 +344,7 @@ void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks15_align4( query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks15_align2( +void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks25_align2( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -362,7 +362,7 @@ void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks15_align2( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 9, 1, 7>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 13, 1, 12>; using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; using AConfig = natten::gemm::detail::AlignmentConfig<2, 2, 1>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -372,7 +372,7 @@ void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks15_align2( query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks17_align8( +void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks27_align8( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -390,7 +390,7 @@ void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks17_align8( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 11, 1, 8>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 14, 1, 13>; using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; using AConfig = natten::gemm::detail::AlignmentConfig<8, 8, 1>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -400,7 +400,7 @@ void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks17_align8( query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks17_align4( +void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks27_align4( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -418,7 +418,427 @@ void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks17_align4( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 11, 1, 8>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 14, 1, 13>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; + using AConfig = natten::gemm::detail::AlignmentConfig<4, 4, 1>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks27_align2( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 14, 1, 13>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; + using AConfig = natten::gemm::detail::AlignmentConfig<2, 2, 1>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks29_align8( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 17, 1, 14>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; + using AConfig = natten::gemm::detail::AlignmentConfig<8, 8, 1>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks29_align4( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 17, 1, 14>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; + using AConfig = natten::gemm::detail::AlignmentConfig<4, 4, 1>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks29_align2( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 17, 1, 14>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; + using AConfig = natten::gemm::detail::AlignmentConfig<2, 2, 1>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks31_align8( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 17, 1, 15>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; + using AConfig = natten::gemm::detail::AlignmentConfig<8, 8, 1>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks31_align4( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 17, 1, 15>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; + using AConfig = natten::gemm::detail::AlignmentConfig<4, 4, 1>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks31_align2( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 17, 1, 15>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; + using AConfig = natten::gemm::detail::AlignmentConfig<2, 2, 1>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks33_align8( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 17, 1, 16>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; + using AConfig = natten::gemm::detail::AlignmentConfig<8, 8, 1>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks33_align4( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 17, 1, 16>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; + using AConfig = natten::gemm::detail::AlignmentConfig<4, 4, 1>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks33_align2( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 17, 1, 16>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; + using AConfig = natten::gemm::detail::AlignmentConfig<2, 2, 1>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks35_align8( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 22, 1, 17>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; + using AConfig = natten::gemm::detail::AlignmentConfig<8, 8, 1>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks35_align4( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 22, 1, 17>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; + using AConfig = natten::gemm::detail::AlignmentConfig<4, 4, 1>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks35_align2( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 22, 1, 17>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; + using AConfig = natten::gemm::detail::AlignmentConfig<2, 2, 1>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks37_align8( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 22, 1, 18>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; + using AConfig = natten::gemm::detail::AlignmentConfig<8, 8, 1>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks37_align4( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 22, 1, 18>; using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; using AConfig = natten::gemm::detail::AlignmentConfig<4, 4, 1>; using DConfig = natten::gemm::detail::DTypeConfig; diff --git a/csrc/autogen/src/cuda/gemm/2d/sm80/source_9.cu b/csrc/autogen/src/cuda/gemm/2d/sm80/source_9.cu index 46788b7..487e495 100644 --- a/csrc/autogen/src/cuda/gemm/2d/sm80/source_9.cu +++ b/csrc/autogen/src/cuda/gemm/2d/sm80/source_9.cu @@ -1,14 +1,434 @@ #include #include -#include #include -#include #include +#include +#include namespace natten { namespace cuda { namespace gemm { -void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks17_align2( +void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks37_align2( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 22, 1, 18>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; + using AConfig = natten::gemm::detail::AlignmentConfig<2, 2, 1>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks39_align8( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 23, 1, 19>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; + using AConfig = natten::gemm::detail::AlignmentConfig<8, 8, 1>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks39_align4( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 23, 1, 19>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; + using AConfig = natten::gemm::detail::AlignmentConfig<4, 4, 1>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks39_align2( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 23, 1, 19>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; + using AConfig = natten::gemm::detail::AlignmentConfig<2, 2, 1>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks41_align8( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 22, 1, 20>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; + using AConfig = natten::gemm::detail::AlignmentConfig<8, 8, 1>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks41_align4( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 22, 1, 20>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; + using AConfig = natten::gemm::detail::AlignmentConfig<4, 4, 1>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks41_align2( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 22, 1, 20>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; + using AConfig = natten::gemm::detail::AlignmentConfig<2, 2, 1>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks43_align8( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 22, 1, 21>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; + using AConfig = natten::gemm::detail::AlignmentConfig<8, 8, 1>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks43_align4( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 22, 1, 21>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; + using AConfig = natten::gemm::detail::AlignmentConfig<4, 4, 1>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks43_align2( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 22, 1, 21>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; + using AConfig = natten::gemm::detail::AlignmentConfig<2, 2, 1>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks45_align8( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 23, 1, 22>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; + using AConfig = natten::gemm::detail::AlignmentConfig<8, 8, 1>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks45_align4( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 23, 1, 22>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; + using AConfig = natten::gemm::detail::AlignmentConfig<4, 4, 1>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks45_align2( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 23, 1, 22>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; + using AConfig = natten::gemm::detail::AlignmentConfig<2, 2, 1>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks47_align8( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 33, 1, 23>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; + using AConfig = natten::gemm::detail::AlignmentConfig<8, 8, 1>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks47_align4( + void * query_ptr, + void * key_ptr, + void * attn_ptr, + void * bias_ptr, + int batch_size, + int heads, + int height, + int width, + int dim, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int kernel_size, + int dilation, + float scale, + cudaStream_t stream) { + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 33, 1, 23>; + using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; + using AConfig = natten::gemm::detail::AlignmentConfig<4, 4, 1>; + using DConfig = natten::gemm::detail::DTypeConfig; + using Kernel = PointwiseNeighborhood2D; + Kernel kernel; + kernel( +query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); +} + +void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks47_align2( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -26,7 +446,7 @@ void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks17_align2( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 11, 1, 8>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 33, 1, 23>; using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; using AConfig = natten::gemm::detail::AlignmentConfig<2, 2, 1>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -36,7 +456,7 @@ void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks17_align2( query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks19_align8( +void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks49_align8( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -54,7 +474,7 @@ void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks19_align8( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 13, 1, 9>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 33, 1, 24>; using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; using AConfig = natten::gemm::detail::AlignmentConfig<8, 8, 1>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -64,7 +484,7 @@ void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks19_align8( query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks19_align4( +void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks49_align4( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -82,7 +502,7 @@ void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks19_align4( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 13, 1, 9>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 33, 1, 24>; using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; using AConfig = natten::gemm::detail::AlignmentConfig<4, 4, 1>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -92,7 +512,7 @@ void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks19_align4( query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks19_align2( +void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks49_align2( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -110,7 +530,7 @@ void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks19_align2( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 13, 1, 9>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 33, 1, 24>; using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; using AConfig = natten::gemm::detail::AlignmentConfig<2, 2, 1>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -120,7 +540,7 @@ void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks19_align2( query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks21_align8( +void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks51_align8( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -138,7 +558,7 @@ void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks21_align8( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 12, 1, 10>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 33, 1, 25>; using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; using AConfig = natten::gemm::detail::AlignmentConfig<8, 8, 1>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -148,7 +568,7 @@ void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks21_align8( query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks21_align4( +void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks51_align4( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -166,7 +586,7 @@ void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks21_align4( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 12, 1, 10>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 33, 1, 25>; using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; using AConfig = natten::gemm::detail::AlignmentConfig<4, 4, 1>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -176,7 +596,7 @@ void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks21_align4( query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks21_align2( +void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks51_align2( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -194,7 +614,7 @@ void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks21_align2( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 12, 1, 10>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 33, 1, 25>; using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; using AConfig = natten::gemm::detail::AlignmentConfig<2, 2, 1>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -204,7 +624,7 @@ void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks21_align2( query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks23_align8( +void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks53_align8( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -222,7 +642,7 @@ void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks23_align8( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 14, 1, 11>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 33, 1, 26>; using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; using AConfig = natten::gemm::detail::AlignmentConfig<8, 8, 1>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -232,7 +652,7 @@ void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks23_align8( query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks23_align4( +void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks53_align4( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -250,7 +670,7 @@ void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks23_align4( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 14, 1, 11>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 33, 1, 26>; using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; using AConfig = natten::gemm::detail::AlignmentConfig<4, 4, 1>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -260,7 +680,7 @@ void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks23_align4( query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks23_align2( +void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks53_align2( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -278,7 +698,7 @@ void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks23_align2( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 14, 1, 11>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 33, 1, 26>; using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; using AConfig = natten::gemm::detail::AlignmentConfig<2, 2, 1>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -288,7 +708,7 @@ void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks23_align2( query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks25_align8( +void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks55_align8( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -306,7 +726,7 @@ void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks25_align8( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 13, 1, 12>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 33, 1, 27>; using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; using AConfig = natten::gemm::detail::AlignmentConfig<8, 8, 1>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -316,7 +736,7 @@ void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks25_align8( query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks25_align4( +void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks55_align4( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -334,7 +754,7 @@ void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks25_align4( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 13, 1, 12>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 33, 1, 27>; using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; using AConfig = natten::gemm::detail::AlignmentConfig<4, 4, 1>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -344,7 +764,7 @@ void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks25_align4( query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks25_align2( +void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks55_align2( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -362,7 +782,7 @@ void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks25_align2( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 13, 1, 12>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 33, 1, 27>; using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; using AConfig = natten::gemm::detail::AlignmentConfig<2, 2, 1>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -372,7 +792,7 @@ void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks25_align2( query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks27_align8( +void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks57_align8( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -390,7 +810,7 @@ void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks27_align8( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 14, 1, 13>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 33, 1, 28>; using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; using AConfig = natten::gemm::detail::AlignmentConfig<8, 8, 1>; using DConfig = natten::gemm::detail::DTypeConfig; @@ -400,7 +820,7 @@ void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks27_align8( query_ptr, key_ptr, attn_ptr, bias_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation, scale, stream); } -void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks27_align4( +void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks57_align4( void * query_ptr, void * key_ptr, void * attn_ptr, @@ -418,7 +838,7 @@ void na2d_pn_cuda_gemm_bfloat16_128x128x32_64x64x32_16x8x16_3_sm80_ks27_align4( int dilation, float scale, cudaStream_t stream) { - using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 14, 1, 13>; + using GConfig = natten::gemm::detail::GemmConfig2D<128, 128, 32, 64, 64, 32, 16, 8, 16, 3, 33, 1, 28>; using ArchConfig = natten::gemm::detail::ArchArgs<80, bfloat16>; using AConfig = natten::gemm::detail::AlignmentConfig<4, 4, 1>; using DConfig = natten::gemm::detail::DTypeConfig; diff --git a/csrc/autogen/src/cuda/naive/source_0.cu b/csrc/autogen/src/cuda/naive/source_0.cu index 9a253d0..6197797 100644 --- a/csrc/autogen/src/cuda/naive/source_0.cu +++ b/csrc/autogen/src/cuda/naive/source_0.cu @@ -2,1013 +2,1155 @@ #include #include #include -#include #include -#include -#include #include +#include +#include +#include +#include namespace natten { namespace cuda { namespace naive { -void na1d_pn_cuda_naive_double_ks_any_di_any( - const int cc, +void na1d_pn_cuda_naive_double_cm_0( + int32_t cc, cudaStream_t stream, + bool is_grad, void * query_ptr, void * key_ptr, void * attn_ptr, - int batch_size, - int heads, - int length, - int dim, + int32_t batch_size, + int32_t heads, + int32_t length, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; + const std::tuple& kernel_size, + const std::tuple& dilation) { + using Arguments = natten::naive::ArgumentPack1D; using Kernel = PointwiseNeighborhood1D; Kernel kernel; kernel( -cc, stream, query_ptr, key_ptr, attn_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); +cc, stream, is_grad, query_ptr, key_ptr, attn_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); } -void na1d_pn_cuda_naive_double_ks_any_di_1( - const int cc, +void na1d_pn_cuda_naive_double_cm_1( + int32_t cc, cudaStream_t stream, + bool is_grad, void * query_ptr, void * key_ptr, void * attn_ptr, - int batch_size, - int heads, - int length, - int dim, + int32_t batch_size, + int32_t heads, + int32_t length, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; + const std::tuple& kernel_size, + const std::tuple& dilation) { + using Arguments = natten::naive::ArgumentPack1D; using Kernel = PointwiseNeighborhood1D; Kernel kernel; kernel( -cc, stream, query_ptr, key_ptr, attn_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); +cc, stream, is_grad, query_ptr, key_ptr, attn_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); } -void na1d_pn_cuda_naive_double_ks_3_di_any( - const int cc, +void na1d_pn_cuda_naive_float_cm_0( + int32_t cc, cudaStream_t stream, + bool is_grad, void * query_ptr, void * key_ptr, void * attn_ptr, - int batch_size, - int heads, - int length, - int dim, + int32_t batch_size, + int32_t heads, + int32_t length, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; + const std::tuple& kernel_size, + const std::tuple& dilation) { + using Arguments = natten::naive::ArgumentPack1D; using Kernel = PointwiseNeighborhood1D; Kernel kernel; kernel( -cc, stream, query_ptr, key_ptr, attn_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); +cc, stream, is_grad, query_ptr, key_ptr, attn_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); } -void na1d_pn_cuda_naive_double_ks_3_di_1( - const int cc, +void na1d_pn_cuda_naive_float_cm_1( + int32_t cc, cudaStream_t stream, + bool is_grad, void * query_ptr, void * key_ptr, void * attn_ptr, - int batch_size, - int heads, - int length, - int dim, + int32_t batch_size, + int32_t heads, + int32_t length, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; + const std::tuple& kernel_size, + const std::tuple& dilation) { + using Arguments = natten::naive::ArgumentPack1D; using Kernel = PointwiseNeighborhood1D; Kernel kernel; kernel( -cc, stream, query_ptr, key_ptr, attn_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); +cc, stream, is_grad, query_ptr, key_ptr, attn_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); } -void na1d_pn_cuda_naive_double_ks_5_di_any( - const int cc, +void na1d_pn_cuda_naive_half_cm_0( + int32_t cc, cudaStream_t stream, + bool is_grad, void * query_ptr, void * key_ptr, void * attn_ptr, - int batch_size, - int heads, - int length, - int dim, + int32_t batch_size, + int32_t heads, + int32_t length, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; + const std::tuple& kernel_size, + const std::tuple& dilation) { + +if(cc >= 60) { + using Arguments = natten::naive::ArgumentPack1D; using Kernel = PointwiseNeighborhood1D; Kernel kernel; kernel( -cc, stream, query_ptr, key_ptr, attn_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); +cc, stream, is_grad, query_ptr, key_ptr, attn_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); + +} else { +std::cerr << "This half type is not supported on the selected device." << std::endl; +exit(EXIT_FAILURE); + +} } -void na1d_pn_cuda_naive_double_ks_5_di_1( - const int cc, +void na1d_pn_cuda_naive_half_cm_1( + int32_t cc, cudaStream_t stream, + bool is_grad, void * query_ptr, void * key_ptr, void * attn_ptr, - int batch_size, - int heads, - int length, - int dim, + int32_t batch_size, + int32_t heads, + int32_t length, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; + const std::tuple& kernel_size, + const std::tuple& dilation) { + +if(cc >= 60) { + using Arguments = natten::naive::ArgumentPack1D; using Kernel = PointwiseNeighborhood1D; Kernel kernel; kernel( -cc, stream, query_ptr, key_ptr, attn_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); +cc, stream, is_grad, query_ptr, key_ptr, attn_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); + +} else { +std::cerr << "This half type is not supported on the selected device." << std::endl; +exit(EXIT_FAILURE); + +} } -void na1d_pn_cuda_naive_double_ks_7_di_any( - const int cc, +void na1d_pn_cuda_naive_bfloat16_cm_0( + int32_t cc, cudaStream_t stream, + bool is_grad, void * query_ptr, void * key_ptr, void * attn_ptr, - int batch_size, - int heads, - int length, - int dim, + int32_t batch_size, + int32_t heads, + int32_t length, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; + const std::tuple& kernel_size, + const std::tuple& dilation) { + +if(cc >= 80) { + using Arguments = natten::naive::ArgumentPack1D; using Kernel = PointwiseNeighborhood1D; Kernel kernel; kernel( -cc, stream, query_ptr, key_ptr, attn_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); +cc, stream, is_grad, query_ptr, key_ptr, attn_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); + +} else { +std::cerr << "This half type is not supported on the selected device." << std::endl; +exit(EXIT_FAILURE); + +} } -void na1d_pn_cuda_naive_double_ks_7_di_1( - const int cc, +void na1d_pn_cuda_naive_bfloat16_cm_1( + int32_t cc, cudaStream_t stream, + bool is_grad, void * query_ptr, void * key_ptr, void * attn_ptr, - int batch_size, - int heads, - int length, - int dim, + int32_t batch_size, + int32_t heads, + int32_t length, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; + const std::tuple& kernel_size, + const std::tuple& dilation) { + +if(cc >= 80) { + using Arguments = natten::naive::ArgumentPack1D; using Kernel = PointwiseNeighborhood1D; Kernel kernel; kernel( -cc, stream, query_ptr, key_ptr, attn_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); +cc, stream, is_grad, query_ptr, key_ptr, attn_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); + +} else { +std::cerr << "This half type is not supported on the selected device." << std::endl; +exit(EXIT_FAILURE); + +} } -void na1d_pn_cuda_naive_double_ks_9_di_any( - const int cc, +void na2d_pn_cuda_naive_double_cm_0_0( + int32_t cc, cudaStream_t stream, + bool is_grad, void * query_ptr, void * key_ptr, void * attn_ptr, - int batch_size, - int heads, - int length, - int dim, + int32_t batch_size, + int32_t heads, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood1D; + int64_t attn_stride_3, + const std::tuple& kernel_size, + const std::tuple& dilation) { + using Arguments = natten::naive::ArgumentPack2D; + using Kernel = PointwiseNeighborhood2D; Kernel kernel; kernel( -cc, stream, query_ptr, key_ptr, attn_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); +cc, stream, is_grad, query_ptr, key_ptr, attn_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); } -void na1d_pn_cuda_naive_double_ks_9_di_1( - const int cc, +void na2d_pn_cuda_naive_double_cm_0_1( + int32_t cc, cudaStream_t stream, + bool is_grad, void * query_ptr, void * key_ptr, void * attn_ptr, - int batch_size, - int heads, - int length, - int dim, + int32_t batch_size, + int32_t heads, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood1D; + int64_t attn_stride_3, + const std::tuple& kernel_size, + const std::tuple& dilation) { + using Arguments = natten::naive::ArgumentPack2D; + using Kernel = PointwiseNeighborhood2D; Kernel kernel; kernel( -cc, stream, query_ptr, key_ptr, attn_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); +cc, stream, is_grad, query_ptr, key_ptr, attn_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); } -void na1d_pn_cuda_naive_double_ks_11_di_any( - const int cc, +void na2d_pn_cuda_naive_double_cm_1_0( + int32_t cc, cudaStream_t stream, + bool is_grad, void * query_ptr, void * key_ptr, void * attn_ptr, - int batch_size, - int heads, - int length, - int dim, + int32_t batch_size, + int32_t heads, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood1D; + int64_t attn_stride_3, + const std::tuple& kernel_size, + const std::tuple& dilation) { + using Arguments = natten::naive::ArgumentPack2D; + using Kernel = PointwiseNeighborhood2D; Kernel kernel; kernel( -cc, stream, query_ptr, key_ptr, attn_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); +cc, stream, is_grad, query_ptr, key_ptr, attn_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); } -void na1d_pn_cuda_naive_double_ks_11_di_1( - const int cc, +void na2d_pn_cuda_naive_double_cm_1_1( + int32_t cc, cudaStream_t stream, + bool is_grad, void * query_ptr, void * key_ptr, void * attn_ptr, - int batch_size, - int heads, - int length, - int dim, + int32_t batch_size, + int32_t heads, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood1D; + int64_t attn_stride_3, + const std::tuple& kernel_size, + const std::tuple& dilation) { + using Arguments = natten::naive::ArgumentPack2D; + using Kernel = PointwiseNeighborhood2D; Kernel kernel; kernel( -cc, stream, query_ptr, key_ptr, attn_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); +cc, stream, is_grad, query_ptr, key_ptr, attn_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); } -void na1d_pn_cuda_naive_double_ks_13_di_any( - const int cc, +void na2d_pn_cuda_naive_float_cm_0_0( + int32_t cc, cudaStream_t stream, + bool is_grad, void * query_ptr, void * key_ptr, void * attn_ptr, - int batch_size, - int heads, - int length, - int dim, + int32_t batch_size, + int32_t heads, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood1D; + int64_t attn_stride_3, + const std::tuple& kernel_size, + const std::tuple& dilation) { + using Arguments = natten::naive::ArgumentPack2D; + using Kernel = PointwiseNeighborhood2D; Kernel kernel; kernel( -cc, stream, query_ptr, key_ptr, attn_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); +cc, stream, is_grad, query_ptr, key_ptr, attn_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); } -void na1d_pn_cuda_naive_double_ks_13_di_1( - const int cc, +void na2d_pn_cuda_naive_float_cm_0_1( + int32_t cc, cudaStream_t stream, + bool is_grad, void * query_ptr, void * key_ptr, void * attn_ptr, - int batch_size, - int heads, - int length, - int dim, + int32_t batch_size, + int32_t heads, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood1D; + int64_t attn_stride_3, + const std::tuple& kernel_size, + const std::tuple& dilation) { + using Arguments = natten::naive::ArgumentPack2D; + using Kernel = PointwiseNeighborhood2D; Kernel kernel; kernel( -cc, stream, query_ptr, key_ptr, attn_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); +cc, stream, is_grad, query_ptr, key_ptr, attn_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); } -void na1d_pn_cuda_naive_float_ks_any_di_any( - const int cc, +void na2d_pn_cuda_naive_float_cm_1_0( + int32_t cc, cudaStream_t stream, + bool is_grad, void * query_ptr, void * key_ptr, void * attn_ptr, - int batch_size, - int heads, - int length, - int dim, + int32_t batch_size, + int32_t heads, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood1D; + int64_t attn_stride_3, + const std::tuple& kernel_size, + const std::tuple& dilation) { + using Arguments = natten::naive::ArgumentPack2D; + using Kernel = PointwiseNeighborhood2D; Kernel kernel; kernel( -cc, stream, query_ptr, key_ptr, attn_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); +cc, stream, is_grad, query_ptr, key_ptr, attn_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); } -void na1d_pn_cuda_naive_float_ks_any_di_1( - const int cc, +void na2d_pn_cuda_naive_float_cm_1_1( + int32_t cc, cudaStream_t stream, + bool is_grad, void * query_ptr, void * key_ptr, void * attn_ptr, - int batch_size, - int heads, - int length, - int dim, + int32_t batch_size, + int32_t heads, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood1D; + int64_t attn_stride_3, + const std::tuple& kernel_size, + const std::tuple& dilation) { + using Arguments = natten::naive::ArgumentPack2D; + using Kernel = PointwiseNeighborhood2D; Kernel kernel; kernel( -cc, stream, query_ptr, key_ptr, attn_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); +cc, stream, is_grad, query_ptr, key_ptr, attn_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); } -void na1d_pn_cuda_naive_float_ks_3_di_any( - const int cc, +void na2d_pn_cuda_naive_half_cm_0_0( + int32_t cc, cudaStream_t stream, + bool is_grad, void * query_ptr, void * key_ptr, void * attn_ptr, - int batch_size, - int heads, - int length, - int dim, + int32_t batch_size, + int32_t heads, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood1D; + int64_t attn_stride_3, + const std::tuple& kernel_size, + const std::tuple& dilation) { + +if(cc >= 60) { + using Arguments = natten::naive::ArgumentPack2D; + using Kernel = PointwiseNeighborhood2D; Kernel kernel; kernel( -cc, stream, query_ptr, key_ptr, attn_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); +cc, stream, is_grad, query_ptr, key_ptr, attn_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); + +} else { +std::cerr << "This half type is not supported on the selected device." << std::endl; +exit(EXIT_FAILURE); + +} } -void na1d_pn_cuda_naive_float_ks_3_di_1( - const int cc, +void na2d_pn_cuda_naive_half_cm_0_1( + int32_t cc, cudaStream_t stream, + bool is_grad, void * query_ptr, void * key_ptr, void * attn_ptr, - int batch_size, - int heads, - int length, - int dim, + int32_t batch_size, + int32_t heads, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood1D; + int64_t attn_stride_3, + const std::tuple& kernel_size, + const std::tuple& dilation) { + +if(cc >= 60) { + using Arguments = natten::naive::ArgumentPack2D; + using Kernel = PointwiseNeighborhood2D; Kernel kernel; kernel( -cc, stream, query_ptr, key_ptr, attn_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); +cc, stream, is_grad, query_ptr, key_ptr, attn_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); + +} else { +std::cerr << "This half type is not supported on the selected device." << std::endl; +exit(EXIT_FAILURE); + +} } -void na1d_pn_cuda_naive_float_ks_5_di_any( - const int cc, +void na2d_pn_cuda_naive_half_cm_1_0( + int32_t cc, cudaStream_t stream, + bool is_grad, void * query_ptr, void * key_ptr, void * attn_ptr, - int batch_size, - int heads, - int length, - int dim, + int32_t batch_size, + int32_t heads, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood1D; + int64_t attn_stride_3, + const std::tuple& kernel_size, + const std::tuple& dilation) { + +if(cc >= 60) { + using Arguments = natten::naive::ArgumentPack2D; + using Kernel = PointwiseNeighborhood2D; Kernel kernel; kernel( -cc, stream, query_ptr, key_ptr, attn_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); +cc, stream, is_grad, query_ptr, key_ptr, attn_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); + +} else { +std::cerr << "This half type is not supported on the selected device." << std::endl; +exit(EXIT_FAILURE); + +} } -void na1d_pn_cuda_naive_float_ks_5_di_1( - const int cc, +void na2d_pn_cuda_naive_half_cm_1_1( + int32_t cc, cudaStream_t stream, + bool is_grad, void * query_ptr, void * key_ptr, void * attn_ptr, - int batch_size, - int heads, - int length, - int dim, + int32_t batch_size, + int32_t heads, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood1D; + int64_t attn_stride_3, + const std::tuple& kernel_size, + const std::tuple& dilation) { + +if(cc >= 60) { + using Arguments = natten::naive::ArgumentPack2D; + using Kernel = PointwiseNeighborhood2D; Kernel kernel; kernel( -cc, stream, query_ptr, key_ptr, attn_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); +cc, stream, is_grad, query_ptr, key_ptr, attn_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); + +} else { +std::cerr << "This half type is not supported on the selected device." << std::endl; +exit(EXIT_FAILURE); + +} } -void na1d_pn_cuda_naive_float_ks_7_di_any( - const int cc, +void na2d_pn_cuda_naive_bfloat16_cm_0_0( + int32_t cc, cudaStream_t stream, + bool is_grad, void * query_ptr, void * key_ptr, void * attn_ptr, - int batch_size, - int heads, - int length, - int dim, + int32_t batch_size, + int32_t heads, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood1D; + int64_t attn_stride_3, + const std::tuple& kernel_size, + const std::tuple& dilation) { + +if(cc >= 80) { + using Arguments = natten::naive::ArgumentPack2D; + using Kernel = PointwiseNeighborhood2D; Kernel kernel; kernel( -cc, stream, query_ptr, key_ptr, attn_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); +cc, stream, is_grad, query_ptr, key_ptr, attn_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); + +} else { +std::cerr << "This half type is not supported on the selected device." << std::endl; +exit(EXIT_FAILURE); + +} } -void na1d_pn_cuda_naive_float_ks_7_di_1( - const int cc, +void na2d_pn_cuda_naive_bfloat16_cm_0_1( + int32_t cc, cudaStream_t stream, + bool is_grad, void * query_ptr, void * key_ptr, void * attn_ptr, - int batch_size, - int heads, - int length, - int dim, + int32_t batch_size, + int32_t heads, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood1D; + int64_t attn_stride_3, + const std::tuple& kernel_size, + const std::tuple& dilation) { + +if(cc >= 80) { + using Arguments = natten::naive::ArgumentPack2D; + using Kernel = PointwiseNeighborhood2D; Kernel kernel; kernel( -cc, stream, query_ptr, key_ptr, attn_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); +cc, stream, is_grad, query_ptr, key_ptr, attn_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); + +} else { +std::cerr << "This half type is not supported on the selected device." << std::endl; +exit(EXIT_FAILURE); + +} } -void na1d_pn_cuda_naive_float_ks_9_di_any( - const int cc, +void na2d_pn_cuda_naive_bfloat16_cm_1_0( + int32_t cc, cudaStream_t stream, + bool is_grad, void * query_ptr, void * key_ptr, void * attn_ptr, - int batch_size, - int heads, - int length, - int dim, + int32_t batch_size, + int32_t heads, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood1D; + int64_t attn_stride_3, + const std::tuple& kernel_size, + const std::tuple& dilation) { + +if(cc >= 80) { + using Arguments = natten::naive::ArgumentPack2D; + using Kernel = PointwiseNeighborhood2D; Kernel kernel; kernel( -cc, stream, query_ptr, key_ptr, attn_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); +cc, stream, is_grad, query_ptr, key_ptr, attn_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); + +} else { +std::cerr << "This half type is not supported on the selected device." << std::endl; +exit(EXIT_FAILURE); + +} } -void na1d_pn_cuda_naive_float_ks_9_di_1( - const int cc, +void na2d_pn_cuda_naive_bfloat16_cm_1_1( + int32_t cc, cudaStream_t stream, + bool is_grad, void * query_ptr, void * key_ptr, void * attn_ptr, - int batch_size, - int heads, - int length, - int dim, + int32_t batch_size, + int32_t heads, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood1D; + int64_t attn_stride_3, + const std::tuple& kernel_size, + const std::tuple& dilation) { + +if(cc >= 80) { + using Arguments = natten::naive::ArgumentPack2D; + using Kernel = PointwiseNeighborhood2D; Kernel kernel; kernel( -cc, stream, query_ptr, key_ptr, attn_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); +cc, stream, is_grad, query_ptr, key_ptr, attn_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); + +} else { +std::cerr << "This half type is not supported on the selected device." << std::endl; +exit(EXIT_FAILURE); + +} } -void na1d_pn_cuda_naive_float_ks_11_di_any( - const int cc, +void na3d_pn_cuda_naive_double_cm_0_0_0( + int32_t cc, cudaStream_t stream, + bool is_grad, void * query_ptr, void * key_ptr, void * attn_ptr, - int batch_size, - int heads, - int length, - int dim, + int32_t batch_size, + int32_t heads, + int32_t depth, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood1D; + int64_t attn_stride_3, + int64_t attn_stride_4, + const std::tuple& kernel_size, + const std::tuple& dilation) { + using Arguments = natten::naive::ArgumentPack3D; + using Kernel = PointwiseNeighborhood3D; Kernel kernel; kernel( -cc, stream, query_ptr, key_ptr, attn_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); +cc, stream, is_grad, query_ptr, key_ptr, attn_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation); } -void na1d_pn_cuda_naive_float_ks_11_di_1( - const int cc, +void na3d_pn_cuda_naive_double_cm_0_0_1( + int32_t cc, cudaStream_t stream, + bool is_grad, void * query_ptr, void * key_ptr, void * attn_ptr, - int batch_size, - int heads, - int length, - int dim, + int32_t batch_size, + int32_t heads, + int32_t depth, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood1D; + int64_t attn_stride_3, + int64_t attn_stride_4, + const std::tuple& kernel_size, + const std::tuple& dilation) { + using Arguments = natten::naive::ArgumentPack3D; + using Kernel = PointwiseNeighborhood3D; Kernel kernel; kernel( -cc, stream, query_ptr, key_ptr, attn_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); +cc, stream, is_grad, query_ptr, key_ptr, attn_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation); } -void na1d_pn_cuda_naive_float_ks_13_di_any( - const int cc, +void na3d_pn_cuda_naive_double_cm_0_1_0( + int32_t cc, cudaStream_t stream, + bool is_grad, void * query_ptr, void * key_ptr, void * attn_ptr, - int batch_size, - int heads, - int length, - int dim, + int32_t batch_size, + int32_t heads, + int32_t depth, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood1D; + int64_t attn_stride_3, + int64_t attn_stride_4, + const std::tuple& kernel_size, + const std::tuple& dilation) { + using Arguments = natten::naive::ArgumentPack3D; + using Kernel = PointwiseNeighborhood3D; Kernel kernel; kernel( -cc, stream, query_ptr, key_ptr, attn_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); +cc, stream, is_grad, query_ptr, key_ptr, attn_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation); } -void na1d_pn_cuda_naive_float_ks_13_di_1( - const int cc, +void na3d_pn_cuda_naive_double_cm_0_1_1( + int32_t cc, cudaStream_t stream, + bool is_grad, void * query_ptr, void * key_ptr, void * attn_ptr, - int batch_size, - int heads, - int length, - int dim, + int32_t batch_size, + int32_t heads, + int32_t depth, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood1D; + int64_t attn_stride_3, + int64_t attn_stride_4, + const std::tuple& kernel_size, + const std::tuple& dilation) { + using Arguments = natten::naive::ArgumentPack3D; + using Kernel = PointwiseNeighborhood3D; Kernel kernel; kernel( -cc, stream, query_ptr, key_ptr, attn_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); +cc, stream, is_grad, query_ptr, key_ptr, attn_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation); } -void na1d_pn_cuda_naive_half_ks_any_di_any( - const int cc, +void na3d_pn_cuda_naive_double_cm_1_0_0( + int32_t cc, cudaStream_t stream, + bool is_grad, void * query_ptr, void * key_ptr, void * attn_ptr, - int batch_size, - int heads, - int length, - int dim, + int32_t batch_size, + int32_t heads, + int32_t depth, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, - int kernel_size, - int dilation) { - -if(cc >= 60) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood1D; + int64_t attn_stride_3, + int64_t attn_stride_4, + const std::tuple& kernel_size, + const std::tuple& dilation) { + using Arguments = natten::naive::ArgumentPack3D; + using Kernel = PointwiseNeighborhood3D; Kernel kernel; kernel( -cc, stream, query_ptr, key_ptr, attn_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} +cc, stream, is_grad, query_ptr, key_ptr, attn_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation); } -void na1d_pn_cuda_naive_half_ks_any_di_1( - const int cc, +void na3d_pn_cuda_naive_double_cm_1_0_1( + int32_t cc, cudaStream_t stream, + bool is_grad, void * query_ptr, void * key_ptr, void * attn_ptr, - int batch_size, - int heads, - int length, - int dim, + int32_t batch_size, + int32_t heads, + int32_t depth, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, - int kernel_size, - int dilation) { - -if(cc >= 60) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood1D; + int64_t attn_stride_3, + int64_t attn_stride_4, + const std::tuple& kernel_size, + const std::tuple& dilation) { + using Arguments = natten::naive::ArgumentPack3D; + using Kernel = PointwiseNeighborhood3D; Kernel kernel; kernel( -cc, stream, query_ptr, key_ptr, attn_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} +cc, stream, is_grad, query_ptr, key_ptr, attn_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation); } -void na1d_pn_cuda_naive_half_ks_3_di_any( - const int cc, +void na3d_pn_cuda_naive_double_cm_1_1_0( + int32_t cc, cudaStream_t stream, + bool is_grad, void * query_ptr, void * key_ptr, void * attn_ptr, - int batch_size, - int heads, - int length, - int dim, + int32_t batch_size, + int32_t heads, + int32_t depth, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, - int kernel_size, - int dilation) { - -if(cc >= 60) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood1D; + int64_t attn_stride_3, + int64_t attn_stride_4, + const std::tuple& kernel_size, + const std::tuple& dilation) { + using Arguments = natten::naive::ArgumentPack3D; + using Kernel = PointwiseNeighborhood3D; Kernel kernel; kernel( -cc, stream, query_ptr, key_ptr, attn_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} +cc, stream, is_grad, query_ptr, key_ptr, attn_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation); } -void na1d_pn_cuda_naive_half_ks_3_di_1( - const int cc, +void na3d_pn_cuda_naive_double_cm_1_1_1( + int32_t cc, cudaStream_t stream, + bool is_grad, void * query_ptr, void * key_ptr, void * attn_ptr, - int batch_size, - int heads, - int length, - int dim, + int32_t batch_size, + int32_t heads, + int32_t depth, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, - int kernel_size, - int dilation) { - -if(cc >= 60) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood1D; + int64_t attn_stride_3, + int64_t attn_stride_4, + const std::tuple& kernel_size, + const std::tuple& dilation) { + using Arguments = natten::naive::ArgumentPack3D; + using Kernel = PointwiseNeighborhood3D; Kernel kernel; kernel( -cc, stream, query_ptr, key_ptr, attn_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} +cc, stream, is_grad, query_ptr, key_ptr, attn_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation); } -void na1d_pn_cuda_naive_half_ks_5_di_any( - const int cc, +void na3d_pn_cuda_naive_float_cm_0_0_0( + int32_t cc, cudaStream_t stream, + bool is_grad, void * query_ptr, void * key_ptr, void * attn_ptr, - int batch_size, - int heads, - int length, - int dim, + int32_t batch_size, + int32_t heads, + int32_t depth, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, - int kernel_size, - int dilation) { - -if(cc >= 60) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood1D; + int64_t attn_stride_3, + int64_t attn_stride_4, + const std::tuple& kernel_size, + const std::tuple& dilation) { + using Arguments = natten::naive::ArgumentPack3D; + using Kernel = PointwiseNeighborhood3D; Kernel kernel; kernel( -cc, stream, query_ptr, key_ptr, attn_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} +cc, stream, is_grad, query_ptr, key_ptr, attn_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation); } -void na1d_pn_cuda_naive_half_ks_5_di_1( - const int cc, +void na3d_pn_cuda_naive_float_cm_0_0_1( + int32_t cc, cudaStream_t stream, + bool is_grad, void * query_ptr, void * key_ptr, void * attn_ptr, - int batch_size, - int heads, - int length, - int dim, + int32_t batch_size, + int32_t heads, + int32_t depth, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, - int kernel_size, - int dilation) { - -if(cc >= 60) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood1D; + int64_t attn_stride_3, + int64_t attn_stride_4, + const std::tuple& kernel_size, + const std::tuple& dilation) { + using Arguments = natten::naive::ArgumentPack3D; + using Kernel = PointwiseNeighborhood3D; Kernel kernel; kernel( -cc, stream, query_ptr, key_ptr, attn_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} +cc, stream, is_grad, query_ptr, key_ptr, attn_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation); } -void na1d_pn_cuda_naive_half_ks_7_di_any( - const int cc, +void na3d_pn_cuda_naive_float_cm_0_1_0( + int32_t cc, cudaStream_t stream, + bool is_grad, void * query_ptr, void * key_ptr, void * attn_ptr, - int batch_size, - int heads, - int length, - int dim, + int32_t batch_size, + int32_t heads, + int32_t depth, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, - int kernel_size, - int dilation) { - -if(cc >= 60) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood1D; + int64_t attn_stride_3, + int64_t attn_stride_4, + const std::tuple& kernel_size, + const std::tuple& dilation) { + using Arguments = natten::naive::ArgumentPack3D; + using Kernel = PointwiseNeighborhood3D; Kernel kernel; kernel( -cc, stream, query_ptr, key_ptr, attn_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} +cc, stream, is_grad, query_ptr, key_ptr, attn_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation); } -void na1d_pn_cuda_naive_half_ks_7_di_1( - const int cc, +void na3d_pn_cuda_naive_float_cm_0_1_1( + int32_t cc, cudaStream_t stream, + bool is_grad, void * query_ptr, void * key_ptr, void * attn_ptr, - int batch_size, - int heads, - int length, - int dim, + int32_t batch_size, + int32_t heads, + int32_t depth, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, - int kernel_size, - int dilation) { - -if(cc >= 60) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood1D; + int64_t attn_stride_3, + int64_t attn_stride_4, + const std::tuple& kernel_size, + const std::tuple& dilation) { + using Arguments = natten::naive::ArgumentPack3D; + using Kernel = PointwiseNeighborhood3D; Kernel kernel; kernel( -cc, stream, query_ptr, key_ptr, attn_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} +cc, stream, is_grad, query_ptr, key_ptr, attn_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation); } -void na1d_pn_cuda_naive_half_ks_9_di_any( - const int cc, +void na3d_pn_cuda_naive_float_cm_1_0_0( + int32_t cc, cudaStream_t stream, + bool is_grad, void * query_ptr, void * key_ptr, void * attn_ptr, - int batch_size, - int heads, - int length, - int dim, + int32_t batch_size, + int32_t heads, + int32_t depth, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, - int kernel_size, - int dilation) { - -if(cc >= 60) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood1D; + int64_t attn_stride_3, + int64_t attn_stride_4, + const std::tuple& kernel_size, + const std::tuple& dilation) { + using Arguments = natten::naive::ArgumentPack3D; + using Kernel = PointwiseNeighborhood3D; Kernel kernel; kernel( -cc, stream, query_ptr, key_ptr, attn_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} +cc, stream, is_grad, query_ptr, key_ptr, attn_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation); } -void na1d_pn_cuda_naive_half_ks_9_di_1( - const int cc, +void na3d_pn_cuda_naive_float_cm_1_0_1( + int32_t cc, cudaStream_t stream, + bool is_grad, void * query_ptr, void * key_ptr, void * attn_ptr, - int batch_size, - int heads, - int length, - int dim, + int32_t batch_size, + int32_t heads, + int32_t depth, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, - int kernel_size, - int dilation) { - -if(cc >= 60) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood1D; + int64_t attn_stride_3, + int64_t attn_stride_4, + const std::tuple& kernel_size, + const std::tuple& dilation) { + using Arguments = natten::naive::ArgumentPack3D; + using Kernel = PointwiseNeighborhood3D; Kernel kernel; kernel( -cc, stream, query_ptr, key_ptr, attn_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} +cc, stream, is_grad, query_ptr, key_ptr, attn_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation); } -void na1d_pn_cuda_naive_half_ks_11_di_any( - const int cc, +void na3d_pn_cuda_naive_float_cm_1_1_0( + int32_t cc, cudaStream_t stream, + bool is_grad, void * query_ptr, void * key_ptr, void * attn_ptr, - int batch_size, - int heads, - int length, - int dim, + int32_t batch_size, + int32_t heads, + int32_t depth, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, - int kernel_size, - int dilation) { - -if(cc >= 60) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood1D; + int64_t attn_stride_3, + int64_t attn_stride_4, + const std::tuple& kernel_size, + const std::tuple& dilation) { + using Arguments = natten::naive::ArgumentPack3D; + using Kernel = PointwiseNeighborhood3D; Kernel kernel; kernel( -cc, stream, query_ptr, key_ptr, attn_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} +cc, stream, is_grad, query_ptr, key_ptr, attn_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation); } -void na1d_pn_cuda_naive_half_ks_11_di_1( - const int cc, +void na3d_pn_cuda_naive_float_cm_1_1_1( + int32_t cc, cudaStream_t stream, + bool is_grad, void * query_ptr, void * key_ptr, void * attn_ptr, - int batch_size, - int heads, - int length, - int dim, + int32_t batch_size, + int32_t heads, + int32_t depth, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, - int kernel_size, - int dilation) { - -if(cc >= 60) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood1D; + int64_t attn_stride_3, + int64_t attn_stride_4, + const std::tuple& kernel_size, + const std::tuple& dilation) { + using Arguments = natten::naive::ArgumentPack3D; + using Kernel = PointwiseNeighborhood3D; Kernel kernel; kernel( -cc, stream, query_ptr, key_ptr, attn_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} +cc, stream, is_grad, query_ptr, key_ptr, attn_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation); } -void na1d_pn_cuda_naive_half_ks_13_di_any( - const int cc, +void na3d_pn_cuda_naive_half_cm_0_0_0( + int32_t cc, cudaStream_t stream, + bool is_grad, void * query_ptr, void * key_ptr, void * attn_ptr, - int batch_size, - int heads, - int length, - int dim, + int32_t batch_size, + int32_t heads, + int32_t depth, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, - int kernel_size, - int dilation) { + int64_t attn_stride_3, + int64_t attn_stride_4, + const std::tuple& kernel_size, + const std::tuple& dilation) { if(cc >= 60) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood1D; + using Arguments = natten::naive::ArgumentPack3D; + using Kernel = PointwiseNeighborhood3D; Kernel kernel; kernel( -cc, stream, query_ptr, key_ptr, attn_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); +cc, stream, is_grad, query_ptr, key_ptr, attn_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation); } else { std::cerr << "This half type is not supported on the selected device." << std::endl; @@ -1017,28 +1159,33 @@ exit(EXIT_FAILURE); } } -void na1d_pn_cuda_naive_half_ks_13_di_1( - const int cc, +void na3d_pn_cuda_naive_half_cm_0_0_1( + int32_t cc, cudaStream_t stream, + bool is_grad, void * query_ptr, void * key_ptr, void * attn_ptr, - int batch_size, - int heads, - int length, - int dim, + int32_t batch_size, + int32_t heads, + int32_t depth, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, - int kernel_size, - int dilation) { + int64_t attn_stride_3, + int64_t attn_stride_4, + const std::tuple& kernel_size, + const std::tuple& dilation) { if(cc >= 60) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood1D; + using Arguments = natten::naive::ArgumentPack3D; + using Kernel = PointwiseNeighborhood3D; Kernel kernel; kernel( -cc, stream, query_ptr, key_ptr, attn_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); +cc, stream, is_grad, query_ptr, key_ptr, attn_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation); } else { std::cerr << "This half type is not supported on the selected device." << std::endl; @@ -1047,28 +1194,33 @@ exit(EXIT_FAILURE); } } -void na1d_pn_cuda_naive_bfloat16_ks_any_di_any( - const int cc, +void na3d_pn_cuda_naive_half_cm_0_1_0( + int32_t cc, cudaStream_t stream, + bool is_grad, void * query_ptr, void * key_ptr, void * attn_ptr, - int batch_size, - int heads, - int length, - int dim, + int32_t batch_size, + int32_t heads, + int32_t depth, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, - int kernel_size, - int dilation) { + int64_t attn_stride_3, + int64_t attn_stride_4, + const std::tuple& kernel_size, + const std::tuple& dilation) { -if(cc >= 80) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood1D; +if(cc >= 60) { + using Arguments = natten::naive::ArgumentPack3D; + using Kernel = PointwiseNeighborhood3D; Kernel kernel; kernel( -cc, stream, query_ptr, key_ptr, attn_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); +cc, stream, is_grad, query_ptr, key_ptr, attn_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation); } else { std::cerr << "This half type is not supported on the selected device." << std::endl; @@ -1077,28 +1229,33 @@ exit(EXIT_FAILURE); } } -void na1d_pn_cuda_naive_bfloat16_ks_any_di_1( - const int cc, +void na3d_pn_cuda_naive_half_cm_0_1_1( + int32_t cc, cudaStream_t stream, + bool is_grad, void * query_ptr, void * key_ptr, void * attn_ptr, - int batch_size, - int heads, - int length, - int dim, + int32_t batch_size, + int32_t heads, + int32_t depth, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, - int kernel_size, - int dilation) { + int64_t attn_stride_3, + int64_t attn_stride_4, + const std::tuple& kernel_size, + const std::tuple& dilation) { -if(cc >= 80) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood1D; +if(cc >= 60) { + using Arguments = natten::naive::ArgumentPack3D; + using Kernel = PointwiseNeighborhood3D; Kernel kernel; kernel( -cc, stream, query_ptr, key_ptr, attn_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); +cc, stream, is_grad, query_ptr, key_ptr, attn_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation); } else { std::cerr << "This half type is not supported on the selected device." << std::endl; @@ -1107,28 +1264,33 @@ exit(EXIT_FAILURE); } } -void na1d_pn_cuda_naive_bfloat16_ks_3_di_any( - const int cc, +void na3d_pn_cuda_naive_half_cm_1_0_0( + int32_t cc, cudaStream_t stream, + bool is_grad, void * query_ptr, void * key_ptr, void * attn_ptr, - int batch_size, - int heads, - int length, - int dim, + int32_t batch_size, + int32_t heads, + int32_t depth, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, - int kernel_size, - int dilation) { + int64_t attn_stride_3, + int64_t attn_stride_4, + const std::tuple& kernel_size, + const std::tuple& dilation) { -if(cc >= 80) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood1D; +if(cc >= 60) { + using Arguments = natten::naive::ArgumentPack3D; + using Kernel = PointwiseNeighborhood3D; Kernel kernel; kernel( -cc, stream, query_ptr, key_ptr, attn_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); +cc, stream, is_grad, query_ptr, key_ptr, attn_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation); } else { std::cerr << "This half type is not supported on the selected device." << std::endl; @@ -1137,28 +1299,33 @@ exit(EXIT_FAILURE); } } -void na1d_pn_cuda_naive_bfloat16_ks_3_di_1( - const int cc, +void na3d_pn_cuda_naive_half_cm_1_0_1( + int32_t cc, cudaStream_t stream, + bool is_grad, void * query_ptr, void * key_ptr, void * attn_ptr, - int batch_size, - int heads, - int length, - int dim, + int32_t batch_size, + int32_t heads, + int32_t depth, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, - int kernel_size, - int dilation) { + int64_t attn_stride_3, + int64_t attn_stride_4, + const std::tuple& kernel_size, + const std::tuple& dilation) { -if(cc >= 80) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood1D; +if(cc >= 60) { + using Arguments = natten::naive::ArgumentPack3D; + using Kernel = PointwiseNeighborhood3D; Kernel kernel; kernel( -cc, stream, query_ptr, key_ptr, attn_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); +cc, stream, is_grad, query_ptr, key_ptr, attn_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation); } else { std::cerr << "This half type is not supported on the selected device." << std::endl; @@ -1167,28 +1334,33 @@ exit(EXIT_FAILURE); } } -void na1d_pn_cuda_naive_bfloat16_ks_5_di_any( - const int cc, +void na3d_pn_cuda_naive_half_cm_1_1_0( + int32_t cc, cudaStream_t stream, + bool is_grad, void * query_ptr, void * key_ptr, void * attn_ptr, - int batch_size, - int heads, - int length, - int dim, + int32_t batch_size, + int32_t heads, + int32_t depth, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, - int kernel_size, - int dilation) { + int64_t attn_stride_3, + int64_t attn_stride_4, + const std::tuple& kernel_size, + const std::tuple& dilation) { -if(cc >= 80) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood1D; +if(cc >= 60) { + using Arguments = natten::naive::ArgumentPack3D; + using Kernel = PointwiseNeighborhood3D; Kernel kernel; kernel( -cc, stream, query_ptr, key_ptr, attn_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); +cc, stream, is_grad, query_ptr, key_ptr, attn_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation); } else { std::cerr << "This half type is not supported on the selected device." << std::endl; @@ -1197,28 +1369,33 @@ exit(EXIT_FAILURE); } } -void na1d_pn_cuda_naive_bfloat16_ks_5_di_1( - const int cc, +void na3d_pn_cuda_naive_half_cm_1_1_1( + int32_t cc, cudaStream_t stream, + bool is_grad, void * query_ptr, void * key_ptr, void * attn_ptr, - int batch_size, - int heads, - int length, - int dim, + int32_t batch_size, + int32_t heads, + int32_t depth, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, - int kernel_size, - int dilation) { + int64_t attn_stride_3, + int64_t attn_stride_4, + const std::tuple& kernel_size, + const std::tuple& dilation) { -if(cc >= 80) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood1D; +if(cc >= 60) { + using Arguments = natten::naive::ArgumentPack3D; + using Kernel = PointwiseNeighborhood3D; Kernel kernel; kernel( -cc, stream, query_ptr, key_ptr, attn_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); +cc, stream, is_grad, query_ptr, key_ptr, attn_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation); } else { std::cerr << "This half type is not supported on the selected device." << std::endl; @@ -1227,28 +1404,33 @@ exit(EXIT_FAILURE); } } -void na1d_pn_cuda_naive_bfloat16_ks_7_di_any( - const int cc, +void na3d_pn_cuda_naive_bfloat16_cm_0_0_0( + int32_t cc, cudaStream_t stream, + bool is_grad, void * query_ptr, void * key_ptr, void * attn_ptr, - int batch_size, - int heads, - int length, - int dim, + int32_t batch_size, + int32_t heads, + int32_t depth, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, - int kernel_size, - int dilation) { + int64_t attn_stride_3, + int64_t attn_stride_4, + const std::tuple& kernel_size, + const std::tuple& dilation) { if(cc >= 80) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood1D; + using Arguments = natten::naive::ArgumentPack3D; + using Kernel = PointwiseNeighborhood3D; Kernel kernel; kernel( -cc, stream, query_ptr, key_ptr, attn_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); +cc, stream, is_grad, query_ptr, key_ptr, attn_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation); } else { std::cerr << "This half type is not supported on the selected device." << std::endl; @@ -1257,28 +1439,33 @@ exit(EXIT_FAILURE); } } -void na1d_pn_cuda_naive_bfloat16_ks_7_di_1( - const int cc, +void na3d_pn_cuda_naive_bfloat16_cm_0_0_1( + int32_t cc, cudaStream_t stream, + bool is_grad, void * query_ptr, void * key_ptr, void * attn_ptr, - int batch_size, - int heads, - int length, - int dim, + int32_t batch_size, + int32_t heads, + int32_t depth, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, - int kernel_size, - int dilation) { + int64_t attn_stride_3, + int64_t attn_stride_4, + const std::tuple& kernel_size, + const std::tuple& dilation) { if(cc >= 80) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood1D; + using Arguments = natten::naive::ArgumentPack3D; + using Kernel = PointwiseNeighborhood3D; Kernel kernel; kernel( -cc, stream, query_ptr, key_ptr, attn_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); +cc, stream, is_grad, query_ptr, key_ptr, attn_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation); } else { std::cerr << "This half type is not supported on the selected device." << std::endl; @@ -1287,28 +1474,33 @@ exit(EXIT_FAILURE); } } -void na1d_pn_cuda_naive_bfloat16_ks_9_di_any( - const int cc, +void na3d_pn_cuda_naive_bfloat16_cm_0_1_0( + int32_t cc, cudaStream_t stream, + bool is_grad, void * query_ptr, void * key_ptr, void * attn_ptr, - int batch_size, - int heads, - int length, - int dim, + int32_t batch_size, + int32_t heads, + int32_t depth, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, - int kernel_size, - int dilation) { + int64_t attn_stride_3, + int64_t attn_stride_4, + const std::tuple& kernel_size, + const std::tuple& dilation) { if(cc >= 80) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood1D; + using Arguments = natten::naive::ArgumentPack3D; + using Kernel = PointwiseNeighborhood3D; Kernel kernel; kernel( -cc, stream, query_ptr, key_ptr, attn_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); +cc, stream, is_grad, query_ptr, key_ptr, attn_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation); } else { std::cerr << "This half type is not supported on the selected device." << std::endl; @@ -1317,28 +1509,33 @@ exit(EXIT_FAILURE); } } -void na1d_pn_cuda_naive_bfloat16_ks_9_di_1( - const int cc, +void na3d_pn_cuda_naive_bfloat16_cm_0_1_1( + int32_t cc, cudaStream_t stream, + bool is_grad, void * query_ptr, void * key_ptr, void * attn_ptr, - int batch_size, - int heads, - int length, - int dim, + int32_t batch_size, + int32_t heads, + int32_t depth, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, - int kernel_size, - int dilation) { + int64_t attn_stride_3, + int64_t attn_stride_4, + const std::tuple& kernel_size, + const std::tuple& dilation) { if(cc >= 80) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood1D; + using Arguments = natten::naive::ArgumentPack3D; + using Kernel = PointwiseNeighborhood3D; Kernel kernel; kernel( -cc, stream, query_ptr, key_ptr, attn_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); +cc, stream, is_grad, query_ptr, key_ptr, attn_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation); } else { std::cerr << "This half type is not supported on the selected device." << std::endl; @@ -1347,28 +1544,33 @@ exit(EXIT_FAILURE); } } -void na1d_pn_cuda_naive_bfloat16_ks_11_di_any( - const int cc, +void na3d_pn_cuda_naive_bfloat16_cm_1_0_0( + int32_t cc, cudaStream_t stream, + bool is_grad, void * query_ptr, void * key_ptr, void * attn_ptr, - int batch_size, - int heads, - int length, - int dim, + int32_t batch_size, + int32_t heads, + int32_t depth, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, - int kernel_size, - int dilation) { + int64_t attn_stride_3, + int64_t attn_stride_4, + const std::tuple& kernel_size, + const std::tuple& dilation) { if(cc >= 80) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood1D; + using Arguments = natten::naive::ArgumentPack3D; + using Kernel = PointwiseNeighborhood3D; Kernel kernel; kernel( -cc, stream, query_ptr, key_ptr, attn_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); +cc, stream, is_grad, query_ptr, key_ptr, attn_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation); } else { std::cerr << "This half type is not supported on the selected device." << std::endl; @@ -1377,28 +1579,33 @@ exit(EXIT_FAILURE); } } -void na1d_pn_cuda_naive_bfloat16_ks_11_di_1( - const int cc, +void na3d_pn_cuda_naive_bfloat16_cm_1_0_1( + int32_t cc, cudaStream_t stream, + bool is_grad, void * query_ptr, void * key_ptr, void * attn_ptr, - int batch_size, - int heads, - int length, - int dim, + int32_t batch_size, + int32_t heads, + int32_t depth, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, - int kernel_size, - int dilation) { + int64_t attn_stride_3, + int64_t attn_stride_4, + const std::tuple& kernel_size, + const std::tuple& dilation) { if(cc >= 80) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood1D; + using Arguments = natten::naive::ArgumentPack3D; + using Kernel = PointwiseNeighborhood3D; Kernel kernel; kernel( -cc, stream, query_ptr, key_ptr, attn_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); +cc, stream, is_grad, query_ptr, key_ptr, attn_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation); } else { std::cerr << "This half type is not supported on the selected device." << std::endl; @@ -1407,28 +1614,33 @@ exit(EXIT_FAILURE); } } -void na1d_pn_cuda_naive_bfloat16_ks_13_di_any( - const int cc, +void na3d_pn_cuda_naive_bfloat16_cm_1_1_0( + int32_t cc, cudaStream_t stream, + bool is_grad, void * query_ptr, void * key_ptr, void * attn_ptr, - int batch_size, - int heads, - int length, - int dim, + int32_t batch_size, + int32_t heads, + int32_t depth, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, - int kernel_size, - int dilation) { + int64_t attn_stride_3, + int64_t attn_stride_4, + const std::tuple& kernel_size, + const std::tuple& dilation) { if(cc >= 80) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood1D; + using Arguments = natten::naive::ArgumentPack3D; + using Kernel = PointwiseNeighborhood3D; Kernel kernel; kernel( -cc, stream, query_ptr, key_ptr, attn_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); +cc, stream, is_grad, query_ptr, key_ptr, attn_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation); } else { std::cerr << "This half type is not supported on the selected device." << std::endl; @@ -1437,28 +1649,33 @@ exit(EXIT_FAILURE); } } -void na1d_pn_cuda_naive_bfloat16_ks_13_di_1( - const int cc, +void na3d_pn_cuda_naive_bfloat16_cm_1_1_1( + int32_t cc, cudaStream_t stream, + bool is_grad, void * query_ptr, void * key_ptr, void * attn_ptr, - int batch_size, - int heads, - int length, - int dim, + int32_t batch_size, + int32_t heads, + int32_t depth, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, - int kernel_size, - int dilation) { + int64_t attn_stride_3, + int64_t attn_stride_4, + const std::tuple& kernel_size, + const std::tuple& dilation) { if(cc >= 80) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood1D; + using Arguments = natten::naive::ArgumentPack3D; + using Kernel = PointwiseNeighborhood3D; Kernel kernel; kernel( -cc, stream, query_ptr, key_ptr, attn_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); +cc, stream, is_grad, query_ptr, key_ptr, attn_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation); } else { std::cerr << "This half type is not supported on the selected device." << std::endl; @@ -1467,9528 +1684,311 @@ exit(EXIT_FAILURE); } } -void na2d_pn_cuda_naive_double_ks_any_di_any( - const int cc, +void na1d_pn_bias_cuda_naive_double_cm_0( + int32_t cc, cudaStream_t stream, void * query_ptr, void * key_ptr, + void * bias_ptr, void * attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, + int32_t batch_size, + int32_t heads, + int32_t length, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood2D; + const std::tuple& kernel_size, + const std::tuple& dilation) { + using Arguments = natten::naive::ArgumentPack1D; + using Kernel = PointwiseNeighborhood1DWithBias; Kernel kernel; kernel( -cc, stream, query_ptr, key_ptr, attn_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); +cc, stream, query_ptr, key_ptr, bias_ptr, attn_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); } -void na2d_pn_cuda_naive_double_ks_any_di_1( - const int cc, +void na1d_pn_bias_cuda_naive_float_cm_0( + int32_t cc, cudaStream_t stream, void * query_ptr, void * key_ptr, + void * bias_ptr, void * attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, + int32_t batch_size, + int32_t heads, + int32_t length, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood2D; + const std::tuple& kernel_size, + const std::tuple& dilation) { + using Arguments = natten::naive::ArgumentPack1D; + using Kernel = PointwiseNeighborhood1DWithBias; Kernel kernel; kernel( -cc, stream, query_ptr, key_ptr, attn_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); +cc, stream, query_ptr, key_ptr, bias_ptr, attn_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); } -void na2d_pn_cuda_naive_double_ks_3_di_any( - const int cc, +void na1d_pn_bias_cuda_naive_half_cm_0( + int32_t cc, cudaStream_t stream, void * query_ptr, void * key_ptr, + void * bias_ptr, void * attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, + int32_t batch_size, + int32_t heads, + int32_t length, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood2D; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, attn_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); -} + const std::tuple& kernel_size, + const std::tuple& dilation) { -void na2d_pn_cuda_naive_double_ks_3_di_1( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood2D; +if(cc >= 60) { + using Arguments = natten::naive::ArgumentPack1D; + using Kernel = PointwiseNeighborhood1DWithBias; Kernel kernel; kernel( -cc, stream, query_ptr, key_ptr, attn_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); -} +cc, stream, query_ptr, key_ptr, bias_ptr, attn_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); -void na2d_pn_cuda_naive_double_ks_5_di_any( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood2D; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, attn_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); -} +} else { +std::cerr << "This half type is not supported on the selected device." << std::endl; +exit(EXIT_FAILURE); -void na2d_pn_cuda_naive_double_ks_5_di_1( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood2D; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, attn_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); } - -void na2d_pn_cuda_naive_double_ks_7_di_any( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood2D; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, attn_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); } -void na2d_pn_cuda_naive_double_ks_7_di_1( - const int cc, +void na1d_pn_bias_cuda_naive_bfloat16_cm_0( + int32_t cc, cudaStream_t stream, void * query_ptr, void * key_ptr, + void * bias_ptr, void * attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, + int32_t batch_size, + int32_t heads, + int32_t length, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood2D; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, attn_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); -} + const std::tuple& kernel_size, + const std::tuple& dilation) { -void na2d_pn_cuda_naive_double_ks_9_di_any( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood2D; +if(cc >= 80) { + using Arguments = natten::naive::ArgumentPack1D; + using Kernel = PointwiseNeighborhood1DWithBias; Kernel kernel; kernel( -cc, stream, query_ptr, key_ptr, attn_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); +cc, stream, query_ptr, key_ptr, bias_ptr, attn_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); + +} else { +std::cerr << "This half type is not supported on the selected device." << std::endl; +exit(EXIT_FAILURE); + +} } -void na2d_pn_cuda_naive_double_ks_9_di_1( - const int cc, +void na2d_pn_bias_cuda_naive_double_cm_0_0( + int32_t cc, cudaStream_t stream, void * query_ptr, void * key_ptr, + void * bias_ptr, void * attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, + int32_t batch_size, + int32_t heads, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood2D; + const std::tuple& kernel_size, + const std::tuple& dilation) { + using Arguments = natten::naive::ArgumentPack2D; + using Kernel = PointwiseNeighborhood2DWithBias; Kernel kernel; kernel( -cc, stream, query_ptr, key_ptr, attn_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); +cc, stream, query_ptr, key_ptr, bias_ptr, attn_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); } -void na2d_pn_cuda_naive_double_ks_11_di_any( - const int cc, +void na2d_pn_bias_cuda_naive_float_cm_0_0( + int32_t cc, cudaStream_t stream, void * query_ptr, void * key_ptr, + void * bias_ptr, void * attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, + int32_t batch_size, + int32_t heads, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood2D; + const std::tuple& kernel_size, + const std::tuple& dilation) { + using Arguments = natten::naive::ArgumentPack2D; + using Kernel = PointwiseNeighborhood2DWithBias; Kernel kernel; kernel( -cc, stream, query_ptr, key_ptr, attn_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); +cc, stream, query_ptr, key_ptr, bias_ptr, attn_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); } -void na2d_pn_cuda_naive_double_ks_11_di_1( - const int cc, +void na2d_pn_bias_cuda_naive_half_cm_0_0( + int32_t cc, cudaStream_t stream, void * query_ptr, void * key_ptr, + void * bias_ptr, void * attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, + int32_t batch_size, + int32_t heads, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood2D; + const std::tuple& kernel_size, + const std::tuple& dilation) { + +if(cc >= 60) { + using Arguments = natten::naive::ArgumentPack2D; + using Kernel = PointwiseNeighborhood2DWithBias; Kernel kernel; kernel( -cc, stream, query_ptr, key_ptr, attn_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); +cc, stream, query_ptr, key_ptr, bias_ptr, attn_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); + +} else { +std::cerr << "This half type is not supported on the selected device." << std::endl; +exit(EXIT_FAILURE); + +} } -void na2d_pn_cuda_naive_double_ks_13_di_any( - const int cc, +void na2d_pn_bias_cuda_naive_bfloat16_cm_0_0( + int32_t cc, cudaStream_t stream, void * query_ptr, void * key_ptr, + void * bias_ptr, void * attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, + int32_t batch_size, + int32_t heads, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood2D; + const std::tuple& kernel_size, + const std::tuple& dilation) { + +if(cc >= 80) { + using Arguments = natten::naive::ArgumentPack2D; + using Kernel = PointwiseNeighborhood2DWithBias; Kernel kernel; kernel( -cc, stream, query_ptr, key_ptr, attn_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); +cc, stream, query_ptr, key_ptr, bias_ptr, attn_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); + +} else { +std::cerr << "This half type is not supported on the selected device." << std::endl; +exit(EXIT_FAILURE); + +} } -void na2d_pn_cuda_naive_double_ks_13_di_1( - const int cc, +void na3d_pn_bias_cuda_naive_double_cm_0_0_0( + int32_t cc, cudaStream_t stream, void * query_ptr, void * key_ptr, + void * bias_ptr, void * attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, + int32_t batch_size, + int32_t heads, + int32_t depth, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood2D; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, attn_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); -} - -void na2d_pn_cuda_naive_float_ks_any_di_any( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood2D; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, attn_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); -} - -void na2d_pn_cuda_naive_float_ks_any_di_1( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood2D; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, attn_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); -} - -void na2d_pn_cuda_naive_float_ks_3_di_any( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood2D; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, attn_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); -} - -void na2d_pn_cuda_naive_float_ks_3_di_1( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood2D; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, attn_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); -} - -void na2d_pn_cuda_naive_float_ks_5_di_any( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood2D; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, attn_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); -} - -void na2d_pn_cuda_naive_float_ks_5_di_1( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood2D; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, attn_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); -} - -void na2d_pn_cuda_naive_float_ks_7_di_any( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood2D; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, attn_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); -} - -void na2d_pn_cuda_naive_float_ks_7_di_1( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood2D; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, attn_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); -} - -void na2d_pn_cuda_naive_float_ks_9_di_any( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood2D; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, attn_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); -} - -void na2d_pn_cuda_naive_float_ks_9_di_1( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood2D; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, attn_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); -} - -void na2d_pn_cuda_naive_float_ks_11_di_any( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood2D; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, attn_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); -} - -void na2d_pn_cuda_naive_float_ks_11_di_1( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood2D; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, attn_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); -} - -void na2d_pn_cuda_naive_float_ks_13_di_any( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood2D; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, attn_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); -} - -void na2d_pn_cuda_naive_float_ks_13_di_1( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood2D; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, attn_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); -} - -void na2d_pn_cuda_naive_half_ks_any_di_any( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation) { - -if(cc >= 60) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood2D; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, attn_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na2d_pn_cuda_naive_half_ks_any_di_1( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation) { - -if(cc >= 60) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood2D; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, attn_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na2d_pn_cuda_naive_half_ks_3_di_any( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation) { - -if(cc >= 60) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood2D; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, attn_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na2d_pn_cuda_naive_half_ks_3_di_1( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation) { - -if(cc >= 60) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood2D; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, attn_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na2d_pn_cuda_naive_half_ks_5_di_any( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation) { - -if(cc >= 60) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood2D; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, attn_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na2d_pn_cuda_naive_half_ks_5_di_1( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation) { - -if(cc >= 60) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood2D; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, attn_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na2d_pn_cuda_naive_half_ks_7_di_any( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation) { - -if(cc >= 60) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood2D; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, attn_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na2d_pn_cuda_naive_half_ks_7_di_1( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation) { - -if(cc >= 60) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood2D; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, attn_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na2d_pn_cuda_naive_half_ks_9_di_any( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation) { - -if(cc >= 60) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood2D; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, attn_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na2d_pn_cuda_naive_half_ks_9_di_1( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation) { - -if(cc >= 60) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood2D; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, attn_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na2d_pn_cuda_naive_half_ks_11_di_any( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation) { - -if(cc >= 60) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood2D; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, attn_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na2d_pn_cuda_naive_half_ks_11_di_1( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation) { - -if(cc >= 60) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood2D; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, attn_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na2d_pn_cuda_naive_half_ks_13_di_any( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation) { - -if(cc >= 60) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood2D; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, attn_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na2d_pn_cuda_naive_half_ks_13_di_1( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation) { - -if(cc >= 60) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood2D; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, attn_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na2d_pn_cuda_naive_bfloat16_ks_any_di_any( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation) { - -if(cc >= 80) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood2D; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, attn_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na2d_pn_cuda_naive_bfloat16_ks_any_di_1( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation) { - -if(cc >= 80) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood2D; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, attn_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na2d_pn_cuda_naive_bfloat16_ks_3_di_any( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation) { - -if(cc >= 80) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood2D; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, attn_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na2d_pn_cuda_naive_bfloat16_ks_3_di_1( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation) { - -if(cc >= 80) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood2D; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, attn_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na2d_pn_cuda_naive_bfloat16_ks_5_di_any( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation) { - -if(cc >= 80) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood2D; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, attn_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na2d_pn_cuda_naive_bfloat16_ks_5_di_1( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation) { - -if(cc >= 80) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood2D; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, attn_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na2d_pn_cuda_naive_bfloat16_ks_7_di_any( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation) { - -if(cc >= 80) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood2D; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, attn_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na2d_pn_cuda_naive_bfloat16_ks_7_di_1( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation) { - -if(cc >= 80) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood2D; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, attn_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na2d_pn_cuda_naive_bfloat16_ks_9_di_any( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation) { - -if(cc >= 80) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood2D; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, attn_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na2d_pn_cuda_naive_bfloat16_ks_9_di_1( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation) { - -if(cc >= 80) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood2D; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, attn_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na2d_pn_cuda_naive_bfloat16_ks_11_di_any( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation) { - -if(cc >= 80) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood2D; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, attn_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na2d_pn_cuda_naive_bfloat16_ks_11_di_1( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation) { - -if(cc >= 80) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood2D; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, attn_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na2d_pn_cuda_naive_bfloat16_ks_13_di_any( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation) { - -if(cc >= 80) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood2D; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, attn_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na2d_pn_cuda_naive_bfloat16_ks_13_di_1( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation) { - -if(cc >= 80) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood2D; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, attn_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na3d_pn_cuda_naive_double_ks_any_di_any( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood3D; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, attn_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); -} - -void na3d_pn_cuda_naive_double_ks_any_di_1( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood3D; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, attn_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); -} - -void na3d_pn_cuda_naive_double_ks_3_di_any( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood3D; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, attn_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); -} - -void na3d_pn_cuda_naive_double_ks_3_di_1( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood3D; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, attn_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); -} - -void na3d_pn_cuda_naive_double_ks_5_di_any( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood3D; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, attn_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); -} - -void na3d_pn_cuda_naive_double_ks_5_di_1( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood3D; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, attn_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); -} - -void na3d_pn_cuda_naive_double_ks_7_di_any( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood3D; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, attn_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); -} - -void na3d_pn_cuda_naive_double_ks_7_di_1( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood3D; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, attn_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); -} - -void na3d_pn_cuda_naive_double_ks_9_di_any( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood3D; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, attn_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); -} - -void na3d_pn_cuda_naive_double_ks_9_di_1( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood3D; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, attn_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); -} - -void na3d_pn_cuda_naive_double_ks_11_di_any( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood3D; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, attn_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); -} - -void na3d_pn_cuda_naive_double_ks_11_di_1( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood3D; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, attn_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); -} - -void na3d_pn_cuda_naive_double_ks_13_di_any( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood3D; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, attn_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); -} - -void na3d_pn_cuda_naive_double_ks_13_di_1( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood3D; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, attn_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); -} - -void na3d_pn_cuda_naive_float_ks_any_di_any( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood3D; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, attn_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); -} - -void na3d_pn_cuda_naive_float_ks_any_di_1( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood3D; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, attn_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); -} - -void na3d_pn_cuda_naive_float_ks_3_di_any( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood3D; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, attn_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); -} - -void na3d_pn_cuda_naive_float_ks_3_di_1( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood3D; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, attn_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); -} - -void na3d_pn_cuda_naive_float_ks_5_di_any( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood3D; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, attn_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); -} - -void na3d_pn_cuda_naive_float_ks_5_di_1( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood3D; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, attn_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); -} - -void na3d_pn_cuda_naive_float_ks_7_di_any( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood3D; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, attn_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); -} - -void na3d_pn_cuda_naive_float_ks_7_di_1( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood3D; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, attn_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); -} - -void na3d_pn_cuda_naive_float_ks_9_di_any( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood3D; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, attn_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); -} - -void na3d_pn_cuda_naive_float_ks_9_di_1( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood3D; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, attn_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); -} - -void na3d_pn_cuda_naive_float_ks_11_di_any( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood3D; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, attn_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); -} - -void na3d_pn_cuda_naive_float_ks_11_di_1( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood3D; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, attn_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); -} - -void na3d_pn_cuda_naive_float_ks_13_di_any( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood3D; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, attn_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); -} - -void na3d_pn_cuda_naive_float_ks_13_di_1( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood3D; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, attn_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); -} - -void na3d_pn_cuda_naive_half_ks_any_di_any( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { - -if(cc >= 60) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood3D; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, attn_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na3d_pn_cuda_naive_half_ks_any_di_1( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { - -if(cc >= 60) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood3D; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, attn_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na3d_pn_cuda_naive_half_ks_3_di_any( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { - -if(cc >= 60) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood3D; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, attn_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na3d_pn_cuda_naive_half_ks_3_di_1( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { - -if(cc >= 60) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood3D; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, attn_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na3d_pn_cuda_naive_half_ks_5_di_any( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { - -if(cc >= 60) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood3D; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, attn_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na3d_pn_cuda_naive_half_ks_5_di_1( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { - -if(cc >= 60) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood3D; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, attn_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na3d_pn_cuda_naive_half_ks_7_di_any( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { - -if(cc >= 60) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood3D; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, attn_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na3d_pn_cuda_naive_half_ks_7_di_1( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { - -if(cc >= 60) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood3D; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, attn_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na3d_pn_cuda_naive_half_ks_9_di_any( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { - -if(cc >= 60) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood3D; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, attn_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na3d_pn_cuda_naive_half_ks_9_di_1( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { - -if(cc >= 60) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood3D; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, attn_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na3d_pn_cuda_naive_half_ks_11_di_any( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { - -if(cc >= 60) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood3D; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, attn_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na3d_pn_cuda_naive_half_ks_11_di_1( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { - -if(cc >= 60) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood3D; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, attn_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na3d_pn_cuda_naive_half_ks_13_di_any( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { - -if(cc >= 60) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood3D; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, attn_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na3d_pn_cuda_naive_half_ks_13_di_1( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { - -if(cc >= 60) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood3D; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, attn_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na3d_pn_cuda_naive_bfloat16_ks_any_di_any( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { - -if(cc >= 80) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood3D; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, attn_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na3d_pn_cuda_naive_bfloat16_ks_any_di_1( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { - -if(cc >= 80) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood3D; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, attn_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na3d_pn_cuda_naive_bfloat16_ks_3_di_any( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { - -if(cc >= 80) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood3D; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, attn_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na3d_pn_cuda_naive_bfloat16_ks_3_di_1( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { - -if(cc >= 80) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood3D; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, attn_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na3d_pn_cuda_naive_bfloat16_ks_5_di_any( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { - -if(cc >= 80) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood3D; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, attn_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na3d_pn_cuda_naive_bfloat16_ks_5_di_1( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { - -if(cc >= 80) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood3D; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, attn_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na3d_pn_cuda_naive_bfloat16_ks_7_di_any( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { - -if(cc >= 80) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood3D; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, attn_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na3d_pn_cuda_naive_bfloat16_ks_7_di_1( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { - -if(cc >= 80) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood3D; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, attn_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na3d_pn_cuda_naive_bfloat16_ks_9_di_any( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { - -if(cc >= 80) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood3D; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, attn_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na3d_pn_cuda_naive_bfloat16_ks_9_di_1( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { - -if(cc >= 80) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood3D; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, attn_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na3d_pn_cuda_naive_bfloat16_ks_11_di_any( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { - -if(cc >= 80) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood3D; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, attn_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na3d_pn_cuda_naive_bfloat16_ks_11_di_1( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { - -if(cc >= 80) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood3D; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, attn_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na3d_pn_cuda_naive_bfloat16_ks_13_di_any( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { - -if(cc >= 80) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood3D; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, attn_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na3d_pn_cuda_naive_bfloat16_ks_13_di_1( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { - -if(cc >= 80) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood3D; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, attn_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na1d_pn_bias_cuda_naive_double_ks_any_di_any( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood1DWithBias; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, bias_ptr, attn_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); -} - -void na1d_pn_bias_cuda_naive_double_ks_any_di_1( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood1DWithBias; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, bias_ptr, attn_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); -} - -void na1d_pn_bias_cuda_naive_double_ks_3_di_any( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood1DWithBias; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, bias_ptr, attn_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); -} - -void na1d_pn_bias_cuda_naive_double_ks_3_di_1( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood1DWithBias; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, bias_ptr, attn_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); -} - -void na1d_pn_bias_cuda_naive_double_ks_5_di_any( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood1DWithBias; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, bias_ptr, attn_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); -} - -void na1d_pn_bias_cuda_naive_double_ks_5_di_1( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood1DWithBias; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, bias_ptr, attn_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); -} - -void na1d_pn_bias_cuda_naive_double_ks_7_di_any( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood1DWithBias; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, bias_ptr, attn_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); -} - -void na1d_pn_bias_cuda_naive_double_ks_7_di_1( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood1DWithBias; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, bias_ptr, attn_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); -} - -void na1d_pn_bias_cuda_naive_double_ks_9_di_any( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood1DWithBias; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, bias_ptr, attn_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); -} - -void na1d_pn_bias_cuda_naive_double_ks_9_di_1( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood1DWithBias; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, bias_ptr, attn_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); -} - -void na1d_pn_bias_cuda_naive_double_ks_11_di_any( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood1DWithBias; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, bias_ptr, attn_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); -} - -void na1d_pn_bias_cuda_naive_double_ks_11_di_1( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood1DWithBias; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, bias_ptr, attn_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); -} - -void na1d_pn_bias_cuda_naive_double_ks_13_di_any( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood1DWithBias; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, bias_ptr, attn_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); -} - -void na1d_pn_bias_cuda_naive_double_ks_13_di_1( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood1DWithBias; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, bias_ptr, attn_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); -} - -void na1d_pn_bias_cuda_naive_float_ks_any_di_any( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood1DWithBias; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, bias_ptr, attn_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); -} - -void na1d_pn_bias_cuda_naive_float_ks_any_di_1( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood1DWithBias; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, bias_ptr, attn_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); -} - -void na1d_pn_bias_cuda_naive_float_ks_3_di_any( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood1DWithBias; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, bias_ptr, attn_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); -} - -void na1d_pn_bias_cuda_naive_float_ks_3_di_1( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood1DWithBias; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, bias_ptr, attn_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); -} - -void na1d_pn_bias_cuda_naive_float_ks_5_di_any( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood1DWithBias; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, bias_ptr, attn_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); -} - -void na1d_pn_bias_cuda_naive_float_ks_5_di_1( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood1DWithBias; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, bias_ptr, attn_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); -} - -void na1d_pn_bias_cuda_naive_float_ks_7_di_any( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood1DWithBias; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, bias_ptr, attn_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); -} - -void na1d_pn_bias_cuda_naive_float_ks_7_di_1( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood1DWithBias; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, bias_ptr, attn_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); -} - -void na1d_pn_bias_cuda_naive_float_ks_9_di_any( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood1DWithBias; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, bias_ptr, attn_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); -} - -void na1d_pn_bias_cuda_naive_float_ks_9_di_1( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood1DWithBias; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, bias_ptr, attn_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); -} - -void na1d_pn_bias_cuda_naive_float_ks_11_di_any( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood1DWithBias; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, bias_ptr, attn_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); -} - -void na1d_pn_bias_cuda_naive_float_ks_11_di_1( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood1DWithBias; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, bias_ptr, attn_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); -} - -void na1d_pn_bias_cuda_naive_float_ks_13_di_any( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood1DWithBias; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, bias_ptr, attn_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); -} - -void na1d_pn_bias_cuda_naive_float_ks_13_di_1( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood1DWithBias; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, bias_ptr, attn_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); -} - -void na1d_pn_bias_cuda_naive_half_ks_any_di_any( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation) { - -if(cc >= 60) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood1DWithBias; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, bias_ptr, attn_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na1d_pn_bias_cuda_naive_half_ks_any_di_1( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation) { - -if(cc >= 60) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood1DWithBias; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, bias_ptr, attn_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na1d_pn_bias_cuda_naive_half_ks_3_di_any( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation) { - -if(cc >= 60) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood1DWithBias; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, bias_ptr, attn_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na1d_pn_bias_cuda_naive_half_ks_3_di_1( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation) { - -if(cc >= 60) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood1DWithBias; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, bias_ptr, attn_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na1d_pn_bias_cuda_naive_half_ks_5_di_any( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation) { - -if(cc >= 60) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood1DWithBias; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, bias_ptr, attn_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na1d_pn_bias_cuda_naive_half_ks_5_di_1( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation) { - -if(cc >= 60) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood1DWithBias; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, bias_ptr, attn_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na1d_pn_bias_cuda_naive_half_ks_7_di_any( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation) { - -if(cc >= 60) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood1DWithBias; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, bias_ptr, attn_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na1d_pn_bias_cuda_naive_half_ks_7_di_1( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation) { - -if(cc >= 60) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood1DWithBias; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, bias_ptr, attn_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na1d_pn_bias_cuda_naive_half_ks_9_di_any( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation) { - -if(cc >= 60) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood1DWithBias; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, bias_ptr, attn_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na1d_pn_bias_cuda_naive_half_ks_9_di_1( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation) { - -if(cc >= 60) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood1DWithBias; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, bias_ptr, attn_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na1d_pn_bias_cuda_naive_half_ks_11_di_any( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation) { - -if(cc >= 60) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood1DWithBias; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, bias_ptr, attn_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na1d_pn_bias_cuda_naive_half_ks_11_di_1( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation) { - -if(cc >= 60) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood1DWithBias; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, bias_ptr, attn_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na1d_pn_bias_cuda_naive_half_ks_13_di_any( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation) { - -if(cc >= 60) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood1DWithBias; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, bias_ptr, attn_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na1d_pn_bias_cuda_naive_half_ks_13_di_1( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation) { - -if(cc >= 60) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood1DWithBias; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, bias_ptr, attn_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na1d_pn_bias_cuda_naive_bfloat16_ks_any_di_any( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation) { - -if(cc >= 80) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood1DWithBias; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, bias_ptr, attn_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na1d_pn_bias_cuda_naive_bfloat16_ks_any_di_1( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation) { - -if(cc >= 80) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood1DWithBias; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, bias_ptr, attn_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na1d_pn_bias_cuda_naive_bfloat16_ks_3_di_any( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation) { - -if(cc >= 80) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood1DWithBias; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, bias_ptr, attn_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na1d_pn_bias_cuda_naive_bfloat16_ks_3_di_1( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation) { - -if(cc >= 80) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood1DWithBias; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, bias_ptr, attn_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na1d_pn_bias_cuda_naive_bfloat16_ks_5_di_any( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation) { - -if(cc >= 80) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood1DWithBias; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, bias_ptr, attn_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na1d_pn_bias_cuda_naive_bfloat16_ks_5_di_1( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation) { - -if(cc >= 80) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood1DWithBias; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, bias_ptr, attn_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na1d_pn_bias_cuda_naive_bfloat16_ks_7_di_any( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation) { - -if(cc >= 80) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood1DWithBias; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, bias_ptr, attn_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na1d_pn_bias_cuda_naive_bfloat16_ks_7_di_1( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation) { - -if(cc >= 80) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood1DWithBias; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, bias_ptr, attn_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na1d_pn_bias_cuda_naive_bfloat16_ks_9_di_any( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation) { - -if(cc >= 80) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood1DWithBias; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, bias_ptr, attn_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na1d_pn_bias_cuda_naive_bfloat16_ks_9_di_1( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation) { - -if(cc >= 80) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood1DWithBias; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, bias_ptr, attn_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na1d_pn_bias_cuda_naive_bfloat16_ks_11_di_any( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation) { - -if(cc >= 80) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood1DWithBias; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, bias_ptr, attn_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na1d_pn_bias_cuda_naive_bfloat16_ks_11_di_1( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation) { - -if(cc >= 80) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood1DWithBias; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, bias_ptr, attn_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na1d_pn_bias_cuda_naive_bfloat16_ks_13_di_any( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation) { - -if(cc >= 80) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood1DWithBias; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, bias_ptr, attn_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na1d_pn_bias_cuda_naive_bfloat16_ks_13_di_1( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation) { - -if(cc >= 80) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood1DWithBias; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, bias_ptr, attn_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na2d_pn_bias_cuda_naive_double_ks_any_di_any( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood2DWithBias; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, bias_ptr, attn_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); -} - -void na2d_pn_bias_cuda_naive_double_ks_any_di_1( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood2DWithBias; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, bias_ptr, attn_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); -} - -void na2d_pn_bias_cuda_naive_double_ks_3_di_any( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood2DWithBias; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, bias_ptr, attn_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); -} - -void na2d_pn_bias_cuda_naive_double_ks_3_di_1( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood2DWithBias; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, bias_ptr, attn_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); -} - -void na2d_pn_bias_cuda_naive_double_ks_5_di_any( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood2DWithBias; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, bias_ptr, attn_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); -} - -void na2d_pn_bias_cuda_naive_double_ks_5_di_1( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood2DWithBias; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, bias_ptr, attn_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); -} - -void na2d_pn_bias_cuda_naive_double_ks_7_di_any( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood2DWithBias; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, bias_ptr, attn_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); -} - -void na2d_pn_bias_cuda_naive_double_ks_7_di_1( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood2DWithBias; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, bias_ptr, attn_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); -} - -void na2d_pn_bias_cuda_naive_double_ks_9_di_any( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood2DWithBias; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, bias_ptr, attn_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); -} - -void na2d_pn_bias_cuda_naive_double_ks_9_di_1( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood2DWithBias; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, bias_ptr, attn_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); -} - -void na2d_pn_bias_cuda_naive_double_ks_11_di_any( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood2DWithBias; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, bias_ptr, attn_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); -} - -void na2d_pn_bias_cuda_naive_double_ks_11_di_1( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood2DWithBias; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, bias_ptr, attn_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); -} - -void na2d_pn_bias_cuda_naive_double_ks_13_di_any( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood2DWithBias; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, bias_ptr, attn_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); -} - -void na2d_pn_bias_cuda_naive_double_ks_13_di_1( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood2DWithBias; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, bias_ptr, attn_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); -} - -void na2d_pn_bias_cuda_naive_float_ks_any_di_any( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood2DWithBias; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, bias_ptr, attn_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); -} - -void na2d_pn_bias_cuda_naive_float_ks_any_di_1( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood2DWithBias; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, bias_ptr, attn_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); -} - -void na2d_pn_bias_cuda_naive_float_ks_3_di_any( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood2DWithBias; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, bias_ptr, attn_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); -} - -void na2d_pn_bias_cuda_naive_float_ks_3_di_1( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood2DWithBias; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, bias_ptr, attn_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); -} - -void na2d_pn_bias_cuda_naive_float_ks_5_di_any( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood2DWithBias; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, bias_ptr, attn_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); -} - -void na2d_pn_bias_cuda_naive_float_ks_5_di_1( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood2DWithBias; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, bias_ptr, attn_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); -} - -void na2d_pn_bias_cuda_naive_float_ks_7_di_any( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood2DWithBias; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, bias_ptr, attn_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); -} - -void na2d_pn_bias_cuda_naive_float_ks_7_di_1( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood2DWithBias; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, bias_ptr, attn_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); -} - -void na2d_pn_bias_cuda_naive_float_ks_9_di_any( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood2DWithBias; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, bias_ptr, attn_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); -} - -void na2d_pn_bias_cuda_naive_float_ks_9_di_1( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood2DWithBias; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, bias_ptr, attn_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); -} - -void na2d_pn_bias_cuda_naive_float_ks_11_di_any( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood2DWithBias; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, bias_ptr, attn_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); -} - -void na2d_pn_bias_cuda_naive_float_ks_11_di_1( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood2DWithBias; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, bias_ptr, attn_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); -} - -void na2d_pn_bias_cuda_naive_float_ks_13_di_any( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood2DWithBias; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, bias_ptr, attn_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); -} - -void na2d_pn_bias_cuda_naive_float_ks_13_di_1( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood2DWithBias; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, bias_ptr, attn_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); -} - -void na2d_pn_bias_cuda_naive_half_ks_any_di_any( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation) { - -if(cc >= 60) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood2DWithBias; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, bias_ptr, attn_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na2d_pn_bias_cuda_naive_half_ks_any_di_1( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation) { - -if(cc >= 60) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood2DWithBias; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, bias_ptr, attn_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na2d_pn_bias_cuda_naive_half_ks_3_di_any( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation) { - -if(cc >= 60) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood2DWithBias; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, bias_ptr, attn_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na2d_pn_bias_cuda_naive_half_ks_3_di_1( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation) { - -if(cc >= 60) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood2DWithBias; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, bias_ptr, attn_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na2d_pn_bias_cuda_naive_half_ks_5_di_any( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation) { - -if(cc >= 60) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood2DWithBias; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, bias_ptr, attn_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na2d_pn_bias_cuda_naive_half_ks_5_di_1( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation) { - -if(cc >= 60) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood2DWithBias; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, bias_ptr, attn_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na2d_pn_bias_cuda_naive_half_ks_7_di_any( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation) { - -if(cc >= 60) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood2DWithBias; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, bias_ptr, attn_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na2d_pn_bias_cuda_naive_half_ks_7_di_1( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation) { - -if(cc >= 60) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood2DWithBias; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, bias_ptr, attn_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na2d_pn_bias_cuda_naive_half_ks_9_di_any( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation) { - -if(cc >= 60) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood2DWithBias; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, bias_ptr, attn_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na2d_pn_bias_cuda_naive_half_ks_9_di_1( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation) { - -if(cc >= 60) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood2DWithBias; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, bias_ptr, attn_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na2d_pn_bias_cuda_naive_half_ks_11_di_any( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation) { - -if(cc >= 60) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood2DWithBias; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, bias_ptr, attn_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na2d_pn_bias_cuda_naive_half_ks_11_di_1( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation) { - -if(cc >= 60) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood2DWithBias; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, bias_ptr, attn_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na2d_pn_bias_cuda_naive_half_ks_13_di_any( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation) { - -if(cc >= 60) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood2DWithBias; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, bias_ptr, attn_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na2d_pn_bias_cuda_naive_half_ks_13_di_1( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation) { - -if(cc >= 60) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood2DWithBias; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, bias_ptr, attn_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na2d_pn_bias_cuda_naive_bfloat16_ks_any_di_any( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation) { - -if(cc >= 80) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood2DWithBias; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, bias_ptr, attn_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na2d_pn_bias_cuda_naive_bfloat16_ks_any_di_1( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation) { - -if(cc >= 80) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood2DWithBias; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, bias_ptr, attn_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na2d_pn_bias_cuda_naive_bfloat16_ks_3_di_any( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation) { - -if(cc >= 80) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood2DWithBias; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, bias_ptr, attn_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na2d_pn_bias_cuda_naive_bfloat16_ks_3_di_1( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation) { - -if(cc >= 80) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood2DWithBias; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, bias_ptr, attn_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na2d_pn_bias_cuda_naive_bfloat16_ks_5_di_any( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation) { - -if(cc >= 80) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood2DWithBias; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, bias_ptr, attn_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na2d_pn_bias_cuda_naive_bfloat16_ks_5_di_1( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation) { - -if(cc >= 80) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood2DWithBias; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, bias_ptr, attn_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na2d_pn_bias_cuda_naive_bfloat16_ks_7_di_any( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation) { - -if(cc >= 80) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood2DWithBias; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, bias_ptr, attn_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na2d_pn_bias_cuda_naive_bfloat16_ks_7_di_1( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation) { - -if(cc >= 80) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood2DWithBias; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, bias_ptr, attn_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na2d_pn_bias_cuda_naive_bfloat16_ks_9_di_any( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation) { - -if(cc >= 80) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood2DWithBias; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, bias_ptr, attn_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na2d_pn_bias_cuda_naive_bfloat16_ks_9_di_1( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation) { - -if(cc >= 80) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood2DWithBias; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, bias_ptr, attn_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na2d_pn_bias_cuda_naive_bfloat16_ks_11_di_any( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation) { - -if(cc >= 80) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood2DWithBias; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, bias_ptr, attn_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na2d_pn_bias_cuda_naive_bfloat16_ks_11_di_1( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation) { - -if(cc >= 80) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood2DWithBias; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, bias_ptr, attn_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na2d_pn_bias_cuda_naive_bfloat16_ks_13_di_any( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation) { - -if(cc >= 80) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood2DWithBias; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, bias_ptr, attn_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na2d_pn_bias_cuda_naive_bfloat16_ks_13_di_1( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation) { - -if(cc >= 80) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood2DWithBias; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, bias_ptr, attn_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na3d_pn_bias_cuda_naive_double_ks_any_di_any( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood3DWithBias; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, bias_ptr, attn_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); -} - -void na3d_pn_bias_cuda_naive_double_ks_any_di_1( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood3DWithBias; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, bias_ptr, attn_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); -} - -void na3d_pn_bias_cuda_naive_double_ks_3_di_any( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood3DWithBias; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, bias_ptr, attn_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); -} - -void na3d_pn_bias_cuda_naive_double_ks_3_di_1( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood3DWithBias; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, bias_ptr, attn_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); -} - -void na3d_pn_bias_cuda_naive_double_ks_5_di_any( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood3DWithBias; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, bias_ptr, attn_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); -} - -void na3d_pn_bias_cuda_naive_double_ks_5_di_1( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood3DWithBias; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, bias_ptr, attn_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); -} - -void na3d_pn_bias_cuda_naive_double_ks_7_di_any( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood3DWithBias; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, bias_ptr, attn_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); -} - -void na3d_pn_bias_cuda_naive_double_ks_7_di_1( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood3DWithBias; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, bias_ptr, attn_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); -} - -void na3d_pn_bias_cuda_naive_double_ks_9_di_any( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood3DWithBias; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, bias_ptr, attn_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); -} - -void na3d_pn_bias_cuda_naive_double_ks_9_di_1( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood3DWithBias; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, bias_ptr, attn_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); -} - -void na3d_pn_bias_cuda_naive_double_ks_11_di_any( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood3DWithBias; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, bias_ptr, attn_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); -} - -void na3d_pn_bias_cuda_naive_double_ks_11_di_1( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood3DWithBias; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, bias_ptr, attn_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); -} - -void na3d_pn_bias_cuda_naive_double_ks_13_di_any( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood3DWithBias; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, bias_ptr, attn_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); -} - -void na3d_pn_bias_cuda_naive_double_ks_13_di_1( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood3DWithBias; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, bias_ptr, attn_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); -} - -void na3d_pn_bias_cuda_naive_float_ks_any_di_any( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood3DWithBias; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, bias_ptr, attn_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); -} - -void na3d_pn_bias_cuda_naive_float_ks_any_di_1( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood3DWithBias; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, bias_ptr, attn_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); -} - -void na3d_pn_bias_cuda_naive_float_ks_3_di_any( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood3DWithBias; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, bias_ptr, attn_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); -} - -void na3d_pn_bias_cuda_naive_float_ks_3_di_1( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood3DWithBias; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, bias_ptr, attn_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); -} - -void na3d_pn_bias_cuda_naive_float_ks_5_di_any( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood3DWithBias; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, bias_ptr, attn_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); -} - -void na3d_pn_bias_cuda_naive_float_ks_5_di_1( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood3DWithBias; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, bias_ptr, attn_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); -} - -void na3d_pn_bias_cuda_naive_float_ks_7_di_any( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood3DWithBias; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, bias_ptr, attn_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); -} - -void na3d_pn_bias_cuda_naive_float_ks_7_di_1( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood3DWithBias; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, bias_ptr, attn_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); -} - -void na3d_pn_bias_cuda_naive_float_ks_9_di_any( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood3DWithBias; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, bias_ptr, attn_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); -} - -void na3d_pn_bias_cuda_naive_float_ks_9_di_1( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood3DWithBias; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, bias_ptr, attn_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); -} - -void na3d_pn_bias_cuda_naive_float_ks_11_di_any( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood3DWithBias; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, bias_ptr, attn_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); -} - -void na3d_pn_bias_cuda_naive_float_ks_11_di_1( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood3DWithBias; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, bias_ptr, attn_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); -} - -void na3d_pn_bias_cuda_naive_float_ks_13_di_any( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood3DWithBias; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, bias_ptr, attn_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); -} - -void na3d_pn_bias_cuda_naive_float_ks_13_di_1( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood3DWithBias; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, bias_ptr, attn_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); -} - -void na3d_pn_bias_cuda_naive_half_ks_any_di_any( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { - -if(cc >= 60) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood3DWithBias; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, bias_ptr, attn_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na3d_pn_bias_cuda_naive_half_ks_any_di_1( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { - -if(cc >= 60) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood3DWithBias; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, bias_ptr, attn_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na3d_pn_bias_cuda_naive_half_ks_3_di_any( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { - -if(cc >= 60) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood3DWithBias; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, bias_ptr, attn_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na3d_pn_bias_cuda_naive_half_ks_3_di_1( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { - -if(cc >= 60) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood3DWithBias; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, bias_ptr, attn_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na3d_pn_bias_cuda_naive_half_ks_5_di_any( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { - -if(cc >= 60) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood3DWithBias; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, bias_ptr, attn_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na3d_pn_bias_cuda_naive_half_ks_5_di_1( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { - -if(cc >= 60) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood3DWithBias; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, bias_ptr, attn_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na3d_pn_bias_cuda_naive_half_ks_7_di_any( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { - -if(cc >= 60) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood3DWithBias; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, bias_ptr, attn_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na3d_pn_bias_cuda_naive_half_ks_7_di_1( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { - -if(cc >= 60) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood3DWithBias; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, bias_ptr, attn_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na3d_pn_bias_cuda_naive_half_ks_9_di_any( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { - -if(cc >= 60) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood3DWithBias; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, bias_ptr, attn_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na3d_pn_bias_cuda_naive_half_ks_9_di_1( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { - -if(cc >= 60) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood3DWithBias; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, bias_ptr, attn_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na3d_pn_bias_cuda_naive_half_ks_11_di_any( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { - -if(cc >= 60) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood3DWithBias; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, bias_ptr, attn_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na3d_pn_bias_cuda_naive_half_ks_11_di_1( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { - -if(cc >= 60) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood3DWithBias; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, bias_ptr, attn_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na3d_pn_bias_cuda_naive_half_ks_13_di_any( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { - -if(cc >= 60) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood3DWithBias; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, bias_ptr, attn_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na3d_pn_bias_cuda_naive_half_ks_13_di_1( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { - -if(cc >= 60) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood3DWithBias; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, bias_ptr, attn_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na3d_pn_bias_cuda_naive_bfloat16_ks_any_di_any( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { - -if(cc >= 80) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood3DWithBias; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, bias_ptr, attn_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na3d_pn_bias_cuda_naive_bfloat16_ks_any_di_1( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { - -if(cc >= 80) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood3DWithBias; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, bias_ptr, attn_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na3d_pn_bias_cuda_naive_bfloat16_ks_3_di_any( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { - -if(cc >= 80) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood3DWithBias; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, bias_ptr, attn_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na3d_pn_bias_cuda_naive_bfloat16_ks_3_di_1( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { - -if(cc >= 80) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood3DWithBias; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, bias_ptr, attn_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na3d_pn_bias_cuda_naive_bfloat16_ks_5_di_any( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { - -if(cc >= 80) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood3DWithBias; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, bias_ptr, attn_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na3d_pn_bias_cuda_naive_bfloat16_ks_5_di_1( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { - -if(cc >= 80) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood3DWithBias; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, bias_ptr, attn_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na3d_pn_bias_cuda_naive_bfloat16_ks_7_di_any( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { - -if(cc >= 80) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood3DWithBias; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, bias_ptr, attn_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na3d_pn_bias_cuda_naive_bfloat16_ks_7_di_1( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { - -if(cc >= 80) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood3DWithBias; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, bias_ptr, attn_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na3d_pn_bias_cuda_naive_bfloat16_ks_9_di_any( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { - -if(cc >= 80) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood3DWithBias; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, bias_ptr, attn_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na3d_pn_bias_cuda_naive_bfloat16_ks_9_di_1( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { - -if(cc >= 80) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood3DWithBias; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, bias_ptr, attn_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na3d_pn_bias_cuda_naive_bfloat16_ks_11_di_any( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { - -if(cc >= 80) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood3DWithBias; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, bias_ptr, attn_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na3d_pn_bias_cuda_naive_bfloat16_ks_11_di_1( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { - -if(cc >= 80) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood3DWithBias; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, bias_ptr, attn_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na3d_pn_bias_cuda_naive_bfloat16_ks_13_di_any( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { - -if(cc >= 80) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood3DWithBias; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, bias_ptr, attn_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na3d_pn_bias_cuda_naive_bfloat16_ks_13_di_1( - const int cc, - cudaStream_t stream, - void * query_ptr, - void * key_ptr, - void * bias_ptr, - void * attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { - -if(cc >= 80) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = PointwiseNeighborhood3DWithBias; - Kernel kernel; - kernel( -cc, stream, query_ptr, key_ptr, bias_ptr, attn_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na1d_nn_cuda_naive_double_ks_any_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = NeighborhoodNeighborhood1D; - Kernel kernel; - kernel( -cc, stream, attn_ptr, value_ptr, output_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); -} - -void na1d_nn_cuda_naive_double_ks_any_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = NeighborhoodNeighborhood1D; - Kernel kernel; - kernel( -cc, stream, attn_ptr, value_ptr, output_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); -} - -void na1d_nn_cuda_naive_double_ks_3_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = NeighborhoodNeighborhood1D; - Kernel kernel; - kernel( -cc, stream, attn_ptr, value_ptr, output_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); -} - -void na1d_nn_cuda_naive_double_ks_3_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = NeighborhoodNeighborhood1D; - Kernel kernel; - kernel( -cc, stream, attn_ptr, value_ptr, output_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); -} - -void na1d_nn_cuda_naive_double_ks_5_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = NeighborhoodNeighborhood1D; - Kernel kernel; - kernel( -cc, stream, attn_ptr, value_ptr, output_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); -} - -void na1d_nn_cuda_naive_double_ks_5_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = NeighborhoodNeighborhood1D; - Kernel kernel; - kernel( -cc, stream, attn_ptr, value_ptr, output_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); -} - -void na1d_nn_cuda_naive_double_ks_7_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = NeighborhoodNeighborhood1D; - Kernel kernel; - kernel( -cc, stream, attn_ptr, value_ptr, output_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); -} - -void na1d_nn_cuda_naive_double_ks_7_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = NeighborhoodNeighborhood1D; - Kernel kernel; - kernel( -cc, stream, attn_ptr, value_ptr, output_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); -} - -void na1d_nn_cuda_naive_double_ks_9_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = NeighborhoodNeighborhood1D; - Kernel kernel; - kernel( -cc, stream, attn_ptr, value_ptr, output_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); -} - -void na1d_nn_cuda_naive_double_ks_9_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = NeighborhoodNeighborhood1D; - Kernel kernel; - kernel( -cc, stream, attn_ptr, value_ptr, output_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); -} - -void na1d_nn_cuda_naive_double_ks_11_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = NeighborhoodNeighborhood1D; - Kernel kernel; - kernel( -cc, stream, attn_ptr, value_ptr, output_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); -} - -void na1d_nn_cuda_naive_double_ks_11_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = NeighborhoodNeighborhood1D; - Kernel kernel; - kernel( -cc, stream, attn_ptr, value_ptr, output_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); -} - -void na1d_nn_cuda_naive_double_ks_13_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = NeighborhoodNeighborhood1D; - Kernel kernel; - kernel( -cc, stream, attn_ptr, value_ptr, output_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); -} - -void na1d_nn_cuda_naive_double_ks_13_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = NeighborhoodNeighborhood1D; - Kernel kernel; - kernel( -cc, stream, attn_ptr, value_ptr, output_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); -} - -void na1d_nn_cuda_naive_float_ks_any_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = NeighborhoodNeighborhood1D; - Kernel kernel; - kernel( -cc, stream, attn_ptr, value_ptr, output_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); -} - -void na1d_nn_cuda_naive_float_ks_any_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = NeighborhoodNeighborhood1D; - Kernel kernel; - kernel( -cc, stream, attn_ptr, value_ptr, output_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); -} - -void na1d_nn_cuda_naive_float_ks_3_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = NeighborhoodNeighborhood1D; - Kernel kernel; - kernel( -cc, stream, attn_ptr, value_ptr, output_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); -} - -void na1d_nn_cuda_naive_float_ks_3_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = NeighborhoodNeighborhood1D; - Kernel kernel; - kernel( -cc, stream, attn_ptr, value_ptr, output_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); -} - -void na1d_nn_cuda_naive_float_ks_5_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = NeighborhoodNeighborhood1D; - Kernel kernel; - kernel( -cc, stream, attn_ptr, value_ptr, output_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); -} - -void na1d_nn_cuda_naive_float_ks_5_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = NeighborhoodNeighborhood1D; - Kernel kernel; - kernel( -cc, stream, attn_ptr, value_ptr, output_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); -} - -void na1d_nn_cuda_naive_float_ks_7_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = NeighborhoodNeighborhood1D; - Kernel kernel; - kernel( -cc, stream, attn_ptr, value_ptr, output_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); -} - -void na1d_nn_cuda_naive_float_ks_7_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = NeighborhoodNeighborhood1D; - Kernel kernel; - kernel( -cc, stream, attn_ptr, value_ptr, output_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); -} - -void na1d_nn_cuda_naive_float_ks_9_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = NeighborhoodNeighborhood1D; - Kernel kernel; - kernel( -cc, stream, attn_ptr, value_ptr, output_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); -} - -void na1d_nn_cuda_naive_float_ks_9_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = NeighborhoodNeighborhood1D; - Kernel kernel; - kernel( -cc, stream, attn_ptr, value_ptr, output_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); -} - -void na1d_nn_cuda_naive_float_ks_11_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = NeighborhoodNeighborhood1D; - Kernel kernel; - kernel( -cc, stream, attn_ptr, value_ptr, output_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); -} - -void na1d_nn_cuda_naive_float_ks_11_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = NeighborhoodNeighborhood1D; - Kernel kernel; - kernel( -cc, stream, attn_ptr, value_ptr, output_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); -} - -void na1d_nn_cuda_naive_float_ks_13_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = NeighborhoodNeighborhood1D; - Kernel kernel; - kernel( -cc, stream, attn_ptr, value_ptr, output_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); -} - -void na1d_nn_cuda_naive_float_ks_13_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = NeighborhoodNeighborhood1D; - Kernel kernel; - kernel( -cc, stream, attn_ptr, value_ptr, output_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); -} - -void na1d_nn_cuda_naive_half_ks_any_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation) { - -if(cc >= 60) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = NeighborhoodNeighborhood1D; - Kernel kernel; - kernel( -cc, stream, attn_ptr, value_ptr, output_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na1d_nn_cuda_naive_half_ks_any_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation) { - -if(cc >= 60) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = NeighborhoodNeighborhood1D; - Kernel kernel; - kernel( -cc, stream, attn_ptr, value_ptr, output_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na1d_nn_cuda_naive_half_ks_3_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation) { - -if(cc >= 60) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = NeighborhoodNeighborhood1D; - Kernel kernel; - kernel( -cc, stream, attn_ptr, value_ptr, output_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na1d_nn_cuda_naive_half_ks_3_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation) { - -if(cc >= 60) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = NeighborhoodNeighborhood1D; - Kernel kernel; - kernel( -cc, stream, attn_ptr, value_ptr, output_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na1d_nn_cuda_naive_half_ks_5_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation) { - -if(cc >= 60) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = NeighborhoodNeighborhood1D; - Kernel kernel; - kernel( -cc, stream, attn_ptr, value_ptr, output_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na1d_nn_cuda_naive_half_ks_5_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation) { - -if(cc >= 60) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = NeighborhoodNeighborhood1D; - Kernel kernel; - kernel( -cc, stream, attn_ptr, value_ptr, output_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na1d_nn_cuda_naive_half_ks_7_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation) { - -if(cc >= 60) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = NeighborhoodNeighborhood1D; - Kernel kernel; - kernel( -cc, stream, attn_ptr, value_ptr, output_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na1d_nn_cuda_naive_half_ks_7_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation) { - -if(cc >= 60) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = NeighborhoodNeighborhood1D; - Kernel kernel; - kernel( -cc, stream, attn_ptr, value_ptr, output_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na1d_nn_cuda_naive_half_ks_9_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation) { - -if(cc >= 60) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = NeighborhoodNeighborhood1D; - Kernel kernel; - kernel( -cc, stream, attn_ptr, value_ptr, output_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na1d_nn_cuda_naive_half_ks_9_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation) { - -if(cc >= 60) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = NeighborhoodNeighborhood1D; - Kernel kernel; - kernel( -cc, stream, attn_ptr, value_ptr, output_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na1d_nn_cuda_naive_half_ks_11_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation) { - -if(cc >= 60) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = NeighborhoodNeighborhood1D; - Kernel kernel; - kernel( -cc, stream, attn_ptr, value_ptr, output_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na1d_nn_cuda_naive_half_ks_11_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation) { - -if(cc >= 60) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = NeighborhoodNeighborhood1D; - Kernel kernel; - kernel( -cc, stream, attn_ptr, value_ptr, output_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na1d_nn_cuda_naive_half_ks_13_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation) { - -if(cc >= 60) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = NeighborhoodNeighborhood1D; - Kernel kernel; - kernel( -cc, stream, attn_ptr, value_ptr, output_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na1d_nn_cuda_naive_half_ks_13_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation) { - -if(cc >= 60) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = NeighborhoodNeighborhood1D; - Kernel kernel; - kernel( -cc, stream, attn_ptr, value_ptr, output_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na1d_nn_cuda_naive_bfloat16_ks_any_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation) { - -if(cc >= 80) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = NeighborhoodNeighborhood1D; - Kernel kernel; - kernel( -cc, stream, attn_ptr, value_ptr, output_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na1d_nn_cuda_naive_bfloat16_ks_any_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation) { - -if(cc >= 80) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = NeighborhoodNeighborhood1D; - Kernel kernel; - kernel( -cc, stream, attn_ptr, value_ptr, output_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na1d_nn_cuda_naive_bfloat16_ks_3_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation) { - -if(cc >= 80) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = NeighborhoodNeighborhood1D; + int64_t attn_stride_4, + const std::tuple& kernel_size, + const std::tuple& dilation) { + using Arguments = natten::naive::ArgumentPack3D; + using Kernel = PointwiseNeighborhood3DWithBias; Kernel kernel; kernel( -cc, stream, attn_ptr, value_ptr, output_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} +cc, stream, query_ptr, key_ptr, bias_ptr, attn_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation); } -void na1d_nn_cuda_naive_bfloat16_ks_3_di_1( - const int cc, +void na3d_pn_bias_cuda_naive_float_cm_0_0_0( + int32_t cc, cudaStream_t stream, + void * query_ptr, + void * key_ptr, + void * bias_ptr, void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int length, - int dim, + int32_t batch_size, + int32_t heads, + int32_t depth, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, - int kernel_size, - int dilation) { - -if(cc >= 80) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = NeighborhoodNeighborhood1D; + int64_t attn_stride_3, + int64_t attn_stride_4, + const std::tuple& kernel_size, + const std::tuple& dilation) { + using Arguments = natten::naive::ArgumentPack3D; + using Kernel = PointwiseNeighborhood3DWithBias; Kernel kernel; kernel( -cc, stream, attn_ptr, value_ptr, output_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} +cc, stream, query_ptr, key_ptr, bias_ptr, attn_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation); } -void na1d_nn_cuda_naive_bfloat16_ks_5_di_any( - const int cc, +void na3d_pn_bias_cuda_naive_half_cm_0_0_0( + int32_t cc, cudaStream_t stream, + void * query_ptr, + void * key_ptr, + void * bias_ptr, void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int length, - int dim, + int32_t batch_size, + int32_t heads, + int32_t depth, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, - int kernel_size, - int dilation) { + int64_t attn_stride_3, + int64_t attn_stride_4, + const std::tuple& kernel_size, + const std::tuple& dilation) { -if(cc >= 80) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = NeighborhoodNeighborhood1D; +if(cc >= 60) { + using Arguments = natten::naive::ArgumentPack3D; + using Kernel = PointwiseNeighborhood3DWithBias; Kernel kernel; kernel( -cc, stream, attn_ptr, value_ptr, output_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); +cc, stream, query_ptr, key_ptr, bias_ptr, attn_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation); } else { std::cerr << "This half type is not supported on the selected device." << std::endl; @@ -10997,28 +1997,33 @@ exit(EXIT_FAILURE); } } -void na1d_nn_cuda_naive_bfloat16_ks_5_di_1( - const int cc, +void na3d_pn_bias_cuda_naive_bfloat16_cm_0_0_0( + int32_t cc, cudaStream_t stream, + void * query_ptr, + void * key_ptr, + void * bias_ptr, void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int length, - int dim, + int32_t batch_size, + int32_t heads, + int32_t depth, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, - int kernel_size, - int dilation) { + int64_t attn_stride_3, + int64_t attn_stride_4, + const std::tuple& kernel_size, + const std::tuple& dilation) { if(cc >= 80) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = NeighborhoodNeighborhood1D; + using Arguments = natten::naive::ArgumentPack3D; + using Kernel = PointwiseNeighborhood3DWithBias; Kernel kernel; kernel( -cc, stream, attn_ptr, value_ptr, output_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); +cc, stream, query_ptr, key_ptr, bias_ptr, attn_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation); } else { std::cerr << "This half type is not supported on the selected device." << std::endl; @@ -11027,144 +2032,112 @@ exit(EXIT_FAILURE); } } -void na1d_nn_cuda_naive_bfloat16_ks_7_di_any( - const int cc, +void na1d_nn_cuda_naive_double_cm_0( + int32_t cc, cudaStream_t stream, void * attn_ptr, void * value_ptr, void * output_ptr, - int batch_size, - int heads, - int length, - int dim, + int32_t batch_size, + int32_t heads, + int32_t length, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, - int kernel_size, - int dilation) { - -if(cc >= 80) { - using Arguments = natten::naive::ArgumentPack; + const std::tuple& kernel_size, + const std::tuple& dilation) { + using Arguments = natten::naive::ArgumentPack1D; using Kernel = NeighborhoodNeighborhood1D; Kernel kernel; kernel( cc, stream, attn_ptr, value_ptr, output_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} } -void na1d_nn_cuda_naive_bfloat16_ks_7_di_1( - const int cc, +void na1d_nn_cuda_naive_double_cm_1( + int32_t cc, cudaStream_t stream, void * attn_ptr, void * value_ptr, void * output_ptr, - int batch_size, - int heads, - int length, - int dim, + int32_t batch_size, + int32_t heads, + int32_t length, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, - int kernel_size, - int dilation) { - -if(cc >= 80) { - using Arguments = natten::naive::ArgumentPack; + const std::tuple& kernel_size, + const std::tuple& dilation) { + using Arguments = natten::naive::ArgumentPack1D; using Kernel = NeighborhoodNeighborhood1D; Kernel kernel; kernel( cc, stream, attn_ptr, value_ptr, output_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} } -void na1d_nn_cuda_naive_bfloat16_ks_9_di_any( - const int cc, +void na1d_nn_cuda_naive_float_cm_0( + int32_t cc, cudaStream_t stream, void * attn_ptr, void * value_ptr, void * output_ptr, - int batch_size, - int heads, - int length, - int dim, + int32_t batch_size, + int32_t heads, + int32_t length, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, - int kernel_size, - int dilation) { - -if(cc >= 80) { - using Arguments = natten::naive::ArgumentPack; + const std::tuple& kernel_size, + const std::tuple& dilation) { + using Arguments = natten::naive::ArgumentPack1D; using Kernel = NeighborhoodNeighborhood1D; Kernel kernel; kernel( cc, stream, attn_ptr, value_ptr, output_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} } -void na1d_nn_cuda_naive_bfloat16_ks_9_di_1( - const int cc, +void na1d_nn_cuda_naive_float_cm_1( + int32_t cc, cudaStream_t stream, void * attn_ptr, void * value_ptr, void * output_ptr, - int batch_size, - int heads, - int length, - int dim, + int32_t batch_size, + int32_t heads, + int32_t length, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, - int kernel_size, - int dilation) { - -if(cc >= 80) { - using Arguments = natten::naive::ArgumentPack; + const std::tuple& kernel_size, + const std::tuple& dilation) { + using Arguments = natten::naive::ArgumentPack1D; using Kernel = NeighborhoodNeighborhood1D; Kernel kernel; kernel( cc, stream, attn_ptr, value_ptr, output_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} } -void na1d_nn_cuda_naive_bfloat16_ks_11_di_any( - const int cc, +void na1d_nn_cuda_naive_half_cm_0( + int32_t cc, cudaStream_t stream, void * attn_ptr, void * value_ptr, void * output_ptr, - int batch_size, - int heads, - int length, - int dim, + int32_t batch_size, + int32_t heads, + int32_t length, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, - int kernel_size, - int dilation) { + const std::tuple& kernel_size, + const std::tuple& dilation) { -if(cc >= 80) { - using Arguments = natten::naive::ArgumentPack; +if(cc >= 60) { + using Arguments = natten::naive::ArgumentPack1D; using Kernel = NeighborhoodNeighborhood1D; Kernel kernel; kernel( @@ -11177,24 +2150,24 @@ exit(EXIT_FAILURE); } } -void na1d_nn_cuda_naive_bfloat16_ks_11_di_1( - const int cc, +void na1d_nn_cuda_naive_half_cm_1( + int32_t cc, cudaStream_t stream, void * attn_ptr, void * value_ptr, void * output_ptr, - int batch_size, - int heads, - int length, - int dim, + int32_t batch_size, + int32_t heads, + int32_t length, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, - int kernel_size, - int dilation) { + const std::tuple& kernel_size, + const std::tuple& dilation) { -if(cc >= 80) { - using Arguments = natten::naive::ArgumentPack; +if(cc >= 60) { + using Arguments = natten::naive::ArgumentPack1D; using Kernel = NeighborhoodNeighborhood1D; Kernel kernel; kernel( @@ -11207,24 +2180,24 @@ exit(EXIT_FAILURE); } } -void na1d_nn_cuda_naive_bfloat16_ks_13_di_any( - const int cc, +void na1d_nn_cuda_naive_bfloat16_cm_0( + int32_t cc, cudaStream_t stream, void * attn_ptr, void * value_ptr, void * output_ptr, - int batch_size, - int heads, - int length, - int dim, + int32_t batch_size, + int32_t heads, + int32_t length, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, - int kernel_size, - int dilation) { + const std::tuple& kernel_size, + const std::tuple& dilation) { if(cc >= 80) { - using Arguments = natten::naive::ArgumentPack; + using Arguments = natten::naive::ArgumentPack1D; using Kernel = NeighborhoodNeighborhood1D; Kernel kernel; kernel( @@ -11237,24 +2210,24 @@ exit(EXIT_FAILURE); } } -void na1d_nn_cuda_naive_bfloat16_ks_13_di_1( - const int cc, +void na1d_nn_cuda_naive_bfloat16_cm_1( + int32_t cc, cudaStream_t stream, void * attn_ptr, void * value_ptr, void * output_ptr, - int batch_size, - int heads, - int length, - int dim, + int32_t batch_size, + int32_t heads, + int32_t length, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, - int kernel_size, - int dilation) { + const std::tuple& kernel_size, + const std::tuple& dilation) { if(cc >= 80) { - using Arguments = natten::naive::ArgumentPack; + using Arguments = natten::naive::ArgumentPack1D; using Kernel = NeighborhoodNeighborhood1D; Kernel kernel; kernel( @@ -11267,676 +2240,556 @@ exit(EXIT_FAILURE); } } -void na2d_nn_cuda_naive_double_ks_any_di_any( - const int cc, +void na2d_nn_cuda_naive_double_cm_0_0( + int32_t cc, cudaStream_t stream, void * attn_ptr, void * value_ptr, void * output_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, + int32_t batch_size, + int32_t heads, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; + const std::tuple& kernel_size, + const std::tuple& dilation) { + using Arguments = natten::naive::ArgumentPack2D; using Kernel = NeighborhoodNeighborhood2D; Kernel kernel; kernel( cc, stream, attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); } -void na2d_nn_cuda_naive_double_ks_any_di_1( - const int cc, +void na2d_nn_cuda_naive_double_cm_0_1( + int32_t cc, cudaStream_t stream, void * attn_ptr, void * value_ptr, void * output_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, + int32_t batch_size, + int32_t heads, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; + const std::tuple& kernel_size, + const std::tuple& dilation) { + using Arguments = natten::naive::ArgumentPack2D; using Kernel = NeighborhoodNeighborhood2D; Kernel kernel; kernel( cc, stream, attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); } -void na2d_nn_cuda_naive_double_ks_3_di_any( - const int cc, +void na2d_nn_cuda_naive_double_cm_1_0( + int32_t cc, cudaStream_t stream, void * attn_ptr, void * value_ptr, void * output_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, + int32_t batch_size, + int32_t heads, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; + const std::tuple& kernel_size, + const std::tuple& dilation) { + using Arguments = natten::naive::ArgumentPack2D; using Kernel = NeighborhoodNeighborhood2D; Kernel kernel; kernel( cc, stream, attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); } -void na2d_nn_cuda_naive_double_ks_3_di_1( - const int cc, +void na2d_nn_cuda_naive_double_cm_1_1( + int32_t cc, cudaStream_t stream, void * attn_ptr, void * value_ptr, void * output_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, + int32_t batch_size, + int32_t heads, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; + const std::tuple& kernel_size, + const std::tuple& dilation) { + using Arguments = natten::naive::ArgumentPack2D; using Kernel = NeighborhoodNeighborhood2D; Kernel kernel; kernel( cc, stream, attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); } -void na2d_nn_cuda_naive_double_ks_5_di_any( - const int cc, +void na2d_nn_cuda_naive_float_cm_0_0( + int32_t cc, cudaStream_t stream, void * attn_ptr, void * value_ptr, void * output_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, + int32_t batch_size, + int32_t heads, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; + const std::tuple& kernel_size, + const std::tuple& dilation) { + using Arguments = natten::naive::ArgumentPack2D; using Kernel = NeighborhoodNeighborhood2D; Kernel kernel; kernel( cc, stream, attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); } -void na2d_nn_cuda_naive_double_ks_5_di_1( - const int cc, +void na2d_nn_cuda_naive_float_cm_0_1( + int32_t cc, cudaStream_t stream, void * attn_ptr, void * value_ptr, void * output_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, + int32_t batch_size, + int32_t heads, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; + const std::tuple& kernel_size, + const std::tuple& dilation) { + using Arguments = natten::naive::ArgumentPack2D; using Kernel = NeighborhoodNeighborhood2D; Kernel kernel; kernel( cc, stream, attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); } -void na2d_nn_cuda_naive_double_ks_7_di_any( - const int cc, +void na2d_nn_cuda_naive_float_cm_1_0( + int32_t cc, cudaStream_t stream, void * attn_ptr, void * value_ptr, void * output_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, + int32_t batch_size, + int32_t heads, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; + const std::tuple& kernel_size, + const std::tuple& dilation) { + using Arguments = natten::naive::ArgumentPack2D; using Kernel = NeighborhoodNeighborhood2D; Kernel kernel; kernel( cc, stream, attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); } -void na2d_nn_cuda_naive_double_ks_7_di_1( - const int cc, +void na2d_nn_cuda_naive_float_cm_1_1( + int32_t cc, cudaStream_t stream, void * attn_ptr, void * value_ptr, void * output_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, + int32_t batch_size, + int32_t heads, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; + const std::tuple& kernel_size, + const std::tuple& dilation) { + using Arguments = natten::naive::ArgumentPack2D; using Kernel = NeighborhoodNeighborhood2D; Kernel kernel; kernel( cc, stream, attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); } -void na2d_nn_cuda_naive_double_ks_9_di_any( - const int cc, +void na2d_nn_cuda_naive_half_cm_0_0( + int32_t cc, cudaStream_t stream, void * attn_ptr, void * value_ptr, void * output_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, + int32_t batch_size, + int32_t heads, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = NeighborhoodNeighborhood2D; - Kernel kernel; - kernel( -cc, stream, attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); -} + const std::tuple& kernel_size, + const std::tuple& dilation) { -void na2d_nn_cuda_naive_double_ks_9_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; +if(cc >= 60) { + using Arguments = natten::naive::ArgumentPack2D; using Kernel = NeighborhoodNeighborhood2D; Kernel kernel; kernel( cc, stream, attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); -} -void na2d_nn_cuda_naive_double_ks_11_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = NeighborhoodNeighborhood2D; - Kernel kernel; - kernel( -cc, stream, attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); -} +} else { +std::cerr << "This half type is not supported on the selected device." << std::endl; +exit(EXIT_FAILURE); -void na2d_nn_cuda_naive_double_ks_11_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = NeighborhoodNeighborhood2D; - Kernel kernel; - kernel( -cc, stream, attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); } - -void na2d_nn_cuda_naive_double_ks_13_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = NeighborhoodNeighborhood2D; - Kernel kernel; - kernel( -cc, stream, attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); } -void na2d_nn_cuda_naive_double_ks_13_di_1( - const int cc, +void na2d_nn_cuda_naive_half_cm_0_1( + int32_t cc, cudaStream_t stream, void * attn_ptr, void * value_ptr, void * output_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, + int32_t batch_size, + int32_t heads, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = NeighborhoodNeighborhood2D; - Kernel kernel; - kernel( -cc, stream, attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); -} + const std::tuple& kernel_size, + const std::tuple& dilation) { -void na2d_nn_cuda_naive_float_ks_any_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; +if(cc >= 60) { + using Arguments = natten::naive::ArgumentPack2D; using Kernel = NeighborhoodNeighborhood2D; Kernel kernel; kernel( cc, stream, attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); -} -void na2d_nn_cuda_naive_float_ks_any_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = NeighborhoodNeighborhood2D; - Kernel kernel; - kernel( -cc, stream, attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); -} +} else { +std::cerr << "This half type is not supported on the selected device." << std::endl; +exit(EXIT_FAILURE); -void na2d_nn_cuda_naive_float_ks_3_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = NeighborhoodNeighborhood2D; - Kernel kernel; - kernel( -cc, stream, attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); } - -void na2d_nn_cuda_naive_float_ks_3_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = NeighborhoodNeighborhood2D; - Kernel kernel; - kernel( -cc, stream, attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); } -void na2d_nn_cuda_naive_float_ks_5_di_any( - const int cc, +void na2d_nn_cuda_naive_half_cm_1_0( + int32_t cc, cudaStream_t stream, void * attn_ptr, void * value_ptr, void * output_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, + int32_t batch_size, + int32_t heads, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; + const std::tuple& kernel_size, + const std::tuple& dilation) { + +if(cc >= 60) { + using Arguments = natten::naive::ArgumentPack2D; using Kernel = NeighborhoodNeighborhood2D; Kernel kernel; kernel( cc, stream, attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); + +} else { +std::cerr << "This half type is not supported on the selected device." << std::endl; +exit(EXIT_FAILURE); + +} } -void na2d_nn_cuda_naive_float_ks_5_di_1( - const int cc, +void na2d_nn_cuda_naive_half_cm_1_1( + int32_t cc, cudaStream_t stream, void * attn_ptr, void * value_ptr, void * output_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, + int32_t batch_size, + int32_t heads, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; + const std::tuple& kernel_size, + const std::tuple& dilation) { + +if(cc >= 60) { + using Arguments = natten::naive::ArgumentPack2D; using Kernel = NeighborhoodNeighborhood2D; Kernel kernel; kernel( cc, stream, attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); + +} else { +std::cerr << "This half type is not supported on the selected device." << std::endl; +exit(EXIT_FAILURE); + +} } -void na2d_nn_cuda_naive_float_ks_7_di_any( - const int cc, +void na2d_nn_cuda_naive_bfloat16_cm_0_0( + int32_t cc, cudaStream_t stream, void * attn_ptr, void * value_ptr, void * output_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, + int32_t batch_size, + int32_t heads, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; + const std::tuple& kernel_size, + const std::tuple& dilation) { + +if(cc >= 80) { + using Arguments = natten::naive::ArgumentPack2D; using Kernel = NeighborhoodNeighborhood2D; Kernel kernel; kernel( cc, stream, attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); + +} else { +std::cerr << "This half type is not supported on the selected device." << std::endl; +exit(EXIT_FAILURE); + +} } -void na2d_nn_cuda_naive_float_ks_7_di_1( - const int cc, +void na2d_nn_cuda_naive_bfloat16_cm_0_1( + int32_t cc, cudaStream_t stream, void * attn_ptr, void * value_ptr, void * output_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, + int32_t batch_size, + int32_t heads, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; + const std::tuple& kernel_size, + const std::tuple& dilation) { + +if(cc >= 80) { + using Arguments = natten::naive::ArgumentPack2D; using Kernel = NeighborhoodNeighborhood2D; Kernel kernel; kernel( cc, stream, attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); + +} else { +std::cerr << "This half type is not supported on the selected device." << std::endl; +exit(EXIT_FAILURE); + +} } -void na2d_nn_cuda_naive_float_ks_9_di_any( - const int cc, +void na2d_nn_cuda_naive_bfloat16_cm_1_0( + int32_t cc, cudaStream_t stream, void * attn_ptr, void * value_ptr, void * output_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, + int32_t batch_size, + int32_t heads, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; + const std::tuple& kernel_size, + const std::tuple& dilation) { + +if(cc >= 80) { + using Arguments = natten::naive::ArgumentPack2D; using Kernel = NeighborhoodNeighborhood2D; Kernel kernel; kernel( cc, stream, attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); + +} else { +std::cerr << "This half type is not supported on the selected device." << std::endl; +exit(EXIT_FAILURE); + +} } -void na2d_nn_cuda_naive_float_ks_9_di_1( - const int cc, +void na2d_nn_cuda_naive_bfloat16_cm_1_1( + int32_t cc, cudaStream_t stream, void * attn_ptr, void * value_ptr, void * output_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, + int32_t batch_size, + int32_t heads, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; + const std::tuple& kernel_size, + const std::tuple& dilation) { + +if(cc >= 80) { + using Arguments = natten::naive::ArgumentPack2D; using Kernel = NeighborhoodNeighborhood2D; Kernel kernel; kernel( cc, stream, attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); + +} else { +std::cerr << "This half type is not supported on the selected device." << std::endl; +exit(EXIT_FAILURE); + +} } -void na2d_nn_cuda_naive_float_ks_11_di_any( - const int cc, +void na3d_nn_cuda_naive_double_cm_0_0_0( + int32_t cc, cudaStream_t stream, void * attn_ptr, void * value_ptr, void * output_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, + int32_t batch_size, + int32_t heads, + int32_t depth, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = NeighborhoodNeighborhood2D; + int64_t attn_stride_4, + const std::tuple& kernel_size, + const std::tuple& dilation) { + using Arguments = natten::naive::ArgumentPack3D; + using Kernel = NeighborhoodNeighborhood3D; Kernel kernel; kernel( -cc, stream, attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); +cc, stream, attn_ptr, value_ptr, output_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation); } -void na2d_nn_cuda_naive_float_ks_11_di_1( - const int cc, +void na3d_nn_cuda_naive_double_cm_0_0_1( + int32_t cc, cudaStream_t stream, void * attn_ptr, void * value_ptr, void * output_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, + int32_t batch_size, + int32_t heads, + int32_t depth, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = NeighborhoodNeighborhood2D; + int64_t attn_stride_4, + const std::tuple& kernel_size, + const std::tuple& dilation) { + using Arguments = natten::naive::ArgumentPack3D; + using Kernel = NeighborhoodNeighborhood3D; Kernel kernel; kernel( -cc, stream, attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); +cc, stream, attn_ptr, value_ptr, output_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation); } -void na2d_nn_cuda_naive_float_ks_13_di_any( - const int cc, +void na3d_nn_cuda_naive_double_cm_0_1_0( + int32_t cc, cudaStream_t stream, void * attn_ptr, void * value_ptr, void * output_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, + int32_t batch_size, + int32_t heads, + int32_t depth, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = NeighborhoodNeighborhood2D; + int64_t attn_stride_4, + const std::tuple& kernel_size, + const std::tuple& dilation) { + using Arguments = natten::naive::ArgumentPack3D; + using Kernel = NeighborhoodNeighborhood3D; Kernel kernel; kernel( -cc, stream, attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); +cc, stream, attn_ptr, value_ptr, output_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation); } -void na2d_nn_cuda_naive_float_ks_13_di_1( - const int cc, +void na3d_nn_cuda_naive_double_cm_0_1_1( + int32_t cc, cudaStream_t stream, void * attn_ptr, void * value_ptr, void * output_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, + int32_t batch_size, + int32_t heads, + int32_t depth, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = NeighborhoodNeighborhood2D; + int64_t attn_stride_4, + const std::tuple& kernel_size, + const std::tuple& dilation) { + using Arguments = natten::naive::ArgumentPack3D; + using Kernel = NeighborhoodNeighborhood3D; Kernel kernel; kernel( -cc, stream, attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); +cc, stream, attn_ptr, value_ptr, output_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation); } } diff --git a/csrc/autogen/src/cuda/naive/source_1.cu b/csrc/autogen/src/cuda/naive/source_1.cu index 4d41f3d..ef1c5d9 100644 --- a/csrc/autogen/src/cuda/naive/source_1.cu +++ b/csrc/autogen/src/cuda/naive/source_1.cu @@ -3,425 +3,354 @@ #include #include #include +#include +#include +#include #include #include -#include #include -#include -#include -#include namespace natten { namespace cuda { namespace naive { -void na2d_nn_cuda_naive_half_ks_any_di_any( - const int cc, +void na3d_nn_cuda_naive_double_cm_1_0_0( + int32_t cc, cudaStream_t stream, void * attn_ptr, void * value_ptr, void * output_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, + int32_t batch_size, + int32_t heads, + int32_t depth, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, - int kernel_size, - int dilation) { - -if(cc >= 60) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = NeighborhoodNeighborhood2D; + int64_t attn_stride_4, + const std::tuple& kernel_size, + const std::tuple& dilation) { + using Arguments = natten::naive::ArgumentPack3D; + using Kernel = NeighborhoodNeighborhood3D; Kernel kernel; kernel( -cc, stream, attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} +cc, stream, attn_ptr, value_ptr, output_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation); } -void na2d_nn_cuda_naive_half_ks_any_di_1( - const int cc, +void na3d_nn_cuda_naive_double_cm_1_0_1( + int32_t cc, cudaStream_t stream, void * attn_ptr, void * value_ptr, void * output_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, + int32_t batch_size, + int32_t heads, + int32_t depth, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, - int kernel_size, - int dilation) { - -if(cc >= 60) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = NeighborhoodNeighborhood2D; + int64_t attn_stride_4, + const std::tuple& kernel_size, + const std::tuple& dilation) { + using Arguments = natten::naive::ArgumentPack3D; + using Kernel = NeighborhoodNeighborhood3D; Kernel kernel; kernel( -cc, stream, attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} +cc, stream, attn_ptr, value_ptr, output_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation); } -void na2d_nn_cuda_naive_half_ks_3_di_any( - const int cc, +void na3d_nn_cuda_naive_double_cm_1_1_0( + int32_t cc, cudaStream_t stream, void * attn_ptr, void * value_ptr, void * output_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, + int32_t batch_size, + int32_t heads, + int32_t depth, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, - int kernel_size, - int dilation) { - -if(cc >= 60) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = NeighborhoodNeighborhood2D; + int64_t attn_stride_4, + const std::tuple& kernel_size, + const std::tuple& dilation) { + using Arguments = natten::naive::ArgumentPack3D; + using Kernel = NeighborhoodNeighborhood3D; Kernel kernel; kernel( -cc, stream, attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} +cc, stream, attn_ptr, value_ptr, output_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation); } -void na2d_nn_cuda_naive_half_ks_3_di_1( - const int cc, +void na3d_nn_cuda_naive_double_cm_1_1_1( + int32_t cc, cudaStream_t stream, void * attn_ptr, void * value_ptr, void * output_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, + int32_t batch_size, + int32_t heads, + int32_t depth, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, - int kernel_size, - int dilation) { - -if(cc >= 60) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = NeighborhoodNeighborhood2D; + int64_t attn_stride_4, + const std::tuple& kernel_size, + const std::tuple& dilation) { + using Arguments = natten::naive::ArgumentPack3D; + using Kernel = NeighborhoodNeighborhood3D; Kernel kernel; kernel( -cc, stream, attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} +cc, stream, attn_ptr, value_ptr, output_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation); } -void na2d_nn_cuda_naive_half_ks_5_di_any( - const int cc, +void na3d_nn_cuda_naive_float_cm_0_0_0( + int32_t cc, cudaStream_t stream, void * attn_ptr, void * value_ptr, void * output_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, + int32_t batch_size, + int32_t heads, + int32_t depth, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, - int kernel_size, - int dilation) { - -if(cc >= 60) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = NeighborhoodNeighborhood2D; + int64_t attn_stride_4, + const std::tuple& kernel_size, + const std::tuple& dilation) { + using Arguments = natten::naive::ArgumentPack3D; + using Kernel = NeighborhoodNeighborhood3D; Kernel kernel; kernel( -cc, stream, attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} +cc, stream, attn_ptr, value_ptr, output_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation); } -void na2d_nn_cuda_naive_half_ks_5_di_1( - const int cc, +void na3d_nn_cuda_naive_float_cm_0_0_1( + int32_t cc, cudaStream_t stream, void * attn_ptr, void * value_ptr, void * output_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, + int32_t batch_size, + int32_t heads, + int32_t depth, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, - int kernel_size, - int dilation) { - -if(cc >= 60) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = NeighborhoodNeighborhood2D; + int64_t attn_stride_4, + const std::tuple& kernel_size, + const std::tuple& dilation) { + using Arguments = natten::naive::ArgumentPack3D; + using Kernel = NeighborhoodNeighborhood3D; Kernel kernel; kernel( -cc, stream, attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} +cc, stream, attn_ptr, value_ptr, output_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation); } -void na2d_nn_cuda_naive_half_ks_7_di_any( - const int cc, +void na3d_nn_cuda_naive_float_cm_0_1_0( + int32_t cc, cudaStream_t stream, void * attn_ptr, void * value_ptr, void * output_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, + int32_t batch_size, + int32_t heads, + int32_t depth, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, - int kernel_size, - int dilation) { - -if(cc >= 60) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = NeighborhoodNeighborhood2D; + int64_t attn_stride_4, + const std::tuple& kernel_size, + const std::tuple& dilation) { + using Arguments = natten::naive::ArgumentPack3D; + using Kernel = NeighborhoodNeighborhood3D; Kernel kernel; kernel( -cc, stream, attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} +cc, stream, attn_ptr, value_ptr, output_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation); } -void na2d_nn_cuda_naive_half_ks_7_di_1( - const int cc, +void na3d_nn_cuda_naive_float_cm_0_1_1( + int32_t cc, cudaStream_t stream, void * attn_ptr, void * value_ptr, void * output_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, + int32_t batch_size, + int32_t heads, + int32_t depth, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, - int kernel_size, - int dilation) { - -if(cc >= 60) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = NeighborhoodNeighborhood2D; + int64_t attn_stride_4, + const std::tuple& kernel_size, + const std::tuple& dilation) { + using Arguments = natten::naive::ArgumentPack3D; + using Kernel = NeighborhoodNeighborhood3D; Kernel kernel; kernel( -cc, stream, attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} +cc, stream, attn_ptr, value_ptr, output_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation); } -void na2d_nn_cuda_naive_half_ks_9_di_any( - const int cc, +void na3d_nn_cuda_naive_float_cm_1_0_0( + int32_t cc, cudaStream_t stream, void * attn_ptr, void * value_ptr, void * output_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, + int32_t batch_size, + int32_t heads, + int32_t depth, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, - int kernel_size, - int dilation) { - -if(cc >= 60) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = NeighborhoodNeighborhood2D; + int64_t attn_stride_4, + const std::tuple& kernel_size, + const std::tuple& dilation) { + using Arguments = natten::naive::ArgumentPack3D; + using Kernel = NeighborhoodNeighborhood3D; Kernel kernel; kernel( -cc, stream, attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} +cc, stream, attn_ptr, value_ptr, output_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation); } -void na2d_nn_cuda_naive_half_ks_9_di_1( - const int cc, +void na3d_nn_cuda_naive_float_cm_1_0_1( + int32_t cc, cudaStream_t stream, void * attn_ptr, void * value_ptr, void * output_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, + int32_t batch_size, + int32_t heads, + int32_t depth, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, - int kernel_size, - int dilation) { - -if(cc >= 60) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = NeighborhoodNeighborhood2D; + int64_t attn_stride_4, + const std::tuple& kernel_size, + const std::tuple& dilation) { + using Arguments = natten::naive::ArgumentPack3D; + using Kernel = NeighborhoodNeighborhood3D; Kernel kernel; kernel( -cc, stream, attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} +cc, stream, attn_ptr, value_ptr, output_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation); } -void na2d_nn_cuda_naive_half_ks_11_di_any( - const int cc, +void na3d_nn_cuda_naive_float_cm_1_1_0( + int32_t cc, cudaStream_t stream, void * attn_ptr, void * value_ptr, void * output_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, + int32_t batch_size, + int32_t heads, + int32_t depth, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, - int kernel_size, - int dilation) { - -if(cc >= 60) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = NeighborhoodNeighborhood2D; + int64_t attn_stride_4, + const std::tuple& kernel_size, + const std::tuple& dilation) { + using Arguments = natten::naive::ArgumentPack3D; + using Kernel = NeighborhoodNeighborhood3D; Kernel kernel; kernel( -cc, stream, attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} +cc, stream, attn_ptr, value_ptr, output_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation); } -void na2d_nn_cuda_naive_half_ks_11_di_1( - const int cc, +void na3d_nn_cuda_naive_float_cm_1_1_1( + int32_t cc, cudaStream_t stream, void * attn_ptr, void * value_ptr, void * output_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, + int32_t batch_size, + int32_t heads, + int32_t depth, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, - int kernel_size, - int dilation) { - -if(cc >= 60) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = NeighborhoodNeighborhood2D; + int64_t attn_stride_4, + const std::tuple& kernel_size, + const std::tuple& dilation) { + using Arguments = natten::naive::ArgumentPack3D; + using Kernel = NeighborhoodNeighborhood3D; Kernel kernel; kernel( -cc, stream, attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} +cc, stream, attn_ptr, value_ptr, output_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation); } -void na2d_nn_cuda_naive_half_ks_13_di_any( - const int cc, +void na3d_nn_cuda_naive_half_cm_0_0_0( + int32_t cc, cudaStream_t stream, void * attn_ptr, void * value_ptr, void * output_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, + int32_t batch_size, + int32_t heads, + int32_t depth, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, - int kernel_size, - int dilation) { + int64_t attn_stride_4, + const std::tuple& kernel_size, + const std::tuple& dilation) { if(cc >= 60) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = NeighborhoodNeighborhood2D; + using Arguments = natten::naive::ArgumentPack3D; + using Kernel = NeighborhoodNeighborhood3D; Kernel kernel; kernel( -cc, stream, attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); +cc, stream, attn_ptr, value_ptr, output_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation); } else { std::cerr << "This half type is not supported on the selected device." << std::endl; @@ -430,30 +359,32 @@ exit(EXIT_FAILURE); } } -void na2d_nn_cuda_naive_half_ks_13_di_1( - const int cc, +void na3d_nn_cuda_naive_half_cm_0_0_1( + int32_t cc, cudaStream_t stream, void * attn_ptr, void * value_ptr, void * output_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, + int32_t batch_size, + int32_t heads, + int32_t depth, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, - int kernel_size, - int dilation) { + int64_t attn_stride_4, + const std::tuple& kernel_size, + const std::tuple& dilation) { if(cc >= 60) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = NeighborhoodNeighborhood2D; + using Arguments = natten::naive::ArgumentPack3D; + using Kernel = NeighborhoodNeighborhood3D; Kernel kernel; kernel( -cc, stream, attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); +cc, stream, attn_ptr, value_ptr, output_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation); } else { std::cerr << "This half type is not supported on the selected device." << std::endl; @@ -462,30 +393,32 @@ exit(EXIT_FAILURE); } } -void na2d_nn_cuda_naive_bfloat16_ks_any_di_any( - const int cc, +void na3d_nn_cuda_naive_half_cm_0_1_0( + int32_t cc, cudaStream_t stream, void * attn_ptr, void * value_ptr, void * output_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, + int32_t batch_size, + int32_t heads, + int32_t depth, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, - int kernel_size, - int dilation) { + int64_t attn_stride_4, + const std::tuple& kernel_size, + const std::tuple& dilation) { -if(cc >= 80) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = NeighborhoodNeighborhood2D; +if(cc >= 60) { + using Arguments = natten::naive::ArgumentPack3D; + using Kernel = NeighborhoodNeighborhood3D; Kernel kernel; kernel( -cc, stream, attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); +cc, stream, attn_ptr, value_ptr, output_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation); } else { std::cerr << "This half type is not supported on the selected device." << std::endl; @@ -494,30 +427,32 @@ exit(EXIT_FAILURE); } } -void na2d_nn_cuda_naive_bfloat16_ks_any_di_1( - const int cc, +void na3d_nn_cuda_naive_half_cm_0_1_1( + int32_t cc, cudaStream_t stream, void * attn_ptr, void * value_ptr, void * output_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, + int32_t batch_size, + int32_t heads, + int32_t depth, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, - int kernel_size, - int dilation) { + int64_t attn_stride_4, + const std::tuple& kernel_size, + const std::tuple& dilation) { -if(cc >= 80) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = NeighborhoodNeighborhood2D; +if(cc >= 60) { + using Arguments = natten::naive::ArgumentPack3D; + using Kernel = NeighborhoodNeighborhood3D; Kernel kernel; kernel( -cc, stream, attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); +cc, stream, attn_ptr, value_ptr, output_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation); } else { std::cerr << "This half type is not supported on the selected device." << std::endl; @@ -526,30 +461,32 @@ exit(EXIT_FAILURE); } } -void na2d_nn_cuda_naive_bfloat16_ks_3_di_any( - const int cc, +void na3d_nn_cuda_naive_half_cm_1_0_0( + int32_t cc, cudaStream_t stream, void * attn_ptr, void * value_ptr, void * output_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, + int32_t batch_size, + int32_t heads, + int32_t depth, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, - int kernel_size, - int dilation) { + int64_t attn_stride_4, + const std::tuple& kernel_size, + const std::tuple& dilation) { -if(cc >= 80) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = NeighborhoodNeighborhood2D; +if(cc >= 60) { + using Arguments = natten::naive::ArgumentPack3D; + using Kernel = NeighborhoodNeighborhood3D; Kernel kernel; kernel( -cc, stream, attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); +cc, stream, attn_ptr, value_ptr, output_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation); } else { std::cerr << "This half type is not supported on the selected device." << std::endl; @@ -558,30 +495,32 @@ exit(EXIT_FAILURE); } } -void na2d_nn_cuda_naive_bfloat16_ks_3_di_1( - const int cc, +void na3d_nn_cuda_naive_half_cm_1_0_1( + int32_t cc, cudaStream_t stream, void * attn_ptr, void * value_ptr, void * output_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, + int32_t batch_size, + int32_t heads, + int32_t depth, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, - int kernel_size, - int dilation) { + int64_t attn_stride_4, + const std::tuple& kernel_size, + const std::tuple& dilation) { -if(cc >= 80) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = NeighborhoodNeighborhood2D; +if(cc >= 60) { + using Arguments = natten::naive::ArgumentPack3D; + using Kernel = NeighborhoodNeighborhood3D; Kernel kernel; kernel( -cc, stream, attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); +cc, stream, attn_ptr, value_ptr, output_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation); } else { std::cerr << "This half type is not supported on the selected device." << std::endl; @@ -590,30 +529,32 @@ exit(EXIT_FAILURE); } } -void na2d_nn_cuda_naive_bfloat16_ks_5_di_any( - const int cc, +void na3d_nn_cuda_naive_half_cm_1_1_0( + int32_t cc, cudaStream_t stream, void * attn_ptr, void * value_ptr, void * output_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, + int32_t batch_size, + int32_t heads, + int32_t depth, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, - int kernel_size, - int dilation) { + int64_t attn_stride_4, + const std::tuple& kernel_size, + const std::tuple& dilation) { -if(cc >= 80) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = NeighborhoodNeighborhood2D; +if(cc >= 60) { + using Arguments = natten::naive::ArgumentPack3D; + using Kernel = NeighborhoodNeighborhood3D; Kernel kernel; kernel( -cc, stream, attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); +cc, stream, attn_ptr, value_ptr, output_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation); } else { std::cerr << "This half type is not supported on the selected device." << std::endl; @@ -622,30 +563,32 @@ exit(EXIT_FAILURE); } } -void na2d_nn_cuda_naive_bfloat16_ks_5_di_1( - const int cc, +void na3d_nn_cuda_naive_half_cm_1_1_1( + int32_t cc, cudaStream_t stream, void * attn_ptr, void * value_ptr, void * output_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, + int32_t batch_size, + int32_t heads, + int32_t depth, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, - int kernel_size, - int dilation) { + int64_t attn_stride_4, + const std::tuple& kernel_size, + const std::tuple& dilation) { -if(cc >= 80) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = NeighborhoodNeighborhood2D; +if(cc >= 60) { + using Arguments = natten::naive::ArgumentPack3D; + using Kernel = NeighborhoodNeighborhood3D; Kernel kernel; kernel( -cc, stream, attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); +cc, stream, attn_ptr, value_ptr, output_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation); } else { std::cerr << "This half type is not supported on the selected device." << std::endl; @@ -654,30 +597,32 @@ exit(EXIT_FAILURE); } } -void na2d_nn_cuda_naive_bfloat16_ks_7_di_any( - const int cc, +void na3d_nn_cuda_naive_bfloat16_cm_0_0_0( + int32_t cc, cudaStream_t stream, void * attn_ptr, void * value_ptr, void * output_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, + int32_t batch_size, + int32_t heads, + int32_t depth, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, - int kernel_size, - int dilation) { + int64_t attn_stride_4, + const std::tuple& kernel_size, + const std::tuple& dilation) { if(cc >= 80) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = NeighborhoodNeighborhood2D; + using Arguments = natten::naive::ArgumentPack3D; + using Kernel = NeighborhoodNeighborhood3D; Kernel kernel; kernel( -cc, stream, attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); +cc, stream, attn_ptr, value_ptr, output_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation); } else { std::cerr << "This half type is not supported on the selected device." << std::endl; @@ -686,30 +631,32 @@ exit(EXIT_FAILURE); } } -void na2d_nn_cuda_naive_bfloat16_ks_7_di_1( - const int cc, +void na3d_nn_cuda_naive_bfloat16_cm_0_0_1( + int32_t cc, cudaStream_t stream, void * attn_ptr, void * value_ptr, void * output_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, + int32_t batch_size, + int32_t heads, + int32_t depth, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, - int kernel_size, - int dilation) { + int64_t attn_stride_4, + const std::tuple& kernel_size, + const std::tuple& dilation) { if(cc >= 80) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = NeighborhoodNeighborhood2D; + using Arguments = natten::naive::ArgumentPack3D; + using Kernel = NeighborhoodNeighborhood3D; Kernel kernel; kernel( -cc, stream, attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); +cc, stream, attn_ptr, value_ptr, output_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation); } else { std::cerr << "This half type is not supported on the selected device." << std::endl; @@ -718,30 +665,32 @@ exit(EXIT_FAILURE); } } -void na2d_nn_cuda_naive_bfloat16_ks_9_di_any( - const int cc, +void na3d_nn_cuda_naive_bfloat16_cm_0_1_0( + int32_t cc, cudaStream_t stream, void * attn_ptr, void * value_ptr, void * output_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, + int32_t batch_size, + int32_t heads, + int32_t depth, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, - int kernel_size, - int dilation) { + int64_t attn_stride_4, + const std::tuple& kernel_size, + const std::tuple& dilation) { if(cc >= 80) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = NeighborhoodNeighborhood2D; + using Arguments = natten::naive::ArgumentPack3D; + using Kernel = NeighborhoodNeighborhood3D; Kernel kernel; kernel( -cc, stream, attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); +cc, stream, attn_ptr, value_ptr, output_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation); } else { std::cerr << "This half type is not supported on the selected device." << std::endl; @@ -750,30 +699,32 @@ exit(EXIT_FAILURE); } } -void na2d_nn_cuda_naive_bfloat16_ks_9_di_1( - const int cc, +void na3d_nn_cuda_naive_bfloat16_cm_0_1_1( + int32_t cc, cudaStream_t stream, void * attn_ptr, void * value_ptr, void * output_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, + int32_t batch_size, + int32_t heads, + int32_t depth, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, - int kernel_size, - int dilation) { + int64_t attn_stride_4, + const std::tuple& kernel_size, + const std::tuple& dilation) { if(cc >= 80) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = NeighborhoodNeighborhood2D; + using Arguments = natten::naive::ArgumentPack3D; + using Kernel = NeighborhoodNeighborhood3D; Kernel kernel; kernel( -cc, stream, attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); +cc, stream, attn_ptr, value_ptr, output_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation); } else { std::cerr << "This half type is not supported on the selected device." << std::endl; @@ -782,30 +733,32 @@ exit(EXIT_FAILURE); } } -void na2d_nn_cuda_naive_bfloat16_ks_11_di_any( - const int cc, +void na3d_nn_cuda_naive_bfloat16_cm_1_0_0( + int32_t cc, cudaStream_t stream, void * attn_ptr, void * value_ptr, void * output_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, + int32_t batch_size, + int32_t heads, + int32_t depth, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, - int kernel_size, - int dilation) { + int64_t attn_stride_4, + const std::tuple& kernel_size, + const std::tuple& dilation) { if(cc >= 80) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = NeighborhoodNeighborhood2D; + using Arguments = natten::naive::ArgumentPack3D; + using Kernel = NeighborhoodNeighborhood3D; Kernel kernel; kernel( -cc, stream, attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); +cc, stream, attn_ptr, value_ptr, output_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation); } else { std::cerr << "This half type is not supported on the selected device." << std::endl; @@ -814,30 +767,32 @@ exit(EXIT_FAILURE); } } -void na2d_nn_cuda_naive_bfloat16_ks_11_di_1( - const int cc, +void na3d_nn_cuda_naive_bfloat16_cm_1_0_1( + int32_t cc, cudaStream_t stream, void * attn_ptr, void * value_ptr, void * output_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, + int32_t batch_size, + int32_t heads, + int32_t depth, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, - int kernel_size, - int dilation) { + int64_t attn_stride_4, + const std::tuple& kernel_size, + const std::tuple& dilation) { if(cc >= 80) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = NeighborhoodNeighborhood2D; + using Arguments = natten::naive::ArgumentPack3D; + using Kernel = NeighborhoodNeighborhood3D; Kernel kernel; kernel( -cc, stream, attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); +cc, stream, attn_ptr, value_ptr, output_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation); } else { std::cerr << "This half type is not supported on the selected device." << std::endl; @@ -846,30 +801,32 @@ exit(EXIT_FAILURE); } } -void na2d_nn_cuda_naive_bfloat16_ks_13_di_any( - const int cc, +void na3d_nn_cuda_naive_bfloat16_cm_1_1_0( + int32_t cc, cudaStream_t stream, void * attn_ptr, void * value_ptr, void * output_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, + int32_t batch_size, + int32_t heads, + int32_t depth, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, - int kernel_size, - int dilation) { + int64_t attn_stride_4, + const std::tuple& kernel_size, + const std::tuple& dilation) { if(cc >= 80) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = NeighborhoodNeighborhood2D; + using Arguments = natten::naive::ArgumentPack3D; + using Kernel = NeighborhoodNeighborhood3D; Kernel kernel; kernel( -cc, stream, attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); +cc, stream, attn_ptr, value_ptr, output_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation); } else { std::cerr << "This half type is not supported on the selected device." << std::endl; @@ -878,30 +835,32 @@ exit(EXIT_FAILURE); } } -void na2d_nn_cuda_naive_bfloat16_ks_13_di_1( - const int cc, +void na3d_nn_cuda_naive_bfloat16_cm_1_1_1( + int32_t cc, cudaStream_t stream, void * attn_ptr, void * value_ptr, void * output_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, + int32_t batch_size, + int32_t heads, + int32_t depth, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, - int kernel_size, - int dilation) { + int64_t attn_stride_4, + const std::tuple& kernel_size, + const std::tuple& dilation) { if(cc >= 80) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = NeighborhoodNeighborhood2D; + using Arguments = natten::naive::ArgumentPack3D; + using Kernel = NeighborhoodNeighborhood3D; Kernel kernel; kernel( -cc, stream, attn_ptr, value_ptr, output_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); +cc, stream, attn_ptr, value_ptr, output_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation); } else { std::cerr << "This half type is not supported on the selected device." << std::endl; @@ -910,10309 +869,1104 @@ exit(EXIT_FAILURE); } } -void na3d_nn_cuda_naive_double_ks_any_di_any( - const int cc, +void na1d_in_cuda_naive_double_cm_0( + int32_t cc, cudaStream_t stream, void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, + void * d_output_ptr, + void * d_value_ptr, + int32_t batch_size, + int32_t heads, + int32_t length, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = NeighborhoodNeighborhood3D; + const std::tuple& kernel_size, + const std::tuple& dilation) { + using Arguments = natten::naive::ArgumentPack1D; + using Kernel = InverseNeighborhood1D; Kernel kernel; kernel( -cc, stream, attn_ptr, value_ptr, output_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); +cc, stream, attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); } -void na3d_nn_cuda_naive_double_ks_any_di_1( - const int cc, +void na1d_in_cuda_naive_double_cm_1( + int32_t cc, cudaStream_t stream, void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, + void * d_output_ptr, + void * d_value_ptr, + int32_t batch_size, + int32_t heads, + int32_t length, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = NeighborhoodNeighborhood3D; + const std::tuple& kernel_size, + const std::tuple& dilation) { + using Arguments = natten::naive::ArgumentPack1D; + using Kernel = InverseNeighborhood1D; Kernel kernel; kernel( -cc, stream, attn_ptr, value_ptr, output_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); +cc, stream, attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); } -void na3d_nn_cuda_naive_double_ks_3_di_any( - const int cc, +void na1d_in_cuda_naive_float_cm_0( + int32_t cc, cudaStream_t stream, void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, + void * d_output_ptr, + void * d_value_ptr, + int32_t batch_size, + int32_t heads, + int32_t length, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = NeighborhoodNeighborhood3D; + const std::tuple& kernel_size, + const std::tuple& dilation) { + using Arguments = natten::naive::ArgumentPack1D; + using Kernel = InverseNeighborhood1D; Kernel kernel; kernel( -cc, stream, attn_ptr, value_ptr, output_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); +cc, stream, attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); } -void na3d_nn_cuda_naive_double_ks_3_di_1( - const int cc, +void na1d_in_cuda_naive_float_cm_1( + int32_t cc, cudaStream_t stream, void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, + void * d_output_ptr, + void * d_value_ptr, + int32_t batch_size, + int32_t heads, + int32_t length, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = NeighborhoodNeighborhood3D; + const std::tuple& kernel_size, + const std::tuple& dilation) { + using Arguments = natten::naive::ArgumentPack1D; + using Kernel = InverseNeighborhood1D; Kernel kernel; kernel( -cc, stream, attn_ptr, value_ptr, output_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); +cc, stream, attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); } -void na3d_nn_cuda_naive_double_ks_5_di_any( - const int cc, +void na1d_in_cuda_naive_half_cm_0( + int32_t cc, cudaStream_t stream, void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, + void * d_output_ptr, + void * d_value_ptr, + int32_t batch_size, + int32_t heads, + int32_t length, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = NeighborhoodNeighborhood3D; + const std::tuple& kernel_size, + const std::tuple& dilation) { + +if(cc >= 60) { + using Arguments = natten::naive::ArgumentPack1D; + using Kernel = InverseNeighborhood1D; Kernel kernel; kernel( -cc, stream, attn_ptr, value_ptr, output_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); +cc, stream, attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); + +} else { +std::cerr << "This half type is not supported on the selected device." << std::endl; +exit(EXIT_FAILURE); + +} } -void na3d_nn_cuda_naive_double_ks_5_di_1( - const int cc, +void na1d_in_cuda_naive_half_cm_1( + int32_t cc, cudaStream_t stream, void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, + void * d_output_ptr, + void * d_value_ptr, + int32_t batch_size, + int32_t heads, + int32_t length, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = NeighborhoodNeighborhood3D; + const std::tuple& kernel_size, + const std::tuple& dilation) { + +if(cc >= 60) { + using Arguments = natten::naive::ArgumentPack1D; + using Kernel = InverseNeighborhood1D; Kernel kernel; kernel( -cc, stream, attn_ptr, value_ptr, output_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); +cc, stream, attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); + +} else { +std::cerr << "This half type is not supported on the selected device." << std::endl; +exit(EXIT_FAILURE); + +} } -void na3d_nn_cuda_naive_double_ks_7_di_any( - const int cc, +void na1d_in_cuda_naive_bfloat16_cm_0( + int32_t cc, cudaStream_t stream, void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, + void * d_output_ptr, + void * d_value_ptr, + int32_t batch_size, + int32_t heads, + int32_t length, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = NeighborhoodNeighborhood3D; + const std::tuple& kernel_size, + const std::tuple& dilation) { + +if(cc >= 80) { + using Arguments = natten::naive::ArgumentPack1D; + using Kernel = InverseNeighborhood1D; Kernel kernel; kernel( -cc, stream, attn_ptr, value_ptr, output_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); -} +cc, stream, attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); -void na3d_nn_cuda_naive_double_ks_7_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, +} else { +std::cerr << "This half type is not supported on the selected device." << std::endl; +exit(EXIT_FAILURE); + +} +} + +void na1d_in_cuda_naive_bfloat16_cm_1( + int32_t cc, + cudaStream_t stream, + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int32_t batch_size, + int32_t heads, + int32_t length, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = NeighborhoodNeighborhood3D; + const std::tuple& kernel_size, + const std::tuple& dilation) { + +if(cc >= 80) { + using Arguments = natten::naive::ArgumentPack1D; + using Kernel = InverseNeighborhood1D; Kernel kernel; kernel( -cc, stream, attn_ptr, value_ptr, output_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); +cc, stream, attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); + +} else { +std::cerr << "This half type is not supported on the selected device." << std::endl; +exit(EXIT_FAILURE); + +} } -void na3d_nn_cuda_naive_double_ks_9_di_any( - const int cc, +void na2d_in_cuda_naive_double_cm_0_0( + int32_t cc, cudaStream_t stream, void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, + void * d_output_ptr, + void * d_value_ptr, + int32_t batch_size, + int32_t heads, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = NeighborhoodNeighborhood3D; + const std::tuple& kernel_size, + const std::tuple& dilation) { + using Arguments = natten::naive::ArgumentPack2D; + using Kernel = InverseNeighborhood2D; Kernel kernel; kernel( -cc, stream, attn_ptr, value_ptr, output_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); +cc, stream, attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); } -void na3d_nn_cuda_naive_double_ks_9_di_1( - const int cc, +void na2d_in_cuda_naive_double_cm_0_1( + int32_t cc, cudaStream_t stream, void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, + void * d_output_ptr, + void * d_value_ptr, + int32_t batch_size, + int32_t heads, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = NeighborhoodNeighborhood3D; + const std::tuple& kernel_size, + const std::tuple& dilation) { + using Arguments = natten::naive::ArgumentPack2D; + using Kernel = InverseNeighborhood2D; Kernel kernel; kernel( -cc, stream, attn_ptr, value_ptr, output_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); +cc, stream, attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); } -void na3d_nn_cuda_naive_double_ks_11_di_any( - const int cc, +void na2d_in_cuda_naive_double_cm_1_0( + int32_t cc, cudaStream_t stream, void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, + void * d_output_ptr, + void * d_value_ptr, + int32_t batch_size, + int32_t heads, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = NeighborhoodNeighborhood3D; + const std::tuple& kernel_size, + const std::tuple& dilation) { + using Arguments = natten::naive::ArgumentPack2D; + using Kernel = InverseNeighborhood2D; Kernel kernel; kernel( -cc, stream, attn_ptr, value_ptr, output_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); +cc, stream, attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); } -void na3d_nn_cuda_naive_double_ks_11_di_1( - const int cc, +void na2d_in_cuda_naive_double_cm_1_1( + int32_t cc, cudaStream_t stream, void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, + void * d_output_ptr, + void * d_value_ptr, + int32_t batch_size, + int32_t heads, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = NeighborhoodNeighborhood3D; + const std::tuple& kernel_size, + const std::tuple& dilation) { + using Arguments = natten::naive::ArgumentPack2D; + using Kernel = InverseNeighborhood2D; Kernel kernel; kernel( -cc, stream, attn_ptr, value_ptr, output_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); +cc, stream, attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); } -void na3d_nn_cuda_naive_double_ks_13_di_any( - const int cc, +void na2d_in_cuda_naive_float_cm_0_0( + int32_t cc, cudaStream_t stream, void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, + void * d_output_ptr, + void * d_value_ptr, + int32_t batch_size, + int32_t heads, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = NeighborhoodNeighborhood3D; + const std::tuple& kernel_size, + const std::tuple& dilation) { + using Arguments = natten::naive::ArgumentPack2D; + using Kernel = InverseNeighborhood2D; Kernel kernel; kernel( -cc, stream, attn_ptr, value_ptr, output_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); +cc, stream, attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); } -void na3d_nn_cuda_naive_double_ks_13_di_1( - const int cc, +void na2d_in_cuda_naive_float_cm_0_1( + int32_t cc, cudaStream_t stream, void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, + void * d_output_ptr, + void * d_value_ptr, + int32_t batch_size, + int32_t heads, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = NeighborhoodNeighborhood3D; + const std::tuple& kernel_size, + const std::tuple& dilation) { + using Arguments = natten::naive::ArgumentPack2D; + using Kernel = InverseNeighborhood2D; Kernel kernel; kernel( -cc, stream, attn_ptr, value_ptr, output_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); +cc, stream, attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); } -void na3d_nn_cuda_naive_float_ks_any_di_any( - const int cc, +void na2d_in_cuda_naive_float_cm_1_0( + int32_t cc, cudaStream_t stream, void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, + void * d_output_ptr, + void * d_value_ptr, + int32_t batch_size, + int32_t heads, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = NeighborhoodNeighborhood3D; + const std::tuple& kernel_size, + const std::tuple& dilation) { + using Arguments = natten::naive::ArgumentPack2D; + using Kernel = InverseNeighborhood2D; Kernel kernel; kernel( -cc, stream, attn_ptr, value_ptr, output_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); +cc, stream, attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); } -void na3d_nn_cuda_naive_float_ks_any_di_1( - const int cc, +void na2d_in_cuda_naive_float_cm_1_1( + int32_t cc, cudaStream_t stream, void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, + void * d_output_ptr, + void * d_value_ptr, + int32_t batch_size, + int32_t heads, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = NeighborhoodNeighborhood3D; + const std::tuple& kernel_size, + const std::tuple& dilation) { + using Arguments = natten::naive::ArgumentPack2D; + using Kernel = InverseNeighborhood2D; Kernel kernel; kernel( -cc, stream, attn_ptr, value_ptr, output_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); +cc, stream, attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); } -void na3d_nn_cuda_naive_float_ks_3_di_any( - const int cc, +void na2d_in_cuda_naive_half_cm_0_0( + int32_t cc, cudaStream_t stream, void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, + void * d_output_ptr, + void * d_value_ptr, + int32_t batch_size, + int32_t heads, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = NeighborhoodNeighborhood3D; + const std::tuple& kernel_size, + const std::tuple& dilation) { + +if(cc >= 60) { + using Arguments = natten::naive::ArgumentPack2D; + using Kernel = InverseNeighborhood2D; Kernel kernel; kernel( -cc, stream, attn_ptr, value_ptr, output_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); +cc, stream, attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); + +} else { +std::cerr << "This half type is not supported on the selected device." << std::endl; +exit(EXIT_FAILURE); + +} } -void na3d_nn_cuda_naive_float_ks_3_di_1( - const int cc, +void na2d_in_cuda_naive_half_cm_0_1( + int32_t cc, cudaStream_t stream, void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, + void * d_output_ptr, + void * d_value_ptr, + int32_t batch_size, + int32_t heads, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = NeighborhoodNeighborhood3D; + const std::tuple& kernel_size, + const std::tuple& dilation) { + +if(cc >= 60) { + using Arguments = natten::naive::ArgumentPack2D; + using Kernel = InverseNeighborhood2D; Kernel kernel; kernel( -cc, stream, attn_ptr, value_ptr, output_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); +cc, stream, attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); + +} else { +std::cerr << "This half type is not supported on the selected device." << std::endl; +exit(EXIT_FAILURE); + +} } -void na3d_nn_cuda_naive_float_ks_5_di_any( - const int cc, +void na2d_in_cuda_naive_half_cm_1_0( + int32_t cc, cudaStream_t stream, void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, + void * d_output_ptr, + void * d_value_ptr, + int32_t batch_size, + int32_t heads, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = NeighborhoodNeighborhood3D; + const std::tuple& kernel_size, + const std::tuple& dilation) { + +if(cc >= 60) { + using Arguments = natten::naive::ArgumentPack2D; + using Kernel = InverseNeighborhood2D; Kernel kernel; kernel( -cc, stream, attn_ptr, value_ptr, output_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); +cc, stream, attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); + +} else { +std::cerr << "This half type is not supported on the selected device." << std::endl; +exit(EXIT_FAILURE); + +} } -void na3d_nn_cuda_naive_float_ks_5_di_1( - const int cc, +void na2d_in_cuda_naive_half_cm_1_1( + int32_t cc, cudaStream_t stream, void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, + void * d_output_ptr, + void * d_value_ptr, + int32_t batch_size, + int32_t heads, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = NeighborhoodNeighborhood3D; + const std::tuple& kernel_size, + const std::tuple& dilation) { + +if(cc >= 60) { + using Arguments = natten::naive::ArgumentPack2D; + using Kernel = InverseNeighborhood2D; Kernel kernel; kernel( -cc, stream, attn_ptr, value_ptr, output_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); +cc, stream, attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); + +} else { +std::cerr << "This half type is not supported on the selected device." << std::endl; +exit(EXIT_FAILURE); + +} } -void na3d_nn_cuda_naive_float_ks_7_di_any( - const int cc, +void na2d_in_cuda_naive_bfloat16_cm_0_0( + int32_t cc, cudaStream_t stream, void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, + void * d_output_ptr, + void * d_value_ptr, + int32_t batch_size, + int32_t heads, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = NeighborhoodNeighborhood3D; + const std::tuple& kernel_size, + const std::tuple& dilation) { + +if(cc >= 80) { + using Arguments = natten::naive::ArgumentPack2D; + using Kernel = InverseNeighborhood2D; Kernel kernel; kernel( -cc, stream, attn_ptr, value_ptr, output_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); +cc, stream, attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); + +} else { +std::cerr << "This half type is not supported on the selected device." << std::endl; +exit(EXIT_FAILURE); + +} } -void na3d_nn_cuda_naive_float_ks_7_di_1( - const int cc, +void na2d_in_cuda_naive_bfloat16_cm_0_1( + int32_t cc, cudaStream_t stream, void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, + void * d_output_ptr, + void * d_value_ptr, + int32_t batch_size, + int32_t heads, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = NeighborhoodNeighborhood3D; + const std::tuple& kernel_size, + const std::tuple& dilation) { + +if(cc >= 80) { + using Arguments = natten::naive::ArgumentPack2D; + using Kernel = InverseNeighborhood2D; Kernel kernel; kernel( -cc, stream, attn_ptr, value_ptr, output_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); +cc, stream, attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); + +} else { +std::cerr << "This half type is not supported on the selected device." << std::endl; +exit(EXIT_FAILURE); + +} } -void na3d_nn_cuda_naive_float_ks_9_di_any( - const int cc, +void na2d_in_cuda_naive_bfloat16_cm_1_0( + int32_t cc, cudaStream_t stream, void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, + void * d_output_ptr, + void * d_value_ptr, + int32_t batch_size, + int32_t heads, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = NeighborhoodNeighborhood3D; + const std::tuple& kernel_size, + const std::tuple& dilation) { + +if(cc >= 80) { + using Arguments = natten::naive::ArgumentPack2D; + using Kernel = InverseNeighborhood2D; Kernel kernel; kernel( -cc, stream, attn_ptr, value_ptr, output_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); +cc, stream, attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); + +} else { +std::cerr << "This half type is not supported on the selected device." << std::endl; +exit(EXIT_FAILURE); + +} } -void na3d_nn_cuda_naive_float_ks_9_di_1( - const int cc, +void na2d_in_cuda_naive_bfloat16_cm_1_1( + int32_t cc, cudaStream_t stream, void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, + void * d_output_ptr, + void * d_value_ptr, + int32_t batch_size, + int32_t heads, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = NeighborhoodNeighborhood3D; + const std::tuple& kernel_size, + const std::tuple& dilation) { + +if(cc >= 80) { + using Arguments = natten::naive::ArgumentPack2D; + using Kernel = InverseNeighborhood2D; Kernel kernel; kernel( -cc, stream, attn_ptr, value_ptr, output_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); +cc, stream, attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); + +} else { +std::cerr << "This half type is not supported on the selected device." << std::endl; +exit(EXIT_FAILURE); + +} } -void na3d_nn_cuda_naive_float_ks_11_di_any( - const int cc, +void na3d_in_cuda_naive_double_cm_0_0_0( + int32_t cc, cudaStream_t stream, void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, + void * d_output_ptr, + void * d_value_ptr, + int32_t batch_size, + int32_t heads, + int32_t depth, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = NeighborhoodNeighborhood3D; + const std::tuple& kernel_size, + const std::tuple& dilation) { + using Arguments = natten::naive::ArgumentPack3D; + using Kernel = InverseNeighborhood3D; Kernel kernel; kernel( -cc, stream, attn_ptr, value_ptr, output_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); +cc, stream, attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation); } -void na3d_nn_cuda_naive_float_ks_11_di_1( - const int cc, +void na3d_in_cuda_naive_double_cm_0_0_1( + int32_t cc, cudaStream_t stream, void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, + void * d_output_ptr, + void * d_value_ptr, + int32_t batch_size, + int32_t heads, + int32_t depth, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = NeighborhoodNeighborhood3D; + const std::tuple& kernel_size, + const std::tuple& dilation) { + using Arguments = natten::naive::ArgumentPack3D; + using Kernel = InverseNeighborhood3D; Kernel kernel; kernel( -cc, stream, attn_ptr, value_ptr, output_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); +cc, stream, attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation); } -void na3d_nn_cuda_naive_float_ks_13_di_any( - const int cc, +void na3d_in_cuda_naive_double_cm_0_1_0( + int32_t cc, cudaStream_t stream, void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, + void * d_output_ptr, + void * d_value_ptr, + int32_t batch_size, + int32_t heads, + int32_t depth, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = NeighborhoodNeighborhood3D; + const std::tuple& kernel_size, + const std::tuple& dilation) { + using Arguments = natten::naive::ArgumentPack3D; + using Kernel = InverseNeighborhood3D; Kernel kernel; kernel( -cc, stream, attn_ptr, value_ptr, output_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); +cc, stream, attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation); } -void na3d_nn_cuda_naive_float_ks_13_di_1( - const int cc, +void na3d_in_cuda_naive_double_cm_0_1_1( + int32_t cc, cudaStream_t stream, void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, + void * d_output_ptr, + void * d_value_ptr, + int32_t batch_size, + int32_t heads, + int32_t depth, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = NeighborhoodNeighborhood3D; + const std::tuple& kernel_size, + const std::tuple& dilation) { + using Arguments = natten::naive::ArgumentPack3D; + using Kernel = InverseNeighborhood3D; Kernel kernel; kernel( -cc, stream, attn_ptr, value_ptr, output_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); +cc, stream, attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation); } -void na3d_nn_cuda_naive_half_ks_any_di_any( - const int cc, +void na3d_in_cuda_naive_double_cm_1_0_0( + int32_t cc, cudaStream_t stream, void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, + void * d_output_ptr, + void * d_value_ptr, + int32_t batch_size, + int32_t heads, + int32_t depth, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { - -if(cc >= 60) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = NeighborhoodNeighborhood3D; + const std::tuple& kernel_size, + const std::tuple& dilation) { + using Arguments = natten::naive::ArgumentPack3D; + using Kernel = InverseNeighborhood3D; Kernel kernel; kernel( -cc, stream, attn_ptr, value_ptr, output_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} +cc, stream, attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation); } -void na3d_nn_cuda_naive_half_ks_any_di_1( - const int cc, +void na3d_in_cuda_naive_double_cm_1_0_1( + int32_t cc, cudaStream_t stream, void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { - -if(cc >= 60) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = NeighborhoodNeighborhood3D; - Kernel kernel; - kernel( -cc, stream, attn_ptr, value_ptr, output_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na3d_nn_cuda_naive_half_ks_3_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { - -if(cc >= 60) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = NeighborhoodNeighborhood3D; - Kernel kernel; - kernel( -cc, stream, attn_ptr, value_ptr, output_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na3d_nn_cuda_naive_half_ks_3_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { - -if(cc >= 60) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = NeighborhoodNeighborhood3D; - Kernel kernel; - kernel( -cc, stream, attn_ptr, value_ptr, output_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na3d_nn_cuda_naive_half_ks_5_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { - -if(cc >= 60) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = NeighborhoodNeighborhood3D; - Kernel kernel; - kernel( -cc, stream, attn_ptr, value_ptr, output_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na3d_nn_cuda_naive_half_ks_5_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { - -if(cc >= 60) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = NeighborhoodNeighborhood3D; - Kernel kernel; - kernel( -cc, stream, attn_ptr, value_ptr, output_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na3d_nn_cuda_naive_half_ks_7_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { - -if(cc >= 60) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = NeighborhoodNeighborhood3D; - Kernel kernel; - kernel( -cc, stream, attn_ptr, value_ptr, output_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na3d_nn_cuda_naive_half_ks_7_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { - -if(cc >= 60) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = NeighborhoodNeighborhood3D; - Kernel kernel; - kernel( -cc, stream, attn_ptr, value_ptr, output_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na3d_nn_cuda_naive_half_ks_9_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { - -if(cc >= 60) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = NeighborhoodNeighborhood3D; - Kernel kernel; - kernel( -cc, stream, attn_ptr, value_ptr, output_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na3d_nn_cuda_naive_half_ks_9_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { - -if(cc >= 60) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = NeighborhoodNeighborhood3D; - Kernel kernel; - kernel( -cc, stream, attn_ptr, value_ptr, output_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na3d_nn_cuda_naive_half_ks_11_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, + void * d_output_ptr, + void * d_value_ptr, + int32_t batch_size, + int32_t heads, + int32_t depth, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { - -if(cc >= 60) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = NeighborhoodNeighborhood3D; + const std::tuple& kernel_size, + const std::tuple& dilation) { + using Arguments = natten::naive::ArgumentPack3D; + using Kernel = InverseNeighborhood3D; Kernel kernel; kernel( -cc, stream, attn_ptr, value_ptr, output_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} +cc, stream, attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation); } -void na3d_nn_cuda_naive_half_ks_11_di_1( - const int cc, +void na3d_in_cuda_naive_double_cm_1_1_0( + int32_t cc, cudaStream_t stream, void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, + void * d_output_ptr, + void * d_value_ptr, + int32_t batch_size, + int32_t heads, + int32_t depth, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { - -if(cc >= 60) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = NeighborhoodNeighborhood3D; + const std::tuple& kernel_size, + const std::tuple& dilation) { + using Arguments = natten::naive::ArgumentPack3D; + using Kernel = InverseNeighborhood3D; Kernel kernel; kernel( -cc, stream, attn_ptr, value_ptr, output_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} +cc, stream, attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation); } -void na3d_nn_cuda_naive_half_ks_13_di_any( - const int cc, +void na3d_in_cuda_naive_double_cm_1_1_1( + int32_t cc, cudaStream_t stream, void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, + void * d_output_ptr, + void * d_value_ptr, + int32_t batch_size, + int32_t heads, + int32_t depth, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { - -if(cc >= 60) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = NeighborhoodNeighborhood3D; + const std::tuple& kernel_size, + const std::tuple& dilation) { + using Arguments = natten::naive::ArgumentPack3D; + using Kernel = InverseNeighborhood3D; Kernel kernel; kernel( -cc, stream, attn_ptr, value_ptr, output_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} +cc, stream, attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation); } -void na3d_nn_cuda_naive_half_ks_13_di_1( - const int cc, +void na3d_in_cuda_naive_float_cm_0_0_0( + int32_t cc, cudaStream_t stream, void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, + void * d_output_ptr, + void * d_value_ptr, + int32_t batch_size, + int32_t heads, + int32_t depth, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { - -if(cc >= 60) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = NeighborhoodNeighborhood3D; + const std::tuple& kernel_size, + const std::tuple& dilation) { + using Arguments = natten::naive::ArgumentPack3D; + using Kernel = InverseNeighborhood3D; Kernel kernel; kernel( -cc, stream, attn_ptr, value_ptr, output_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} +cc, stream, attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation); } -void na3d_nn_cuda_naive_bfloat16_ks_any_di_any( - const int cc, +void na3d_in_cuda_naive_float_cm_0_0_1( + int32_t cc, cudaStream_t stream, void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, + void * d_output_ptr, + void * d_value_ptr, + int32_t batch_size, + int32_t heads, + int32_t depth, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { - -if(cc >= 80) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = NeighborhoodNeighborhood3D; - Kernel kernel; - kernel( -cc, stream, attn_ptr, value_ptr, output_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na3d_nn_cuda_naive_bfloat16_ks_any_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { - -if(cc >= 80) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = NeighborhoodNeighborhood3D; - Kernel kernel; - kernel( -cc, stream, attn_ptr, value_ptr, output_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na3d_nn_cuda_naive_bfloat16_ks_3_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { - -if(cc >= 80) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = NeighborhoodNeighborhood3D; - Kernel kernel; - kernel( -cc, stream, attn_ptr, value_ptr, output_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na3d_nn_cuda_naive_bfloat16_ks_3_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { - -if(cc >= 80) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = NeighborhoodNeighborhood3D; - Kernel kernel; - kernel( -cc, stream, attn_ptr, value_ptr, output_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na3d_nn_cuda_naive_bfloat16_ks_5_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { - -if(cc >= 80) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = NeighborhoodNeighborhood3D; - Kernel kernel; - kernel( -cc, stream, attn_ptr, value_ptr, output_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na3d_nn_cuda_naive_bfloat16_ks_5_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { - -if(cc >= 80) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = NeighborhoodNeighborhood3D; - Kernel kernel; - kernel( -cc, stream, attn_ptr, value_ptr, output_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na3d_nn_cuda_naive_bfloat16_ks_7_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { - -if(cc >= 80) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = NeighborhoodNeighborhood3D; - Kernel kernel; - kernel( -cc, stream, attn_ptr, value_ptr, output_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na3d_nn_cuda_naive_bfloat16_ks_7_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { - -if(cc >= 80) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = NeighborhoodNeighborhood3D; - Kernel kernel; - kernel( -cc, stream, attn_ptr, value_ptr, output_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na3d_nn_cuda_naive_bfloat16_ks_9_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { - -if(cc >= 80) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = NeighborhoodNeighborhood3D; - Kernel kernel; - kernel( -cc, stream, attn_ptr, value_ptr, output_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na3d_nn_cuda_naive_bfloat16_ks_9_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { - -if(cc >= 80) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = NeighborhoodNeighborhood3D; - Kernel kernel; - kernel( -cc, stream, attn_ptr, value_ptr, output_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na3d_nn_cuda_naive_bfloat16_ks_11_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { - -if(cc >= 80) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = NeighborhoodNeighborhood3D; - Kernel kernel; - kernel( -cc, stream, attn_ptr, value_ptr, output_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na3d_nn_cuda_naive_bfloat16_ks_11_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { - -if(cc >= 80) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = NeighborhoodNeighborhood3D; - Kernel kernel; - kernel( -cc, stream, attn_ptr, value_ptr, output_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na3d_nn_cuda_naive_bfloat16_ks_13_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { - -if(cc >= 80) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = NeighborhoodNeighborhood3D; - Kernel kernel; - kernel( -cc, stream, attn_ptr, value_ptr, output_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na3d_nn_cuda_naive_bfloat16_ks_13_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * value_ptr, - void * output_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { - -if(cc >= 80) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = NeighborhoodNeighborhood3D; - Kernel kernel; - kernel( -cc, stream, attn_ptr, value_ptr, output_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na1d_in_cuda_naive_double_ks_any_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = InverseNeighborhood1D; - Kernel kernel; - kernel( -cc, stream, attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); -} - -void na1d_in_cuda_naive_double_ks_any_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = InverseNeighborhood1D; - Kernel kernel; - kernel( -cc, stream, attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); -} - -void na1d_in_cuda_naive_double_ks_3_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = InverseNeighborhood1D; - Kernel kernel; - kernel( -cc, stream, attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); -} - -void na1d_in_cuda_naive_double_ks_3_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = InverseNeighborhood1D; - Kernel kernel; - kernel( -cc, stream, attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); -} - -void na1d_in_cuda_naive_double_ks_5_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = InverseNeighborhood1D; - Kernel kernel; - kernel( -cc, stream, attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); -} - -void na1d_in_cuda_naive_double_ks_5_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = InverseNeighborhood1D; - Kernel kernel; - kernel( -cc, stream, attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); -} - -void na1d_in_cuda_naive_double_ks_7_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = InverseNeighborhood1D; - Kernel kernel; - kernel( -cc, stream, attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); -} - -void na1d_in_cuda_naive_double_ks_7_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = InverseNeighborhood1D; - Kernel kernel; - kernel( -cc, stream, attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); -} - -void na1d_in_cuda_naive_double_ks_9_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = InverseNeighborhood1D; - Kernel kernel; - kernel( -cc, stream, attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); -} - -void na1d_in_cuda_naive_double_ks_9_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = InverseNeighborhood1D; - Kernel kernel; - kernel( -cc, stream, attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); -} - -void na1d_in_cuda_naive_double_ks_11_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = InverseNeighborhood1D; - Kernel kernel; - kernel( -cc, stream, attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); -} - -void na1d_in_cuda_naive_double_ks_11_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = InverseNeighborhood1D; - Kernel kernel; - kernel( -cc, stream, attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); -} - -void na1d_in_cuda_naive_double_ks_13_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = InverseNeighborhood1D; - Kernel kernel; - kernel( -cc, stream, attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); -} - -void na1d_in_cuda_naive_double_ks_13_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = InverseNeighborhood1D; - Kernel kernel; - kernel( -cc, stream, attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); -} - -void na1d_in_cuda_naive_float_ks_any_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = InverseNeighborhood1D; - Kernel kernel; - kernel( -cc, stream, attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); -} - -void na1d_in_cuda_naive_float_ks_any_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = InverseNeighborhood1D; - Kernel kernel; - kernel( -cc, stream, attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); -} - -void na1d_in_cuda_naive_float_ks_3_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = InverseNeighborhood1D; - Kernel kernel; - kernel( -cc, stream, attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); -} - -void na1d_in_cuda_naive_float_ks_3_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = InverseNeighborhood1D; - Kernel kernel; - kernel( -cc, stream, attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); -} - -void na1d_in_cuda_naive_float_ks_5_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = InverseNeighborhood1D; - Kernel kernel; - kernel( -cc, stream, attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); -} - -void na1d_in_cuda_naive_float_ks_5_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = InverseNeighborhood1D; - Kernel kernel; - kernel( -cc, stream, attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); -} - -void na1d_in_cuda_naive_float_ks_7_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = InverseNeighborhood1D; - Kernel kernel; - kernel( -cc, stream, attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); -} - -void na1d_in_cuda_naive_float_ks_7_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = InverseNeighborhood1D; - Kernel kernel; - kernel( -cc, stream, attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); -} - -void na1d_in_cuda_naive_float_ks_9_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = InverseNeighborhood1D; - Kernel kernel; - kernel( -cc, stream, attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); -} - -void na1d_in_cuda_naive_float_ks_9_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = InverseNeighborhood1D; - Kernel kernel; - kernel( -cc, stream, attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); -} - -void na1d_in_cuda_naive_float_ks_11_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = InverseNeighborhood1D; - Kernel kernel; - kernel( -cc, stream, attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); -} - -void na1d_in_cuda_naive_float_ks_11_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = InverseNeighborhood1D; - Kernel kernel; - kernel( -cc, stream, attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); -} - -void na1d_in_cuda_naive_float_ks_13_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = InverseNeighborhood1D; - Kernel kernel; - kernel( -cc, stream, attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); -} - -void na1d_in_cuda_naive_float_ks_13_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = InverseNeighborhood1D; - Kernel kernel; - kernel( -cc, stream, attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); -} - -void na1d_in_cuda_naive_half_ks_any_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation) { - -if(cc >= 60) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = InverseNeighborhood1D; - Kernel kernel; - kernel( -cc, stream, attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na1d_in_cuda_naive_half_ks_any_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation) { - -if(cc >= 60) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = InverseNeighborhood1D; - Kernel kernel; - kernel( -cc, stream, attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na1d_in_cuda_naive_half_ks_3_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation) { - -if(cc >= 60) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = InverseNeighborhood1D; - Kernel kernel; - kernel( -cc, stream, attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na1d_in_cuda_naive_half_ks_3_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation) { - -if(cc >= 60) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = InverseNeighborhood1D; - Kernel kernel; - kernel( -cc, stream, attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na1d_in_cuda_naive_half_ks_5_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation) { - -if(cc >= 60) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = InverseNeighborhood1D; - Kernel kernel; - kernel( -cc, stream, attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na1d_in_cuda_naive_half_ks_5_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation) { - -if(cc >= 60) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = InverseNeighborhood1D; - Kernel kernel; - kernel( -cc, stream, attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na1d_in_cuda_naive_half_ks_7_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation) { - -if(cc >= 60) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = InverseNeighborhood1D; - Kernel kernel; - kernel( -cc, stream, attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na1d_in_cuda_naive_half_ks_7_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation) { - -if(cc >= 60) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = InverseNeighborhood1D; - Kernel kernel; - kernel( -cc, stream, attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na1d_in_cuda_naive_half_ks_9_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation) { - -if(cc >= 60) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = InverseNeighborhood1D; - Kernel kernel; - kernel( -cc, stream, attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na1d_in_cuda_naive_half_ks_9_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation) { - -if(cc >= 60) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = InverseNeighborhood1D; - Kernel kernel; - kernel( -cc, stream, attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na1d_in_cuda_naive_half_ks_11_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation) { - -if(cc >= 60) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = InverseNeighborhood1D; - Kernel kernel; - kernel( -cc, stream, attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na1d_in_cuda_naive_half_ks_11_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation) { - -if(cc >= 60) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = InverseNeighborhood1D; - Kernel kernel; - kernel( -cc, stream, attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na1d_in_cuda_naive_half_ks_13_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation) { - -if(cc >= 60) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = InverseNeighborhood1D; - Kernel kernel; - kernel( -cc, stream, attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na1d_in_cuda_naive_half_ks_13_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation) { - -if(cc >= 60) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = InverseNeighborhood1D; - Kernel kernel; - kernel( -cc, stream, attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na1d_in_cuda_naive_bfloat16_ks_any_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation) { - -if(cc >= 80) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = InverseNeighborhood1D; - Kernel kernel; - kernel( -cc, stream, attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na1d_in_cuda_naive_bfloat16_ks_any_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation) { - -if(cc >= 80) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = InverseNeighborhood1D; - Kernel kernel; - kernel( -cc, stream, attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na1d_in_cuda_naive_bfloat16_ks_3_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation) { - -if(cc >= 80) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = InverseNeighborhood1D; - Kernel kernel; - kernel( -cc, stream, attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na1d_in_cuda_naive_bfloat16_ks_3_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation) { - -if(cc >= 80) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = InverseNeighborhood1D; - Kernel kernel; - kernel( -cc, stream, attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na1d_in_cuda_naive_bfloat16_ks_5_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation) { - -if(cc >= 80) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = InverseNeighborhood1D; - Kernel kernel; - kernel( -cc, stream, attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na1d_in_cuda_naive_bfloat16_ks_5_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation) { - -if(cc >= 80) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = InverseNeighborhood1D; - Kernel kernel; - kernel( -cc, stream, attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na1d_in_cuda_naive_bfloat16_ks_7_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation) { - -if(cc >= 80) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = InverseNeighborhood1D; - Kernel kernel; - kernel( -cc, stream, attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na1d_in_cuda_naive_bfloat16_ks_7_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation) { - -if(cc >= 80) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = InverseNeighborhood1D; - Kernel kernel; - kernel( -cc, stream, attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na1d_in_cuda_naive_bfloat16_ks_9_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation) { - -if(cc >= 80) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = InverseNeighborhood1D; - Kernel kernel; - kernel( -cc, stream, attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na1d_in_cuda_naive_bfloat16_ks_9_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation) { - -if(cc >= 80) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = InverseNeighborhood1D; - Kernel kernel; - kernel( -cc, stream, attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na1d_in_cuda_naive_bfloat16_ks_11_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation) { - -if(cc >= 80) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = InverseNeighborhood1D; - Kernel kernel; - kernel( -cc, stream, attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na1d_in_cuda_naive_bfloat16_ks_11_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation) { - -if(cc >= 80) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = InverseNeighborhood1D; - Kernel kernel; - kernel( -cc, stream, attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na1d_in_cuda_naive_bfloat16_ks_13_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation) { - -if(cc >= 80) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = InverseNeighborhood1D; - Kernel kernel; - kernel( -cc, stream, attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na1d_in_cuda_naive_bfloat16_ks_13_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation) { - -if(cc >= 80) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = InverseNeighborhood1D; - Kernel kernel; - kernel( -cc, stream, attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na2d_in_cuda_naive_double_ks_any_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = InverseNeighborhood2D; - Kernel kernel; - kernel( -cc, stream, attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); -} - -void na2d_in_cuda_naive_double_ks_any_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = InverseNeighborhood2D; - Kernel kernel; - kernel( -cc, stream, attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); -} - -void na2d_in_cuda_naive_double_ks_3_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = InverseNeighborhood2D; - Kernel kernel; - kernel( -cc, stream, attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); -} - -void na2d_in_cuda_naive_double_ks_3_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = InverseNeighborhood2D; - Kernel kernel; - kernel( -cc, stream, attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); -} - -void na2d_in_cuda_naive_double_ks_5_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = InverseNeighborhood2D; - Kernel kernel; - kernel( -cc, stream, attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); -} - -void na2d_in_cuda_naive_double_ks_5_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = InverseNeighborhood2D; - Kernel kernel; - kernel( -cc, stream, attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); -} - -void na2d_in_cuda_naive_double_ks_7_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = InverseNeighborhood2D; - Kernel kernel; - kernel( -cc, stream, attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); -} - -void na2d_in_cuda_naive_double_ks_7_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = InverseNeighborhood2D; - Kernel kernel; - kernel( -cc, stream, attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); -} - -void na2d_in_cuda_naive_double_ks_9_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = InverseNeighborhood2D; - Kernel kernel; - kernel( -cc, stream, attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); -} - -void na2d_in_cuda_naive_double_ks_9_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = InverseNeighborhood2D; - Kernel kernel; - kernel( -cc, stream, attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); -} - -void na2d_in_cuda_naive_double_ks_11_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = InverseNeighborhood2D; - Kernel kernel; - kernel( -cc, stream, attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); -} - -void na2d_in_cuda_naive_double_ks_11_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = InverseNeighborhood2D; - Kernel kernel; - kernel( -cc, stream, attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); -} - -void na2d_in_cuda_naive_double_ks_13_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = InverseNeighborhood2D; - Kernel kernel; - kernel( -cc, stream, attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); -} - -void na2d_in_cuda_naive_double_ks_13_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = InverseNeighborhood2D; - Kernel kernel; - kernel( -cc, stream, attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); -} - -void na2d_in_cuda_naive_float_ks_any_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = InverseNeighborhood2D; - Kernel kernel; - kernel( -cc, stream, attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); -} - -void na2d_in_cuda_naive_float_ks_any_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = InverseNeighborhood2D; - Kernel kernel; - kernel( -cc, stream, attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); -} - -void na2d_in_cuda_naive_float_ks_3_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = InverseNeighborhood2D; - Kernel kernel; - kernel( -cc, stream, attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); -} - -void na2d_in_cuda_naive_float_ks_3_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = InverseNeighborhood2D; - Kernel kernel; - kernel( -cc, stream, attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); -} - -void na2d_in_cuda_naive_float_ks_5_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = InverseNeighborhood2D; - Kernel kernel; - kernel( -cc, stream, attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); -} - -void na2d_in_cuda_naive_float_ks_5_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = InverseNeighborhood2D; - Kernel kernel; - kernel( -cc, stream, attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); -} - -void na2d_in_cuda_naive_float_ks_7_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = InverseNeighborhood2D; - Kernel kernel; - kernel( -cc, stream, attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); -} - -void na2d_in_cuda_naive_float_ks_7_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = InverseNeighborhood2D; - Kernel kernel; - kernel( -cc, stream, attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); -} - -void na2d_in_cuda_naive_float_ks_9_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = InverseNeighborhood2D; - Kernel kernel; - kernel( -cc, stream, attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); -} - -void na2d_in_cuda_naive_float_ks_9_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = InverseNeighborhood2D; - Kernel kernel; - kernel( -cc, stream, attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); -} - -void na2d_in_cuda_naive_float_ks_11_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = InverseNeighborhood2D; - Kernel kernel; - kernel( -cc, stream, attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); -} - -void na2d_in_cuda_naive_float_ks_11_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = InverseNeighborhood2D; - Kernel kernel; - kernel( -cc, stream, attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); -} - -void na2d_in_cuda_naive_float_ks_13_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = InverseNeighborhood2D; - Kernel kernel; - kernel( -cc, stream, attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); -} - -void na2d_in_cuda_naive_float_ks_13_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = InverseNeighborhood2D; - Kernel kernel; - kernel( -cc, stream, attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); -} - -void na2d_in_cuda_naive_half_ks_any_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation) { - -if(cc >= 60) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = InverseNeighborhood2D; - Kernel kernel; - kernel( -cc, stream, attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na2d_in_cuda_naive_half_ks_any_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation) { - -if(cc >= 60) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = InverseNeighborhood2D; - Kernel kernel; - kernel( -cc, stream, attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na2d_in_cuda_naive_half_ks_3_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation) { - -if(cc >= 60) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = InverseNeighborhood2D; - Kernel kernel; - kernel( -cc, stream, attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na2d_in_cuda_naive_half_ks_3_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation) { - -if(cc >= 60) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = InverseNeighborhood2D; - Kernel kernel; - kernel( -cc, stream, attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na2d_in_cuda_naive_half_ks_5_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation) { - -if(cc >= 60) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = InverseNeighborhood2D; - Kernel kernel; - kernel( -cc, stream, attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na2d_in_cuda_naive_half_ks_5_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation) { - -if(cc >= 60) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = InverseNeighborhood2D; - Kernel kernel; - kernel( -cc, stream, attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na2d_in_cuda_naive_half_ks_7_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation) { - -if(cc >= 60) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = InverseNeighborhood2D; - Kernel kernel; - kernel( -cc, stream, attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na2d_in_cuda_naive_half_ks_7_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation) { - -if(cc >= 60) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = InverseNeighborhood2D; - Kernel kernel; - kernel( -cc, stream, attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na2d_in_cuda_naive_half_ks_9_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation) { - -if(cc >= 60) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = InverseNeighborhood2D; - Kernel kernel; - kernel( -cc, stream, attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na2d_in_cuda_naive_half_ks_9_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation) { - -if(cc >= 60) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = InverseNeighborhood2D; - Kernel kernel; - kernel( -cc, stream, attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na2d_in_cuda_naive_half_ks_11_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation) { - -if(cc >= 60) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = InverseNeighborhood2D; - Kernel kernel; - kernel( -cc, stream, attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na2d_in_cuda_naive_half_ks_11_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation) { - -if(cc >= 60) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = InverseNeighborhood2D; - Kernel kernel; - kernel( -cc, stream, attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na2d_in_cuda_naive_half_ks_13_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation) { - -if(cc >= 60) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = InverseNeighborhood2D; - Kernel kernel; - kernel( -cc, stream, attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na2d_in_cuda_naive_half_ks_13_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation) { - -if(cc >= 60) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = InverseNeighborhood2D; - Kernel kernel; - kernel( -cc, stream, attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na2d_in_cuda_naive_bfloat16_ks_any_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation) { - -if(cc >= 80) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = InverseNeighborhood2D; - Kernel kernel; - kernel( -cc, stream, attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na2d_in_cuda_naive_bfloat16_ks_any_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation) { - -if(cc >= 80) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = InverseNeighborhood2D; - Kernel kernel; - kernel( -cc, stream, attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na2d_in_cuda_naive_bfloat16_ks_3_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation) { - -if(cc >= 80) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = InverseNeighborhood2D; - Kernel kernel; - kernel( -cc, stream, attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na2d_in_cuda_naive_bfloat16_ks_3_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation) { - -if(cc >= 80) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = InverseNeighborhood2D; - Kernel kernel; - kernel( -cc, stream, attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na2d_in_cuda_naive_bfloat16_ks_5_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation) { - -if(cc >= 80) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = InverseNeighborhood2D; - Kernel kernel; - kernel( -cc, stream, attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na2d_in_cuda_naive_bfloat16_ks_5_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation) { - -if(cc >= 80) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = InverseNeighborhood2D; - Kernel kernel; - kernel( -cc, stream, attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na2d_in_cuda_naive_bfloat16_ks_7_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation) { - -if(cc >= 80) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = InverseNeighborhood2D; - Kernel kernel; - kernel( -cc, stream, attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na2d_in_cuda_naive_bfloat16_ks_7_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation) { - -if(cc >= 80) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = InverseNeighborhood2D; - Kernel kernel; - kernel( -cc, stream, attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na2d_in_cuda_naive_bfloat16_ks_9_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation) { - -if(cc >= 80) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = InverseNeighborhood2D; - Kernel kernel; - kernel( -cc, stream, attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na2d_in_cuda_naive_bfloat16_ks_9_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation) { - -if(cc >= 80) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = InverseNeighborhood2D; - Kernel kernel; - kernel( -cc, stream, attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na2d_in_cuda_naive_bfloat16_ks_11_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation) { - -if(cc >= 80) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = InverseNeighborhood2D; - Kernel kernel; - kernel( -cc, stream, attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na2d_in_cuda_naive_bfloat16_ks_11_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation) { - -if(cc >= 80) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = InverseNeighborhood2D; - Kernel kernel; - kernel( -cc, stream, attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na2d_in_cuda_naive_bfloat16_ks_13_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation) { - -if(cc >= 80) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = InverseNeighborhood2D; - Kernel kernel; - kernel( -cc, stream, attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na2d_in_cuda_naive_bfloat16_ks_13_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation) { - -if(cc >= 80) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = InverseNeighborhood2D; - Kernel kernel; - kernel( -cc, stream, attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na3d_in_cuda_naive_double_ks_any_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = InverseNeighborhood3D; - Kernel kernel; - kernel( -cc, stream, attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); -} - -void na3d_in_cuda_naive_double_ks_any_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = InverseNeighborhood3D; - Kernel kernel; - kernel( -cc, stream, attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); -} - -void na3d_in_cuda_naive_double_ks_3_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = InverseNeighborhood3D; - Kernel kernel; - kernel( -cc, stream, attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); -} - -void na3d_in_cuda_naive_double_ks_3_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = InverseNeighborhood3D; - Kernel kernel; - kernel( -cc, stream, attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); -} - -void na3d_in_cuda_naive_double_ks_5_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = InverseNeighborhood3D; - Kernel kernel; - kernel( -cc, stream, attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); -} - -void na3d_in_cuda_naive_double_ks_5_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = InverseNeighborhood3D; - Kernel kernel; - kernel( -cc, stream, attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); -} - -void na3d_in_cuda_naive_double_ks_7_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = InverseNeighborhood3D; - Kernel kernel; - kernel( -cc, stream, attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); -} - -void na3d_in_cuda_naive_double_ks_7_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = InverseNeighborhood3D; - Kernel kernel; - kernel( -cc, stream, attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); -} - -void na3d_in_cuda_naive_double_ks_9_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = InverseNeighborhood3D; - Kernel kernel; - kernel( -cc, stream, attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); -} - -void na3d_in_cuda_naive_double_ks_9_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = InverseNeighborhood3D; - Kernel kernel; - kernel( -cc, stream, attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); -} - -void na3d_in_cuda_naive_double_ks_11_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = InverseNeighborhood3D; - Kernel kernel; - kernel( -cc, stream, attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); -} - -void na3d_in_cuda_naive_double_ks_11_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = InverseNeighborhood3D; - Kernel kernel; - kernel( -cc, stream, attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); -} - -void na3d_in_cuda_naive_double_ks_13_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = InverseNeighborhood3D; - Kernel kernel; - kernel( -cc, stream, attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); -} - -void na3d_in_cuda_naive_double_ks_13_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = InverseNeighborhood3D; - Kernel kernel; - kernel( -cc, stream, attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); -} - -void na3d_in_cuda_naive_float_ks_any_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = InverseNeighborhood3D; - Kernel kernel; - kernel( -cc, stream, attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); -} - -void na3d_in_cuda_naive_float_ks_any_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = InverseNeighborhood3D; - Kernel kernel; - kernel( -cc, stream, attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); -} - -void na3d_in_cuda_naive_float_ks_3_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = InverseNeighborhood3D; - Kernel kernel; - kernel( -cc, stream, attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); -} - -void na3d_in_cuda_naive_float_ks_3_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = InverseNeighborhood3D; - Kernel kernel; - kernel( -cc, stream, attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); -} - -void na3d_in_cuda_naive_float_ks_5_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = InverseNeighborhood3D; - Kernel kernel; - kernel( -cc, stream, attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); -} - -void na3d_in_cuda_naive_float_ks_5_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = InverseNeighborhood3D; - Kernel kernel; - kernel( -cc, stream, attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); -} - -void na3d_in_cuda_naive_float_ks_7_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = InverseNeighborhood3D; - Kernel kernel; - kernel( -cc, stream, attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); -} - -void na3d_in_cuda_naive_float_ks_7_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = InverseNeighborhood3D; - Kernel kernel; - kernel( -cc, stream, attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); -} - -void na3d_in_cuda_naive_float_ks_9_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = InverseNeighborhood3D; - Kernel kernel; - kernel( -cc, stream, attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); -} - -void na3d_in_cuda_naive_float_ks_9_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = InverseNeighborhood3D; - Kernel kernel; - kernel( -cc, stream, attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); -} - -void na3d_in_cuda_naive_float_ks_11_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = InverseNeighborhood3D; - Kernel kernel; - kernel( -cc, stream, attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); -} - -void na3d_in_cuda_naive_float_ks_11_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = InverseNeighborhood3D; - Kernel kernel; - kernel( -cc, stream, attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); -} - -void na3d_in_cuda_naive_float_ks_13_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = InverseNeighborhood3D; - Kernel kernel; - kernel( -cc, stream, attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); -} - -void na3d_in_cuda_naive_float_ks_13_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = InverseNeighborhood3D; - Kernel kernel; - kernel( -cc, stream, attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); -} - -void na3d_in_cuda_naive_half_ks_any_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { - -if(cc >= 60) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = InverseNeighborhood3D; - Kernel kernel; - kernel( -cc, stream, attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na3d_in_cuda_naive_half_ks_any_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { - -if(cc >= 60) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = InverseNeighborhood3D; - Kernel kernel; - kernel( -cc, stream, attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na3d_in_cuda_naive_half_ks_3_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { - -if(cc >= 60) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = InverseNeighborhood3D; - Kernel kernel; - kernel( -cc, stream, attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na3d_in_cuda_naive_half_ks_3_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { - -if(cc >= 60) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = InverseNeighborhood3D; - Kernel kernel; - kernel( -cc, stream, attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na3d_in_cuda_naive_half_ks_5_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { - -if(cc >= 60) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = InverseNeighborhood3D; - Kernel kernel; - kernel( -cc, stream, attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na3d_in_cuda_naive_half_ks_5_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { - -if(cc >= 60) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = InverseNeighborhood3D; - Kernel kernel; - kernel( -cc, stream, attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na3d_in_cuda_naive_half_ks_7_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { - -if(cc >= 60) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = InverseNeighborhood3D; - Kernel kernel; - kernel( -cc, stream, attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na3d_in_cuda_naive_half_ks_7_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { - -if(cc >= 60) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = InverseNeighborhood3D; - Kernel kernel; - kernel( -cc, stream, attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na3d_in_cuda_naive_half_ks_9_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { - -if(cc >= 60) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = InverseNeighborhood3D; - Kernel kernel; - kernel( -cc, stream, attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na3d_in_cuda_naive_half_ks_9_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { - -if(cc >= 60) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = InverseNeighborhood3D; - Kernel kernel; - kernel( -cc, stream, attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na3d_in_cuda_naive_half_ks_11_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { - -if(cc >= 60) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = InverseNeighborhood3D; - Kernel kernel; - kernel( -cc, stream, attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na3d_in_cuda_naive_half_ks_11_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { - -if(cc >= 60) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = InverseNeighborhood3D; - Kernel kernel; - kernel( -cc, stream, attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na3d_in_cuda_naive_half_ks_13_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { - -if(cc >= 60) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = InverseNeighborhood3D; - Kernel kernel; - kernel( -cc, stream, attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na3d_in_cuda_naive_half_ks_13_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { - -if(cc >= 60) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = InverseNeighborhood3D; - Kernel kernel; - kernel( -cc, stream, attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na3d_in_cuda_naive_bfloat16_ks_any_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { - -if(cc >= 80) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = InverseNeighborhood3D; - Kernel kernel; - kernel( -cc, stream, attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na3d_in_cuda_naive_bfloat16_ks_any_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { - -if(cc >= 80) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = InverseNeighborhood3D; - Kernel kernel; - kernel( -cc, stream, attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na3d_in_cuda_naive_bfloat16_ks_3_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { - -if(cc >= 80) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = InverseNeighborhood3D; - Kernel kernel; - kernel( -cc, stream, attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na3d_in_cuda_naive_bfloat16_ks_3_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { - -if(cc >= 80) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = InverseNeighborhood3D; - Kernel kernel; - kernel( -cc, stream, attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na3d_in_cuda_naive_bfloat16_ks_5_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { - -if(cc >= 80) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = InverseNeighborhood3D; - Kernel kernel; - kernel( -cc, stream, attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na3d_in_cuda_naive_bfloat16_ks_5_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { - -if(cc >= 80) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = InverseNeighborhood3D; - Kernel kernel; - kernel( -cc, stream, attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na3d_in_cuda_naive_bfloat16_ks_7_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { - -if(cc >= 80) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = InverseNeighborhood3D; - Kernel kernel; - kernel( -cc, stream, attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na3d_in_cuda_naive_bfloat16_ks_7_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { - -if(cc >= 80) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = InverseNeighborhood3D; - Kernel kernel; - kernel( -cc, stream, attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na3d_in_cuda_naive_bfloat16_ks_9_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { - -if(cc >= 80) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = InverseNeighborhood3D; - Kernel kernel; - kernel( -cc, stream, attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na3d_in_cuda_naive_bfloat16_ks_9_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { - -if(cc >= 80) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = InverseNeighborhood3D; - Kernel kernel; - kernel( -cc, stream, attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na3d_in_cuda_naive_bfloat16_ks_11_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { - -if(cc >= 80) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = InverseNeighborhood3D; - Kernel kernel; - kernel( -cc, stream, attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na3d_in_cuda_naive_bfloat16_ks_11_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { - -if(cc >= 80) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = InverseNeighborhood3D; - Kernel kernel; - kernel( -cc, stream, attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na3d_in_cuda_naive_bfloat16_ks_13_di_any( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { - -if(cc >= 80) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = InverseNeighborhood3D; - Kernel kernel; - kernel( -cc, stream, attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na3d_in_cuda_naive_bfloat16_ks_13_di_1( - const int cc, - cudaStream_t stream, - void * attn_ptr, - void * d_output_ptr, - void * d_value_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { - -if(cc >= 80) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = InverseNeighborhood3D; - Kernel kernel; - kernel( -cc, stream, attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na1d_rpbgrad_cuda_naive_double_ks_any_di_any( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = RelPosBiasGradient1D; - Kernel kernel; - kernel( -cc, stream, d_bias_ptr, d_attn_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); -} - -void na1d_rpbgrad_cuda_naive_double_ks_any_di_1( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = RelPosBiasGradient1D; - Kernel kernel; - kernel( -cc, stream, d_bias_ptr, d_attn_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); -} - -void na1d_rpbgrad_cuda_naive_double_ks_3_di_any( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = RelPosBiasGradient1D; - Kernel kernel; - kernel( -cc, stream, d_bias_ptr, d_attn_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); -} - -void na1d_rpbgrad_cuda_naive_double_ks_3_di_1( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = RelPosBiasGradient1D; - Kernel kernel; - kernel( -cc, stream, d_bias_ptr, d_attn_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); -} - -void na1d_rpbgrad_cuda_naive_double_ks_5_di_any( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = RelPosBiasGradient1D; - Kernel kernel; - kernel( -cc, stream, d_bias_ptr, d_attn_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); -} - -void na1d_rpbgrad_cuda_naive_double_ks_5_di_1( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = RelPosBiasGradient1D; - Kernel kernel; - kernel( -cc, stream, d_bias_ptr, d_attn_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); -} - -void na1d_rpbgrad_cuda_naive_double_ks_7_di_any( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = RelPosBiasGradient1D; - Kernel kernel; - kernel( -cc, stream, d_bias_ptr, d_attn_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); -} - -void na1d_rpbgrad_cuda_naive_double_ks_7_di_1( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = RelPosBiasGradient1D; - Kernel kernel; - kernel( -cc, stream, d_bias_ptr, d_attn_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); -} - -void na1d_rpbgrad_cuda_naive_double_ks_9_di_any( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = RelPosBiasGradient1D; - Kernel kernel; - kernel( -cc, stream, d_bias_ptr, d_attn_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); -} - -void na1d_rpbgrad_cuda_naive_double_ks_9_di_1( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = RelPosBiasGradient1D; - Kernel kernel; - kernel( -cc, stream, d_bias_ptr, d_attn_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); -} - -void na1d_rpbgrad_cuda_naive_double_ks_11_di_any( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = RelPosBiasGradient1D; - Kernel kernel; - kernel( -cc, stream, d_bias_ptr, d_attn_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); -} - -void na1d_rpbgrad_cuda_naive_double_ks_11_di_1( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = RelPosBiasGradient1D; - Kernel kernel; - kernel( -cc, stream, d_bias_ptr, d_attn_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); -} - -void na1d_rpbgrad_cuda_naive_double_ks_13_di_any( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = RelPosBiasGradient1D; - Kernel kernel; - kernel( -cc, stream, d_bias_ptr, d_attn_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); -} - -void na1d_rpbgrad_cuda_naive_double_ks_13_di_1( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = RelPosBiasGradient1D; - Kernel kernel; - kernel( -cc, stream, d_bias_ptr, d_attn_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); -} - -void na1d_rpbgrad_cuda_naive_float_ks_any_di_any( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = RelPosBiasGradient1D; - Kernel kernel; - kernel( -cc, stream, d_bias_ptr, d_attn_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); -} - -void na1d_rpbgrad_cuda_naive_float_ks_any_di_1( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = RelPosBiasGradient1D; - Kernel kernel; - kernel( -cc, stream, d_bias_ptr, d_attn_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); -} - -void na1d_rpbgrad_cuda_naive_float_ks_3_di_any( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = RelPosBiasGradient1D; - Kernel kernel; - kernel( -cc, stream, d_bias_ptr, d_attn_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); -} - -void na1d_rpbgrad_cuda_naive_float_ks_3_di_1( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = RelPosBiasGradient1D; - Kernel kernel; - kernel( -cc, stream, d_bias_ptr, d_attn_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); -} - -void na1d_rpbgrad_cuda_naive_float_ks_5_di_any( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = RelPosBiasGradient1D; - Kernel kernel; - kernel( -cc, stream, d_bias_ptr, d_attn_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); -} - -void na1d_rpbgrad_cuda_naive_float_ks_5_di_1( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = RelPosBiasGradient1D; - Kernel kernel; - kernel( -cc, stream, d_bias_ptr, d_attn_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); -} - -void na1d_rpbgrad_cuda_naive_float_ks_7_di_any( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = RelPosBiasGradient1D; - Kernel kernel; - kernel( -cc, stream, d_bias_ptr, d_attn_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); -} - -void na1d_rpbgrad_cuda_naive_float_ks_7_di_1( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = RelPosBiasGradient1D; - Kernel kernel; - kernel( -cc, stream, d_bias_ptr, d_attn_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); -} - -void na1d_rpbgrad_cuda_naive_float_ks_9_di_any( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = RelPosBiasGradient1D; - Kernel kernel; - kernel( -cc, stream, d_bias_ptr, d_attn_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); -} - -void na1d_rpbgrad_cuda_naive_float_ks_9_di_1( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = RelPosBiasGradient1D; - Kernel kernel; - kernel( -cc, stream, d_bias_ptr, d_attn_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); -} - -void na1d_rpbgrad_cuda_naive_float_ks_11_di_any( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = RelPosBiasGradient1D; - Kernel kernel; - kernel( -cc, stream, d_bias_ptr, d_attn_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); -} - -void na1d_rpbgrad_cuda_naive_float_ks_11_di_1( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = RelPosBiasGradient1D; - Kernel kernel; - kernel( -cc, stream, d_bias_ptr, d_attn_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); -} - -void na1d_rpbgrad_cuda_naive_float_ks_13_di_any( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = RelPosBiasGradient1D; - Kernel kernel; - kernel( -cc, stream, d_bias_ptr, d_attn_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); -} - -void na1d_rpbgrad_cuda_naive_float_ks_13_di_1( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = RelPosBiasGradient1D; - Kernel kernel; - kernel( -cc, stream, d_bias_ptr, d_attn_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); -} - -void na1d_rpbgrad_cuda_naive_half_ks_any_di_any( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation) { - -if(cc >= 60) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = RelPosBiasGradient1D; - Kernel kernel; - kernel( -cc, stream, d_bias_ptr, d_attn_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na1d_rpbgrad_cuda_naive_half_ks_any_di_1( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation) { - -if(cc >= 60) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = RelPosBiasGradient1D; - Kernel kernel; - kernel( -cc, stream, d_bias_ptr, d_attn_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na1d_rpbgrad_cuda_naive_half_ks_3_di_any( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation) { - -if(cc >= 60) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = RelPosBiasGradient1D; - Kernel kernel; - kernel( -cc, stream, d_bias_ptr, d_attn_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na1d_rpbgrad_cuda_naive_half_ks_3_di_1( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation) { - -if(cc >= 60) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = RelPosBiasGradient1D; - Kernel kernel; - kernel( -cc, stream, d_bias_ptr, d_attn_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na1d_rpbgrad_cuda_naive_half_ks_5_di_any( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation) { - -if(cc >= 60) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = RelPosBiasGradient1D; - Kernel kernel; - kernel( -cc, stream, d_bias_ptr, d_attn_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na1d_rpbgrad_cuda_naive_half_ks_5_di_1( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation) { - -if(cc >= 60) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = RelPosBiasGradient1D; - Kernel kernel; - kernel( -cc, stream, d_bias_ptr, d_attn_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na1d_rpbgrad_cuda_naive_half_ks_7_di_any( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation) { - -if(cc >= 60) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = RelPosBiasGradient1D; - Kernel kernel; - kernel( -cc, stream, d_bias_ptr, d_attn_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na1d_rpbgrad_cuda_naive_half_ks_7_di_1( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation) { - -if(cc >= 60) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = RelPosBiasGradient1D; - Kernel kernel; - kernel( -cc, stream, d_bias_ptr, d_attn_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na1d_rpbgrad_cuda_naive_half_ks_9_di_any( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation) { - -if(cc >= 60) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = RelPosBiasGradient1D; - Kernel kernel; - kernel( -cc, stream, d_bias_ptr, d_attn_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na1d_rpbgrad_cuda_naive_half_ks_9_di_1( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation) { - -if(cc >= 60) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = RelPosBiasGradient1D; - Kernel kernel; - kernel( -cc, stream, d_bias_ptr, d_attn_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na1d_rpbgrad_cuda_naive_half_ks_11_di_any( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation) { - -if(cc >= 60) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = RelPosBiasGradient1D; - Kernel kernel; - kernel( -cc, stream, d_bias_ptr, d_attn_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na1d_rpbgrad_cuda_naive_half_ks_11_di_1( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation) { - -if(cc >= 60) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = RelPosBiasGradient1D; - Kernel kernel; - kernel( -cc, stream, d_bias_ptr, d_attn_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na1d_rpbgrad_cuda_naive_half_ks_13_di_any( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation) { - -if(cc >= 60) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = RelPosBiasGradient1D; - Kernel kernel; - kernel( -cc, stream, d_bias_ptr, d_attn_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na1d_rpbgrad_cuda_naive_half_ks_13_di_1( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation) { - -if(cc >= 60) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = RelPosBiasGradient1D; - Kernel kernel; - kernel( -cc, stream, d_bias_ptr, d_attn_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na1d_rpbgrad_cuda_naive_bfloat16_ks_any_di_any( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation) { - -if(cc >= 80) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = RelPosBiasGradient1D; - Kernel kernel; - kernel( -cc, stream, d_bias_ptr, d_attn_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na1d_rpbgrad_cuda_naive_bfloat16_ks_any_di_1( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation) { - -if(cc >= 80) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = RelPosBiasGradient1D; - Kernel kernel; - kernel( -cc, stream, d_bias_ptr, d_attn_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na1d_rpbgrad_cuda_naive_bfloat16_ks_3_di_any( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation) { - -if(cc >= 80) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = RelPosBiasGradient1D; - Kernel kernel; - kernel( -cc, stream, d_bias_ptr, d_attn_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na1d_rpbgrad_cuda_naive_bfloat16_ks_3_di_1( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation) { - -if(cc >= 80) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = RelPosBiasGradient1D; - Kernel kernel; - kernel( -cc, stream, d_bias_ptr, d_attn_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na1d_rpbgrad_cuda_naive_bfloat16_ks_5_di_any( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation) { - -if(cc >= 80) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = RelPosBiasGradient1D; - Kernel kernel; - kernel( -cc, stream, d_bias_ptr, d_attn_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na1d_rpbgrad_cuda_naive_bfloat16_ks_5_di_1( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation) { - -if(cc >= 80) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = RelPosBiasGradient1D; - Kernel kernel; - kernel( -cc, stream, d_bias_ptr, d_attn_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na1d_rpbgrad_cuda_naive_bfloat16_ks_7_di_any( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation) { - -if(cc >= 80) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = RelPosBiasGradient1D; - Kernel kernel; - kernel( -cc, stream, d_bias_ptr, d_attn_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na1d_rpbgrad_cuda_naive_bfloat16_ks_7_di_1( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation) { - -if(cc >= 80) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = RelPosBiasGradient1D; - Kernel kernel; - kernel( -cc, stream, d_bias_ptr, d_attn_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na1d_rpbgrad_cuda_naive_bfloat16_ks_9_di_any( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation) { - -if(cc >= 80) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = RelPosBiasGradient1D; - Kernel kernel; - kernel( -cc, stream, d_bias_ptr, d_attn_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na1d_rpbgrad_cuda_naive_bfloat16_ks_9_di_1( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation) { - -if(cc >= 80) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = RelPosBiasGradient1D; - Kernel kernel; - kernel( -cc, stream, d_bias_ptr, d_attn_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na1d_rpbgrad_cuda_naive_bfloat16_ks_11_di_any( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation) { - -if(cc >= 80) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = RelPosBiasGradient1D; - Kernel kernel; - kernel( -cc, stream, d_bias_ptr, d_attn_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na1d_rpbgrad_cuda_naive_bfloat16_ks_11_di_1( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation) { - -if(cc >= 80) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = RelPosBiasGradient1D; - Kernel kernel; - kernel( -cc, stream, d_bias_ptr, d_attn_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na1d_rpbgrad_cuda_naive_bfloat16_ks_13_di_any( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation) { - -if(cc >= 80) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = RelPosBiasGradient1D; - Kernel kernel; - kernel( -cc, stream, d_bias_ptr, d_attn_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na1d_rpbgrad_cuda_naive_bfloat16_ks_13_di_1( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int length, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int kernel_size, - int dilation) { - -if(cc >= 80) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = RelPosBiasGradient1D; - Kernel kernel; - kernel( -cc, stream, d_bias_ptr, d_attn_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na2d_rpbgrad_cuda_naive_double_ks_any_di_any( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = RelPosBiasGradient2D; - Kernel kernel; - kernel( -cc, stream, d_bias_ptr, d_attn_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); -} - -void na2d_rpbgrad_cuda_naive_double_ks_any_di_1( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = RelPosBiasGradient2D; - Kernel kernel; - kernel( -cc, stream, d_bias_ptr, d_attn_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); -} - -void na2d_rpbgrad_cuda_naive_double_ks_3_di_any( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = RelPosBiasGradient2D; - Kernel kernel; - kernel( -cc, stream, d_bias_ptr, d_attn_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); -} - -void na2d_rpbgrad_cuda_naive_double_ks_3_di_1( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = RelPosBiasGradient2D; - Kernel kernel; - kernel( -cc, stream, d_bias_ptr, d_attn_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); -} - -void na2d_rpbgrad_cuda_naive_double_ks_5_di_any( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = RelPosBiasGradient2D; - Kernel kernel; - kernel( -cc, stream, d_bias_ptr, d_attn_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); -} - -void na2d_rpbgrad_cuda_naive_double_ks_5_di_1( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = RelPosBiasGradient2D; - Kernel kernel; - kernel( -cc, stream, d_bias_ptr, d_attn_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); -} - -void na2d_rpbgrad_cuda_naive_double_ks_7_di_any( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = RelPosBiasGradient2D; - Kernel kernel; - kernel( -cc, stream, d_bias_ptr, d_attn_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); -} - -void na2d_rpbgrad_cuda_naive_double_ks_7_di_1( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = RelPosBiasGradient2D; - Kernel kernel; - kernel( -cc, stream, d_bias_ptr, d_attn_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); -} - -void na2d_rpbgrad_cuda_naive_double_ks_9_di_any( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = RelPosBiasGradient2D; - Kernel kernel; - kernel( -cc, stream, d_bias_ptr, d_attn_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); -} - -void na2d_rpbgrad_cuda_naive_double_ks_9_di_1( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = RelPosBiasGradient2D; - Kernel kernel; - kernel( -cc, stream, d_bias_ptr, d_attn_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); -} - -void na2d_rpbgrad_cuda_naive_double_ks_11_di_any( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = RelPosBiasGradient2D; - Kernel kernel; - kernel( -cc, stream, d_bias_ptr, d_attn_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); -} - -void na2d_rpbgrad_cuda_naive_double_ks_11_di_1( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = RelPosBiasGradient2D; - Kernel kernel; - kernel( -cc, stream, d_bias_ptr, d_attn_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); -} - -void na2d_rpbgrad_cuda_naive_double_ks_13_di_any( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = RelPosBiasGradient2D; - Kernel kernel; - kernel( -cc, stream, d_bias_ptr, d_attn_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); -} - -void na2d_rpbgrad_cuda_naive_double_ks_13_di_1( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = RelPosBiasGradient2D; - Kernel kernel; - kernel( -cc, stream, d_bias_ptr, d_attn_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); -} - -void na2d_rpbgrad_cuda_naive_float_ks_any_di_any( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = RelPosBiasGradient2D; - Kernel kernel; - kernel( -cc, stream, d_bias_ptr, d_attn_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); -} - -void na2d_rpbgrad_cuda_naive_float_ks_any_di_1( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = RelPosBiasGradient2D; - Kernel kernel; - kernel( -cc, stream, d_bias_ptr, d_attn_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); -} - -void na2d_rpbgrad_cuda_naive_float_ks_3_di_any( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = RelPosBiasGradient2D; - Kernel kernel; - kernel( -cc, stream, d_bias_ptr, d_attn_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); -} - -void na2d_rpbgrad_cuda_naive_float_ks_3_di_1( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = RelPosBiasGradient2D; - Kernel kernel; - kernel( -cc, stream, d_bias_ptr, d_attn_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); -} - -void na2d_rpbgrad_cuda_naive_float_ks_5_di_any( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = RelPosBiasGradient2D; - Kernel kernel; - kernel( -cc, stream, d_bias_ptr, d_attn_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); -} - -void na2d_rpbgrad_cuda_naive_float_ks_5_di_1( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = RelPosBiasGradient2D; - Kernel kernel; - kernel( -cc, stream, d_bias_ptr, d_attn_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); -} - -void na2d_rpbgrad_cuda_naive_float_ks_7_di_any( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = RelPosBiasGradient2D; - Kernel kernel; - kernel( -cc, stream, d_bias_ptr, d_attn_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); -} - -void na2d_rpbgrad_cuda_naive_float_ks_7_di_1( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = RelPosBiasGradient2D; - Kernel kernel; - kernel( -cc, stream, d_bias_ptr, d_attn_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); -} - -void na2d_rpbgrad_cuda_naive_float_ks_9_di_any( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = RelPosBiasGradient2D; - Kernel kernel; - kernel( -cc, stream, d_bias_ptr, d_attn_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); -} - -void na2d_rpbgrad_cuda_naive_float_ks_9_di_1( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = RelPosBiasGradient2D; - Kernel kernel; - kernel( -cc, stream, d_bias_ptr, d_attn_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); -} - -void na2d_rpbgrad_cuda_naive_float_ks_11_di_any( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = RelPosBiasGradient2D; - Kernel kernel; - kernel( -cc, stream, d_bias_ptr, d_attn_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); -} - -void na2d_rpbgrad_cuda_naive_float_ks_11_di_1( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = RelPosBiasGradient2D; - Kernel kernel; - kernel( -cc, stream, d_bias_ptr, d_attn_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); -} - -void na2d_rpbgrad_cuda_naive_float_ks_13_di_any( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = RelPosBiasGradient2D; - Kernel kernel; - kernel( -cc, stream, d_bias_ptr, d_attn_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); -} - -void na2d_rpbgrad_cuda_naive_float_ks_13_di_1( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = RelPosBiasGradient2D; - Kernel kernel; - kernel( -cc, stream, d_bias_ptr, d_attn_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); -} - -void na2d_rpbgrad_cuda_naive_half_ks_any_di_any( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation) { - -if(cc >= 60) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = RelPosBiasGradient2D; - Kernel kernel; - kernel( -cc, stream, d_bias_ptr, d_attn_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na2d_rpbgrad_cuda_naive_half_ks_any_di_1( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation) { - -if(cc >= 60) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = RelPosBiasGradient2D; - Kernel kernel; - kernel( -cc, stream, d_bias_ptr, d_attn_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na2d_rpbgrad_cuda_naive_half_ks_3_di_any( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation) { - -if(cc >= 60) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = RelPosBiasGradient2D; - Kernel kernel; - kernel( -cc, stream, d_bias_ptr, d_attn_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na2d_rpbgrad_cuda_naive_half_ks_3_di_1( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation) { - -if(cc >= 60) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = RelPosBiasGradient2D; - Kernel kernel; - kernel( -cc, stream, d_bias_ptr, d_attn_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na2d_rpbgrad_cuda_naive_half_ks_5_di_any( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation) { - -if(cc >= 60) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = RelPosBiasGradient2D; - Kernel kernel; - kernel( -cc, stream, d_bias_ptr, d_attn_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na2d_rpbgrad_cuda_naive_half_ks_5_di_1( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation) { - -if(cc >= 60) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = RelPosBiasGradient2D; - Kernel kernel; - kernel( -cc, stream, d_bias_ptr, d_attn_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na2d_rpbgrad_cuda_naive_half_ks_7_di_any( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation) { - -if(cc >= 60) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = RelPosBiasGradient2D; - Kernel kernel; - kernel( -cc, stream, d_bias_ptr, d_attn_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na2d_rpbgrad_cuda_naive_half_ks_7_di_1( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation) { - -if(cc >= 60) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = RelPosBiasGradient2D; - Kernel kernel; - kernel( -cc, stream, d_bias_ptr, d_attn_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na2d_rpbgrad_cuda_naive_half_ks_9_di_any( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation) { - -if(cc >= 60) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = RelPosBiasGradient2D; - Kernel kernel; - kernel( -cc, stream, d_bias_ptr, d_attn_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na2d_rpbgrad_cuda_naive_half_ks_9_di_1( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation) { - -if(cc >= 60) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = RelPosBiasGradient2D; - Kernel kernel; - kernel( -cc, stream, d_bias_ptr, d_attn_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na2d_rpbgrad_cuda_naive_half_ks_11_di_any( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation) { - -if(cc >= 60) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = RelPosBiasGradient2D; - Kernel kernel; - kernel( -cc, stream, d_bias_ptr, d_attn_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na2d_rpbgrad_cuda_naive_half_ks_11_di_1( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation) { - -if(cc >= 60) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = RelPosBiasGradient2D; - Kernel kernel; - kernel( -cc, stream, d_bias_ptr, d_attn_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na2d_rpbgrad_cuda_naive_half_ks_13_di_any( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation) { - -if(cc >= 60) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = RelPosBiasGradient2D; - Kernel kernel; - kernel( -cc, stream, d_bias_ptr, d_attn_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na2d_rpbgrad_cuda_naive_half_ks_13_di_1( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation) { - -if(cc >= 60) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = RelPosBiasGradient2D; - Kernel kernel; - kernel( -cc, stream, d_bias_ptr, d_attn_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na2d_rpbgrad_cuda_naive_bfloat16_ks_any_di_any( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation) { - -if(cc >= 80) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = RelPosBiasGradient2D; - Kernel kernel; - kernel( -cc, stream, d_bias_ptr, d_attn_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na2d_rpbgrad_cuda_naive_bfloat16_ks_any_di_1( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation) { - -if(cc >= 80) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = RelPosBiasGradient2D; - Kernel kernel; - kernel( -cc, stream, d_bias_ptr, d_attn_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na2d_rpbgrad_cuda_naive_bfloat16_ks_3_di_any( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation) { - -if(cc >= 80) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = RelPosBiasGradient2D; - Kernel kernel; - kernel( -cc, stream, d_bias_ptr, d_attn_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na2d_rpbgrad_cuda_naive_bfloat16_ks_3_di_1( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation) { - -if(cc >= 80) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = RelPosBiasGradient2D; - Kernel kernel; - kernel( -cc, stream, d_bias_ptr, d_attn_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na2d_rpbgrad_cuda_naive_bfloat16_ks_5_di_any( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation) { - -if(cc >= 80) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = RelPosBiasGradient2D; - Kernel kernel; - kernel( -cc, stream, d_bias_ptr, d_attn_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na2d_rpbgrad_cuda_naive_bfloat16_ks_5_di_1( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation) { - -if(cc >= 80) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = RelPosBiasGradient2D; - Kernel kernel; - kernel( -cc, stream, d_bias_ptr, d_attn_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na2d_rpbgrad_cuda_naive_bfloat16_ks_7_di_any( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation) { - -if(cc >= 80) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = RelPosBiasGradient2D; - Kernel kernel; - kernel( -cc, stream, d_bias_ptr, d_attn_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na2d_rpbgrad_cuda_naive_bfloat16_ks_7_di_1( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation) { - -if(cc >= 80) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = RelPosBiasGradient2D; - Kernel kernel; - kernel( -cc, stream, d_bias_ptr, d_attn_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na2d_rpbgrad_cuda_naive_bfloat16_ks_9_di_any( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation) { - -if(cc >= 80) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = RelPosBiasGradient2D; - Kernel kernel; - kernel( -cc, stream, d_bias_ptr, d_attn_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na2d_rpbgrad_cuda_naive_bfloat16_ks_9_di_1( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation) { - -if(cc >= 80) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = RelPosBiasGradient2D; - Kernel kernel; - kernel( -cc, stream, d_bias_ptr, d_attn_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na2d_rpbgrad_cuda_naive_bfloat16_ks_11_di_any( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation) { - -if(cc >= 80) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = RelPosBiasGradient2D; - Kernel kernel; - kernel( -cc, stream, d_bias_ptr, d_attn_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na2d_rpbgrad_cuda_naive_bfloat16_ks_11_di_1( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation) { - -if(cc >= 80) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = RelPosBiasGradient2D; - Kernel kernel; - kernel( -cc, stream, d_bias_ptr, d_attn_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na2d_rpbgrad_cuda_naive_bfloat16_ks_13_di_any( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation) { - -if(cc >= 80) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = RelPosBiasGradient2D; - Kernel kernel; - kernel( -cc, stream, d_bias_ptr, d_attn_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na2d_rpbgrad_cuda_naive_bfloat16_ks_13_di_1( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int kernel_size, - int dilation) { - -if(cc >= 80) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = RelPosBiasGradient2D; - Kernel kernel; - kernel( -cc, stream, d_bias_ptr, d_attn_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} -} - -void na3d_rpbgrad_cuda_naive_double_ks_any_di_any( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = RelPosBiasGradient3D; - Kernel kernel; - kernel( -cc, stream, d_bias_ptr, d_attn_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); -} - -void na3d_rpbgrad_cuda_naive_double_ks_any_di_1( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = RelPosBiasGradient3D; - Kernel kernel; - kernel( -cc, stream, d_bias_ptr, d_attn_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); -} - -void na3d_rpbgrad_cuda_naive_double_ks_3_di_any( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = RelPosBiasGradient3D; - Kernel kernel; - kernel( -cc, stream, d_bias_ptr, d_attn_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); -} - -void na3d_rpbgrad_cuda_naive_double_ks_3_di_1( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = RelPosBiasGradient3D; - Kernel kernel; - kernel( -cc, stream, d_bias_ptr, d_attn_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); -} - -void na3d_rpbgrad_cuda_naive_double_ks_5_di_any( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = RelPosBiasGradient3D; - Kernel kernel; - kernel( -cc, stream, d_bias_ptr, d_attn_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); -} - -void na3d_rpbgrad_cuda_naive_double_ks_5_di_1( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = RelPosBiasGradient3D; - Kernel kernel; - kernel( -cc, stream, d_bias_ptr, d_attn_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); -} - -void na3d_rpbgrad_cuda_naive_double_ks_7_di_any( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = RelPosBiasGradient3D; - Kernel kernel; - kernel( -cc, stream, d_bias_ptr, d_attn_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); -} - -void na3d_rpbgrad_cuda_naive_double_ks_7_di_1( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = RelPosBiasGradient3D; - Kernel kernel; - kernel( -cc, stream, d_bias_ptr, d_attn_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); -} - -void na3d_rpbgrad_cuda_naive_double_ks_9_di_any( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = RelPosBiasGradient3D; - Kernel kernel; - kernel( -cc, stream, d_bias_ptr, d_attn_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); -} - -void na3d_rpbgrad_cuda_naive_double_ks_9_di_1( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = RelPosBiasGradient3D; - Kernel kernel; - kernel( -cc, stream, d_bias_ptr, d_attn_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); -} - -void na3d_rpbgrad_cuda_naive_double_ks_11_di_any( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = RelPosBiasGradient3D; - Kernel kernel; - kernel( -cc, stream, d_bias_ptr, d_attn_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); -} - -void na3d_rpbgrad_cuda_naive_double_ks_11_di_1( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = RelPosBiasGradient3D; - Kernel kernel; - kernel( -cc, stream, d_bias_ptr, d_attn_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); -} - -void na3d_rpbgrad_cuda_naive_double_ks_13_di_any( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = RelPosBiasGradient3D; - Kernel kernel; - kernel( -cc, stream, d_bias_ptr, d_attn_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); -} - -void na3d_rpbgrad_cuda_naive_double_ks_13_di_1( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = RelPosBiasGradient3D; - Kernel kernel; - kernel( -cc, stream, d_bias_ptr, d_attn_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); -} - -void na3d_rpbgrad_cuda_naive_float_ks_any_di_any( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = RelPosBiasGradient3D; - Kernel kernel; - kernel( -cc, stream, d_bias_ptr, d_attn_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); -} - -void na3d_rpbgrad_cuda_naive_float_ks_any_di_1( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = RelPosBiasGradient3D; - Kernel kernel; - kernel( -cc, stream, d_bias_ptr, d_attn_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); -} - -void na3d_rpbgrad_cuda_naive_float_ks_3_di_any( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = RelPosBiasGradient3D; - Kernel kernel; - kernel( -cc, stream, d_bias_ptr, d_attn_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); -} - -void na3d_rpbgrad_cuda_naive_float_ks_3_di_1( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = RelPosBiasGradient3D; - Kernel kernel; - kernel( -cc, stream, d_bias_ptr, d_attn_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); -} - -void na3d_rpbgrad_cuda_naive_float_ks_5_di_any( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = RelPosBiasGradient3D; - Kernel kernel; - kernel( -cc, stream, d_bias_ptr, d_attn_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); -} - -void na3d_rpbgrad_cuda_naive_float_ks_5_di_1( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = RelPosBiasGradient3D; - Kernel kernel; - kernel( -cc, stream, d_bias_ptr, d_attn_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); -} - -void na3d_rpbgrad_cuda_naive_float_ks_7_di_any( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = RelPosBiasGradient3D; - Kernel kernel; - kernel( -cc, stream, d_bias_ptr, d_attn_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); -} - -void na3d_rpbgrad_cuda_naive_float_ks_7_di_1( - const int cc, - cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, - int64_t attn_stride_0, - int64_t attn_stride_1, - int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = RelPosBiasGradient3D; + const std::tuple& kernel_size, + const std::tuple& dilation) { + using Arguments = natten::naive::ArgumentPack3D; + using Kernel = InverseNeighborhood3D; Kernel kernel; kernel( -cc, stream, d_bias_ptr, d_attn_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); +cc, stream, attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation); } -void na3d_rpbgrad_cuda_naive_float_ks_9_di_any( - const int cc, +void na3d_in_cuda_naive_float_cm_0_1_0( + int32_t cc, cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int32_t batch_size, + int32_t heads, + int32_t depth, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = RelPosBiasGradient3D; + const std::tuple& kernel_size, + const std::tuple& dilation) { + using Arguments = natten::naive::ArgumentPack3D; + using Kernel = InverseNeighborhood3D; Kernel kernel; kernel( -cc, stream, d_bias_ptr, d_attn_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); +cc, stream, attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation); } -void na3d_rpbgrad_cuda_naive_float_ks_9_di_1( - const int cc, +void na3d_in_cuda_naive_float_cm_0_1_1( + int32_t cc, cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int32_t batch_size, + int32_t heads, + int32_t depth, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = RelPosBiasGradient3D; + const std::tuple& kernel_size, + const std::tuple& dilation) { + using Arguments = natten::naive::ArgumentPack3D; + using Kernel = InverseNeighborhood3D; Kernel kernel; kernel( -cc, stream, d_bias_ptr, d_attn_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); +cc, stream, attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation); } -void na3d_rpbgrad_cuda_naive_float_ks_11_di_any( - const int cc, +void na3d_in_cuda_naive_float_cm_1_0_0( + int32_t cc, cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int32_t batch_size, + int32_t heads, + int32_t depth, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = RelPosBiasGradient3D; + const std::tuple& kernel_size, + const std::tuple& dilation) { + using Arguments = natten::naive::ArgumentPack3D; + using Kernel = InverseNeighborhood3D; Kernel kernel; kernel( -cc, stream, d_bias_ptr, d_attn_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); +cc, stream, attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation); } -void na3d_rpbgrad_cuda_naive_float_ks_11_di_1( - const int cc, +void na3d_in_cuda_naive_float_cm_1_0_1( + int32_t cc, cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int32_t batch_size, + int32_t heads, + int32_t depth, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = RelPosBiasGradient3D; + const std::tuple& kernel_size, + const std::tuple& dilation) { + using Arguments = natten::naive::ArgumentPack3D; + using Kernel = InverseNeighborhood3D; Kernel kernel; kernel( -cc, stream, d_bias_ptr, d_attn_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); +cc, stream, attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation); } -void na3d_rpbgrad_cuda_naive_float_ks_13_di_any( - const int cc, +void na3d_in_cuda_naive_float_cm_1_1_0( + int32_t cc, cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int32_t batch_size, + int32_t heads, + int32_t depth, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = RelPosBiasGradient3D; + const std::tuple& kernel_size, + const std::tuple& dilation) { + using Arguments = natten::naive::ArgumentPack3D; + using Kernel = InverseNeighborhood3D; Kernel kernel; kernel( -cc, stream, d_bias_ptr, d_attn_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); +cc, stream, attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation); } -void na3d_rpbgrad_cuda_naive_float_ks_13_di_1( - const int cc, +void na3d_in_cuda_naive_float_cm_1_1_1( + int32_t cc, cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int32_t batch_size, + int32_t heads, + int32_t depth, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = RelPosBiasGradient3D; + const std::tuple& kernel_size, + const std::tuple& dilation) { + using Arguments = natten::naive::ArgumentPack3D; + using Kernel = InverseNeighborhood3D; Kernel kernel; kernel( -cc, stream, d_bias_ptr, d_attn_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); +cc, stream, attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation); } -void na3d_rpbgrad_cuda_naive_half_ks_any_di_any( - const int cc, +void na3d_in_cuda_naive_half_cm_0_0_0( + int32_t cc, cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int32_t batch_size, + int32_t heads, + int32_t depth, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { + const std::tuple& kernel_size, + const std::tuple& dilation) { if(cc >= 60) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = RelPosBiasGradient3D; + using Arguments = natten::naive::ArgumentPack3D; + using Kernel = InverseNeighborhood3D; Kernel kernel; kernel( -cc, stream, d_bias_ptr, d_attn_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); +cc, stream, attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation); } else { std::cerr << "This half type is not supported on the selected device." << std::endl; @@ -11221,33 +1975,32 @@ exit(EXIT_FAILURE); } } -void na3d_rpbgrad_cuda_naive_half_ks_any_di_1( - const int cc, +void na3d_in_cuda_naive_half_cm_0_0_1( + int32_t cc, cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int32_t batch_size, + int32_t heads, + int32_t depth, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { + const std::tuple& kernel_size, + const std::tuple& dilation) { if(cc >= 60) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = RelPosBiasGradient3D; + using Arguments = natten::naive::ArgumentPack3D; + using Kernel = InverseNeighborhood3D; Kernel kernel; kernel( -cc, stream, d_bias_ptr, d_attn_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); +cc, stream, attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation); } else { std::cerr << "This half type is not supported on the selected device." << std::endl; @@ -11256,33 +2009,32 @@ exit(EXIT_FAILURE); } } -void na3d_rpbgrad_cuda_naive_half_ks_3_di_any( - const int cc, +void na3d_in_cuda_naive_half_cm_0_1_0( + int32_t cc, cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int32_t batch_size, + int32_t heads, + int32_t depth, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { + const std::tuple& kernel_size, + const std::tuple& dilation) { if(cc >= 60) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = RelPosBiasGradient3D; + using Arguments = natten::naive::ArgumentPack3D; + using Kernel = InverseNeighborhood3D; Kernel kernel; kernel( -cc, stream, d_bias_ptr, d_attn_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); +cc, stream, attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation); } else { std::cerr << "This half type is not supported on the selected device." << std::endl; @@ -11291,33 +2043,32 @@ exit(EXIT_FAILURE); } } -void na3d_rpbgrad_cuda_naive_half_ks_3_di_1( - const int cc, +void na3d_in_cuda_naive_half_cm_0_1_1( + int32_t cc, cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int32_t batch_size, + int32_t heads, + int32_t depth, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { + const std::tuple& kernel_size, + const std::tuple& dilation) { if(cc >= 60) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = RelPosBiasGradient3D; + using Arguments = natten::naive::ArgumentPack3D; + using Kernel = InverseNeighborhood3D; Kernel kernel; kernel( -cc, stream, d_bias_ptr, d_attn_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); +cc, stream, attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation); } else { std::cerr << "This half type is not supported on the selected device." << std::endl; @@ -11326,33 +2077,32 @@ exit(EXIT_FAILURE); } } -void na3d_rpbgrad_cuda_naive_half_ks_5_di_any( - const int cc, +void na3d_in_cuda_naive_half_cm_1_0_0( + int32_t cc, cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int32_t batch_size, + int32_t heads, + int32_t depth, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { + const std::tuple& kernel_size, + const std::tuple& dilation) { if(cc >= 60) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = RelPosBiasGradient3D; + using Arguments = natten::naive::ArgumentPack3D; + using Kernel = InverseNeighborhood3D; Kernel kernel; kernel( -cc, stream, d_bias_ptr, d_attn_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); +cc, stream, attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation); } else { std::cerr << "This half type is not supported on the selected device." << std::endl; @@ -11361,33 +2111,32 @@ exit(EXIT_FAILURE); } } -void na3d_rpbgrad_cuda_naive_half_ks_5_di_1( - const int cc, +void na3d_in_cuda_naive_half_cm_1_0_1( + int32_t cc, cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int32_t batch_size, + int32_t heads, + int32_t depth, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { + const std::tuple& kernel_size, + const std::tuple& dilation) { if(cc >= 60) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = RelPosBiasGradient3D; + using Arguments = natten::naive::ArgumentPack3D; + using Kernel = InverseNeighborhood3D; Kernel kernel; kernel( -cc, stream, d_bias_ptr, d_attn_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); +cc, stream, attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation); } else { std::cerr << "This half type is not supported on the selected device." << std::endl; @@ -11396,33 +2145,32 @@ exit(EXIT_FAILURE); } } -void na3d_rpbgrad_cuda_naive_half_ks_7_di_any( - const int cc, +void na3d_in_cuda_naive_half_cm_1_1_0( + int32_t cc, cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int32_t batch_size, + int32_t heads, + int32_t depth, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { + const std::tuple& kernel_size, + const std::tuple& dilation) { if(cc >= 60) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = RelPosBiasGradient3D; + using Arguments = natten::naive::ArgumentPack3D; + using Kernel = InverseNeighborhood3D; Kernel kernel; kernel( -cc, stream, d_bias_ptr, d_attn_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); +cc, stream, attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation); } else { std::cerr << "This half type is not supported on the selected device." << std::endl; @@ -11431,33 +2179,32 @@ exit(EXIT_FAILURE); } } -void na3d_rpbgrad_cuda_naive_half_ks_7_di_1( - const int cc, +void na3d_in_cuda_naive_half_cm_1_1_1( + int32_t cc, cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int32_t batch_size, + int32_t heads, + int32_t depth, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { + const std::tuple& kernel_size, + const std::tuple& dilation) { if(cc >= 60) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = RelPosBiasGradient3D; + using Arguments = natten::naive::ArgumentPack3D; + using Kernel = InverseNeighborhood3D; Kernel kernel; kernel( -cc, stream, d_bias_ptr, d_attn_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); +cc, stream, attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation); } else { std::cerr << "This half type is not supported on the selected device." << std::endl; @@ -11466,33 +2213,32 @@ exit(EXIT_FAILURE); } } -void na3d_rpbgrad_cuda_naive_half_ks_9_di_any( - const int cc, +void na3d_in_cuda_naive_bfloat16_cm_0_0_0( + int32_t cc, cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int32_t batch_size, + int32_t heads, + int32_t depth, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { + const std::tuple& kernel_size, + const std::tuple& dilation) { -if(cc >= 60) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = RelPosBiasGradient3D; +if(cc >= 80) { + using Arguments = natten::naive::ArgumentPack3D; + using Kernel = InverseNeighborhood3D; Kernel kernel; kernel( -cc, stream, d_bias_ptr, d_attn_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); +cc, stream, attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation); } else { std::cerr << "This half type is not supported on the selected device." << std::endl; @@ -11501,33 +2247,32 @@ exit(EXIT_FAILURE); } } -void na3d_rpbgrad_cuda_naive_half_ks_9_di_1( - const int cc, +void na3d_in_cuda_naive_bfloat16_cm_0_0_1( + int32_t cc, cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int32_t batch_size, + int32_t heads, + int32_t depth, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { + const std::tuple& kernel_size, + const std::tuple& dilation) { -if(cc >= 60) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = RelPosBiasGradient3D; +if(cc >= 80) { + using Arguments = natten::naive::ArgumentPack3D; + using Kernel = InverseNeighborhood3D; Kernel kernel; kernel( -cc, stream, d_bias_ptr, d_attn_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); +cc, stream, attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation); } else { std::cerr << "This half type is not supported on the selected device." << std::endl; @@ -11536,33 +2281,32 @@ exit(EXIT_FAILURE); } } -void na3d_rpbgrad_cuda_naive_half_ks_11_di_any( - const int cc, +void na3d_in_cuda_naive_bfloat16_cm_0_1_0( + int32_t cc, cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int32_t batch_size, + int32_t heads, + int32_t depth, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { + const std::tuple& kernel_size, + const std::tuple& dilation) { -if(cc >= 60) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = RelPosBiasGradient3D; +if(cc >= 80) { + using Arguments = natten::naive::ArgumentPack3D; + using Kernel = InverseNeighborhood3D; Kernel kernel; kernel( -cc, stream, d_bias_ptr, d_attn_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); +cc, stream, attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation); } else { std::cerr << "This half type is not supported on the selected device." << std::endl; @@ -11571,33 +2315,32 @@ exit(EXIT_FAILURE); } } -void na3d_rpbgrad_cuda_naive_half_ks_11_di_1( - const int cc, +void na3d_in_cuda_naive_bfloat16_cm_0_1_1( + int32_t cc, cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int32_t batch_size, + int32_t heads, + int32_t depth, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { + const std::tuple& kernel_size, + const std::tuple& dilation) { -if(cc >= 60) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = RelPosBiasGradient3D; +if(cc >= 80) { + using Arguments = natten::naive::ArgumentPack3D; + using Kernel = InverseNeighborhood3D; Kernel kernel; kernel( -cc, stream, d_bias_ptr, d_attn_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); +cc, stream, attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation); } else { std::cerr << "This half type is not supported on the selected device." << std::endl; @@ -11606,33 +2349,32 @@ exit(EXIT_FAILURE); } } -void na3d_rpbgrad_cuda_naive_half_ks_13_di_any( - const int cc, +void na3d_in_cuda_naive_bfloat16_cm_1_0_0( + int32_t cc, cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int32_t batch_size, + int32_t heads, + int32_t depth, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { + const std::tuple& kernel_size, + const std::tuple& dilation) { -if(cc >= 60) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = RelPosBiasGradient3D; +if(cc >= 80) { + using Arguments = natten::naive::ArgumentPack3D; + using Kernel = InverseNeighborhood3D; Kernel kernel; kernel( -cc, stream, d_bias_ptr, d_attn_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); +cc, stream, attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation); } else { std::cerr << "This half type is not supported on the selected device." << std::endl; @@ -11641,33 +2383,32 @@ exit(EXIT_FAILURE); } } -void na3d_rpbgrad_cuda_naive_half_ks_13_di_1( - const int cc, +void na3d_in_cuda_naive_bfloat16_cm_1_0_1( + int32_t cc, cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int32_t batch_size, + int32_t heads, + int32_t depth, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { + const std::tuple& kernel_size, + const std::tuple& dilation) { -if(cc >= 60) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = RelPosBiasGradient3D; +if(cc >= 80) { + using Arguments = natten::naive::ArgumentPack3D; + using Kernel = InverseNeighborhood3D; Kernel kernel; kernel( -cc, stream, d_bias_ptr, d_attn_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); +cc, stream, attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation); } else { std::cerr << "This half type is not supported on the selected device." << std::endl; @@ -11676,33 +2417,32 @@ exit(EXIT_FAILURE); } } -void na3d_rpbgrad_cuda_naive_bfloat16_ks_any_di_any( - const int cc, +void na3d_in_cuda_naive_bfloat16_cm_1_1_0( + int32_t cc, cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int32_t batch_size, + int32_t heads, + int32_t depth, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { + const std::tuple& kernel_size, + const std::tuple& dilation) { if(cc >= 80) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = RelPosBiasGradient3D; + using Arguments = natten::naive::ArgumentPack3D; + using Kernel = InverseNeighborhood3D; Kernel kernel; kernel( -cc, stream, d_bias_ptr, d_attn_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); +cc, stream, attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation); } else { std::cerr << "This half type is not supported on the selected device." << std::endl; @@ -11711,33 +2451,32 @@ exit(EXIT_FAILURE); } } -void na3d_rpbgrad_cuda_naive_bfloat16_ks_any_di_1( - const int cc, +void na3d_in_cuda_naive_bfloat16_cm_1_1_1( + int32_t cc, cudaStream_t stream, - void * d_bias_ptr, - void * d_attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, + void * attn_ptr, + void * d_output_ptr, + void * d_value_ptr, + int32_t batch_size, + int32_t heads, + int32_t depth, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { + const std::tuple& kernel_size, + const std::tuple& dilation) { if(cc >= 80) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = RelPosBiasGradient3D; + using Arguments = natten::naive::ArgumentPack3D; + using Kernel = InverseNeighborhood3D; Kernel kernel; kernel( -cc, stream, d_bias_ptr, d_attn_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); +cc, stream, attn_ptr, d_output_ptr, d_value_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation); } else { std::cerr << "This half type is not supported on the selected device." << std::endl; @@ -11746,103 +2485,69 @@ exit(EXIT_FAILURE); } } -void na3d_rpbgrad_cuda_naive_bfloat16_ks_3_di_any( - const int cc, +void na1d_rpbgrad_cuda_naive_double_cm_0( + int32_t cc, cudaStream_t stream, void * d_bias_ptr, void * d_attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, + int32_t batch_size, + int32_t heads, + int32_t length, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { - -if(cc >= 80) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = RelPosBiasGradient3D; + const std::tuple& kernel_size, + const std::tuple& dilation) { + using Arguments = natten::naive::ArgumentPack1D; + using Kernel = RelPosBiasGradient1D; Kernel kernel; kernel( -cc, stream, d_bias_ptr, d_attn_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} +cc, stream, d_bias_ptr, d_attn_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); } -void na3d_rpbgrad_cuda_naive_bfloat16_ks_3_di_1( - const int cc, +void na1d_rpbgrad_cuda_naive_float_cm_0( + int32_t cc, cudaStream_t stream, void * d_bias_ptr, void * d_attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, + int32_t batch_size, + int32_t heads, + int32_t length, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { - -if(cc >= 80) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = RelPosBiasGradient3D; + const std::tuple& kernel_size, + const std::tuple& dilation) { + using Arguments = natten::naive::ArgumentPack1D; + using Kernel = RelPosBiasGradient1D; Kernel kernel; kernel( -cc, stream, d_bias_ptr, d_attn_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} +cc, stream, d_bias_ptr, d_attn_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); } -void na3d_rpbgrad_cuda_naive_bfloat16_ks_5_di_any( - const int cc, +void na1d_rpbgrad_cuda_naive_half_cm_0( + int32_t cc, cudaStream_t stream, void * d_bias_ptr, void * d_attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, + int32_t batch_size, + int32_t heads, + int32_t length, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { + const std::tuple& kernel_size, + const std::tuple& dilation) { -if(cc >= 80) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = RelPosBiasGradient3D; +if(cc >= 60) { + using Arguments = natten::naive::ArgumentPack1D; + using Kernel = RelPosBiasGradient1D; Kernel kernel; kernel( -cc, stream, d_bias_ptr, d_attn_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); +cc, stream, d_bias_ptr, d_attn_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); } else { std::cerr << "This half type is not supported on the selected device." << std::endl; @@ -11851,33 +2556,27 @@ exit(EXIT_FAILURE); } } -void na3d_rpbgrad_cuda_naive_bfloat16_ks_5_di_1( - const int cc, +void na1d_rpbgrad_cuda_naive_bfloat16_cm_0( + int32_t cc, cudaStream_t stream, void * d_bias_ptr, void * d_attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, + int32_t batch_size, + int32_t heads, + int32_t length, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, - int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { + const std::tuple& kernel_size, + const std::tuple& dilation) { if(cc >= 80) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = RelPosBiasGradient3D; + using Arguments = natten::naive::ArgumentPack1D; + using Kernel = RelPosBiasGradient1D; Kernel kernel; kernel( -cc, stream, d_bias_ptr, d_attn_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); +cc, stream, d_bias_ptr, d_attn_ptr, batch_size, heads, length, dim, attn_stride_0, attn_stride_1, attn_stride_2, kernel_size, dilation); } else { std::cerr << "This half type is not supported on the selected device." << std::endl; @@ -11886,103 +2585,75 @@ exit(EXIT_FAILURE); } } -void na3d_rpbgrad_cuda_naive_bfloat16_ks_7_di_any( - const int cc, +void na2d_rpbgrad_cuda_naive_double_cm_0_0( + int32_t cc, cudaStream_t stream, void * d_bias_ptr, void * d_attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, + int32_t batch_size, + int32_t heads, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { - -if(cc >= 80) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = RelPosBiasGradient3D; + const std::tuple& kernel_size, + const std::tuple& dilation) { + using Arguments = natten::naive::ArgumentPack2D; + using Kernel = RelPosBiasGradient2D; Kernel kernel; kernel( -cc, stream, d_bias_ptr, d_attn_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} +cc, stream, d_bias_ptr, d_attn_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); } -void na3d_rpbgrad_cuda_naive_bfloat16_ks_7_di_1( - const int cc, +void na2d_rpbgrad_cuda_naive_float_cm_0_0( + int32_t cc, cudaStream_t stream, void * d_bias_ptr, void * d_attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, + int32_t batch_size, + int32_t heads, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { - -if(cc >= 80) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = RelPosBiasGradient3D; + const std::tuple& kernel_size, + const std::tuple& dilation) { + using Arguments = natten::naive::ArgumentPack2D; + using Kernel = RelPosBiasGradient2D; Kernel kernel; kernel( -cc, stream, d_bias_ptr, d_attn_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} +cc, stream, d_bias_ptr, d_attn_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); } -void na3d_rpbgrad_cuda_naive_bfloat16_ks_9_di_any( - const int cc, +void na2d_rpbgrad_cuda_naive_half_cm_0_0( + int32_t cc, cudaStream_t stream, void * d_bias_ptr, void * d_attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, + int32_t batch_size, + int32_t heads, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { + const std::tuple& kernel_size, + const std::tuple& dilation) { -if(cc >= 80) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = RelPosBiasGradient3D; +if(cc >= 60) { + using Arguments = natten::naive::ArgumentPack2D; + using Kernel = RelPosBiasGradient2D; Kernel kernel; kernel( -cc, stream, d_bias_ptr, d_attn_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); +cc, stream, d_bias_ptr, d_attn_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); } else { std::cerr << "This half type is not supported on the selected device." << std::endl; @@ -11991,33 +2662,29 @@ exit(EXIT_FAILURE); } } -void na3d_rpbgrad_cuda_naive_bfloat16_ks_9_di_1( - const int cc, +void na2d_rpbgrad_cuda_naive_bfloat16_cm_0_0( + int32_t cc, cudaStream_t stream, void * d_bias_ptr, void * d_attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, + int32_t batch_size, + int32_t heads, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, - int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { + const std::tuple& kernel_size, + const std::tuple& dilation) { if(cc >= 80) { - using Arguments = natten::naive::ArgumentPack; - using Kernel = RelPosBiasGradient3D; + using Arguments = natten::naive::ArgumentPack2D; + using Kernel = RelPosBiasGradient2D; Kernel kernel; kernel( -cc, stream, d_bias_ptr, d_attn_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); +cc, stream, d_bias_ptr, d_attn_ptr, batch_size, heads, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, kernel_size, dilation); } else { std::cerr << "This half type is not supported on the selected device." << std::endl; @@ -12026,103 +2693,81 @@ exit(EXIT_FAILURE); } } -void na3d_rpbgrad_cuda_naive_bfloat16_ks_11_di_any( - const int cc, +void na3d_rpbgrad_cuda_naive_double_cm_0_0_0( + int32_t cc, cudaStream_t stream, void * d_bias_ptr, void * d_attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, + int32_t batch_size, + int32_t heads, + int32_t depth, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { - -if(cc >= 80) { - using Arguments = natten::naive::ArgumentPack; + const std::tuple& kernel_size, + const std::tuple& dilation) { + using Arguments = natten::naive::ArgumentPack3D; using Kernel = RelPosBiasGradient3D; Kernel kernel; kernel( -cc, stream, d_bias_ptr, d_attn_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} +cc, stream, d_bias_ptr, d_attn_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation); } -void na3d_rpbgrad_cuda_naive_bfloat16_ks_11_di_1( - const int cc, +void na3d_rpbgrad_cuda_naive_float_cm_0_0_0( + int32_t cc, cudaStream_t stream, void * d_bias_ptr, void * d_attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, + int32_t batch_size, + int32_t heads, + int32_t depth, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { - -if(cc >= 80) { - using Arguments = natten::naive::ArgumentPack; + const std::tuple& kernel_size, + const std::tuple& dilation) { + using Arguments = natten::naive::ArgumentPack3D; using Kernel = RelPosBiasGradient3D; Kernel kernel; kernel( -cc, stream, d_bias_ptr, d_attn_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); - -} else { -std::cerr << "This half type is not supported on the selected device." << std::endl; -exit(EXIT_FAILURE); - -} +cc, stream, d_bias_ptr, d_attn_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation); } -void na3d_rpbgrad_cuda_naive_bfloat16_ks_13_di_any( - const int cc, +void na3d_rpbgrad_cuda_naive_half_cm_0_0_0( + int32_t cc, cudaStream_t stream, void * d_bias_ptr, void * d_attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, + int32_t batch_size, + int32_t heads, + int32_t depth, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { + const std::tuple& kernel_size, + const std::tuple& dilation) { -if(cc >= 80) { - using Arguments = natten::naive::ArgumentPack; +if(cc >= 60) { + using Arguments = natten::naive::ArgumentPack3D; using Kernel = RelPosBiasGradient3D; Kernel kernel; kernel( -cc, stream, d_bias_ptr, d_attn_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); +cc, stream, d_bias_ptr, d_attn_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation); } else { std::cerr << "This half type is not supported on the selected device." << std::endl; @@ -12131,33 +2776,31 @@ exit(EXIT_FAILURE); } } -void na3d_rpbgrad_cuda_naive_bfloat16_ks_13_di_1( - const int cc, +void na3d_rpbgrad_cuda_naive_bfloat16_cm_0_0_0( + int32_t cc, cudaStream_t stream, void * d_bias_ptr, void * d_attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, + int32_t batch_size, + int32_t heads, + int32_t depth, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, int64_t attn_stride_4, - int kernel_size, - int dilation, - int kernel_size_d, - int dilation_d) { + const std::tuple& kernel_size, + const std::tuple& dilation) { if(cc >= 80) { - using Arguments = natten::naive::ArgumentPack; + using Arguments = natten::naive::ArgumentPack3D; using Kernel = RelPosBiasGradient3D; Kernel kernel; kernel( -cc, stream, d_bias_ptr, d_attn_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation, kernel_size_d, dilation_d); +cc, stream, d_bias_ptr, d_attn_ptr, batch_size, heads, depth, height, width, dim, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4, kernel_size, dilation); } else { std::cerr << "This half type is not supported on the selected device." << std::endl; diff --git a/csrc/include/natten/config.h b/csrc/include/natten/config.h index 98a8d14..a5a4050 100644 --- a/csrc/include/natten/config.h +++ b/csrc/include/natten/config.h @@ -9,7 +9,7 @@ * furnished to do so, subject to the following conditions: * * The above copyright notice and this permission notice shall be included in - *all copies or substantial portions of the Software. + * all copies or substantial portions of the Software. * * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, diff --git a/csrc/include/natten/cpu/na1d.h b/csrc/include/natten/cpu/na1d.h index 5c5f1fa..049c467 100644 --- a/csrc/include/natten/cpu/na1d.h +++ b/csrc/include/natten/cpu/na1d.h @@ -25,6 +25,7 @@ */ #pragma once +#include #include namespace natten { @@ -36,18 +37,20 @@ void na1d_qk_forward( void* key_ptr, void* bias_ptr, void* attn_ptr, - int batch_size, - int heads, - int length, - int dim, + int32_t batch_size, + int32_t heads, + int32_t length, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, - int kernel_size, - int dilation) { + const std::tuple& kernel_size, + const std::tuple& dilation, + const std::tuple& is_causal) { if (bias_ptr == nullptr) { DISPATCH_DTYPE_na1d_pn_cpu_naive( T, + /* is_grad = */ false, query_ptr, key_ptr, attn_ptr, @@ -59,8 +62,12 @@ void na1d_qk_forward( attn_stride_1, attn_stride_2, kernel_size, - dilation); + dilation, + is_causal); } else { + NATTEN_CHECK( + !any_true(is_causal), + "Neighborhood attention with causal masking does not support positional biases yet."); DISPATCH_DTYPE_na1d_pn_bias_cpu_naive( T, query_ptr, @@ -75,7 +82,8 @@ void na1d_qk_forward( attn_stride_1, attn_stride_2, kernel_size, - dilation); + dilation, + is_causal); } } @@ -87,15 +95,16 @@ void na1d_qk_backward( void* d_query_ptr, void* d_key_ptr, void* d_bias_ptr, - int batch_size, - int heads, - int length, - int dim, + int32_t batch_size, + int32_t heads, + int32_t length, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, - int kernel_size, - int dilation) { + const std::tuple& kernel_size, + const std::tuple& dilation, + const std::tuple& is_causal) { DISPATCH_DTYPE_na1d_nn_cpu_naive( T, d_attn_ptr, @@ -109,7 +118,8 @@ void na1d_qk_backward( attn_stride_1, attn_stride_2, kernel_size, - dilation); + dilation, + is_causal); DISPATCH_DTYPE_na1d_in_cpu_naive( T, d_attn_ptr, @@ -123,8 +133,12 @@ void na1d_qk_backward( attn_stride_1, attn_stride_2, kernel_size, - dilation); + dilation, + is_causal); if (d_bias_ptr != nullptr) { + NATTEN_CHECK( + !any_true(is_causal), + "Neighborhood attention with causal masking does not support positional biases yet."); DISPATCH_DTYPE_na1d_rpbgrad_cpu_naive( T, d_bias_ptr, @@ -137,7 +151,8 @@ void na1d_qk_backward( attn_stride_1, attn_stride_2, kernel_size, - dilation); + dilation, + is_causal); } } @@ -146,15 +161,16 @@ void na1d_av_forward( void* attn_ptr, void* value_ptr, void* output_ptr, - int batch_size, - int heads, - int length, - int dim, + int32_t batch_size, + int32_t heads, + int32_t length, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, - int kernel_size, - int dilation) { + const std::tuple& kernel_size, + const std::tuple& dilation, + const std::tuple& is_causal) { DISPATCH_DTYPE_na1d_nn_cpu_naive( T, attn_ptr, @@ -168,7 +184,8 @@ void na1d_av_forward( attn_stride_1, attn_stride_2, kernel_size, - dilation); + dilation, + is_causal); } template @@ -178,17 +195,19 @@ void na1d_av_backward( void* d_output_ptr, void* d_attn_ptr, void* d_value_ptr, - int batch_size, - int heads, - int length, - int dim, + int32_t batch_size, + int32_t heads, + int32_t length, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, - int kernel_size, - int dilation) { + const std::tuple& kernel_size, + const std::tuple& dilation, + const std::tuple& is_causal) { DISPATCH_DTYPE_na1d_pn_cpu_naive( T, + /* is_grad = */ true, d_output_ptr, value_ptr, d_attn_ptr, @@ -200,7 +219,8 @@ void na1d_av_backward( attn_stride_1, attn_stride_2, kernel_size, - dilation); + dilation, + is_causal); DISPATCH_DTYPE_na1d_in_cpu_naive( T, attn_ptr, @@ -214,7 +234,8 @@ void na1d_av_backward( attn_stride_1, attn_stride_2, kernel_size, - dilation); + dilation, + is_causal); } } // namespace cpu diff --git a/csrc/include/natten/cpu/na2d.h b/csrc/include/natten/cpu/na2d.h index 02cc793..210066c 100644 --- a/csrc/include/natten/cpu/na2d.h +++ b/csrc/include/natten/cpu/na2d.h @@ -25,6 +25,7 @@ */ #pragma once +#include #include namespace natten { @@ -36,20 +37,22 @@ void na2d_qk_forward( void* key_ptr, void* bias_ptr, void* attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, + int32_t batch_size, + int32_t heads, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, - int kernel_size, - int dilation) { + const std::tuple& kernel_size, + const std::tuple& dilation, + const std::tuple& is_causal) { if (bias_ptr == nullptr) { DISPATCH_DTYPE_na2d_pn_cpu_naive( T, + /* is_grad = */ false, query_ptr, key_ptr, attn_ptr, @@ -63,8 +66,12 @@ void na2d_qk_forward( attn_stride_2, attn_stride_3, kernel_size, - dilation); + dilation, + is_causal); } else { + NATTEN_CHECK( + !any_true(is_causal), + "Neighborhood attention with causal masking does not support positional biases yet."); DISPATCH_DTYPE_na2d_pn_bias_cpu_naive( T, query_ptr, @@ -81,7 +88,8 @@ void na2d_qk_forward( attn_stride_2, attn_stride_3, kernel_size, - dilation); + dilation, + is_causal); } } @@ -93,17 +101,18 @@ void na2d_qk_backward( void* d_query_ptr, void* d_key_ptr, void* d_bias_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, + int32_t batch_size, + int32_t heads, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, - int kernel_size, - int dilation) { + const std::tuple& kernel_size, + const std::tuple& dilation, + const std::tuple& is_causal) { DISPATCH_DTYPE_na2d_nn_cpu_naive( T, d_attn_ptr, @@ -119,7 +128,8 @@ void na2d_qk_backward( attn_stride_2, attn_stride_3, kernel_size, - dilation); + dilation, + is_causal); DISPATCH_DTYPE_na2d_in_cpu_naive( T, d_attn_ptr, @@ -135,8 +145,12 @@ void na2d_qk_backward( attn_stride_2, attn_stride_3, kernel_size, - dilation); + dilation, + is_causal); if (d_bias_ptr != nullptr) { + NATTEN_CHECK( + !any_true(is_causal), + "Neighborhood attention with causal masking does not support positional biases yet."); DISPATCH_DTYPE_na2d_rpbgrad_cpu_naive( T, d_bias_ptr, @@ -151,7 +165,8 @@ void na2d_qk_backward( attn_stride_2, attn_stride_3, kernel_size, - dilation); + dilation, + is_causal); } } @@ -160,17 +175,18 @@ void na2d_av_forward( void* attn_ptr, void* value_ptr, void* output_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, + int32_t batch_size, + int32_t heads, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, - int kernel_size, - int dilation) { + const std::tuple& kernel_size, + const std::tuple& dilation, + const std::tuple& is_causal) { DISPATCH_DTYPE_na2d_nn_cpu_naive( T, attn_ptr, @@ -186,7 +202,8 @@ void na2d_av_forward( attn_stride_2, attn_stride_3, kernel_size, - dilation); + dilation, + is_causal); } template @@ -196,19 +213,21 @@ void na2d_av_backward( void* d_output_ptr, void* d_attn_ptr, void* d_value_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, + int32_t batch_size, + int32_t heads, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, - int kernel_size, - int dilation) { + const std::tuple& kernel_size, + const std::tuple& dilation, + const std::tuple& is_causal) { DISPATCH_DTYPE_na2d_pn_cpu_naive( T, + /* is_grad = */ true, d_output_ptr, value_ptr, d_attn_ptr, @@ -222,7 +241,8 @@ void na2d_av_backward( attn_stride_2, attn_stride_3, kernel_size, - dilation); + dilation, + is_causal); DISPATCH_DTYPE_na2d_in_cpu_naive( T, attn_ptr, @@ -238,7 +258,8 @@ void na2d_av_backward( attn_stride_2, attn_stride_3, kernel_size, - dilation); + dilation, + is_causal); } } // namespace cpu diff --git a/csrc/include/natten/cpu/na3d.h b/csrc/include/natten/cpu/na3d.h index 8a88681..4c1fe90 100644 --- a/csrc/include/natten/cpu/na3d.h +++ b/csrc/include/natten/cpu/na3d.h @@ -25,6 +25,7 @@ */ #pragma once +#include #include namespace natten { @@ -36,24 +37,24 @@ void na3d_qk_forward( void* key_ptr, void* bias_ptr, void* attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, + int32_t batch_size, + int32_t heads, + int32_t depth, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, int64_t attn_stride_4, - int kernel_size, - int dilation, - int depth_kernel_size, - int depth_dilation) { + const std::tuple& kernel_size, + const std::tuple& dilation, + const std::tuple& is_causal) { if (bias_ptr == nullptr) { DISPATCH_DTYPE_na3d_pn_cpu_naive( T, + /* is_grad = */ false, query_ptr, key_ptr, attn_ptr, @@ -69,10 +70,12 @@ void na3d_qk_forward( attn_stride_3, attn_stride_4, kernel_size, - depth_kernel_size, dilation, - depth_dilation); + is_causal); } else { + NATTEN_CHECK( + !any_true(is_causal), + "Neighborhood attention with causal masking does not support positional biases yet."); DISPATCH_DTYPE_na3d_pn_bias_cpu_naive( T, query_ptr, @@ -91,9 +94,8 @@ void na3d_qk_forward( attn_stride_3, attn_stride_4, kernel_size, - depth_kernel_size, dilation, - depth_dilation); + is_causal); } } @@ -105,21 +107,20 @@ void na3d_qk_backward( void* d_query_ptr, void* d_key_ptr, void* d_bias_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, + int32_t batch_size, + int32_t heads, + int32_t depth, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, int64_t attn_stride_4, - int kernel_size, - int dilation, - int depth_kernel_size, - int depth_dilation) { + const std::tuple& kernel_size, + const std::tuple& dilation, + const std::tuple& is_causal) { DISPATCH_DTYPE_na3d_nn_cpu_naive( T, d_attn_ptr, @@ -137,9 +138,8 @@ void na3d_qk_backward( attn_stride_3, attn_stride_4, kernel_size, - depth_kernel_size, dilation, - depth_dilation); + is_causal); DISPATCH_DTYPE_na3d_in_cpu_naive( T, d_attn_ptr, @@ -157,10 +157,12 @@ void na3d_qk_backward( attn_stride_3, attn_stride_4, kernel_size, - depth_kernel_size, dilation, - depth_dilation); + is_causal); if (d_bias_ptr != nullptr) { + NATTEN_CHECK( + !any_true(is_causal), + "Neighborhood attention with causal masking does not support positional biases yet."); DISPATCH_DTYPE_na3d_rpbgrad_cpu_naive( T, d_bias_ptr, @@ -177,9 +179,8 @@ void na3d_qk_backward( attn_stride_3, attn_stride_4, kernel_size, - depth_kernel_size, dilation, - depth_dilation); + is_causal); } } @@ -188,21 +189,20 @@ void na3d_av_forward( void* attn_ptr, void* value_ptr, void* output_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, + int32_t batch_size, + int32_t heads, + int32_t depth, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, int64_t attn_stride_4, - int kernel_size, - int dilation, - int depth_kernel_size, - int depth_dilation) { + const std::tuple& kernel_size, + const std::tuple& dilation, + const std::tuple& is_causal) { DISPATCH_DTYPE_na3d_nn_cpu_naive( T, attn_ptr, @@ -220,9 +220,8 @@ void na3d_av_forward( attn_stride_3, attn_stride_4, kernel_size, - depth_kernel_size, dilation, - depth_dilation); + is_causal); } template @@ -232,23 +231,23 @@ void na3d_av_backward( void* d_output_ptr, void* d_attn_ptr, void* d_value_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, + int32_t batch_size, + int32_t heads, + int32_t depth, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, int64_t attn_stride_4, - int kernel_size, - int dilation, - int depth_kernel_size, - int depth_dilation) { + const std::tuple& kernel_size, + const std::tuple& dilation, + const std::tuple& is_causal) { DISPATCH_DTYPE_na3d_pn_cpu_naive( T, + /* is_grad = */ true, d_output_ptr, value_ptr, d_attn_ptr, @@ -264,9 +263,8 @@ void na3d_av_backward( attn_stride_3, attn_stride_4, kernel_size, - depth_kernel_size, dilation, - depth_dilation); + is_causal); DISPATCH_DTYPE_na3d_in_cpu_naive( T, attn_ptr, @@ -284,9 +282,8 @@ void na3d_av_backward( attn_stride_3, attn_stride_4, kernel_size, - depth_kernel_size, dilation, - depth_dilation); + is_causal); } } // namespace cpu diff --git a/csrc/include/natten/cpu/naive/inverse_neighborhood_1d.hpp b/csrc/include/natten/cpu/naive/inverse_neighborhood_1d.hpp index e2d91c0..1df9678 100644 --- a/csrc/include/natten/cpu/naive/inverse_neighborhood_1d.hpp +++ b/csrc/include/natten/cpu/naive/inverse_neighborhood_1d.hpp @@ -39,7 +39,8 @@ #include #endif -#include "natten/cpu/naive/natten_cpu_commons.h" +#include +#include namespace natten { namespace cpu { @@ -55,71 +56,85 @@ struct InverseNeighborhood1D { void* attn_ptr, void* d_output_ptr, void* d_value_ptr, - int batch_size, - int heads, - int length, - int dim, + int32_t batch_size, + int32_t heads, + int32_t length, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, - int kernel_size, - int dilation) { + const std::tuple& kernel_size, + const std::tuple& dilation, + const std::tuple& is_causal) { launch( reinterpret_cast(attn_ptr), reinterpret_cast(d_output_ptr), reinterpret_cast(d_value_ptr), length, heads, - kernel_size, - dilation, + std::get<0>(kernel_size), + std::get<0>(dilation), dim, batch_size, attn_stride_0, attn_stride_1, - attn_stride_2); + attn_stride_2, + is_causal); } void launch( // K-grad / V-grad scalar_t* weights, // d_attn / attn scalar_t* values, // query / d_out scalar_t* output, // d_key / d_value - const int length, - const int heads, - const int kernel_size, - const int dilation, - const int dim, - const int batch_size, - const int64_t weights_stride_0, - const int64_t weights_stride_1, - const int64_t weights_stride_2) { - const int neighborhood_size = kernel_size / 2; - const int values_stride_2 = dim; - const int values_stride_1 = length * values_stride_2; - const int values_stride_0 = heads * values_stride_1; - for (int b = 0; b < batch_size; b++) { - at::parallel_for(0, heads, GRAIN_SIZE, [&](int start, int end) { - for (int h = start; h < end; h++) { - for (int i = 0; i < length; i++) { - const int ni = get_backward_window_start( - i, kernel_size, neighborhood_size, dilation); - const int ei = get_backward_window_end( - i, length, kernel_size, neighborhood_size, dilation); - for (int d = 0; d < dim; d++) { - const int64_t weightsOffset = + int32_t length, + int32_t heads, + int32_t kernel_size, + int32_t dilation, + int32_t dim, + int32_t batch_size, + int64_t weights_stride_0, + int64_t weights_stride_1, + int64_t weights_stride_2, + const std::tuple& is_causal) { + auto is_causal_0 = std::get<0>(is_causal); + auto neighborhood_size = kernel_size / 2; + int64_t values_stride_2 = dim; + int64_t values_stride_1 = length * values_stride_2; + int64_t values_stride_0 = heads * values_stride_1; + for (int32_t b = 0; b < batch_size; b++) { + at::parallel_for(0, heads, GRAIN_SIZE, [&](int32_t start, int32_t end) { + for (int32_t h = start; h < end; h++) { + for (int32_t i = 0; i < length; i++) { + auto ni = get_backward_window_start( + i, kernel_size, neighborhood_size, dilation, is_causal_0); + auto ei = get_backward_window_end( + i, + length, + kernel_size, + neighborhood_size, + dilation, + is_causal_0); + for (int32_t d = 0; d < dim; d++) { + int64_t weightsOffset = b * weights_stride_0 + h * weights_stride_1; - const int64_t valuesOffset = + int64_t valuesOffset = b * values_stride_0 + h * values_stride_1 + d; scalar_t output_update = scalar_t(0); - for (int xi = ni; xi < ei; xi += dilation) { - const int oni = get_window_start( - xi, length, kernel_size, neighborhood_size, dilation); - const int64_t valuesIndex = valuesOffset + xi * values_stride_2; - const int64_t weightsIndex = weightsOffset + xi * weights_stride_2 + - int((i - oni) / dilation); + for (int32_t xi = ni; xi < ei; xi += dilation) { + auto oni = get_window_start( + xi, + length, + kernel_size, + neighborhood_size, + dilation, + is_causal_0); + int64_t valuesIndex = valuesOffset + xi * values_stride_2; + int64_t weightsIndex = weightsOffset + xi * weights_stride_2 + + int32_t((i - oni) / dilation); output_update += values[valuesIndex] * weights[weightsIndex]; } - const int64_t linearIndex = b * values_stride_0 + - h * values_stride_1 + i * values_stride_2 + d; + int64_t linearIndex = b * values_stride_0 + h * values_stride_1 + + i * values_stride_2 + d; output[linearIndex] = output_update; } } diff --git a/csrc/include/natten/cpu/naive/inverse_neighborhood_2d.hpp b/csrc/include/natten/cpu/naive/inverse_neighborhood_2d.hpp index 7f5f3d7..a79acfa 100644 --- a/csrc/include/natten/cpu/naive/inverse_neighborhood_2d.hpp +++ b/csrc/include/natten/cpu/naive/inverse_neighborhood_2d.hpp @@ -39,7 +39,8 @@ #include #endif -#include "natten/cpu/naive/natten_cpu_commons.h" +#include +#include namespace natten { namespace cpu { @@ -55,17 +56,18 @@ struct InverseNeighborhood2D { void* attn_ptr, void* d_output_ptr, void* d_value_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, + int32_t batch_size, + int32_t heads, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, - int kernel_size, - int dilation) { + const std::tuple& kernel_size, + const std::tuple& dilation, + const std::tuple& is_causal) { launch( reinterpret_cast(attn_ptr), reinterpret_cast(d_output_ptr), @@ -73,71 +75,104 @@ struct InverseNeighborhood2D { height, width, heads, - kernel_size, - dilation, + std::get<0>(kernel_size), + std::get<1>(kernel_size), + std::get<0>(dilation), + std::get<1>(dilation), dim, batch_size, attn_stride_0, attn_stride_1, attn_stride_2, - attn_stride_3); + attn_stride_3, + is_causal); } void launch( // K-grad / V-grad scalar_t* weights, // d_attn / attn scalar_t* values, // query / d_out scalar_t* output, // d_key / d_value - const int height, - const int width, - const int heads, - const int kernel_size, - const int dilation, - const int dim, - const int batch_size, - const int64_t weights_stride_0, - const int64_t weights_stride_1, - const int64_t weights_stride_2, - const int64_t weights_stride_3) { - const int neighborhood_size = kernel_size / 2; - const int values_stride_3 = dim; - const int values_stride_2 = width * values_stride_3; - const int values_stride_1 = height * values_stride_2; - const int values_stride_0 = heads * values_stride_1; - for (int b = 0; b < batch_size; b++) { - at::parallel_for(0, heads, GRAIN_SIZE, [&](int start, int end) { - for (int h = start; h < end; h++) { - for (int i = 0; i < height; i++) { - const int ni = get_backward_window_start( - i, kernel_size, neighborhood_size, dilation); - const int ei = get_backward_window_end( - i, height, kernel_size, neighborhood_size, dilation); - for (int j = 0; j < width; j++) { - const int nj = get_backward_window_start( - j, kernel_size, neighborhood_size, dilation); - const int ej = get_backward_window_end( - j, width, kernel_size, neighborhood_size, dilation); - for (int d = 0; d < dim; d++) { - const int64_t weightsOffset = + int32_t height, + int32_t width, + int32_t heads, + int32_t kernel_size_0, + int32_t kernel_size_1, + int32_t dilation_0, + int32_t dilation_1, + int32_t dim, + int32_t batch_size, + int64_t weights_stride_0, + int64_t weights_stride_1, + int64_t weights_stride_2, + int64_t weights_stride_3, + const std::tuple& is_causal) { + auto is_causal_0 = std::get<0>(is_causal); + auto is_causal_1 = std::get<1>(is_causal); + auto neighborhood_size_0 = kernel_size_0 / 2; + auto neighborhood_size_1 = kernel_size_1 / 2; + int64_t values_stride_3 = dim; + int64_t values_stride_2 = width * values_stride_3; + int64_t values_stride_1 = height * values_stride_2; + int64_t values_stride_0 = heads * values_stride_1; + for (int32_t b = 0; b < batch_size; b++) { + at::parallel_for(0, heads, GRAIN_SIZE, [&](int32_t start, int32_t end) { + for (int32_t h = start; h < end; h++) { + for (int32_t i = 0; i < height; i++) { + auto ni = get_backward_window_start( + i, kernel_size_0, neighborhood_size_0, dilation_0, is_causal_0); + auto ei = get_backward_window_end( + i, + height, + kernel_size_0, + neighborhood_size_0, + dilation_0, + is_causal_0); + for (int32_t j = 0; j < width; j++) { + auto nj = get_backward_window_start( + j, + kernel_size_1, + neighborhood_size_1, + dilation_1, + is_causal_1); + auto ej = get_backward_window_end( + j, + width, + kernel_size_1, + neighborhood_size_1, + dilation_1, + is_causal_1); + for (int32_t d = 0; d < dim; d++) { + int64_t weightsOffset = b * weights_stride_0 + h * weights_stride_1; - const int64_t outOffset = + int64_t outOffset = b * values_stride_0 + h * values_stride_1 + d; scalar_t output_update = scalar_t(0); - for (int xi = ni; xi < ei; xi += dilation) { - const int oni = get_window_start( - xi, height, kernel_size, neighborhood_size, dilation); - for (int xj = nj; xj < ej; xj += dilation) { - const int onj = get_window_start( - xj, width, kernel_size, neighborhood_size, dilation); - const int64_t outIndex = + for (int32_t xi = ni; xi < ei; xi += dilation_0) { + auto oni = get_window_start( + xi, + height, + kernel_size_0, + neighborhood_size_0, + dilation_0, + is_causal_0); + for (int32_t xj = nj; xj < ej; xj += dilation_1) { + auto onj = get_window_start( + xj, + width, + kernel_size_1, + neighborhood_size_1, + dilation_1, + is_causal_1); + int64_t outIndex = outOffset + xi * values_stride_2 + xj * values_stride_3; - const int64_t weightsIndex = weightsOffset + + int64_t weightsIndex = weightsOffset + xi * weights_stride_2 + xj * weights_stride_3 + - int((i - oni) / dilation) * kernel_size + - int((j - onj) / dilation); + int32_t((i - oni) / dilation_0) * kernel_size_1 + + int32_t((j - onj) / dilation_1); output_update += values[outIndex] * weights[weightsIndex]; } } - const int64_t linearIndex = b * values_stride_0 + + int64_t linearIndex = b * values_stride_0 + h * values_stride_1 + i * values_stride_2 + j * values_stride_3 + d; output[linearIndex] = output_update; diff --git a/csrc/include/natten/cpu/naive/inverse_neighborhood_3d.hpp b/csrc/include/natten/cpu/naive/inverse_neighborhood_3d.hpp index a9f2789..a926d1a 100644 --- a/csrc/include/natten/cpu/naive/inverse_neighborhood_3d.hpp +++ b/csrc/include/natten/cpu/naive/inverse_neighborhood_3d.hpp @@ -39,7 +39,8 @@ #include #endif -#include "natten/cpu/naive/natten_cpu_commons.h" +#include +#include namespace natten { namespace cpu { @@ -53,21 +54,20 @@ struct InverseNeighborhood3D { void* attn_ptr, void* d_output_ptr, void* d_value_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, + int32_t batch_size, + int32_t heads, + int32_t depth, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, int64_t attn_stride_4, - int kernel_size, - int kernel_size_depth, - int dilation, - int dilation_depth) { + const std::tuple& kernel_size, + const std::tuple& dilation, + const std::tuple& is_causal) { launch( reinterpret_cast(attn_ptr), reinterpret_cast(d_output_ptr), @@ -76,101 +76,141 @@ struct InverseNeighborhood3D { height, width, heads, - kernel_size, - kernel_size_depth, - dilation, - dilation_depth, + std::get<0>(kernel_size), + std::get<1>(kernel_size), + std::get<2>(kernel_size), + std::get<0>(dilation), + std::get<1>(dilation), + std::get<2>(dilation), dim, batch_size, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, - attn_stride_4); + attn_stride_4, + is_causal); } void launch( // K-grad / V-grad scalar_t* weights, // d_attn / attn scalar_t* values, // query / d_out scalar_t* output, // d_key / d_value - const int depth, - const int height, - const int width, - const int heads, - const int kernel_size, - const int kernel_size_d, - const int dilation, - const int dilation_d, - const int dim, - const int batch_size, - const int64_t weights_stride_0, - const int64_t weights_stride_1, - const int64_t weights_stride_2, - const int64_t weights_stride_3, - const int64_t weights_stride_4) { - const int neighborhood_size = kernel_size / 2; - const int neighborhood_size_d = kernel_size_d / 2; - const int values_stride_4 = dim; - const int values_stride_3 = width * values_stride_4; - const int values_stride_2 = height * values_stride_3; - const int values_stride_1 = depth * values_stride_2; - const int values_stride_0 = heads * values_stride_1; - for (int b = 0; b < batch_size; b++) { - at::parallel_for(0, heads, GRAIN_SIZE, [&](int start, int end) { - for (int h = start; h < end; h++) { - for (int k = 0; k < depth; k++) { - const int nk = get_backward_window_start( - k, kernel_size_d, neighborhood_size_d, dilation_d); - const int ek = get_backward_window_end( - k, depth, kernel_size_d, neighborhood_size_d, dilation_d); - for (int i = 0; i < height; i++) { - const int ni = get_backward_window_start( - i, kernel_size, neighborhood_size, dilation); - const int ei = get_backward_window_end( - i, height, kernel_size, neighborhood_size, dilation); - for (int j = 0; j < width; j++) { - const int nj = get_backward_window_start( - j, kernel_size, neighborhood_size, dilation); - const int ej = get_backward_window_end( - j, width, kernel_size, neighborhood_size, dilation); - for (int d = 0; d < dim; d++) { - const int64_t weightsOffset = + int32_t depth, + int32_t height, + int32_t width, + int32_t heads, + int32_t kernel_size_0, + int32_t kernel_size_1, + int32_t kernel_size_2, + int32_t dilation_0, + int32_t dilation_1, + int32_t dilation_2, + int32_t dim, + int32_t batch_size, + int64_t weights_stride_0, + int64_t weights_stride_1, + int64_t weights_stride_2, + int64_t weights_stride_3, + int64_t weights_stride_4, + const std::tuple& is_causal) { + auto is_causal_0 = std::get<0>(is_causal); + auto is_causal_1 = std::get<1>(is_causal); + auto is_causal_2 = std::get<2>(is_causal); + auto neighborhood_size_0 = kernel_size_0 / 2; + auto neighborhood_size_1 = kernel_size_1 / 2; + auto neighborhood_size_2 = kernel_size_2 / 2; + int64_t values_stride_4 = dim; + int64_t values_stride_3 = width * values_stride_4; + int64_t values_stride_2 = height * values_stride_3; + int64_t values_stride_1 = depth * values_stride_2; + int64_t values_stride_0 = heads * values_stride_1; + for (int32_t b = 0; b < batch_size; b++) { + at::parallel_for(0, heads, GRAIN_SIZE, [&](int32_t start, int32_t end) { + for (int32_t h = start; h < end; h++) { + for (int32_t k = 0; k < depth; k++) { + auto nk = get_backward_window_start( + k, kernel_size_0, neighborhood_size_0, dilation_0, is_causal_0); + auto ek = get_backward_window_end( + k, + depth, + kernel_size_0, + neighborhood_size_0, + dilation_0, + is_causal_0); + for (int32_t i = 0; i < height; i++) { + auto ni = get_backward_window_start( + i, + kernel_size_1, + neighborhood_size_1, + dilation_1, + is_causal_1); + auto ei = get_backward_window_end( + i, + height, + kernel_size_1, + neighborhood_size_1, + dilation_1, + is_causal_1); + for (int32_t j = 0; j < width; j++) { + auto nj = get_backward_window_start( + j, + kernel_size_2, + neighborhood_size_2, + dilation_2, + is_causal_2); + auto ej = get_backward_window_end( + j, + width, + kernel_size_2, + neighborhood_size_2, + dilation_2, + is_causal_2); + for (int32_t d = 0; d < dim; d++) { + int64_t weightsOffset = b * weights_stride_0 + h * weights_stride_1; - const int64_t outOffset = + int64_t outOffset = b * values_stride_0 + h * values_stride_1 + d; scalar_t output_update = scalar_t(0); - for (int xk = nk; xk < ek; xk += dilation_d) { - const int onk = get_window_start( + for (int32_t xk = nk; xk < ek; xk += dilation_0) { + auto onk = get_window_start( xk, depth, - kernel_size_d, - neighborhood_size_d, - dilation_d); - for (int xi = ni; xi < ei; xi += dilation) { - const int oni = get_window_start( - xi, height, kernel_size, neighborhood_size, dilation); - for (int xj = nj; xj < ej; xj += dilation) { - const int onj = get_window_start( + kernel_size_0, + neighborhood_size_0, + dilation_0, + is_causal_0); + for (int32_t xi = ni; xi < ei; xi += dilation_1) { + auto oni = get_window_start( + xi, + height, + kernel_size_1, + neighborhood_size_1, + dilation_1, + is_causal_1); + for (int32_t xj = nj; xj < ej; xj += dilation_2) { + auto onj = get_window_start( xj, width, - kernel_size, - neighborhood_size, - dilation); - const int64_t outIndex = outOffset + xk * values_stride_2 + + kernel_size_2, + neighborhood_size_2, + dilation_2, + is_causal_2); + int64_t outIndex = outOffset + xk * values_stride_2 + xi * values_stride_3 + xj * values_stride_4; - const int64_t weightsIndex = weightsOffset + + int64_t weightsIndex = weightsOffset + xk * weights_stride_2 + xi * weights_stride_3 + xj * weights_stride_4 + - int((k - onk) / dilation_d) * - (kernel_size * kernel_size) + - int((i - oni) / dilation) * kernel_size + - int((j - onj) / dilation); + int32_t((k - onk) / dilation_0) * + (kernel_size_1 * kernel_size_2) + + int32_t((i - oni) / dilation_1) * kernel_size_2 + + int32_t((j - onj) / dilation_2); output_update += values[outIndex] * weights[weightsIndex]; } } } - const int64_t linearIndex = b * values_stride_0 + + int64_t linearIndex = b * values_stride_0 + h * values_stride_1 + k * values_stride_2 + i * values_stride_3 + j * values_stride_4 + d; output[linearIndex] = output_update; diff --git a/csrc/include/natten/cpu/naive/natten_cpu_commons.h b/csrc/include/natten/cpu/naive/natten_cpu_commons.h index b47cfb7..c9306f7 100644 --- a/csrc/include/natten/cpu/naive/natten_cpu_commons.h +++ b/csrc/include/natten/cpu/naive/natten_cpu_commons.h @@ -21,7 +21,7 @@ * **************************************************************************************************/ /*! \file - \brief Holds dispatchers, and common functions shared between ops. + \brief Holds common functions shared between ops. */ #pragma once @@ -30,67 +30,97 @@ namespace natten { namespace cpu { namespace naive { -inline int get_backward_window_start( - const int index, - const int KERNEL_SIZE, - const int NEIGHBORHOOD_SIZE, - const int dilation) { - return (index < KERNEL_SIZE * dilation) - ? (index % dilation) - : index - NEIGHBORHOOD_SIZE * dilation; +template +struct AttnMask { + static_assert(std::numeric_limits::is_iec559); + + static inline auto value(bool is_grad) { + return is_grad ? T(0.0) : -std::numeric_limits::infinity(); + } +}; + +inline int32_t get_backward_window_start( + int32_t index, + int32_t kernel_size, + int32_t neighborhood_size, + int32_t dilation, + bool is_causal) { + if (!is_causal) { + return (index < kernel_size * dilation) + ? (index % dilation) + : index - neighborhood_size * dilation; + } + return index; } -inline int get_backward_window_end( - const int index, - const int length, - const int KERNEL_SIZE, - const int NEIGHBORHOOD_SIZE, - const int dilation) { - return (index >= length - KERNEL_SIZE * dilation) - ? (length) - : (index + (NEIGHBORHOOD_SIZE + 1) * dilation); +inline int32_t get_backward_window_end( + int32_t index, + int32_t length, + int32_t kernel_size, + int32_t neighborhood_size, + int32_t dilation, + bool is_causal) { + if (!is_causal) { + return (index >= length - kernel_size * dilation) + ? (length) + : (index + (neighborhood_size + 1) * dilation); + } + return std::min(index + kernel_size * dilation, length); } -inline int get_window_start( - const int index, - const int length, - const int KERNEL_SIZE, - const int NEIGHBORHOOD_SIZE, - const int dilation) { - if (dilation <= 1) - return std::max(index - NEIGHBORHOOD_SIZE, 0) + - (index + NEIGHBORHOOD_SIZE >= length) * - (length - index - NEIGHBORHOOD_SIZE - 1); - int ni = index - NEIGHBORHOOD_SIZE * dilation; - if (ni < 0) - return index % dilation; - if (index + NEIGHBORHOOD_SIZE * dilation >= length) { - const int imodd = index % dilation; - const int a = int(length / dilation) * dilation; - const int b = length - a; - if (imodd < b) - return length - b + imodd - 2 * NEIGHBORHOOD_SIZE * dilation; - return a + imodd - KERNEL_SIZE * dilation; +inline int32_t get_window_start( + int32_t index, + int32_t length, + int32_t kernel_size, + int32_t neighborhood_size, + int32_t dilation, + bool is_causal) { + auto dilation_idx = index % dilation; + auto index_pdp = index / dilation; + auto length_pdp = (length + dilation - 1) / dilation; + auto num_padded = (length_pdp * dilation) - length; + length_pdp -= (dilation_idx >= dilation - num_padded) ? 1 : 0; + int32_t start_idx; + if (!is_causal) { + start_idx = std::max(index_pdp - neighborhood_size, 0) + + (index_pdp + neighborhood_size >= length_pdp) * + (length_pdp - index_pdp - neighborhood_size - 1); + } else { + start_idx = std::max(index_pdp - kernel_size + 1, 0); + } + return start_idx * dilation + dilation_idx; +} + +inline int32_t get_window_end( + int32_t index, + int32_t start_index, + int32_t length, + int32_t kernel_size, + int32_t neighborhood_size, + int32_t dilation, + bool is_causal) { + if (!is_causal) { + return std::min(length, start_index + kernel_size * dilation); } - return ni; + return std::min(length, index + dilation); } -inline int get_pb_start( - const int index, - const int length, - const int KERNEL_SIZE, - const int NEIGHBORHOOD_SIZE, - const int dilation) { +inline int32_t get_pb_start( + int32_t index, + int32_t length, + int32_t kernel_size, + int32_t neighborhood_size, + int32_t dilation) { if (dilation <= 1) - return NEIGHBORHOOD_SIZE + - (index < NEIGHBORHOOD_SIZE) * (NEIGHBORHOOD_SIZE - index) + - (index + NEIGHBORHOOD_SIZE >= length) * - (length - index - 1 - NEIGHBORHOOD_SIZE); - if (index - NEIGHBORHOOD_SIZE * dilation < 0) - return KERNEL_SIZE - 1 - (index / dilation); - if (index + NEIGHBORHOOD_SIZE * dilation >= length) + return neighborhood_size + + (index < neighborhood_size) * (neighborhood_size - index) + + (index + neighborhood_size >= length) * + (length - index - 1 - neighborhood_size); + if (index - neighborhood_size * dilation < 0) + return kernel_size - 1 - (index / dilation); + if (index + neighborhood_size * dilation >= length) return (length - index - 1) / dilation; - return NEIGHBORHOOD_SIZE; + return neighborhood_size; } } // namespace naive diff --git a/csrc/include/natten/cpu/naive/neighborhood_neighborhood_1d.hpp b/csrc/include/natten/cpu/naive/neighborhood_neighborhood_1d.hpp index af98ba5..11f8875 100644 --- a/csrc/include/natten/cpu/naive/neighborhood_neighborhood_1d.hpp +++ b/csrc/include/natten/cpu/naive/neighborhood_neighborhood_1d.hpp @@ -39,7 +39,8 @@ #include #endif -#include "natten/cpu/naive/natten_cpu_commons.h" +#include +#include namespace natten { namespace cpu { @@ -55,67 +56,83 @@ struct NeighborhoodNeighborhood1D { void* attn_ptr, void* value_ptr, void* output_ptr, - int batch_size, - int heads, - int length, - int dim, + int32_t batch_size, + int32_t heads, + int32_t length, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, - int kernel_size, - int dilation) { + const std::tuple& kernel_size, + const std::tuple& dilation, + const std::tuple& is_causal) { launch( reinterpret_cast(attn_ptr), reinterpret_cast(value_ptr), reinterpret_cast(output_ptr), length, heads, - kernel_size, - dilation, + std::get<0>(kernel_size), + std::get<0>(dilation), dim, batch_size, attn_stride_0, attn_stride_1, - attn_stride_2); + attn_stride_2, + is_causal); } void launch( // AV / Q-grad scalar_t* weights, // attn / d_attn scalar_t* values, // value / key scalar_t* output, // output / d_query - const int length, - const int heads, - const int kernel_size, - const int dilation, - const int dim, - const int batch_size, - const int64_t weights_stride_0, - const int64_t weights_stride_1, - const int64_t weights_stride_2) { - const int neighborhood_size = kernel_size / 2; - const int values_stride_2 = dim; - const int values_stride_1 = length * values_stride_2; - const int values_stride_0 = heads * values_stride_1; - for (int b = 0; b < batch_size; b++) { - at::parallel_for(0, heads, GRAIN_SIZE, [&](int start, int end) { - for (int h = start; h < end; h++) { - for (int i = 0; i < length; i++) { - const int ni = get_window_start( - i, length, kernel_size, neighborhood_size, dilation); - for (int d = 0; d < dim; d++) { + int32_t length, + int32_t heads, + int32_t kernel_size, + int32_t dilation, + int32_t dim, + int32_t batch_size, + int64_t weights_stride_0, + int64_t weights_stride_1, + int64_t weights_stride_2, + const std::tuple& is_causal) { + auto is_causal_0 = std::get<0>(is_causal); + auto neighborhood_size = kernel_size / 2; + int64_t values_stride_2 = dim; + int64_t values_stride_1 = length * values_stride_2; + int64_t values_stride_0 = heads * values_stride_1; + for (int32_t b = 0; b < batch_size; b++) { + at::parallel_for(0, heads, GRAIN_SIZE, [&](int32_t start, int32_t end) { + for (int32_t h = start; h < end; h++) { + for (int32_t i = 0; i < length; i++) { + auto ni = get_window_start( + i, + length, + kernel_size, + neighborhood_size, + dilation, + is_causal_0); + auto ei = get_window_end( + i, + ni, + length, + kernel_size, + neighborhood_size, + dilation, + is_causal_0); + for (int32_t d = 0; d < dim; d++) { scalar_t output_update = scalar_t(0); int64_t attnOffset = b * weights_stride_0 + h * weights_stride_1 + i * weights_stride_2; - const int64_t valuesOffset = + int64_t valuesOffset = b * values_stride_0 + h * values_stride_1 + d; - for (int xi = ni; xi < ni + kernel_size * dilation; - xi += dilation) { - const int64_t valuesIndex = valuesOffset + xi * values_stride_2; + for (int32_t xi = ni; xi < ei; xi += dilation) { + int64_t valuesIndex = valuesOffset + xi * values_stride_2; output_update += weights[attnOffset] * values[valuesIndex]; ++attnOffset; } - const int64_t linearIndex = b * values_stride_0 + - h * values_stride_1 + i * values_stride_2 + d; + int64_t linearIndex = b * values_stride_0 + h * values_stride_1 + + i * values_stride_2 + d; output[linearIndex] = output_update; } } diff --git a/csrc/include/natten/cpu/naive/neighborhood_neighborhood_2d.hpp b/csrc/include/natten/cpu/naive/neighborhood_neighborhood_2d.hpp index 02b5f66..ec3cafb 100644 --- a/csrc/include/natten/cpu/naive/neighborhood_neighborhood_2d.hpp +++ b/csrc/include/natten/cpu/naive/neighborhood_neighborhood_2d.hpp @@ -39,7 +39,8 @@ #include #endif -#include "natten/cpu/naive/natten_cpu_commons.h" +#include +#include namespace natten { namespace cpu { @@ -53,17 +54,18 @@ struct NeighborhoodNeighborhood2D { void* attn_ptr, void* value_ptr, void* output_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, + int32_t batch_size, + int32_t heads, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, - int kernel_size, - int dilation) { + const std::tuple& kernel_size, + const std::tuple& dilation, + const std::tuple& is_causal) { launch( reinterpret_cast(attn_ptr), reinterpret_cast(value_ptr), @@ -71,36 +73,45 @@ struct NeighborhoodNeighborhood2D { height, width, heads, - kernel_size, - dilation, + std::get<0>(kernel_size), + std::get<1>(kernel_size), + std::get<0>(dilation), + std::get<1>(dilation), dim, batch_size, attn_stride_0, attn_stride_1, attn_stride_2, - attn_stride_3); + attn_stride_3, + is_causal); } void launch( // AV / Q-grad scalar_t* weights, // attn / d_attn scalar_t* values, // value / key scalar_t* output, // output / d_query - const int height, - const int width, - const int heads, - const int kernel_size, - const int dilation, - const int dim, - const int batch_size, - const int64_t weights_stride_0, - const int64_t weights_stride_1, - const int64_t weights_stride_2, - const int64_t weights_stride_3) { - const int neighborhood_size = kernel_size / 2; - const int values_stride_3 = dim; - const int values_stride_2 = width * values_stride_3; - const int values_stride_1 = height * values_stride_2; - const int values_stride_0 = heads * values_stride_1; + int32_t height, + int32_t width, + int32_t heads, + int32_t kernel_size_0, + int32_t kernel_size_1, + int32_t dilation_0, + int32_t dilation_1, + int32_t dim, + int32_t batch_size, + int64_t weights_stride_0, + int64_t weights_stride_1, + int64_t weights_stride_2, + int64_t weights_stride_3, + const std::tuple& is_causal) { + auto is_causal_0 = std::get<0>(is_causal); + auto is_causal_1 = std::get<1>(is_causal); + auto neighborhood_size_0 = kernel_size_0 / 2; + auto neighborhood_size_1 = kernel_size_1 / 2; + int64_t values_stride_3 = dim; + int64_t values_stride_2 = width * values_stride_3; + int64_t values_stride_1 = height * values_stride_2; + int64_t values_stride_0 = heads * values_stride_1; // NOTE: this function originally had an AVX impl, // but it was removed when migrating to the new NATTEN api // Unsure what the issue was; I wrote this well over a year ago so... @@ -110,39 +121,65 @@ struct NeighborhoodNeighborhood2D { 0, batch_size * heads * height * width, GRAIN_SIZE, - [&](int start, int end) { - for (int x = start; x < end; x++) { - int indtmp1 = x / width; - const int j = x - indtmp1 * width; - int indtmp2 = indtmp1 / height; - const int i = indtmp1 - indtmp2 * height; + [&](int32_t start, int32_t end) { + for (int32_t x = start; x < end; x++) { + int32_t indtmp1 = x / width; + auto j = x - indtmp1 * width; + int32_t indtmp2 = indtmp1 / height; + auto i = indtmp1 - indtmp2 * height; indtmp1 = indtmp2; indtmp2 = indtmp1 / heads; - const int h = indtmp1 - indtmp2 * heads; - const int b = indtmp2; - const int ni = get_window_start( - i, height, kernel_size, neighborhood_size, dilation); - const int nj = get_window_start( - j, width, kernel_size, neighborhood_size, dilation); - for (int d = 0; d < dim; d++) { + auto h = indtmp1 - indtmp2 * heads; + auto& b = indtmp2; + auto ni = get_window_start( + i, + height, + kernel_size_0, + neighborhood_size_0, + dilation_0, + is_causal_0); + auto nj = get_window_start( + j, + width, + kernel_size_1, + neighborhood_size_1, + dilation_1, + is_causal_1); + auto ei = get_window_end( + i, + ni, + height, + kernel_size_0, + neighborhood_size_0, + dilation_0, + is_causal_0); + auto ej = get_window_end( + j, + nj, + width, + kernel_size_1, + neighborhood_size_1, + dilation_1, + is_causal_1); + for (int32_t d = 0; d < dim; d++) { scalar_t updt = scalar_t(0); - int64_t weightsOffset = b * weights_stride_0 + h * weights_stride_1 + - i * weights_stride_2 + j * weights_stride_3; - const int64_t valuesOffset = + int64_t weightsOffset = b * weights_stride_0 + + h * weights_stride_1 + i * weights_stride_2 + + j * weights_stride_3; + int64_t valuesOffset = b * values_stride_0 + h * values_stride_1 + d; - for (int xi = ni; xi < ni + kernel_size * dilation; - xi += dilation) { - for (int xj = nj; xj < nj + kernel_size * dilation; - xj += dilation) { - const int64_t valuesIndex = valuesOffset + xi * values_stride_2 + + for (int32_t xi = ni; xi < ei; xi += dilation_0) { + for (int32_t xj = nj; xj < ej; xj += dilation_1) { + int64_t valuesIndex = valuesOffset + xi * values_stride_2 + xj * values_stride_3; - updt += weights[weightsOffset] * values[valuesIndex]; - ++weightsOffset; + int64_t weightsIndex = weightsOffset + + int32_t((xi - ni) / dilation_0) * kernel_size_1 + + int32_t((xj - nj) / dilation_1); + updt += weights[weightsIndex] * values[valuesIndex]; } } - const int64_t linearIndex = b * values_stride_0 + - h * values_stride_1 + i * values_stride_2 + - j * values_stride_3 + d; + int64_t linearIndex = b * values_stride_0 + h * values_stride_1 + + i * values_stride_2 + j * values_stride_3 + d; output[linearIndex] = updt; } } diff --git a/csrc/include/natten/cpu/naive/neighborhood_neighborhood_3d.hpp b/csrc/include/natten/cpu/naive/neighborhood_neighborhood_3d.hpp index 8797e3f..20bbabb 100644 --- a/csrc/include/natten/cpu/naive/neighborhood_neighborhood_3d.hpp +++ b/csrc/include/natten/cpu/naive/neighborhood_neighborhood_3d.hpp @@ -39,7 +39,8 @@ #include #endif -#include "natten/cpu/naive/natten_cpu_commons.h" +#include +#include namespace natten { namespace cpu { @@ -53,21 +54,20 @@ struct NeighborhoodNeighborhood3D { void* attn_ptr, void* value_ptr, void* output_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, + int32_t batch_size, + int32_t heads, + int32_t depth, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, int64_t attn_stride_4, - int kernel_size, - int kernel_size_depth, - int dilation, - int dilation_depth) { + const std::tuple& kernel_size, + const std::tuple& dilation, + const std::tuple& is_causal) { launch( reinterpret_cast(attn_ptr), reinterpret_cast(value_ptr), @@ -76,92 +76,141 @@ struct NeighborhoodNeighborhood3D { height, width, heads, - kernel_size, - kernel_size_depth, - dilation, - dilation_depth, + std::get<0>(kernel_size), + std::get<1>(kernel_size), + std::get<2>(kernel_size), + std::get<0>(dilation), + std::get<1>(dilation), + std::get<2>(dilation), dim, batch_size, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, - attn_stride_4); + attn_stride_4, + is_causal); } void launch( // AV / Q-grad scalar_t* weights, // attn / d_attn scalar_t* values, // value / key scalar_t* output, // output / d_query - const int depth, - const int height, - const int width, - const int heads, - const int kernel_size, - const int kernel_size_d, - const int dilation, - const int dilation_d, - const int dim, - const int batch_size, - const int64_t weights_stride_0, - const int64_t weights_stride_1, - const int64_t weights_stride_2, - const int64_t weights_stride_3, - const int64_t weights_stride_4) { - const int neighborhood_size = kernel_size / 2; - const int neighborhood_size_d = kernel_size_d / 2; - const int values_stride_4 = dim; - const int values_stride_3 = width * values_stride_4; - const int values_stride_2 = height * values_stride_3; - const int values_stride_1 = depth * values_stride_2; - const int values_stride_0 = heads * values_stride_1; + int32_t depth, + int32_t height, + int32_t width, + int32_t heads, + int32_t kernel_size_0, + int32_t kernel_size_1, + int32_t kernel_size_2, + int32_t dilation_0, + int32_t dilation_1, + int32_t dilation_2, + int32_t dim, + int32_t batch_size, + int64_t weights_stride_0, + int64_t weights_stride_1, + int64_t weights_stride_2, + int64_t weights_stride_3, + int64_t weights_stride_4, + const std::tuple& is_causal) { + auto is_causal_0 = std::get<0>(is_causal); + auto is_causal_1 = std::get<1>(is_causal); + auto is_causal_2 = std::get<2>(is_causal); + auto neighborhood_size_0 = kernel_size_0 / 2; + auto neighborhood_size_1 = kernel_size_1 / 2; + auto neighborhood_size_2 = kernel_size_2 / 2; + int64_t values_stride_4 = dim; + int64_t values_stride_3 = width * values_stride_4; + int64_t values_stride_2 = height * values_stride_3; + int64_t values_stride_1 = depth * values_stride_2; + int64_t values_stride_0 = heads * values_stride_1; at::parallel_for( 0, batch_size * heads * depth * height * width, GRAIN_SIZE, - [&](int start, int end) { - for (int x = start; x < end; x++) { - int indtmp1 = x / width; - const int j = x - indtmp1 * width; - int indtmp2 = indtmp1 / height; - const int i = indtmp1 - indtmp2 * height; + [&](int32_t start, int32_t end) { + for (int32_t x = start; x < end; x++) { + int32_t indtmp1 = x / width; + auto j = x - indtmp1 * width; + int32_t indtmp2 = indtmp1 / height; + auto i = indtmp1 - indtmp2 * height; indtmp1 = indtmp2; indtmp2 = indtmp1 / depth; - const int k = indtmp1 - indtmp2 * depth; + auto k = indtmp1 - indtmp2 * depth; indtmp1 = indtmp2; indtmp2 = indtmp1 / heads; - const int h = indtmp1 - indtmp2 * heads; - const int b = indtmp2; - const int ni = get_window_start( - i, height, kernel_size, neighborhood_size, dilation); - const int nj = get_window_start( - j, width, kernel_size, neighborhood_size, dilation); - const int nk = get_window_start( - k, depth, kernel_size_d, neighborhood_size_d, dilation_d); - for (int d = 0; d < dim; d++) { + auto h = indtmp1 - indtmp2 * heads; + auto& b = indtmp2; + auto nk = get_window_start( + k, + depth, + kernel_size_0, + neighborhood_size_0, + dilation_0, + is_causal_0); + auto ni = get_window_start( + i, + height, + kernel_size_1, + neighborhood_size_1, + dilation_1, + is_causal_1); + auto nj = get_window_start( + j, + width, + kernel_size_2, + neighborhood_size_2, + dilation_2, + is_causal_2); + auto ek = get_window_end( + k, + nk, + depth, + kernel_size_0, + neighborhood_size_0, + dilation_0, + is_causal_0); + auto ei = get_window_end( + i, + ni, + height, + kernel_size_1, + neighborhood_size_1, + dilation_1, + is_causal_1); + auto ej = get_window_end( + j, + nj, + width, + kernel_size_2, + neighborhood_size_2, + dilation_2, + is_causal_2); + for (int32_t d = 0; d < dim; d++) { scalar_t updt = scalar_t(0); - int64_t weightsOffset = b * weights_stride_0 + h * weights_stride_1 + - k * weights_stride_2 + i * weights_stride_3 + - j * weights_stride_4; - const int64_t valuesOffset = + int64_t weightsOffset = b * weights_stride_0 + + h * weights_stride_1 + k * weights_stride_2 + + i * weights_stride_3 + j * weights_stride_4; + int64_t valuesOffset = b * values_stride_0 + h * values_stride_1 + d; - for (int xk = nk; xk < nk + kernel_size_d * dilation_d; - xk += dilation_d) { - for (int xi = ni; xi < ni + kernel_size * dilation; - xi += dilation) { - for (int xj = nj; xj < nj + kernel_size * dilation; - xj += dilation) { - const int64_t valuesIndex = valuesOffset + - xk * values_stride_2 + xi * values_stride_3 + - xj * values_stride_4; - updt += weights[weightsOffset] * values[valuesIndex]; - ++weightsOffset; + for (int32_t xk = nk; xk < ek; xk += dilation_0) { + for (int32_t xi = ni; xi < ei; xi += dilation_1) { + for (int32_t xj = nj; xj < ej; xj += dilation_2) { + int64_t valuesIndex = valuesOffset + xk * values_stride_2 + + xi * values_stride_3 + xj * values_stride_4; + int64_t weightsIndex = weightsOffset + + int32_t((xk - nk) / dilation_0) * + (kernel_size_1 * kernel_size_2) + + int32_t((xi - ni) / dilation_1) * kernel_size_2 + + int32_t((xj - nj) / dilation_2); + updt += weights[weightsIndex] * values[valuesIndex]; } } } - const int64_t linearIndex = b * values_stride_0 + - h * values_stride_1 + k * values_stride_2 + - i * values_stride_3 + j * values_stride_4 + d; + int64_t linearIndex = b * values_stride_0 + h * values_stride_1 + + k * values_stride_2 + i * values_stride_3 + + j * values_stride_4 + d; output[linearIndex] = updt; } } diff --git a/csrc/include/natten/cpu/naive/pointwise_neighborhood_1d.hpp b/csrc/include/natten/cpu/naive/pointwise_neighborhood_1d.hpp index a269856..a84af36 100644 --- a/csrc/include/natten/cpu/naive/pointwise_neighborhood_1d.hpp +++ b/csrc/include/natten/cpu/naive/pointwise_neighborhood_1d.hpp @@ -40,7 +40,8 @@ #include #endif -#include "natten/cpu/naive/natten_cpu_commons.h" +#include +#include namespace natten { namespace cpu { @@ -51,103 +52,148 @@ namespace naive { template struct PointwiseNeighborhood1D { void operator()( + bool is_grad, void* query_ptr, void* key_ptr, void* attn_ptr, - int batch_size, - int heads, - int length, - int dim, + int32_t batch_size, + int32_t heads, + int32_t length, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, - int kernel_size, - int dilation) { + const std::tuple& kernel_size, + const std::tuple& dilation, + const std::tuple& is_causal) { launch( + is_grad, reinterpret_cast(query_ptr), reinterpret_cast(key_ptr), reinterpret_cast(attn_ptr), length, heads, - kernel_size, - dilation, + std::get<0>(kernel_size), + std::get<0>(dilation), dim, batch_size, attn_stride_0, attn_stride_1, - attn_stride_2); + attn_stride_2, + is_causal); } void launch( // QK / A-grad + bool is_grad, scalar_t* query, // query / d_out scalar_t* key, // key / value scalar_t* attn, // attn / d_attn - const int length, - const int heads, - const int kernel_size, - const int dilation, - const int dim, - const int batch_size, - const int64_t attn_stride_0, - const int64_t attn_stride_1, - const int64_t attn_stride_2) { - const int neighborhood_size = kernel_size / 2; - const int query_stride_2 = dim; - const int query_stride_1 = length * query_stride_2; - const int query_stride_0 = heads * query_stride_1; + int32_t length, + int32_t heads, + int32_t kernel_size, + int32_t dilation, + int32_t dim, + int32_t batch_size, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + const std::tuple& is_causal) { + auto is_causal_0 = std::get<0>(is_causal); + auto neighborhood_size = kernel_size / 2; + int64_t query_stride_2 = dim; + int64_t query_stride_1 = length * query_stride_2; + int64_t query_stride_0 = heads * query_stride_1; + auto mask_value = AttnMask::value(is_grad); #if defined(AVX_INT) using Vec = at::vec::Vectorized; at::parallel_for( - 0, batch_size * heads * length, GRAIN_SIZE, [&](int start, int end) { - for (int x = start; x < end; x++) { - int indtmp1 = x / length; - const int i = x - indtmp1 * length; - int indtmp2 = indtmp1 / heads; - const int h = indtmp1 - indtmp2 * heads; - const int b = indtmp2; - const int ni = get_window_start( - i, length, kernel_size, neighborhood_size, dilation); - const int64_t batchHeadOffset = b * query_stride_0 + h * query_stride_1; - const int64_t queryOffset = batchHeadOffset + i * query_stride_2; + 0, + batch_size * heads * length, + GRAIN_SIZE, + [&](int32_t start, int32_t end) { + for (int32_t x = start; x < end; x++) { + int32_t indtmp1 = x / length; + auto i = x - indtmp1 * length; + int32_t indtmp2 = indtmp1 / heads; + auto h = indtmp1 - indtmp2 * heads; + auto& b = indtmp2; + auto ni = get_window_start( + i, + length, + kernel_size, + neighborhood_size, + dilation, + is_causal_0); + auto ei = get_window_end( + i, + ni, + length, + kernel_size, + neighborhood_size, + dilation, + is_causal_0); + int64_t batchHeadOffset = b * query_stride_0 + h * query_stride_1; + int64_t queryOffset = batchHeadOffset + i * query_stride_2; int64_t index = b * attn_stride_0 + h * attn_stride_1 + i * attn_stride_2; scalar_t* _qaddr = query + queryOffset; - for (int ki = 0; ki < kernel_size; ki++) { + for (int32_t ki = 0; ki < kernel_size; ki++) { Vec updt = Vec(scalar_t(0)); - const int64_t keyOffset = - batchHeadOffset + (ki * dilation + ni) * query_stride_2; - scalar_t* _kaddr = key + keyOffset; - int64_t d1 = 0; - for (; d1 < dim - (dim % Vec::size()); d1 += Vec::size()) - updt = at::vec::fmadd( - Vec::loadu(_qaddr + d1), Vec::loadu(_kaddr + d1), updt); - scalar_t sum_val = at::vec::vec_reduce_all( - [](Vec& x, Vec& y) { return x + y; }, updt, Vec::size()); - for (; d1 < dim; ++d1) - sum_val += _qaddr[d1] * _kaddr[d1]; + scalar_t sum_val; + auto key_idx = ki * dilation + ni; + if (key_idx < ei) { + int64_t keyOffset = batchHeadOffset + key_idx * query_stride_2; + scalar_t* _kaddr = key + keyOffset; + int64_t d1 = 0; + for (; d1 < dim - (dim % Vec::size()); d1 += Vec::size()) + updt = at::vec::fmadd( + Vec::loadu(_qaddr + d1), Vec::loadu(_kaddr + d1), updt); + sum_val = at::vec::vec_reduce_all( + [](Vec& x, Vec& y) { return x + y; }, updt, Vec::size()); + for (; d1 < dim; ++d1) + sum_val += _qaddr[d1] * _kaddr[d1]; + } else { + sum_val = mask_value; + } attn[index] = sum_val; index++; } } }); #else - for (int b = 0; b < batch_size; b++) { - at::parallel_for(0, heads, GRAIN_SIZE, [&](int start, int end) { - for (int h = start; h < end; h++) { - for (int i = 0; i < length; i++) { - const int ni = get_window_start( - i, length, kernel_size, neighborhood_size, dilation); - for (int ki = 0; ki < kernel_size; ki++) { + for (int32_t b = 0; b < batch_size; b++) { + at::parallel_for(0, heads, GRAIN_SIZE, [&](int32_t start, int32_t end) { + for (int32_t h = start; h < end; h++) { + for (int32_t i = 0; i < length; i++) { + auto ni = get_window_start( + i, + length, + kernel_size, + neighborhood_size, + dilation, + is_causal_0); + auto ei = get_window_end( + i, + ni, + length, + kernel_size, + neighborhood_size, + dilation, + is_causal_0); + for (int32_t ki = 0; ki < kernel_size; ki++) { scalar_t updt = scalar_t(0); - const int64_t batchHeadOffset = - b * query_stride_0 + h * query_stride_1; - const int64_t queryOffset = batchHeadOffset + i * query_stride_2; - const int64_t keyOffset = - batchHeadOffset + (ki * dilation + ni) * query_stride_2; - for (int64_t dimOffset = 0; dimOffset < dim; ++dimOffset) - updt += - query[queryOffset + dimOffset] * key[keyOffset + dimOffset]; - const int64_t index = b * attn_stride_0 + h * attn_stride_1 + + int64_t batchHeadOffset = b * query_stride_0 + h * query_stride_1; + int64_t queryOffset = batchHeadOffset + i * query_stride_2; + auto key_idx = ki * dilation + ni; + if (key_idx < ei) { + int64_t keyOffset = batchHeadOffset + key_idx * query_stride_2; + for (int64_t dimOffset = 0; dimOffset < dim; ++dimOffset) + updt += query[queryOffset + dimOffset] * + key[keyOffset + dimOffset]; + } else { + updt = mask_value; + } + int64_t index = b * attn_stride_0 + h * attn_stride_1 + i * attn_stride_2 + ki; attn[index] = updt; } @@ -166,15 +212,19 @@ struct PointwiseNeighborhood1DWithBias { void* key_ptr, void* bias_ptr, void* attn_ptr, - int batch_size, - int heads, - int length, - int dim, + int32_t batch_size, + int32_t heads, + int32_t length, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, - int kernel_size, - int dilation) { + const std::tuple& kernel_size, + const std::tuple& dilation, + const std::tuple& is_causal) { + NATTEN_CHECK( + !any_true(is_causal), + "Neighborhood attention with causal masking does not support positional biases yet."); launch( reinterpret_cast(query_ptr), reinterpret_cast(key_ptr), @@ -182,8 +232,8 @@ struct PointwiseNeighborhood1DWithBias { reinterpret_cast(attn_ptr), length, heads, - kernel_size, - dilation, + std::get<0>(kernel_size), + std::get<0>(dilation), dim, batch_size, attn_stride_0, @@ -196,42 +246,45 @@ struct PointwiseNeighborhood1DWithBias { scalar_t* key, // key scalar_t* bias, // relative positional bias tensor scalar_t* attn, // attn - const int length, - const int heads, - const int kernel_size, - const int dilation, - const int dim, - const int batch_size, - const int64_t attn_stride_0, - const int64_t attn_stride_1, - const int64_t attn_stride_2) { - const int neighborhood_size = kernel_size / 2; - const int bias_stride_0 = 2 * kernel_size - 1; - const int query_stride_2 = dim; - const int query_stride_1 = length * query_stride_2; - const int query_stride_0 = heads * query_stride_1; + int32_t length, + int32_t heads, + int32_t kernel_size, + int32_t dilation, + int32_t dim, + int32_t batch_size, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2) { + auto neighborhood_size = kernel_size / 2; + int64_t bias_stride_0 = 2 * kernel_size - 1; + int64_t query_stride_2 = dim; + int64_t query_stride_1 = length * query_stride_2; + int64_t query_stride_0 = heads * query_stride_1; #if defined(AVX_INT) using Vec = at::vec::Vectorized; at::parallel_for( - 0, batch_size * heads * length, GRAIN_SIZE, [&](int start, int end) { - for (int x = start; x < end; x++) { - int indtmp1 = x / length; - const int i = x - indtmp1 * length; - int indtmp2 = indtmp1 / heads; - const int h = indtmp1 - indtmp2 * heads; - const int b = indtmp2; - const int ni = get_window_start( - i, length, kernel_size, neighborhood_size, dilation); - const int pi = get_pb_start( + 0, + batch_size * heads * length, + GRAIN_SIZE, + [&](int32_t start, int32_t end) { + for (int32_t x = start; x < end; x++) { + int32_t indtmp1 = x / length; + auto i = x - indtmp1 * length; + int32_t indtmp2 = indtmp1 / heads; + auto h = indtmp1 - indtmp2 * heads; + auto& b = indtmp2; + auto ni = get_window_start( + i, length, kernel_size, neighborhood_size, dilation, false); + auto pi = get_pb_start( i, length, kernel_size, neighborhood_size, dilation); - const int64_t batchHeadOffset = b * query_stride_0 + h * query_stride_1; - const int64_t queryOffset = batchHeadOffset + i * query_stride_2; + int64_t batchHeadOffset = b * query_stride_0 + h * query_stride_1; + int64_t queryOffset = batchHeadOffset + i * query_stride_2; int64_t index = b * attn_stride_0 + h * attn_stride_1 + i * attn_stride_2; scalar_t* _qaddr = query + queryOffset; - for (int ki = 0; ki < kernel_size; ki++) { + for (int32_t ki = 0; ki < kernel_size; ki++) { Vec updt = Vec(scalar_t(0)); - const int64_t keyOffset = + int64_t keyOffset = batchHeadOffset + (ki * dilation + ni) * query_stride_2; scalar_t* _kaddr = key + keyOffset; int64_t d1 = 0; @@ -242,34 +295,33 @@ struct PointwiseNeighborhood1DWithBias { [](Vec& x, Vec& y) { return x + y; }, updt, Vec::size()); for (; d1 < dim; ++d1) sum_val += _qaddr[d1] * _kaddr[d1]; - const int64_t biasIndex = h * bias_stride_0 + (pi + ki); + int64_t biasIndex = h * bias_stride_0 + (pi + ki); attn[index] = bias[biasIndex] + sum_val; index++; } } }); #else - for (int b = 0; b < batch_size; b++) { - at::parallel_for(0, heads, GRAIN_SIZE, [&](int start, int end) { - for (int h = start; h < end; h++) { - for (int i = 0; i < length; i++) { - const int ni = get_window_start( - i, length, kernel_size, neighborhood_size, dilation); - const int pi = get_pb_start( + for (int32_t b = 0; b < batch_size; b++) { + at::parallel_for(0, heads, GRAIN_SIZE, [&](int32_t start, int32_t end) { + for (int32_t h = start; h < end; h++) { + for (int32_t i = 0; i < length; i++) { + auto ni = get_window_start( + i, length, kernel_size, neighborhood_size, dilation, false); + auto pi = get_pb_start( i, length, kernel_size, neighborhood_size, dilation); - for (int ki = 0; ki < kernel_size; ki++) { + for (int32_t ki = 0; ki < kernel_size; ki++) { scalar_t updt = scalar_t(0); - const int64_t batchHeadOffset = - b * query_stride_0 + h * query_stride_1; - const int64_t queryOffset = batchHeadOffset + i * query_stride_2; - const int64_t keyOffset = + int64_t batchHeadOffset = b * query_stride_0 + h * query_stride_1; + int64_t queryOffset = batchHeadOffset + i * query_stride_2; + int64_t keyOffset = batchHeadOffset + (ki * dilation + ni) * query_stride_2; for (int64_t dimOffset = 0; dimOffset < dim; ++dimOffset) updt += query[queryOffset + dimOffset] * key[keyOffset + dimOffset]; - const int64_t index = b * attn_stride_0 + h * attn_stride_1 + + int64_t index = b * attn_stride_0 + h * attn_stride_1 + i * attn_stride_2 + ki; - const int64_t biasIndex = h * bias_stride_0 + (pi + ki); + int64_t biasIndex = h * bias_stride_0 + (pi + ki); updt += bias[biasIndex]; attn[index] = updt; } diff --git a/csrc/include/natten/cpu/naive/pointwise_neighborhood_2d.hpp b/csrc/include/natten/cpu/naive/pointwise_neighborhood_2d.hpp index 7088c8c..4505065 100644 --- a/csrc/include/natten/cpu/naive/pointwise_neighborhood_2d.hpp +++ b/csrc/include/natten/cpu/naive/pointwise_neighborhood_2d.hpp @@ -40,7 +40,8 @@ #include #endif -#include "natten/cpu/naive/natten_cpu_commons.h" +#include +#include namespace natten { namespace cpu { @@ -51,98 +52,144 @@ namespace naive { template struct PointwiseNeighborhood2D { void operator()( + bool is_grad, void* query_ptr, void* key_ptr, void* attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, + int32_t batch_size, + int32_t heads, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, - int kernel_size, - int dilation) { + const std::tuple& kernel_size, + const std::tuple& dilation, + const std::tuple& is_causal) { launch( + is_grad, reinterpret_cast(query_ptr), reinterpret_cast(key_ptr), reinterpret_cast(attn_ptr), height, width, heads, - kernel_size, - dilation, + std::get<0>(kernel_size), + std::get<1>(kernel_size), + std::get<0>(dilation), + std::get<1>(dilation), dim, batch_size, attn_stride_0, attn_stride_1, attn_stride_2, - attn_stride_3); + attn_stride_3, + is_causal); } void launch( // QK / A-grad + bool is_grad, scalar_t* query, // query / d_out scalar_t* key, // key / value scalar_t* attn, // attn / d_attn - const int height, - const int width, - const int heads, - const int kernel_size, - const int dilation, - const int dim, - const int batch_size, - const int64_t attn_stride_0, - const int64_t attn_stride_1, - const int64_t attn_stride_2, - const int64_t attn_stride_3) { - const int neighborhood_size = kernel_size / 2; - const int query_stride_3 = dim; - const int query_stride_2 = width * query_stride_3; - const int query_stride_1 = height * query_stride_2; - const int query_stride_0 = heads * query_stride_1; + int32_t height, + int32_t width, + int32_t heads, + int32_t kernel_size_0, + int32_t kernel_size_1, + int32_t dilation_0, + int32_t dilation_1, + int32_t dim, + int32_t batch_size, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + const std::tuple& is_causal) { + auto is_causal_0 = std::get<0>(is_causal); + auto is_causal_1 = std::get<1>(is_causal); + auto neighborhood_size_0 = kernel_size_0 / 2; + auto neighborhood_size_1 = kernel_size_1 / 2; + int64_t query_stride_3 = dim; + int64_t query_stride_2 = width * query_stride_3; + int64_t query_stride_1 = height * query_stride_2; + int64_t query_stride_0 = heads * query_stride_1; + auto mask_value = AttnMask::value(is_grad); #if defined(AVX_INT) using Vec = at::vec::Vectorized; at::parallel_for( 0, batch_size * heads * height * width, GRAIN_SIZE, - [&](int start, int end) { - for (int x = start; x < end; x++) { - int indtmp1 = x / width; - const int j = x - indtmp1 * width; - int indtmp2 = indtmp1 / height; - const int i = indtmp1 - indtmp2 * height; + [&](int32_t start, int32_t end) { + for (int32_t x = start; x < end; x++) { + int32_t indtmp1 = x / width; + auto j = x - indtmp1 * width; + int32_t indtmp2 = indtmp1 / height; + auto i = indtmp1 - indtmp2 * height; indtmp1 = indtmp2; indtmp2 = indtmp1 / heads; - const int h = indtmp1 - indtmp2 * heads; - const int b = indtmp2; - const int ni = get_window_start( - i, height, kernel_size, neighborhood_size, dilation); - const int nj = get_window_start( - j, width, kernel_size, neighborhood_size, dilation); - const int64_t batchHeadOffset = b * query_stride_0 + h * query_stride_1; - const int64_t queryOffset = + auto h = indtmp1 - indtmp2 * heads; + auto& b = indtmp2; + auto ni = get_window_start( + i, + height, + kernel_size_0, + neighborhood_size_0, + dilation_0, + is_causal_0); + auto nj = get_window_start( + j, + width, + kernel_size_1, + neighborhood_size_1, + dilation_1, + is_causal_1); + auto ei = get_window_end( + i, + ni, + height, + kernel_size_0, + neighborhood_size_0, + dilation_0, + is_causal_0); + auto ej = get_window_end( + j, + nj, + width, + kernel_size_1, + neighborhood_size_1, + dilation_1, + is_causal_1); + int64_t batchHeadOffset = b * query_stride_0 + h * query_stride_1; + int64_t queryOffset = batchHeadOffset + i * query_stride_2 + j * query_stride_3; int64_t index = b * attn_stride_0 + h * attn_stride_1 + i * attn_stride_2 + j * attn_stride_3; scalar_t* _qaddr = query + queryOffset; - for (int ki = 0; ki < kernel_size; ki++) { - for (int kj = 0; kj < kernel_size; kj++) { + for (int32_t ki = 0; ki < kernel_size_0; ki++) { + auto key_idx_i = ki * dilation_0 + ni; + for (int32_t kj = 0; kj < kernel_size_1; kj++) { + auto key_idx_j = kj * dilation_1 + nj; Vec updt = Vec(scalar_t(0)); - const int64_t keyOffset = batchHeadOffset + - (ki * dilation + ni) * query_stride_2 + - (kj * dilation + nj) * query_stride_3; - scalar_t* _kaddr = key + keyOffset; - int64_t d1 = 0; - for (; d1 < dim - (dim % Vec::size()); d1 += Vec::size()) - updt = at::vec::fmadd( - Vec::loadu(_qaddr + d1), Vec::loadu(_kaddr + d1), updt); - scalar_t sum_val = at::vec::vec_reduce_all( - [](Vec& x, Vec& y) { return x + y; }, updt, Vec::size()); - for (; d1 < dim; ++d1) - sum_val += _qaddr[d1] * _kaddr[d1]; + scalar_t sum_val; + if (key_idx_i < ei && key_idx_j < ej) { + int64_t keyOffset = batchHeadOffset + + key_idx_i * query_stride_2 + key_idx_j * query_stride_3; + scalar_t* _kaddr = key + keyOffset; + int64_t d1 = 0; + for (; d1 < dim - (dim % Vec::size()); d1 += Vec::size()) + updt = at::vec::fmadd( + Vec::loadu(_qaddr + d1), Vec::loadu(_kaddr + d1), updt); + sum_val = at::vec::vec_reduce_all( + [](Vec& x, Vec& y) { return x + y; }, updt, Vec::size()); + for (; d1 < dim; ++d1) + sum_val += _qaddr[d1] * _kaddr[d1]; + } else { + sum_val = mask_value; + } attn[index] = sum_val; index++; } @@ -154,35 +201,67 @@ struct PointwiseNeighborhood2D { 0, batch_size * heads * height * width, GRAIN_SIZE, - [&](int start, int end) { - for (int x = start; x < end; x++) { - int indtmp1 = x / width; - const int j = x - indtmp1 * width; - int indtmp2 = indtmp1 / height; - const int i = indtmp1 - indtmp2 * height; + [&](int32_t start, int32_t end) { + for (int32_t x = start; x < end; x++) { + int32_t indtmp1 = x / width; + auto j = x - indtmp1 * width; + int32_t indtmp2 = indtmp1 / height; + auto i = indtmp1 - indtmp2 * height; indtmp1 = indtmp2; indtmp2 = indtmp1 / heads; - const int h = indtmp1 - indtmp2 * heads; - const int b = indtmp2; - const int ni = get_window_start( - i, height, kernel_size, neighborhood_size, dilation); - const int nj = get_window_start( - j, width, kernel_size, neighborhood_size, dilation); - for (int ki = 0; ki < kernel_size; ki++) { - for (int kj = 0; kj < kernel_size; kj++) { + auto h = indtmp1 - indtmp2 * heads; + auto& b = indtmp2; + auto ni = get_window_start( + i, + height, + kernel_size_0, + neighborhood_size_0, + dilation_0, + is_causal_0); + auto nj = get_window_start( + j, + width, + kernel_size_1, + neighborhood_size_1, + dilation_1, + is_causal_1); + auto ni = get_window_end( + i, + ni, + height, + kernel_size_0, + neighborhood_size_0, + dilation_0, + is_causal_0); + auto nj = get_window_end( + j, + nj, + width, + kernel_size_1, + neighborhood_size_1, + dilation_1, + is_causal_1); + for (int32_t ki = 0; ki < kernel_size_0; ki++) { + auto key_idx_i = ki * dilation_0 + ni; + for (int32_t kj = 0; kj < kernel_size_1; kj++) { + auto key_idx_j = kj * dilation_1 + nj; scalar_t updt = scalar_t(0); - const int64_t batchHeadOffset = + int64_t batchHeadOffset = b * query_stride_0 + h * query_stride_1; - const int64_t queryOffset = + int64_t queryOffset = batchHeadOffset + i * query_stride_2 + j * query_stride_3; - const int64_t keyOffset = batchHeadOffset + - (ki * dilation + ni) * query_stride_2 + - (kj * dilation + nj) * query_stride_3; - for (int64_t dimOffset = 0; dimOffset < dim; ++dimOffset) - updt += query[queryOffset + dimOffset] * - key[keyOffset + dimOffset]; - const int64_t index = b * attn_stride_0 + h * attn_stride_1 + - i * attn_stride_2 + j * attn_stride_3 + ki * kernel_size + + if (key_idx_i < ei && key_idx_j < ej) { + int64_t keyOffset = batchHeadOffset + + (ki * dilation_0 + ni) * query_stride_2 + + (kj * dilation_1 + nj) * query_stride_3; + for (int64_t dimOffset = 0; dimOffset < dim; ++dimOffset) + updt += query[queryOffset + dimOffset] * + key[keyOffset + dimOffset]; + } else { + updt = mask_value; + } + int64_t index = b * attn_stride_0 + h * attn_stride_1 + + i * attn_stride_2 + j * attn_stride_3 + ki * kernel_size_1 + kj; attn[index] = updt; } @@ -200,17 +279,21 @@ struct PointwiseNeighborhood2DWithBias { void* key_ptr, void* bias_ptr, void* attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, + int32_t batch_size, + int32_t heads, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, - int kernel_size, - int dilation) { + const std::tuple& kernel_size, + const std::tuple& dilation, + const std::tuple& is_causal) { + NATTEN_CHECK( + !any_true(is_causal), + "Neighborhood attention with causal masking does not support positional biases yet."); launch( reinterpret_cast(query_ptr), reinterpret_cast(key_ptr), @@ -219,8 +302,10 @@ struct PointwiseNeighborhood2DWithBias { height, width, heads, - kernel_size, - dilation, + std::get<0>(kernel_size), + std::get<1>(kernel_size), + std::get<0>(dilation), + std::get<1>(dilation), dim, batch_size, attn_stride_0, @@ -234,60 +319,73 @@ struct PointwiseNeighborhood2DWithBias { scalar_t* key, // key / value scalar_t* bias, // relative positional bias tensor scalar_t* attn, // attn / d_attn - const int height, - const int width, - const int heads, - const int kernel_size, - const int dilation, - const int dim, - const int batch_size, - const int64_t attn_stride_0, - const int64_t attn_stride_1, - const int64_t attn_stride_2, - const int64_t attn_stride_3) { - const int neighborhood_size = kernel_size / 2; - const int bias_stride_1 = (2 * kernel_size - 1); - const int bias_stride_0 = (2 * kernel_size - 1) * bias_stride_1; - const int query_stride_3 = dim; - const int query_stride_2 = width * query_stride_3; - const int query_stride_1 = height * query_stride_2; - const int query_stride_0 = heads * query_stride_1; + int32_t height, + int32_t width, + int32_t heads, + int32_t kernel_size_0, + int32_t kernel_size_1, + int32_t dilation_0, + int32_t dilation_1, + int32_t dim, + int32_t batch_size, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3) { + auto neighborhood_size_0 = kernel_size_0 / 2; + auto neighborhood_size_1 = kernel_size_1 / 2; + int64_t bias_stride_1 = (2 * kernel_size_1 - 1); + int64_t bias_stride_0 = (2 * kernel_size_0 - 1) * bias_stride_1; + int64_t query_stride_3 = dim; + int64_t query_stride_2 = width * query_stride_3; + int64_t query_stride_1 = height * query_stride_2; + int64_t query_stride_0 = heads * query_stride_1; #if defined(AVX_INT) using Vec = at::vec::Vectorized; at::parallel_for( 0, batch_size * heads * height * width, GRAIN_SIZE, - [&](int start, int end) { - for (int x = start; x < end; x++) { - int indtmp1 = x / width; - const int j = x - indtmp1 * width; - int indtmp2 = indtmp1 / height; - const int i = indtmp1 - indtmp2 * height; + [&](int32_t start, int32_t end) { + for (int32_t x = start; x < end; x++) { + int32_t indtmp1 = x / width; + auto j = x - indtmp1 * width; + int32_t indtmp2 = indtmp1 / height; + auto i = indtmp1 - indtmp2 * height; indtmp1 = indtmp2; indtmp2 = indtmp1 / heads; - const int h = indtmp1 - indtmp2 * heads; - const int b = indtmp2; - const int ni = get_window_start( - i, height, kernel_size, neighborhood_size, dilation); - const int pi = get_pb_start( - i, height, kernel_size, neighborhood_size, dilation); - const int nj = get_window_start( - j, width, kernel_size, neighborhood_size, dilation); - const int pj = get_pb_start( - j, width, kernel_size, neighborhood_size, dilation); - const int64_t batchHeadOffset = b * query_stride_0 + h * query_stride_1; - const int64_t queryOffset = + auto h = indtmp1 - indtmp2 * heads; + auto& b = indtmp2; + auto ni = get_window_start( + i, + height, + kernel_size_0, + neighborhood_size_0, + dilation_0, + false); + int32_t pi = get_pb_start( + i, height, kernel_size_0, neighborhood_size_0, dilation_0); + auto nj = get_window_start( + j, + width, + kernel_size_1, + neighborhood_size_1, + dilation_1, + false); + int32_t pj = get_pb_start( + j, width, kernel_size_1, neighborhood_size_1, dilation_1); + int64_t batchHeadOffset = b * query_stride_0 + h * query_stride_1; + int64_t queryOffset = batchHeadOffset + i * query_stride_2 + j * query_stride_3; int64_t index = b * attn_stride_0 + h * attn_stride_1 + i * attn_stride_2 + j * attn_stride_3; scalar_t* _qaddr = query + queryOffset; - for (int ki = 0; ki < kernel_size; ki++) { - for (int kj = 0; kj < kernel_size; kj++) { + for (int32_t ki = 0; ki < kernel_size_0; ki++) { + for (int32_t kj = 0; kj < kernel_size_1; kj++) { Vec updt = Vec(scalar_t(0)); - const int64_t keyOffset = batchHeadOffset + - (ki * dilation + ni) * query_stride_2 + - (kj * dilation + nj) * query_stride_3; + int64_t keyOffset = batchHeadOffset + + (ki * dilation_0 + ni) * query_stride_2 + + (kj * dilation_1 + nj) * query_stride_3; scalar_t* _kaddr = key + keyOffset; int64_t d1 = 0; for (; d1 < dim - (dim % Vec::size()); d1 += Vec::size()) @@ -297,7 +395,7 @@ struct PointwiseNeighborhood2DWithBias { [](Vec& x, Vec& y) { return x + y; }, updt, Vec::size()); for (; d1 < dim; ++d1) sum_val += _qaddr[d1] * _kaddr[d1]; - const int64_t biasIndex = + int64_t biasIndex = h * bias_stride_0 + (pi + ki) * bias_stride_1 + (pj + kj); attn[index] = bias[biasIndex] + sum_val; index++; @@ -310,41 +408,51 @@ struct PointwiseNeighborhood2DWithBias { 0, batch_size * heads * height * width, GRAIN_SIZE, - [&](int start, int end) { - for (int x = start; x < end; x++) { - int indtmp1 = x / width; - const int j = x - indtmp1 * width; - int indtmp2 = indtmp1 / height; - const int i = indtmp1 - indtmp2 * height; + [&](int32_t start, int32_t end) { + for (int32_t x = start; x < end; x++) { + int32_t indtmp1 = x / width; + auto j = x - indtmp1 * width; + int32_t indtmp2 = indtmp1 / height; + auto i = indtmp1 - indtmp2 * height; indtmp1 = indtmp2; indtmp2 = indtmp1 / heads; - const int h = indtmp1 - indtmp2 * heads; - const int b = indtmp2; - const int ni = get_window_start( - i, height, kernel_size, neighborhood_size, dilation); - const int pi = get_pb_start( - i, height, kernel_size, neighborhood_size, dilation); - const int nj = get_window_start( - j, width, kernel_size, neighborhood_size, dilation); - const int pj = get_pb_start( - j, width, kernel_size, neighborhood_size, dilation); - for (int ki = 0; ki < kernel_size; ki++) { - for (int kj = 0; kj < kernel_size; kj++) { + auto h = indtmp1 - indtmp2 * heads; + auto& b = indtmp2; + auto ni = get_window_start( + i, + height, + kernel_size_0, + neighborhood_size_0, + dilation_0, + false); + auto pi = get_pb_start( + i, height, kernel_size_0, neighborhood_size_0, dilation_0); + auto nj = get_window_start( + j, + width, + kernel_size_1, + neighborhood_size_1, + dilation_1, + false); + auto pj = get_pb_start( + j, width, kernel_size_1, neighborhood_size_1, dilation_1); + for (int32_t ki = 0; ki < kernel_size_0; ki++) { + for (int32_t kj = 0; kj < kernel_size_1; kj++) { scalar_t updt = scalar_t(0); - const int64_t batchHeadOffset = + int64_t batchHeadOffset = b * query_stride_0 + h * query_stride_1; - const int64_t queryOffset = + int64_t queryOffset = batchHeadOffset + i * query_stride_2 + j * query_stride_3; - const int64_t keyOffset = batchHeadOffset + - (ki * dilation + ni) * query_stride_2 + - (kj * dilation + nj) * query_stride_3; + int64_t keyOffset = batchHeadOffset + + (ki * dilation_0 + ni) * query_stride_2 + + (kj * dilation_1 + nj) * query_stride_3; for (int64_t dimOffset = 0; dimOffset < dim; ++dimOffset) updt += query[queryOffset + dimOffset] * key[keyOffset + dimOffset]; - const int64_t index = b * attn_stride_0 + h * attn_stride_1 + - i * attn_stride_2 + j * attn_stride_3 + ki * kernel_size + + int64_t index = b * attn_stride_0 + h * attn_stride_1 + + i * attn_stride_2 + j * attn_stride_3 + ki * kernel_size_1 + kj; - const int64_t biasIndex = + int64_t biasIndex = h * bias_stride_0 + (pi + ki) * bias_stride_1 + (pj + kj); updt += bias[biasIndex]; attn[index] = updt; diff --git a/csrc/include/natten/cpu/naive/pointwise_neighborhood_3d.hpp b/csrc/include/natten/cpu/naive/pointwise_neighborhood_3d.hpp index acb9e29..aee826b 100644 --- a/csrc/include/natten/cpu/naive/pointwise_neighborhood_3d.hpp +++ b/csrc/include/natten/cpu/naive/pointwise_neighborhood_3d.hpp @@ -40,7 +40,8 @@ #include #endif -#include "natten/cpu/naive/natten_cpu_commons.h" +#include +#include namespace natten { namespace cpu { @@ -51,25 +52,26 @@ namespace naive { template struct PointwiseNeighborhood3D { void operator()( + bool is_grad, void* query_ptr, void* key_ptr, void* attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, + int32_t batch_size, + int32_t heads, + int32_t depth, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, int64_t attn_stride_4, - int kernel_size, - int kernel_size_depth, - int dilation, - int dilation_depth) { + const std::tuple& kernel_size, + const std::tuple& dilation, + const std::tuple& is_causal) { launch( + is_grad, reinterpret_cast(query_ptr), reinterpret_cast(key_ptr), reinterpret_cast(attn_ptr), @@ -77,87 +79,144 @@ struct PointwiseNeighborhood3D { height, width, heads, - kernel_size, - kernel_size_depth, - dilation, - dilation_depth, + std::get<0>(kernel_size), + std::get<1>(kernel_size), + std::get<2>(kernel_size), + std::get<0>(dilation), + std::get<1>(dilation), + std::get<2>(dilation), dim, batch_size, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, - attn_stride_4); + attn_stride_4, + is_causal); } void launch( // QK / A-grad + bool is_grad, scalar_t* query, // query / d_out scalar_t* key, // key / value scalar_t* attn, // attn / d_attn - const int depth, - const int height, - const int width, - const int heads, - const int kernel_size, - const int kernel_size_d, - const int dilation, - const int dilation_d, - const int dim, - const int batch_size, - const int64_t attn_stride_0, - const int64_t attn_stride_1, - const int64_t attn_stride_2, - const int64_t attn_stride_3, - const int64_t attn_stride_4) { - const int neighborhood_size = kernel_size / 2; - const int neighborhood_size_d = kernel_size_d / 2; - const int query_stride_4 = dim; - const int query_stride_3 = width * query_stride_4; - const int query_stride_2 = height * query_stride_3; - const int query_stride_1 = depth * query_stride_2; - const int query_stride_0 = heads * query_stride_1; + int32_t depth, + int32_t height, + int32_t width, + int32_t heads, + int32_t kernel_size_0, + int32_t kernel_size_1, + int32_t kernel_size_2, + int32_t dilation_0, + int32_t dilation_1, + int32_t dilation_2, + int32_t dim, + int32_t batch_size, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int64_t attn_stride_4, + const std::tuple& is_causal) { + auto is_causal_0 = std::get<0>(is_causal); + auto is_causal_1 = std::get<1>(is_causal); + auto is_causal_2 = std::get<2>(is_causal); + auto neighborhood_size_0 = kernel_size_0 / 2; + auto neighborhood_size_1 = kernel_size_1 / 2; + auto neighborhood_size_2 = kernel_size_2 / 2; + int64_t query_stride_4 = dim; + int64_t query_stride_3 = width * query_stride_4; + int64_t query_stride_2 = height * query_stride_3; + int64_t query_stride_1 = depth * query_stride_2; + int64_t query_stride_0 = heads * query_stride_1; + auto mask_value = AttnMask::value(is_grad); at::parallel_for( 0, batch_size * heads * depth * height * width, GRAIN_SIZE, - [&](int start, int end) { - for (int x = start; x < end; x++) { - int indtmp1 = x / width; - const int j = x - indtmp1 * width; - int indtmp2 = indtmp1 / height; - const int i = indtmp1 - indtmp2 * height; + [&](int32_t start, int32_t end) { + for (int32_t x = start; x < end; x++) { + int32_t indtmp1 = x / width; + auto j = x - indtmp1 * width; + int32_t indtmp2 = indtmp1 / height; + auto i = indtmp1 - indtmp2 * height; indtmp1 = indtmp2; indtmp2 = indtmp1 / depth; - const int k = indtmp1 - indtmp2 * depth; + auto k = indtmp1 - indtmp2 * depth; indtmp1 = indtmp2; indtmp2 = indtmp1 / heads; - const int h = indtmp1 - indtmp2 * heads; - const int b = indtmp2; - const int ni = get_window_start( - i, height, kernel_size, neighborhood_size, dilation); - const int nj = get_window_start( - j, width, kernel_size, neighborhood_size, dilation); - const int nk = get_window_start( - k, depth, kernel_size_d, neighborhood_size_d, dilation_d); - for (int kk = 0; kk < kernel_size_d; kk++) { - for (int ki = 0; ki < kernel_size; ki++) { - for (int kj = 0; kj < kernel_size; kj++) { + auto h = indtmp1 - indtmp2 * heads; + auto& b = indtmp2; + auto nk = get_window_start( + k, + depth, + kernel_size_0, + neighborhood_size_0, + dilation_0, + is_causal_0); + auto ni = get_window_start( + i, + height, + kernel_size_1, + neighborhood_size_1, + dilation_1, + is_causal_1); + auto nj = get_window_start( + j, + width, + kernel_size_2, + neighborhood_size_2, + dilation_2, + is_causal_2); + auto ek = get_window_end( + k, + nk, + depth, + kernel_size_0, + neighborhood_size_0, + dilation_0, + is_causal_0); + auto ei = get_window_end( + i, + ni, + height, + kernel_size_1, + neighborhood_size_1, + dilation_1, + is_causal_1); + auto ej = get_window_end( + j, + nj, + width, + kernel_size_2, + neighborhood_size_2, + dilation_2, + is_causal_2); + for (int32_t kk = 0; kk < kernel_size_0; kk++) { + auto key_idx_k = kk * dilation_0 + nk; + for (int32_t ki = 0; ki < kernel_size_1; ki++) { + auto key_idx_i = ki * dilation_1 + ni; + for (int32_t kj = 0; kj < kernel_size_2; kj++) { + auto key_idx_j = kj * dilation_2 + nj; scalar_t updt = scalar_t(0); - const int64_t batchHeadOffset = + int64_t batchHeadOffset = b * query_stride_0 + h * query_stride_1; - const int64_t queryOffset = batchHeadOffset + k * query_stride_2 + + int64_t queryOffset = batchHeadOffset + k * query_stride_2 + i * query_stride_3 + j * query_stride_4; - const int64_t keyOffset = batchHeadOffset + - (kk * dilation_d + nk) * query_stride_2 + - (ki * dilation + ni) * query_stride_3 + - (kj * dilation + nj) * query_stride_4; - for (int64_t dimOffset = 0; dimOffset < dim; ++dimOffset) - updt += query[queryOffset + dimOffset] * - key[keyOffset + dimOffset]; - const int64_t index = b * attn_stride_0 + h * attn_stride_1 + + if (key_idx_k < ek && key_idx_i < ei && key_idx_j < ej) { + int64_t keyOffset = batchHeadOffset + + key_idx_k * query_stride_2 + + key_idx_i * query_stride_3 + key_idx_j * query_stride_4; + for (int64_t dimOffset = 0; dimOffset < dim; ++dimOffset) + updt += query[queryOffset + dimOffset] * + key[keyOffset + dimOffset]; + } else { + updt = mask_value; + } + int64_t index = b * attn_stride_0 + h * attn_stride_1 + k * attn_stride_2 + i * attn_stride_3 + - j * attn_stride_4 + kk * (kernel_size * kernel_size) + - ki * kernel_size + kj; + j * attn_stride_4 + kk * (kernel_size_1 * kernel_size_2) + + ki * kernel_size_2 + kj; attn[index] = updt; } } @@ -174,21 +233,23 @@ struct PointwiseNeighborhood3DWithBias { void* key_ptr, void* bias_ptr, void* attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, + int32_t batch_size, + int32_t heads, + int32_t depth, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, int64_t attn_stride_4, - int kernel_size, - int kernel_size_depth, - int dilation, - int dilation_depth) { + const std::tuple& kernel_size, + const std::tuple& dilation, + const std::tuple& is_causal) { + NATTEN_CHECK( + !any_true(is_causal), + "Neighborhood attention with causal masking does not support positional biases yet."); launch( reinterpret_cast(query_ptr), reinterpret_cast(key_ptr), @@ -198,10 +259,12 @@ struct PointwiseNeighborhood3DWithBias { height, width, heads, - kernel_size, - kernel_size_depth, - dilation, - dilation_depth, + std::get<0>(kernel_size), + std::get<1>(kernel_size), + std::get<2>(kernel_size), + std::get<0>(dilation), + std::get<1>(dilation), + std::get<2>(dilation), dim, batch_size, attn_stride_0, @@ -216,80 +279,98 @@ struct PointwiseNeighborhood3DWithBias { scalar_t* key, // key / value scalar_t* bias, // relative positional bias tensor scalar_t* attn, // attn / d_attn - const int depth, - const int height, - const int width, - const int heads, - const int kernel_size, - const int kernel_size_d, - const int dilation, - const int dilation_d, - const int dim, - const int batch_size, - const int64_t attn_stride_0, - const int64_t attn_stride_1, - const int64_t attn_stride_2, - const int64_t attn_stride_3, - const int64_t attn_stride_4) { - const int neighborhood_size = kernel_size / 2; - const int neighborhood_size_d = kernel_size_d / 2; - const int bias_stride_2 = (2 * kernel_size - 1); - const int bias_stride_1 = (2 * kernel_size - 1) * bias_stride_2; - const int bias_stride_0 = (2 * kernel_size_d - 1) * bias_stride_1; - const int query_stride_4 = dim; - const int query_stride_3 = width * query_stride_4; - const int query_stride_2 = height * query_stride_3; - const int query_stride_1 = depth * query_stride_2; - const int query_stride_0 = heads * query_stride_1; + int32_t depth, + int32_t height, + int32_t width, + int32_t heads, + int32_t kernel_size_0, + int32_t kernel_size_1, + int32_t kernel_size_2, + int32_t dilation_0, + int32_t dilation_1, + int32_t dilation_2, + int32_t dim, + int32_t batch_size, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int64_t attn_stride_4) { + auto neighborhood_size_0 = kernel_size_0 / 2; + auto neighborhood_size_1 = kernel_size_1 / 2; + auto neighborhood_size_2 = kernel_size_2 / 2; + int64_t bias_stride_2 = (2 * kernel_size_2 - 1); + int64_t bias_stride_1 = (2 * kernel_size_1 - 1) * bias_stride_2; + int64_t bias_stride_0 = (2 * kernel_size_0 - 1) * bias_stride_1; + int64_t query_stride_4 = dim; + int64_t query_stride_3 = width * query_stride_4; + int64_t query_stride_2 = height * query_stride_3; + int64_t query_stride_1 = depth * query_stride_2; + int64_t query_stride_0 = heads * query_stride_1; at::parallel_for( 0, batch_size * heads * depth * height * width, GRAIN_SIZE, - [&](int start, int end) { - for (int x = start; x < end; x++) { - int indtmp1 = x / width; - const int j = x - indtmp1 * width; - int indtmp2 = indtmp1 / height; - const int i = indtmp1 - indtmp2 * height; + [&](int32_t start, int32_t end) { + for (int32_t x = start; x < end; x++) { + int32_t indtmp1 = x / width; + auto j = x - indtmp1 * width; + int32_t indtmp2 = indtmp1 / height; + auto i = indtmp1 - indtmp2 * height; indtmp1 = indtmp2; indtmp2 = indtmp1 / depth; - const int k = indtmp1 - indtmp2 * depth; + auto k = indtmp1 - indtmp2 * depth; indtmp1 = indtmp2; indtmp2 = indtmp1 / heads; - const int h = indtmp1 - indtmp2 * heads; - const int b = indtmp2; - const int ni = get_window_start( - i, height, kernel_size, neighborhood_size, dilation); - const int nj = get_window_start( - j, width, kernel_size, neighborhood_size, dilation); - const int nk = get_window_start( - k, depth, kernel_size_d, neighborhood_size_d, dilation_d); - const int pi = get_pb_start( - i, height, kernel_size, neighborhood_size, dilation); - const int pj = get_pb_start( - j, width, kernel_size, neighborhood_size, dilation); - const int pk = get_pb_start( - k, depth, kernel_size_d, neighborhood_size_d, dilation_d); - for (int kk = 0; kk < kernel_size_d; kk++) { - for (int ki = 0; ki < kernel_size; ki++) { - for (int kj = 0; kj < kernel_size; kj++) { + auto h = indtmp1 - indtmp2 * heads; + auto& b = indtmp2; + auto ni = get_window_start( + i, + height, + kernel_size_1, + neighborhood_size_1, + dilation_1, + false); + auto nj = get_window_start( + j, + width, + kernel_size_2, + neighborhood_size_2, + dilation_2, + false); + auto nk = get_window_start( + k, + depth, + kernel_size_0, + neighborhood_size_0, + dilation_0, + false); + auto pi = get_pb_start( + i, height, kernel_size_1, neighborhood_size_1, dilation_1); + auto pj = get_pb_start( + j, width, kernel_size_2, neighborhood_size_2, dilation_2); + auto pk = get_pb_start( + k, depth, kernel_size_0, neighborhood_size_0, dilation_0); + for (int32_t kk = 0; kk < kernel_size_0; kk++) { + for (int32_t ki = 0; ki < kernel_size_1; ki++) { + for (int32_t kj = 0; kj < kernel_size_2; kj++) { scalar_t updt = scalar_t(0); - const int64_t batchHeadOffset = + int64_t batchHeadOffset = b * query_stride_0 + h * query_stride_1; - const int64_t queryOffset = batchHeadOffset + k * query_stride_2 + + int64_t queryOffset = batchHeadOffset + k * query_stride_2 + i * query_stride_3 + j * query_stride_4; - const int64_t keyOffset = batchHeadOffset + - (kk * dilation_d + nk) * query_stride_2 + - (ki * dilation + ni) * query_stride_3 + - (kj * dilation + nj) * query_stride_4; + int64_t keyOffset = batchHeadOffset + + (kk * dilation_0 + nk) * query_stride_2 + + (ki * dilation_1 + ni) * query_stride_3 + + (kj * dilation_2 + nj) * query_stride_4; for (int64_t dimOffset = 0; dimOffset < dim; ++dimOffset) updt += query[queryOffset + dimOffset] * key[keyOffset + dimOffset]; - const int64_t index = b * attn_stride_0 + h * attn_stride_1 + + int64_t index = b * attn_stride_0 + h * attn_stride_1 + k * attn_stride_2 + i * attn_stride_3 + - j * attn_stride_4 + kk * (kernel_size * kernel_size) + - ki * kernel_size + kj; - const int64_t biasIndex = h * bias_stride_0 + + j * attn_stride_4 + kk * (kernel_size_1 * kernel_size_2) + + ki * kernel_size_2 + kj; + int64_t biasIndex = h * bias_stride_0 + (pk + kk) * bias_stride_1 + (pi + ki) * bias_stride_2 + (pj + kj); updt += bias[biasIndex]; diff --git a/csrc/include/natten/cpu/naive/rel_pos_bias_1d.hpp b/csrc/include/natten/cpu/naive/rel_pos_bias_1d.hpp index 2828bcc..1866c14 100644 --- a/csrc/include/natten/cpu/naive/rel_pos_bias_1d.hpp +++ b/csrc/include/natten/cpu/naive/rel_pos_bias_1d.hpp @@ -37,7 +37,8 @@ #include #endif -#include "natten/cpu/naive/natten_cpu_commons.h" +#include +#include namespace natten { namespace cpu { @@ -52,22 +53,26 @@ struct RelPosBiasGradient1D { void operator()( void* d_bias_ptr, void* d_attn_ptr, - int batch_size, - int heads, - int length, - int dim, + int32_t batch_size, + int32_t heads, + int32_t length, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, - int kernel_size, - int dilation) { + const std::tuple& kernel_size, + const std::tuple& dilation, + const std::tuple& is_causal) { + NATTEN_CHECK( + !any_true(is_causal), + "Neighborhood attention with causal masking does not support positional biases yet."); launch( reinterpret_cast(d_bias_ptr), reinterpret_cast(d_attn_ptr), length, heads, - kernel_size, - dilation, + std::get<0>(kernel_size), + std::get<0>(dilation), batch_size, attn_stride_0, attn_stride_1, @@ -77,29 +82,29 @@ struct RelPosBiasGradient1D { void launch( scalar_t* d_bias, scalar_t* d_attn, - const int length, - const int heads, - const int kernel_size, - const int dilation, - const int batch_size, - const int64_t d_attn_stride_0, - const int64_t d_attn_stride_1, - const int64_t d_attn_stride_2) { - const int neighborhood_size = kernel_size / 2; - const int d_bias_stride_0 = 2 * kernel_size - 1; - at::parallel_for(0, heads, GRAIN_SIZE, [&](int start, int end) { - for (int h = start; h < end; h++) { - for (int i = 0; i < length; i++) { - const int pi = + int32_t length, + int32_t heads, + int32_t kernel_size, + int32_t dilation, + int32_t batch_size, + int64_t d_attn_stride_0, + int64_t d_attn_stride_1, + int64_t d_attn_stride_2) { + auto neighborhood_size = kernel_size / 2; + int64_t d_bias_stride_0 = 2 * kernel_size - 1; + at::parallel_for(0, heads, GRAIN_SIZE, [&](int32_t start, int32_t end) { + for (int32_t h = start; h < end; h++) { + for (int32_t i = 0; i < length; i++) { + auto pi = get_pb_start(i, length, kernel_size, neighborhood_size, dilation); - for (int ki = 0; ki < kernel_size; ki++) { + for (int32_t ki = 0; ki < kernel_size; ki++) { scalar_t d_bias_update = scalar_t(0); int64_t attnOffset = h * d_attn_stride_1 + i * d_attn_stride_2 + ki; - for (int b = 0; b < batch_size; ++b) { + for (int32_t b = 0; b < batch_size; ++b) { d_bias_update += d_attn[attnOffset]; attnOffset += d_attn_stride_0; } - const int64_t index = h * d_bias_stride_0 + (pi + ki); + int64_t index = h * d_bias_stride_0 + (pi + ki); d_bias[index] += d_bias_update; } } diff --git a/csrc/include/natten/cpu/naive/rel_pos_bias_2d.hpp b/csrc/include/natten/cpu/naive/rel_pos_bias_2d.hpp index 26184d9..e36c8ce 100644 --- a/csrc/include/natten/cpu/naive/rel_pos_bias_2d.hpp +++ b/csrc/include/natten/cpu/naive/rel_pos_bias_2d.hpp @@ -37,7 +37,8 @@ #include #endif -#include "natten/cpu/naive/natten_cpu_commons.h" +#include +#include namespace natten { namespace cpu { @@ -52,25 +53,31 @@ struct RelPosBiasGradient2D { void operator()( void* d_bias_ptr, void* d_attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, + int32_t batch_size, + int32_t heads, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, - int kernel_size, - int dilation) { + const std::tuple& kernel_size, + const std::tuple& dilation, + const std::tuple& is_causal) { + NATTEN_CHECK( + !any_true(is_causal), + "Neighborhood attention with causal masking does not support positional biases yet."); launch( reinterpret_cast(d_bias_ptr), reinterpret_cast(d_attn_ptr), height, width, heads, - kernel_size, - dilation, + std::get<0>(kernel_size), + std::get<1>(kernel_size), + std::get<0>(dilation), + std::get<1>(dilation), batch_size, attn_stride_0, attn_stride_1, @@ -81,37 +88,40 @@ struct RelPosBiasGradient2D { void launch( scalar_t* d_bias, scalar_t* d_attn, - const int height, - const int width, - const int heads, - const int kernel_size, - const int dilation, - const int batch_size, - const int64_t d_attn_stride_0, - const int64_t d_attn_stride_1, - const int64_t d_attn_stride_2, - const int64_t d_attn_stride_3) { - const int neighborhood_size = kernel_size / 2; - const int d_bias_stride_1 = (2 * kernel_size - 1); - const int d_bias_stride_0 = (2 * kernel_size - 1) * d_bias_stride_1; - at::parallel_for(0, heads, GRAIN_SIZE, [&](int start, int end) { - for (int h = start; h < end; h++) { - for (int i = 0; i < height; i++) { - const int pi = - get_pb_start(i, height, kernel_size, neighborhood_size, dilation); - for (int j = 0; j < width; j++) { - const int pj = get_pb_start( - j, width, kernel_size, neighborhood_size, dilation); - for (int ki = 0; ki < kernel_size; ki++) { - for (int kj = 0; kj < kernel_size; kj++) { + int32_t height, + int32_t width, + int32_t heads, + int32_t kernel_size_0, + int32_t kernel_size_1, + int32_t dilation_0, + int32_t dilation_1, + int32_t batch_size, + int64_t d_attn_stride_0, + int64_t d_attn_stride_1, + int64_t d_attn_stride_2, + int64_t d_attn_stride_3) { + auto neighborhood_size_0 = kernel_size_0 / 2; + auto neighborhood_size_1 = kernel_size_1 / 2; + int64_t d_bias_stride_1 = (2 * kernel_size_1 - 1); + int64_t d_bias_stride_0 = (2 * kernel_size_0 - 1) * d_bias_stride_1; + at::parallel_for(0, heads, GRAIN_SIZE, [&](int32_t start, int32_t end) { + for (int32_t h = start; h < end; h++) { + for (int32_t i = 0; i < height; i++) { + auto pi = get_pb_start( + i, height, kernel_size_0, neighborhood_size_0, dilation_0); + for (int32_t j = 0; j < width; j++) { + auto pj = get_pb_start( + j, width, kernel_size_1, neighborhood_size_1, dilation_1); + for (int32_t ki = 0; ki < kernel_size_0; ki++) { + for (int32_t kj = 0; kj < kernel_size_1; kj++) { scalar_t d_bias_update = scalar_t(0); int64_t attnOffset = h * d_attn_stride_1 + i * d_attn_stride_2 + - j * d_attn_stride_3 + ki * kernel_size + kj; - for (int b = 0; b < batch_size; ++b) { + j * d_attn_stride_3 + ki * kernel_size_1 + kj; + for (int32_t b = 0; b < batch_size; ++b) { d_bias_update += d_attn[attnOffset]; attnOffset += d_attn_stride_0; } - const int64_t index = h * d_bias_stride_0 + + int64_t index = h * d_bias_stride_0 + (pi + ki) * d_bias_stride_1 + (pj + kj); d_bias[index] += d_bias_update; } diff --git a/csrc/include/natten/cpu/naive/rel_pos_bias_3d.hpp b/csrc/include/natten/cpu/naive/rel_pos_bias_3d.hpp index d26470e..20062fb 100644 --- a/csrc/include/natten/cpu/naive/rel_pos_bias_3d.hpp +++ b/csrc/include/natten/cpu/naive/rel_pos_bias_3d.hpp @@ -37,7 +37,8 @@ #include #endif -#include "natten/cpu/naive/natten_cpu_commons.h" +#include +#include namespace natten { namespace cpu { @@ -50,21 +51,23 @@ struct RelPosBiasGradient3D { void operator()( void* d_bias_ptr, void* d_attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, + int32_t batch_size, + int32_t heads, + int32_t depth, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, int64_t attn_stride_4, - int kernel_size, - int kernel_size_depth, - int dilation, - int dilation_depth) { + const std::tuple& kernel_size, + const std::tuple& dilation, + const std::tuple& is_causal) { + NATTEN_CHECK( + !any_true(is_causal), + "Neighborhood attention with causal masking does not support positional biases yet."); launch( reinterpret_cast(d_bias_ptr), reinterpret_cast(d_attn_ptr), @@ -72,10 +75,12 @@ struct RelPosBiasGradient3D { height, width, heads, - kernel_size, - kernel_size_depth, - dilation, - dilation_depth, + std::get<0>(kernel_size), + std::get<1>(kernel_size), + std::get<2>(kernel_size), + std::get<0>(dilation), + std::get<1>(dilation), + std::get<2>(dilation), batch_size, attn_stride_0, attn_stride_1, @@ -87,49 +92,53 @@ struct RelPosBiasGradient3D { void launch( scalar_t* d_bias, scalar_t* d_attn, - const int depth, - const int height, - const int width, - const int heads, - const int kernel_size, - const int kernel_size_d, - const int dilation, - const int dilation_d, - const int batch_size, - const int64_t d_attn_stride_0, - const int64_t d_attn_stride_1, - const int64_t d_attn_stride_2, - const int64_t d_attn_stride_3, - const int64_t d_attn_stride_4) { - const int neighborhood_size = kernel_size / 2; - const int neighborhood_size_d = kernel_size_d / 2; - const int d_bias_stride_2 = (2 * kernel_size - 1); - const int d_bias_stride_1 = (2 * kernel_size - 1) * d_bias_stride_2; - const int d_bias_stride_0 = (2 * kernel_size_d - 1) * d_bias_stride_1; - at::parallel_for(0, heads, GRAIN_SIZE, [&](int start, int end) { - for (int h = start; h < end; h++) { - for (int k = 0; k < depth; k++) { - const int pk = get_pb_start( - k, depth, kernel_size_d, neighborhood_size_d, dilation_d); - for (int i = 0; i < height; i++) { - const int pi = get_pb_start( - i, height, kernel_size, neighborhood_size, dilation); - for (int j = 0; j < width; j++) { - const int pj = get_pb_start( - j, width, kernel_size, neighborhood_size, dilation); - for (int kk = 0; kk < kernel_size_d; kk++) { - for (int ki = 0; ki < kernel_size; ki++) { - for (int kj = 0; kj < kernel_size; kj++) { + int32_t depth, + int32_t height, + int32_t width, + int32_t heads, + int32_t kernel_size_0, + int32_t kernel_size_1, + int32_t kernel_size_2, + int32_t dilation_0, + int32_t dilation_1, + int32_t dilation_2, + int32_t batch_size, + int64_t d_attn_stride_0, + int64_t d_attn_stride_1, + int64_t d_attn_stride_2, + int64_t d_attn_stride_3, + int64_t d_attn_stride_4) { + auto neighborhood_size_0 = kernel_size_0 / 2; + auto neighborhood_size_1 = kernel_size_1 / 2; + auto neighborhood_size_2 = kernel_size_2 / 2; + int64_t d_bias_stride_2 = (2 * kernel_size_2 - 1); + int64_t d_bias_stride_1 = (2 * kernel_size_1 - 1) * d_bias_stride_2; + int64_t d_bias_stride_0 = (2 * kernel_size_0 - 1) * d_bias_stride_1; + at::parallel_for(0, heads, GRAIN_SIZE, [&](int32_t start, int32_t end) { + for (int32_t h = start; h < end; h++) { + for (int32_t k = 0; k < depth; k++) { + auto pk = get_pb_start( + k, depth, kernel_size_0, neighborhood_size_0, dilation_0); + for (int32_t i = 0; i < height; i++) { + auto pi = get_pb_start( + i, height, kernel_size_1, neighborhood_size_1, dilation_1); + for (int32_t j = 0; j < width; j++) { + auto pj = get_pb_start( + j, width, kernel_size_2, neighborhood_size_2, dilation_2); + for (int32_t kk = 0; kk < kernel_size_0; kk++) { + for (int32_t ki = 0; ki < kernel_size_1; ki++) { + for (int32_t kj = 0; kj < kernel_size_2; kj++) { scalar_t d_bias_update = scalar_t(0); - int64_t attnOffset = h * d_attn_stride_1 + k * d_attn_stride_2 + - i * d_attn_stride_3 + j * d_attn_stride_4 + - kk * (kernel_size * kernel_size) + ki * kernel_size + - kj; - for (int b = 0; b < batch_size; ++b) { + int64_t attnOffset = h * d_attn_stride_1 + + k * d_attn_stride_2 + i * d_attn_stride_3 + + j * d_attn_stride_4 + + kk * (kernel_size_1 * kernel_size_2) + + ki * kernel_size_2 + kj; + for (int32_t b = 0; b < batch_size; ++b) { d_bias_update += d_attn[attnOffset]; attnOffset += d_attn_stride_0; } - const int64_t index = h * d_bias_stride_0 + + int64_t index = h * d_bias_stride_0 + (pk + kk) * d_bias_stride_1 + (pi + ki) * d_bias_stride_2 + (pj + kj); d_bias[index] += d_bias_update; diff --git a/csrc/include/natten/cuda/fna/epilogue/epilogue_pipelined.h b/csrc/include/natten/cuda/fna/epilogue/epilogue_pipelined.h new file mode 100644 index 0000000..8dfd417 --- /dev/null +++ b/csrc/include/natten/cuda/fna/epilogue/epilogue_pipelined.h @@ -0,0 +1,638 @@ +/* + * Copied from xFormers (https://github.com/facebookresearch/xformers/) + * + * Copyright (c) Meta Platforms, Inc. and affiliates. + * All rights reserved. + */ +/*************************************************************************************************** + * Copyright (c) 2017 - 2023 NVIDIA CORPORATION & AFFILIATES. All rights + *reserved. SPDX-License-Identifier: BSD-3-Clause + * + * Redistribution and use in source and binary forms, with or without + * modification, are permitted provided that the following conditions are met: + * + * 1. Redistributions of source code must retain the above copyright notice, + *this list of conditions and the following disclaimer. + * + * 2. Redistributions in binary form must reproduce the above copyright notice, + * this list of conditions and the following disclaimer in the documentation + * and/or other materials provided with the distribution. + * + * 3. Neither the name of the copyright holder nor the names of its + * contributors may be used to endorse or promote products derived from + * this software without specific prior written permission. + * + * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" + * AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE + * IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE + *ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE + *LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR + *CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF + *SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS + *INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN + *CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) + *ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE + *POSSIBILITY OF SUCH DAMAGE. + * + **************************************************************************************************/ +/*! \file + \brief Epilogue for threadblock scoped GEMMs using Tensor Ops. + + File copied from "cutlass/epilogue/threadblock/epilogue.h" + then modified to: + (1) load 2 source fragments at the same time (pipelining) + (2) support reading from a different dtype + (3) pass the row id to the OutputOp if it takes it + (see MemoryEfficientAttentionNormalize) + Note that in general the fragment passed to the OutputOp could + span multiple rows but it does not happen with the configurations we have +*/ + +#pragma once + +#if defined(__CUDACC_RTC__) +#include +#else +#include +#endif + +#include "cutlass/aligned_buffer.h" +#include "cutlass/array.h" +#include "cutlass/cutlass.h" +#include "cutlass/functional.h" +#include "cutlass/layout/tensor.h" +#include "cutlass/layout/vector.h" +#include "cutlass/numeric_types.h" +#include "cutlass/tensor_coord.h" + +#include "cutlass/gemm/gemm.h" + +#include "cutlass/transform/pitch_linear_thread_map.h" +#include "cutlass/transform/threadblock/regular_tile_iterator.h" + +#include "cutlass/epilogue/threadblock/epilogue_base.h" +#include "cutlass/epilogue/threadblock/predicated_tile_iterator.h" +#include "cutlass/numeric_types.h" + +//////////////////////////////////////////////////////////////////////////////// + +namespace cutlass { +namespace epilogue { +namespace threadblock { + +template +struct ApplyEpilogueOp { + static CUTLASS_DEVICE typename Op::FragmentOutput apply( + Op const& output_op, + int row_id, + typename Op::FragmentAccumulator const& accum, + typename Op::FragmentOutput const& source) { + return output_op(accum, source); + } + static CUTLASS_DEVICE typename Op::FragmentOutput apply( + Op const& output_op, + int row_id, + typename Op::FragmentAccumulator const& accum) { + return output_op(accum); + } +}; + +//////////////////////////////////////////////////////////////////////////////// + +/// Epilogue operator +template < + typename Shape_, ///< Shape of threadblock tile (concept: GemmShape) + typename WarpMmaOperator_, ///< Warp-level MMA operator (concept: + ///< gemm::warp::MmaTensorOp) + int PartitionsK, ///< Number of partitions of the K dimension + typename OutputTileIterator_, ///< Tile iterator writing output tensors + typename AccumulatorFragmentIterator_, ///< Fragment iterator selecting + ///< accumulators + typename WarpTileIterator_, ///< Warp-scoped tile iterator writing + ///< accumulators to SMEM + typename SharedLoadIterator_, ///< Threadblock-scoped tile iterator loading + ///< from SMEM + typename OutputOp_, ///< Output operator + typename Padding_, ///< Padding added to SMEM allocation to avoid bank + ///< conflicts (concept: MatrixShape) + int FragmentsPerPartition = + 1, ///< Used to coarsten the epilogue granularity + int IterationsUnroll = ///< Used to reduce binary size when epilogue op is + ///< large + (!IsEpilogueFunctorHeavy::value), + typename OutputTileSourceIterator_ = + OutputTileIterator_ ///< Tile iterator reading tensors + > +class EpiloguePipelined : public EpilogueBase< + Shape_, + typename WarpMmaOperator_::Shape, + PartitionsK, + AccumulatorFragmentIterator_, + WarpTileIterator_, + Padding_, + FragmentsPerPartition> { + public: + using Base = EpilogueBase< + Shape_, + typename WarpMmaOperator_::Shape, + PartitionsK, + AccumulatorFragmentIterator_, + WarpTileIterator_, + Padding_, + FragmentsPerPartition>; + + using Shape = Shape_; + using WarpMmaOperator = WarpMmaOperator_; + static int const kPartitionsK = PartitionsK; + using OutputTileIterator = OutputTileIterator_; + using OutputTileSourceIterator = OutputTileSourceIterator_; + using AccumulatorFragmentIterator = AccumulatorFragmentIterator_; + using WarpTileIterator = WarpTileIterator_; + using SharedLoadIterator = SharedLoadIterator_; + using OutputOp = OutputOp_; + using Padding = Padding_; + + using Layout = layout::RowMajor; + using LongIndex = typename Layout::LongIndex; + + /// The complete warp-level accumulator tile + using AccumulatorTile = typename Base::AccumulatorTile; + + /// Accumulator element + using ElementAccumulator = typename WarpTileIterator::Element; + + /// Output element + using ElementOutput = typename OutputTileIterator::Element; + using ElementSource = typename OutputTileSourceIterator::Element; + + /// Output access size + static int const kElementsPerAccess = OutputTileIterator::kElementsPerAccess; + + /// Tensor reference to destination tensor + using TensorRef = typename OutputTileIterator::TensorRef; + + /// Tensor reference to sync tensor + using SyncTensorRef = + typename cutlass::TensorRef; + + /// Const tensor reference to source tensor + using ConstTensorRef = typename OutputTileIterator::ConstTensorRef; + + /// Array type used to output + using OutputAccessType = Array< + typename OutputTileIterator::Element, + OutputTileIterator::kElementsPerAccess>; + using SourceAccessType = Array< + typename OutputTileSourceIterator::Element, + OutputTileSourceIterator::kElementsPerAccess>; + + /// Array type used by output functor + using AccumulatorAccessType = Array< + typename WarpTileIterator::Element, + OutputTileIterator::kElementsPerAccess>; + + /// Number of warps + using WarpCount = typename Base::WarpCount; + + static int constexpr kSmemTiles = Base::kFragmentsPerIteration > 1 + ? Base::kFragmentsPerIteration + : kPartitionsK; + static int constexpr kSmemPointerOffset = + Base::SharedStorage::StorageShape::kCount / kSmemTiles; + + public: + static_assert( + OutputTileSourceIterator::Fragment::kElements == + OutputTileIterator::Fragment::kElements, + "Mismatch between input tile and output tile iterator (kElements)"); + static_assert( + OutputTileSourceIterator::kIterations == OutputTileIterator::kIterations, + "Mismatch between input tile and output tile iterator (kIterations)"); + static_assert( + SharedLoadIterator::Fragment::kElements == + OutputTileIterator::Fragment::kElements, + "Mismatch between shared load iterator and output tile iterator."); + + static_assert( + OutputTileIterator::kElementsPerAccess, + "OutputTileIterator::kElementsPerAccess must not be zero."); + + static_assert( + !(OutputTileIterator::Fragment::kElements % + OutputTileIterator::kElementsPerAccess), + "Divisibility"); + + private: + /// Loads fragment from shared memory aligned with output tensor + SharedLoadIterator shared_load_iterator_; + + public: + /// Constructor + CUTLASS_DEVICE + EpiloguePipelined( + typename Base::SharedStorage& shared_storage, ///< Shared storage object + int thread_idx, ///< ID of a thread within the threadblock + int warp_idx, ///< ID of warp within threadblock + int lane_idx ///< Id of thread within warp + ) + : Base(shared_storage, thread_idx, warp_idx, lane_idx), + shared_load_iterator_(shared_storage.reference(), thread_idx) {} + + /// Streams the result to global memory + CUTLASS_DEVICE + void operator()( + OutputOp const& output_op, ///< Output operator + OutputTileIterator + destination_iterator, ///< Tile iterator for destination + AccumulatorTile const& + accumulators, ///< Complete warp-level accumulator tile + OutputTileSourceIterator + source_iterator) { ///< Threadblock tile coordinate in GEMM (in units + ///< of threadblock tiles) + + if (!output_op.is_source_needed()) { + compute_source_not_needed_(output_op, destination_iterator, accumulators); + } else { + compute_source_needed_( + output_op, destination_iterator, accumulators, source_iterator); + } + } + CUTLASS_DEVICE + void operator()( + OutputOp const& output_op, ///< Output operator + OutputTileIterator + destination_iterator, ///< Tile iterator for destination + AccumulatorTile const& + accumulators) { ///< Complete warp-level accumulator tile + compute_source_not_needed_(output_op, destination_iterator, accumulators); + } + + private: + template + struct acc2smem_source_not_needed; + + template + struct acc2smem_source_not_needed> { + template + CUTLASS_DEVICE static void helper( + AccumulatorFragmentIterator accum_fragment_iterator, + WarpTileIterator& warp_tile_iterator) { + CUTLASS_PRAGMA_UNROLL + for (int i = 0; i < Advance; i++) { + ++accum_fragment_iterator; + } + + CUTLASS_PRAGMA_UNROLL + for (int p = 0; p < Base::kFragmentsPerIteration; ++p) { + typename AccumulatorFragmentIterator::Fragment accum_fragment; + + accum_fragment_iterator.load(accum_fragment); + ++accum_fragment_iterator; + + warp_tile_iterator.store(accum_fragment); + if (p < Base::kFragmentsPerIteration - 1) { + warp_tile_iterator.add_pointer_offset(kSmemPointerOffset); + } + } + + if (Base::kFragmentsPerIteration > 1) { + warp_tile_iterator.add_pointer_offset( + kSmemPointerOffset * (1 - Base::kFragmentsPerIteration)); + } + } + + CUTLASS_DEVICE + static void push( + size_t pos, + AccumulatorFragmentIterator const& iterator_begin, + WarpTileIterator& warp_tile_iterator) { + int dummy[] = { + (pos == (Seq * Base::kFragmentsPerIteration)) && + (helper( + iterator_begin, warp_tile_iterator), + 0)...}; + + CUTLASS_UNUSED(dummy[0]); + } + }; + + static_assert( + kPartitionsK == 1 || Base::kFragmentsPerIteration == 1, + "One of these must be exactly 1."); + + /// Streams the result to global memory + CUTLASS_DEVICE + void compute_source_not_needed_( + OutputOp const& output_op, ///< Output operator + OutputTileIterator + destination_iterator, ///< Tile iterator for destination + AccumulatorTile const& + accumulators ///< Complete warp-level accumulator tile + ) { + // + // Iterator over warp-level accumulator fragment + // + + AccumulatorFragmentIterator accum_fragment_iterator(accumulators); + + // + // Iterate over accumulator tile + // + +#pragma unroll( \ + IterationsUnroll \ + ? OutputTileIterator::kIterations / Base::kFragmentsPerIteration \ + : 1) + for (int iter = 0; iter < OutputTileIterator::kIterations; + iter += Base::kFragmentsPerIteration) { + // + // Convert and store fragment + // + + __syncthreads(); + + acc2smem_source_not_needed>:: + push(iter, accum_fragment_iterator, this->warp_tile_iterator_); + + __syncthreads(); + + // + // Load fragments from shared memory + // + + CUTLASS_PRAGMA_UNROLL + for (int p = 0; p < Base::kFragmentsPerIteration; ++p) { + typename SharedLoadIterator::Fragment + aligned_accum_fragment[kPartitionsK]; + + shared_load_iterator_.load(aligned_accum_fragment[0]); + + if (p < Base::kFragmentsPerIteration - 1) { + shared_load_iterator_.add_pointer_offset(kSmemPointerOffset); + } else if (kPartitionsK > 1) { + plus add_fragments; + + CUTLASS_PRAGMA_UNROLL + for (int i = 1; i < kPartitionsK; ++i) { + shared_load_iterator_.add_pointer_offset(kSmemPointerOffset); + shared_load_iterator_.load(aligned_accum_fragment[i]); + aligned_accum_fragment[0] = add_fragments( + aligned_accum_fragment[0], aligned_accum_fragment[i]); + } + + shared_load_iterator_.add_pointer_offset( + (1 - kPartitionsK) * kSmemPointerOffset); + } + + // + // Compute the output result + // + + typename OutputTileIterator::Fragment output_fragment; + + apply_output_operator_source_not_needed_( + destination_iterator.thread_start_row(), + output_fragment, + output_op, + aligned_accum_fragment[0]); + + // + // Store the final result + // + + destination_iterator.store(output_fragment); + ++destination_iterator; + } + + if (Base::kFragmentsPerIteration > 1) { + shared_load_iterator_.add_pointer_offset( + kSmemPointerOffset * (1 - Base::kFragmentsPerIteration)); + } + } + } + + template + struct acc2smem_source_needed; + + template + struct acc2smem_source_needed> { + template + CUTLASS_DEVICE static void helper( + AccumulatorFragmentIterator accum_fragment_iterator, + WarpTileIterator& warp_tile_iterator) { + CUTLASS_PRAGMA_UNROLL + for (int i = 0; i < Advance; i++) { + ++accum_fragment_iterator; + } + + typename AccumulatorFragmentIterator::Fragment accum_fragment; + accum_fragment_iterator.load(accum_fragment); + warp_tile_iterator.store(accum_fragment); + } + + CUTLASS_DEVICE + static void push( + size_t pos, + AccumulatorFragmentIterator const& iterator_begin, + WarpTileIterator& warp_tile_iterator) { + int dummy[] = { + (pos == Seq) && + (helper(iterator_begin, warp_tile_iterator), 0)...}; + } + }; + + /// Streams the result to global memory + CUTLASS_DEVICE + void compute_source_needed_( + OutputOp const& output_op, ///< Output operator + OutputTileIterator + destination_iterator, ///< Tile iterator for destination + AccumulatorTile const& + accumulators, ///< Complete warp-level accumulator tile + OutputTileSourceIterator + source_iterator ///< Threadblock tile coordinate in GEMM (in units of + ///< threadblock tiles) + ) { + typename OutputTileSourceIterator::Fragment source_fragment[2]; + + source_fragment[0].clear(); + source_iterator.load(source_fragment[0]); + ++source_iterator; + source_fragment[1].clear(); + + // + // Iterator over warp-level accumulator fragment + // + + AccumulatorFragmentIterator accum_fragment_iterator(accumulators); + + // + // Iterate over accumulator tile + // + +#pragma unroll(IterationsUnroll ? OutputTileIterator::kIterations : 1) + for (int iter = 0; iter < OutputTileIterator::kIterations; ++iter) { + if (iter > 0) { + __syncthreads(); + } + // + // Load the source for next iteration (pipelining) + // + + if (iter + 1 < OutputTileIterator::kIterations) { + source_iterator.load(source_fragment[(iter + 1) % 2]); + } + ++source_iterator; + acc2smem_source_needed< + cutlass::make_index_sequence>:: + push(iter, accum_fragment_iterator, this->warp_tile_iterator_); + + __syncthreads(); + + // + // Load fragments from shared memory + // + + typename SharedLoadIterator::Fragment + aligned_accum_fragment[kPartitionsK]; + + shared_load_iterator_.load(aligned_accum_fragment[0]); + + // If the number of k-slices is > 1 - perform a reduction amongst the + // k-slices + if (kPartitionsK > 1) { + plus add_fragments; + + CUTLASS_PRAGMA_UNROLL + for (int i = 1; i < kPartitionsK; ++i) { + shared_load_iterator_.add_pointer_offset(kSmemPointerOffset); + shared_load_iterator_.load(aligned_accum_fragment[i]); + aligned_accum_fragment[0] = add_fragments( + aligned_accum_fragment[0], aligned_accum_fragment[i]); + } + + shared_load_iterator_.add_pointer_offset( + (1 - kPartitionsK) * kSmemPointerOffset); + } + + // + // Compute the output result + // + + typename OutputTileIterator::Fragment output_fragment; + + apply_output_operator_( + destination_iterator.thread_start_row(), + output_fragment, + output_op, + aligned_accum_fragment[0], + source_fragment[iter % 2]); + + // + // Store the final result + // + + destination_iterator.store(output_fragment); + ++destination_iterator; + } + } + + /// Helper to invoke the output functor over each vector of output + CUTLASS_DEVICE + void apply_output_operator_( + int begin_row, + typename OutputTileIterator::Fragment& output_fragment, + OutputOp const& output_op, ///< Output operator + typename SharedLoadIterator::Fragment const& aligned_accum_fragment, + typename OutputTileSourceIterator::Fragment const& source_fragment) { + OutputAccessType* output_frag_ptr = + reinterpret_cast(&output_fragment); + + AccumulatorAccessType const* compute_frag_ptr = + reinterpret_cast(&aligned_accum_fragment); + + SourceAccessType const* source_frag_ptr = + reinterpret_cast(&source_fragment); + + int const kOutputOpIterations = OutputTileIterator::Fragment::kElements / + OutputTileIterator::kElementsPerAccess; + + CUTLASS_PRAGMA_UNROLL + for (int i = 0; i < kOutputOpIterations; ++i) { + // Call the output operator + output_frag_ptr[i] = ApplyEpilogueOp::apply( + output_op, + begin_row + getRowOffset(i * OutputTileIterator::kElementsPerAccess), + compute_frag_ptr[i], + source_frag_ptr[i]); + } + } + + /// Helper to invoke the output functor over each vector of output + CUTLASS_DEVICE + void apply_output_operator_source_not_needed_( + int begin_row, + typename OutputTileIterator::Fragment& output_fragment, + OutputOp const& output_op, ///< Output operator + typename SharedLoadIterator::Fragment const& aligned_accum_fragment) { + OutputAccessType* output_frag_ptr = + reinterpret_cast(&output_fragment); + + AccumulatorAccessType const* compute_frag_ptr = + reinterpret_cast(&aligned_accum_fragment); + + int const kOutputOpIterations = OutputTileIterator::Fragment::kElements / + OutputTileIterator::kElementsPerAccess; + + CUTLASS_PRAGMA_UNROLL + for (int i = 0; i < kOutputOpIterations; ++i) { + // Call the output operator + output_frag_ptr[i] = ApplyEpilogueOp::apply( + output_op, + begin_row + getRowOffset(i * OutputTileIterator::kElementsPerAccess), + compute_frag_ptr[i]); + } + } + + // This should be constexpr, but it's only supported on c++14 + static int CUTLASS_HOST_DEVICE getRowOffset(int i) { + using ThreadMap = typename OutputTileIterator::ThreadMap; + + CUTLASS_PRAGMA_UNROLL + for (int cluster = 0; cluster < ThreadMap::Iterations::kCluster; + ++cluster) { + CUTLASS_PRAGMA_UNROLL + for (int group = 0; group < ThreadMap::Iterations::kGroup; ++group) { + CUTLASS_PRAGMA_UNROLL + for (int row = 0; row < ThreadMap::Iterations::kRow; ++row) { + int row_offset = row * ThreadMap::Delta::kRow + + group * ThreadMap::Delta::kGroup + + cluster * ThreadMap::Delta::kCluster; + int frag_row_idx = + (row + + ThreadMap::Iterations::kRow * + (group + ThreadMap::Iterations::kGroup * cluster)); + CUTLASS_PRAGMA_UNROLL + for (int column = 0; column < ThreadMap::Iterations::kColumn; + ++column) { + int frag_idx = ThreadMap::kElementsPerAccess * + (frag_row_idx * ThreadMap::Iterations::kColumn + column); + if (i < frag_idx + ThreadMap::kElementsPerAccess) { + return row_offset; + } + } + } + } + } + return -1; + } +}; + +//////////////////////////////////////////////////////////////////////////////// + +} // namespace threadblock +} // namespace epilogue +} // namespace cutlass + +//////////////////////////////////////////////////////////////////////////////// diff --git a/csrc/include/natten/cuda/fna/epilogue/epilogue_rescale_output.h b/csrc/include/natten/cuda/fna/epilogue/epilogue_rescale_output.h new file mode 100644 index 0000000..8a6c0f9 --- /dev/null +++ b/csrc/include/natten/cuda/fna/epilogue/epilogue_rescale_output.h @@ -0,0 +1,266 @@ +/* + * Copied from xFormers (https://github.com/facebookresearch/xformers/) + * + * Copyright (c) Meta Platforms, Inc. and affiliates. + * All rights reserved. + * + * BSD 3-Clause License + * + * Redistribution and use in source and binary forms, with or without + * modification, are permitted provided that the following conditions are met: + * + * 1. Redistributions of source code must retain the above copyright + * notice, this list of conditions and the following disclaimer. + * + * 2. Redistributions in binary form must reproduce the above copyright + * notice, this list of conditions and the following disclaimer in the + * documentation and/or other materials provided with the distribution. + * + * 3. Neither the names of Facebook, Deepmind Technologies, NYU, NEC + * Laboratories America and IDIAP Research Institute nor the names of its + * contributors may be used to endorse or promote products derived from this + * software without specific prior written permission. + * + * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" + * AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE + * IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE + * ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE + * LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR + * CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF + * SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS + * INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN + * CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) + * ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE + * POSSIBILITY OF SUCH DAMAGE. + */ +/*! \file + \brief Epilogue for threadblock scoped GEMMs using Tensor Ops. + + The epilogue rearranges the result of a matrix product through shared memory + to match canonical tensor layouts in global memory. Epilogues support + conversion and reduction operations. + + This is a copy of cutlass/epilogue/threadblock/epilogue.h that can + handle "row_id" as a first argument, as uses it to get the corresponding + `m_prime` / `s_prime` to rescale the output. +*/ + +#pragma once + +#if defined(__CUDACC_RTC__) +#include +#else +#include +#endif + +#include "cutlass/aligned_buffer.h" +#include "cutlass/array.h" +#include "cutlass/cutlass.h" +#include "cutlass/functional.h" +#include "cutlass/layout/tensor.h" +#include "cutlass/layout/vector.h" +#include "cutlass/numeric_types.h" +#include "cutlass/tensor_coord.h" + +#include "cutlass/gemm/gemm.h" + +#include "cutlass/transform/pitch_linear_thread_map.h" +#include "cutlass/transform/threadblock/regular_tile_iterator.h" + +#include "cutlass/epilogue/threadblock/epilogue_base.h" +#include "cutlass/epilogue/threadblock/predicated_tile_iterator.h" +#include "cutlass/numeric_types.h" + +#include "cutlass/array.h" +#include "cutlass/cutlass.h" +#include "cutlass/epilogue/thread/scale_type.h" +#include "cutlass/functional.h" +#include "cutlass/numeric_conversion.h" +#include "cutlass/numeric_types.h" +#include "epilogue_pipelined.h" + +///////////////////////////////////////////////////////////////////////////////////////////////// + +namespace cutlass { +namespace epilogue { +namespace thread { + +///////////////////////////////////////////////////////////////////////////////////////////////// + +/// Applies a linear combination operator to an array of elements. +// output <- alpha * accumulator + beta * source +// with: +// alpha = 1 / s_prime (to normalize when isLast=True, 1 otherwise) +// beta = alpha / m_prime (renormalize the output when the max changes) +// source is the current output +template < + typename ElementOutput_, ///< Data type used to store tensors + typename ElementSource_, //< Data type for source (usually matches + //`ElementOutput`) + int Count, ///< Number of elements computed per operation. + ///< Usually it is 128/sizeof_bits, + ///< but we use 64 or 32 sometimes when there are not enough data + ///< to store + typename ElementAccumulator_, ///< Accumulator data type + typename ElementCompute_, ///< Data type used to compute linear combination + bool isFirst, + bool isLast, + typename FragmentAlphaBeta_, + FloatRoundStyle Round = FloatRoundStyle::round_to_nearest> +class MemoryEfficientAttentionNormalize { + public: + using ElementOutput = ElementOutput_; + using ElementSource = ElementSource_; + using ElementAccumulator = ElementAccumulator_; + using ElementCompute = ElementCompute_; + + static int const kCount = Count; + + using FragmentOutput = Array; + using FragmentSource = Array; + using FragmentAccumulator = Array; + using ComputeFragment = Array; + using FragmentAlphaBeta = FragmentAlphaBeta_; + + static FloatRoundStyle const kRound = Round; + + private: + // + // Data members + // + + FragmentAlphaBeta const& s_prime_; + FragmentAlphaBeta const& m_prime_; + + public: + /// Constructs the function object, possibly loading from pointers in host + /// memory + CUTLASS_HOST_DEVICE + MemoryEfficientAttentionNormalize( + FragmentAlphaBeta const& s_prime, + FragmentAlphaBeta const& m_prime) + : s_prime_(s_prime), m_prime_(m_prime) {} + + /// Returns true if source is needed + CUTLASS_HOST_DEVICE + bool is_source_needed() const { + return !isFirst; + } + + /// Functionally required for serial reduction in the epilogue + CUTLASS_HOST_DEVICE + void set_k_partition(int k_partition, int k_partition_count) {} + + /// Computes linear scaling: D = alpha * accumulator + beta * source + CUTLASS_HOST_DEVICE + FragmentOutput operator()( + int row, + FragmentAccumulator const& accumulator, + FragmentSource const& source) const { + assert(!isFirst); + + // Convert source to interal compute numeric type + NumericArrayConverter + source_converter; + NumericArrayConverter + accumulator_converter; + + // Convert to destination numeric type + NumericArrayConverter + destination_converter; + + ComputeFragment converted_source = source_converter(source); + ComputeFragment converted_accumulator = accumulator_converter(accumulator); + + // Perform binary operations + ComputeFragment intermediate; + + multiplies mul_add_source; + multiply_add mul_add_accumulator; + + ElementCompute alpha = isLast ? (1 / s_prime_[row]) : 1; + ElementCompute beta = alpha * m_prime_[row]; + + intermediate = mul_add_source(beta, converted_source); // X = beta * C + + intermediate = mul_add_accumulator( + alpha, converted_accumulator, intermediate); // D = alpha * Accum + X + + return destination_converter(intermediate); + } + + /// Computes linear scaling: D = alpha * accumulator + CUTLASS_HOST_DEVICE + FragmentOutput operator()(int row, FragmentAccumulator const& accumulator) + const { + assert(isFirst); + + // Convert source to interal compute numeric type + NumericArrayConverter + accumulator_converter; + + // Convert to destination numeric type + NumericArrayConverter + destination_converter; + + ComputeFragment converted_accumulator = accumulator_converter(accumulator); + + ComputeFragment intermediate; + multiplies mul_accumulator; + + ElementCompute alpha = isLast ? (1 / s_prime_[row]) : 1; + + intermediate = mul_accumulator( + alpha, converted_accumulator); // X = alpha * C + uniform + + return destination_converter(intermediate); + } +}; + +} // namespace thread + +namespace threadblock { +template < + typename EO, + typename ES, + int Count, + typename EA, + typename EC, + bool F, + bool L, + typename FAB, + FloatRoundStyle R> +struct ApplyEpilogueOp> { + using Op = thread:: + MemoryEfficientAttentionNormalize; + static CUTLASS_DEVICE typename Op::FragmentOutput apply( + Op const& output_op, + int row_id, + typename Op::FragmentAccumulator const& accum, + typename Op::FragmentSource const& source) { + return output_op(row_id, accum, source); + } + static CUTLASS_DEVICE typename Op::FragmentOutput apply( + Op const& output_op, + int row_id, + typename Op::FragmentAccumulator const& accum) { + return output_op(row_id, accum); + } +}; + +///////////////////////////////////////////////////////////////////////////////////////////////// + +} // namespace threadblock +} // namespace epilogue +} // namespace cutlass + +///////////////////////////////////////////////////////////////////////////////////////////////// diff --git a/csrc/include/natten/cuda/fna/epilogue/epilogue_thread_apply_logsumexp.h b/csrc/include/natten/cuda/fna/epilogue/epilogue_thread_apply_logsumexp.h new file mode 100644 index 0000000..6a53dc6 --- /dev/null +++ b/csrc/include/natten/cuda/fna/epilogue/epilogue_thread_apply_logsumexp.h @@ -0,0 +1,181 @@ +/* + * Copied from xFormers (https://github.com/facebookresearch/xformers/) + * + * Copyright (c) Meta Platforms, Inc. and affiliates. + * All rights reserved. + */ +/*************************************************************************************************** + * Copyright (c) 2017 - 2023 NVIDIA CORPORATION & AFFILIATES. All rights + *reserved. SPDX-License-Identifier: BSD-3-Clause + * + * Redistribution and use in source and binary forms, with or without + * modification, are permitted provided that the following conditions are met: + * + * 1. Redistributions of source code must retain the above copyright notice, + *this list of conditions and the following disclaimer. + * + * 2. Redistributions in binary form must reproduce the above copyright notice, + * this list of conditions and the following disclaimer in the documentation + * and/or other materials provided with the distribution. + * + * 3. Neither the name of the copyright holder nor the names of its + * contributors may be used to endorse or promote products derived from + * this software without specific prior written permission. + * + * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" + * AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE + * IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE + *ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE + *LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR + *CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF + *SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS + *INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN + *CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) + *ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE + *POSSIBILITY OF SUCH DAMAGE. + * + **************************************************************************************************/ +/*! \file + \brief Functor performing linear combination operations used by epilogues. +*/ + +#pragma once + +#include + +#include "cutlass/array.h" +#include "cutlass/cutlass.h" +#include "cutlass/epilogue/thread/activation.h" +#include "cutlass/functional.h" +#include "cutlass/numeric_conversion.h" +#include "cutlass/numeric_types.h" + +///////////////////////////////////////////////////////////////////////////////////////////////// + +namespace cutlass { +namespace epilogue { +namespace thread { + +///////////////////////////////////////////////////////////////////////////////////////////////// + +namespace detail { + +template +struct ArrayExponential { + CUTLASS_HOST_DEVICE + Array operator()( + Array const& input) const { + Array result; + + CUTLASS_PRAGMA_UNROLL + for (int i = 0; i < ElementsPerAccess; ++i) { + result[i] = expf(input[i]); + } + + return result; + } +}; + +template +struct ArrayExponential { + CUTLASS_DEVICE + Array operator()( + Array const& input) const { + Array result; + + int const kVectorCount = ElementsPerAccess / 2; + + __half2 const* input_ptr = + reinterpret_cast<__half2 const*>(input.raw_data()); + __half2* res_ptr = reinterpret_cast<__half2*>(result.raw_data()); + + CUTLASS_PRAGMA_UNROLL + for (int i = 0; i < kVectorCount; ++i) { + res_ptr[i] = h2exp(input_ptr[i]); + } + + return result; + } +}; +} // namespace detail + +///////////////////////////////////////////////////////////////////////////////////////////////// + +/// Applies: +/// output <- (input - lse).exp() +template < + typename ElementOutput_, // output + typename ElementLSE_, // accumulator from LSE + typename ElementAccumulator_, // accumulator from matmul + typename ElementCompute_, // intermediate compute (and exp calculation) + int ElementsPerAccess> +class ApplyLogSumExp { + public: + using ElementOutput = ElementOutput_; + using ElementAccumulator = ElementAccumulator_; + using ElementCompute = ElementCompute_; + using ElementLSE = ElementLSE_; + + static int const kElementsPerAccess = ElementsPerAccess; + static int const kCount = kElementsPerAccess; + static const ScaleType::Kind kScale = + cutlass::epilogue::thread::ScaleType::NoBetaScaling; + + using FragmentOutput = Array; + using FragmentAccumulator = Array; + using FragmentCompute = Array; + using FragmentLSE = Array; + using FragmentScaleBias = FragmentLSE; // Used by epilogue_smem_accumulator.h + + public: + // + // Methods + // + + CUTLASS_HOST_DEVICE + ApplyLogSumExp() {} + + /// Returns true if source is needed + CUTLASS_HOST_DEVICE + bool is_source_needed() const { + return true; + } + + /// Functionally required for serial reduction in the epilogue + CUTLASS_HOST_DEVICE + void set_k_partition(int k_partition, int k_partition_count) {} + + CUTLASS_HOST_DEVICE + FragmentOutput operator()( + FragmentAccumulator const& AB, + FragmentLSE const& scale_unused, + // bias used as LSE + FragmentLSE const& bias) const { + FragmentCompute frag_AB = NumericArrayConverter< + ElementCompute, + ElementAccumulator, + kElementsPerAccess>()(AB); + FragmentCompute frag_lse_compute = + NumericArrayConverter()( + bias); + FragmentCompute frag_compute; + + minus minus_lse; + detail::ArrayExponential apply_exp; + frag_compute = minus_lse(frag_AB, frag_lse_compute); + frag_compute = apply_exp(frag_compute); + + return NumericArrayConverter< + ElementOutput, + ElementCompute, + kElementsPerAccess>()(frag_compute); + } +}; + +///////////////////////////////////////////////////////////////////////////////////////////////// + +} // namespace thread +} // namespace epilogue +} // namespace cutlass + +///////////////////////////////////////////////////////////////////////////////////////////////// diff --git a/csrc/include/natten/cuda/fna/epilogue/predicated_tile_iterator.h b/csrc/include/natten/cuda/fna/epilogue/predicated_tile_iterator.h new file mode 100644 index 0000000..d328721 --- /dev/null +++ b/csrc/include/natten/cuda/fna/epilogue/predicated_tile_iterator.h @@ -0,0 +1,629 @@ +/* + * Copied from CUTLASS (https://github.com/NVIDIA/cutlass/) and edited. + */ +/*************************************************************************************************** + * Copyright (c) 2017 - 2024 NVIDIA CORPORATION & AFFILIATES. All rights + *reserved. SPDX-License-Identifier: BSD-3-Clause + * + * Redistribution and use in source and binary forms, with or without + * modification, are permitted provided that the following conditions are met: + * + * 1. Redistributions of source code must retain the above copyright notice, + *this list of conditions and the following disclaimer. + * + * 2. Redistributions in binary form must reproduce the above copyright notice, + * this list of conditions and the following disclaimer in the documentation + * and/or other materials provided with the distribution. + * + * 3. Neither the name of the copyright holder nor the names of its + * contributors may be used to endorse or promote products derived from + * this software without specific prior written permission. + * + * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" + * AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE + * IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE + *ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE + *LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR + *CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF + *SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS + *INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN + *CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) + *ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE + *POSSIBILITY OF SUCH DAMAGE. + * + **************************************************************************************************/ +/*! \file + \brief Epilogue for threadblock scoped GEMMs using Tensor Ops. + + The epilogue rearranges the result of a matrix product through shared memory + to match canonical tensor layouts in global memory. Epilogues support + conversion and reduction operations. + +*/ + +#pragma once + +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include + +#include +#include + +//////////////////////////////////////////////////////////////////////////////// + +namespace cutlass { + +//////////////////////////////////////////////////////////////////////////////// + +namespace epilogue { +namespace threadblock { + +//////////////////////////////////////////////////////////////////////////////// + +/// Tile iterator used to load and store output tile from global memory in +/// epilogue. +/// +/// Satisfies: ReadableTileIterator | CustomPredicatedTileIterator | +/// ForwardTileIterator +/// +template < + int NADim, + int kTileSizeRow, + typename ThreadMap_, ///< Thread map (conept: OutputTileThreadMap) + typename Element_ ///< Element data type + > +class CustomPredicatedTileIterator { + public: + static_assert(NADim >= 1 && NADim < 4); + using Dim = typename natten::cuda::fna::GetDim::type; + + using ThreadMap = ThreadMap_; + using Shape = typename ThreadMap::Shape; + + using Element = Element_; + + using Layout = layout::RowMajor; + using TensorRef = TensorRef; + using ConstTensorRef = typename TensorRef::ConstTensorRef; + + using Index = typename Layout::Index; + using LongIndex = typename Layout::LongIndex; + using TensorCoord = MatrixCoord; + + static int const kElementsPerAccess = ThreadMap::kElementsPerAccess; + static int const kThreads = ThreadMap::kThreads; + static int const kIterations = ThreadMap::Count::kTile; + + static_assert( + ThreadMap::Iterations::kRow > 0, + "ThreadMap::Iterations::kRow must be > 0"); + static_assert( + ThreadMap::Iterations::kGroup > 0, + "ThreadMap::Iterations::kGroup must be > 0"); + static_assert( + ThreadMap::Iterations::kCluster > 0, + "ThreadMap::Iterations::kCluster must be > 0"); + static_assert( + ThreadMap::Iterations::kColumn > 0, + "ThreadMap::Iterations::kColumn must be > 0"); + + /// Fragment object + using Fragment = Array< + Element, + ThreadMap::Iterations::kColumn * ThreadMap::Iterations::kRow * + ThreadMap::Iterations::kGroup * ThreadMap::Iterations::kCluster * + ThreadMap::kElementsPerAccess>; + + /// Memory access size + using AccessType = AlignedArray; + + // + // Parameters struct + // + using ParamsBase = CustomPredicatedTileIteratorParams; + + /// Uses a non-template class + struct Params : ParamsBase { + using Base = ParamsBase; + + CUTLASS_HOST_DEVICE + Params() {} + + CUTLASS_HOST_DEVICE + Params(Dim stride, Dim extent_row) + : ParamsBase( + stride, + int32_t(sizeof(AccessType)) / kElementsPerAccess, + extent_row, + make_OutputTileThreadMapDesc()) {} + + CUTLASS_HOST_DEVICE + Params(Base const& base) : Base(base) {} + }; + + /// Mask object + struct Mask { + static int const kCount = ThreadMap::Iterations::kColumn; + + /// Predicate state + bool predicates[kCount]; + + // + // Mask + // + CUTLASS_HOST_DEVICE + Mask() { + enable(); + } + + ///< Efficiently disables all accesses guarded by mask + CUTLASS_HOST_DEVICE void clear() { + CUTLASS_PRAGMA_UNROLL + for (int i = 0; i < kCount; ++i) { + predicates[i] = false; + } + } + + ///< CUTLASS_HOST_DEVICE enables all accesses guarded by mask + CUTLASS_DEVICE void enable() { + CUTLASS_PRAGMA_UNROLL + for (int i = 0; i < kCount; ++i) { + predicates[i] = true; + } + } + }; + + private: + // + // Data members + // + + /// Parameters structure containing reference and precomputed state. + ParamsBase params_; + + /// Byte-level pointer. This pointer is usually for both load() and store(), + /// unless PermuteD is performed. When having PermuteD, byte_pointer_ is only + /// for load(). + uint8_t* byte_pointer_; + + // /// Byte-level pointer for store(). Due to PermuteD Op, store_byte_pointer_ + // may be with different address computation compared to byte_pointer_. + // uint8_t *store_byte_pointer_; + + /// Array of boolean values to contain steady-state predicates + Mask mask_; + + /// Extent of the matrix tile in rows + Dim extent_row_; + int32_t extent_row_int; + + /// Extent of the matrix tile in rows + Index extent_column_; + + /// A thread's starting row position (assuming steady-state predicates have + /// been computed) + Index thread_start_row_; + + /// A thread's starting column + Index thread_start_column_; + + /// Internal state counter + int state_[3]; + + // + // Static asserts about internal strides + // + + static_assert(sizeof(extent_column_) == 4, "Expected 32b extents"); + static_assert(sizeof(thread_start_row_) == 4, "Expected 32b extents"); + + private: + // + // Methods + // + + public: + // + // Methods + // + + /// Constructor + CUTLASS_DEVICE + CustomPredicatedTileIterator( + ParamsBase const& params, + Element* pointer, + Dim extent_row, + int32_t extent_col, + int thread_idx, + TensorCoord threadblock_offset = TensorCoord()) + : params_(params) { + TensorCoord thread_offset = + ThreadMap::initial_offset(thread_idx) + threadblock_offset; + + extent_row_ = extent_row; + extent_column_ = extent_col; + extent_row_int = extent_row.prod32(); + + thread_start_row_ = thread_offset.row(); + thread_start_column_ = thread_offset.column(); + + // Initialize predicates + CUTLASS_PRAGMA_UNROLL + for (int c = 0; c < ThreadMap::Iterations::kColumn; ++c) { + mask_.predicates[c] = + ((thread_offset.column() + ThreadMap::Delta::kColumn * c) < + extent_col); + } + + // Null pointer performs no accesses + if (!pointer) { + mask_.clear(); + } + + // if (ScatterD && !indices) { + // mask_.clear(); + // } + + // Initialize byte_pointer_ + byte_pointer_ = reinterpret_cast(pointer) + + //(natten::cuda::fna::map_index_to_coord(thread_offset.row(), + //extent_row_) * params_.stride).sum() + + LongIndex(thread_offset.column()) * sizeof(AccessType) / + kElementsPerAccess; + + // if (ScatterD) { + // byte_pointer_ = reinterpret_cast(pointer) + + // LongIndex(thread_offset.column()) * sizeof(AccessType) / + // kElementsPerAccess; + //} + + // store_byte_pointer_ is set to be the same with byte_pointer_ unless + // PermuteD is used. + // store_byte_pointer_ = PermuteD ? reinterpret_cast(pointer) : + // byte_pointer_; + // store_byte_pointer_ = byte_pointer_; + + // Initialize internal state counter + state_[0] = state_[1] = state_[2] = 0; + } + + /// Adds a pointer offset in units of Element + CUTLASS_HOST_DEVICE + void add_pointer_offset(LongIndex pointer_offset) { + // store_byte_pointer_ += pointer_offset * sizeof_bits::value / 8; + byte_pointer_ += pointer_offset * sizeof_bits::value / 8; + } + + /// Loads a fragment from memory + CUTLASS_DEVICE + void load_with_byte_offset(Fragment& frag, int64_t byte_offset) const { + uint8_t* byte_pointer = byte_pointer_; + AccessType* frag_ptr = reinterpret_cast(&frag); + + CUTLASS_PRAGMA_UNROLL + for (int cluster = 0; cluster < ThreadMap::Iterations::kCluster; + ++cluster) { + CUTLASS_PRAGMA_UNROLL + for (int group = 0; group < ThreadMap::Iterations::kGroup; ++group) { + CUTLASS_PRAGMA_UNROLL + for (int row = 0; row < ThreadMap::Iterations::kRow; ++row) { + int frag_row_idx = + (row + + ThreadMap::Iterations::kRow * + (group + ThreadMap::Iterations::kGroup * cluster)); + + int32_t row_offset = row * ThreadMap::Delta::kRow + + group * ThreadMap::Delta::kGroup + + cluster * ThreadMap::Delta::kCluster; + + auto row_offset_full_ = natten::cuda::fna::map_index_to_coord( + row_offset + thread_start_row_, extent_row_); + // bool row_guard = + // natten::cuda::fna::is_coord_within_upper_bound(row_offset_full_, + // extent_row_); + bool row_guard = row_offset + thread_start_row_ < extent_row_int; + + AccessType* memory_pointer = reinterpret_cast( + byte_pointer + byte_offset + + (row_offset_full_ * params_.stride).sum()); + + // if (ScatterD && row_guard) { + // assert(indices_); + + // memory_pointer = reinterpret_cast(byte_pointer + + // byte_offset + + // LongIndex(indices_[row_offset + thread_start_row_]) * + // LongIndex(params_.stride)); + //} + + CUTLASS_PRAGMA_UNROLL + for (int column = 0; column < ThreadMap::Iterations::kColumn; + ++column) { + bool guard = row_guard && mask_.predicates[column]; + + cutlass::arch::global_load( + frag_ptr + [frag_row_idx * ThreadMap::Iterations::kColumn + column], + (void*)&memory_pointer + [column * ThreadMap::Delta::kColumn / kElementsPerAccess], + guard); + } + + if (row + 1 < ThreadMap::Iterations::kRow) { + // if (!ScatterD) { + // byte_pointer += params_.increment_row; + //} + } + } + + if (group + 1 < ThreadMap::Iterations::kGroup) { + // byte_pointer += params_.increment_group; + } + } + + if (cluster + 1 < ThreadMap::Iterations::kCluster) { + // byte_pointer += params_.increment_cluster; + } + } + } + + /// Loads a fragment from memory + CUTLASS_DEVICE + void load(Fragment& frag) const { + load_with_byte_offset(frag, 0); + } + + /// Stores a fragment to memory + CUTLASS_DEVICE + void store_with_byte_offset(Fragment const& frag, int64_t byte_offset) const { + // uint8_t *byte_pointer = store_byte_pointer_; + uint8_t* byte_pointer = byte_pointer_; + AccessType const* frag_ptr = reinterpret_cast(&frag); + + CUTLASS_PRAGMA_UNROLL + for (int cluster = 0; cluster < ThreadMap::Iterations::kCluster; + ++cluster) { + CUTLASS_PRAGMA_UNROLL + for (int group = 0; group < ThreadMap::Iterations::kGroup; ++group) { + CUTLASS_PRAGMA_UNROLL + for (int row = 0; row < ThreadMap::Iterations::kRow; ++row) { + int frag_row_idx = + (row + + ThreadMap::Iterations::kRow * + (group + ThreadMap::Iterations::kGroup * cluster)); + + int32_t row_offset = row * ThreadMap::Delta::kRow + + group * ThreadMap::Delta::kGroup + + cluster * ThreadMap::Delta::kCluster; + + auto row_offset_full_ = natten::cuda::fna::map_index_to_coord( + row_offset + thread_start_row_, extent_row_); + // bool row_guard = + // natten::cuda::fna::is_coord_within_upper_bound(row_offset_full_, + // extent_row_); + bool row_guard = row_offset + thread_start_row_ < extent_row_int; + + AccessType* memory_pointer = reinterpret_cast( + byte_pointer + byte_offset + + (row_offset_full_ * params_.stride).sum()); + + // if (ScatterD && row_guard) { + // assert(indices_); + + // memory_pointer = reinterpret_cast(byte_pointer + + // byte_offset + + // LongIndex(indices_[row_offset + thread_start_row_]) * + // LongIndex(params_.stride)); + //} + + CUTLASS_PRAGMA_UNROLL + for (int column = 0; column < ThreadMap::Iterations::kColumn; + ++column) { + bool guard = row_guard && mask_.predicates[column]; + + // if (PermuteD) { + + // int col_offset = column * ThreadMap::Delta::kColumn; + + // int col = col_offset + thread_start_column_; + // int row = row_offset + thread_start_row_; + + // // Locate memory_pointer + // memory_pointer = reinterpret_cast(byte_pointer + + // byte_offset + // + permute_layout_(PitchLinearCoord(col, row)) * + // sizeof(AccessType) / kElementsPerAccess); + // } + + // if (UseCUDAStore) { + // if (guard) { + // memory_pointer[0] = + // frag_ptr[frag_row_idx * ThreadMap::Iterations::kColumn + + // column]; + // } + //} else { + cutlass::arch::global_store( + frag_ptr + [frag_row_idx * ThreadMap::Iterations::kColumn + column], + (void*)&memory_pointer[0], + guard); + //} + + // if (!PermuteD) { + memory_pointer += (ThreadMap::Delta::kColumn / kElementsPerAccess); + //} + } + + if (row + 1 < ThreadMap::Iterations::kRow) { + // if (!ScatterD && !PermuteD) { + // byte_pointer += params_.increment_row; + //} + } + } + + if (group + 1 < ThreadMap::Iterations::kGroup) { + // byte_pointer += params_.increment_group; + } + } + + if (cluster + 1 < ThreadMap::Iterations::kCluster) { + // byte_pointer += params_.increment_cluster; + } + } + } + + /// Stores a fragment to memory + CUTLASS_DEVICE + void store(Fragment const& frag) const { + store_with_byte_offset(frag, 0); + } + + CUTLASS_DEVICE + MatrixCoord thread_start() const { + return MatrixCoord(thread_start_row_, thread_start_column_); + } + + /// Need to get the thread start row from the tile iterator + CUTLASS_DEVICE + int32_t thread_start_row() const { + return thread_start_row_; + } + + /// Need to get the thread start row from the tile iterator + CUTLASS_DEVICE + int32_t thread_start_column() const { + return thread_start_column_; + } + + /// Extent of the matrix in columns + CUTLASS_DEVICE + Index extent_column() const { + return extent_column_; + } + + /// Advances to the next position to load or store + CUTLASS_HOST_DEVICE + CustomPredicatedTileIterator& operator++() { + ++state_[0]; + + // if (!ScatterD && !PermuteD) { + // store_byte_pointer_ += params_.advance_row; + //} + + // if (!ScatterD) { + // byte_pointer_ += params_.advance_row; + //} + + thread_start_row_ += ThreadMap::Shape::kRow; + + if (state_[0] == ThreadMap::Count::kRow) { + state_[0] = 0; + ++state_[1]; + // byte_pointer_ += params_.advance_group; + // store_byte_pointer_ += params_.advance_group; + + thread_start_row_ += (ThreadMap::Shape::kGroup - 1) * + ThreadMap::Shape::kRow * ThreadMap::Count::kRow; + + if (state_[1] == ThreadMap::Count::kGroup) { + state_[1] = 0; + ++state_[2]; + // byte_pointer_ += params_.advance_cluster; + // store_byte_pointer_ += params_.advance_cluster; + + thread_start_row_ += ThreadMap::Count::kGroup * + ThreadMap::Shape::kGroup * ThreadMap::Count::kRow * + ThreadMap::Shape::kRow; + + if (state_[2] == ThreadMap::Count::kCluster) { + state_[2] = 0; + // byte_pointer_ += params_.advance_tile; + // store_byte_pointer_ += params_.advance_tile; + + thread_start_row_ += ThreadMap::Shape::kGroup * + ThreadMap::Shape::kRow * ThreadMap::Shape::kCluster * + ThreadMap::Shape::kTile; + } + } + } + + return *this; + } + + /// Advances a number of positions to load or store + CUTLASS_HOST_DEVICE + CustomPredicatedTileIterator& operator+=(int increment) { + // Row + state_[0] += increment; + int increment_row = state_[0] / ThreadMap::Count::kRow; + state_[0] = state_[0] % ThreadMap::Count::kRow; + + // byte_pointer_ += (params_.advance_row * increment); + // store_byte_pointer_ += (params_.advance_row * increment); + thread_start_row_ += (ThreadMap::Shape::kRow * increment); + + // Group + state_[1] += increment_row; + int increment_group = state_[1] / ThreadMap::Count::kGroup; + state_[1] = state_[1] % ThreadMap::Count::kGroup; + + // byte_pointer_ += (params_.advance_group * increment_row); + // store_byte_pointer_ += (params_.advance_group * increment_row); + thread_start_row_ += (ThreadMap::Shape::kGroup - 1) * + ThreadMap::Shape::kRow * ThreadMap::Count::kRow * increment_row; + + // Cluster + state_[2] += increment_group; + int increment_cluster = state_[2] / ThreadMap::Count::kCluster; + state_[2] = state_[2] % ThreadMap::Count::kCluster; + + // byte_pointer_ += (params_.advance_cluster * increment_group); + // store_byte_pointer_ += (params_.advance_cluster * increment_group); + thread_start_row_ += ThreadMap::Count::kGroup * ThreadMap::Shape::kGroup * + ThreadMap::Count::kRow * ThreadMap::Shape::kRow * increment_group; + + // Tile + // byte_pointer_ += (params_.advance_tile * increment_cluster); + // store_byte_pointer_ += (params_.advance_tile * increment_cluster); + thread_start_row_ += ThreadMap::Shape::kGroup * ThreadMap::Shape::kRow * + ThreadMap::Shape::kCluster * ThreadMap::Shape::kTile * + increment_cluster; + + return *this; + } + + ///< Efficiently disables all accesses guarded by mask + CUTLASS_DEVICE void clear_mask() { + mask_.clear(); + } + + ///< Efficiently enables all accesses guarded by mask + CUTLASS_DEVICE void enable_mask() { + mask_.enable(); + } + + ///< Sets the mask + CUTLASS_DEVICE void get_mask(Mask& mask) const { + mask = mask_; + } + + ///< Sets the mask + CUTLASS_DEVICE void set_mask(Mask const& mask) { + mask_ = mask; + } +}; + +} // namespace threadblock +} // namespace epilogue +} // namespace cutlass + +//////////////////////////////////////////////////////////////////////////////// diff --git a/csrc/include/natten/cuda/fna/epilogue/predicated_tile_iterator_params.h b/csrc/include/natten/cuda/fna/epilogue/predicated_tile_iterator_params.h new file mode 100644 index 0000000..9014c4f --- /dev/null +++ b/csrc/include/natten/cuda/fna/epilogue/predicated_tile_iterator_params.h @@ -0,0 +1,152 @@ +/* + * Copied from CUTLASS (https://github.com/NVIDIA/cutlass/) and edited. + */ +/*************************************************************************************************** + * Copyright (c) 2017 - 2024 NVIDIA CORPORATION & AFFILIATES. All rights + *reserved. SPDX-License-Identifier: BSD-3-Clause + * + * Redistribution and use in source and binary forms, with or without + * modification, are permitted provided that the following conditions are met: + * + * 1. Redistributions of source code must retain the above copyright notice, + *this list of conditions and the following disclaimer. + * + * 2. Redistributions in binary form must reproduce the above copyright notice, + * this list of conditions and the following disclaimer in the documentation + * and/or other materials provided with the distribution. + * + * 3. Neither the name of the copyright holder nor the names of its + * contributors may be used to endorse or promote products derived from + * this software without specific prior written permission. + * + * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" + * AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE + * IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE + *ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE + *LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR + *CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF + *SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS + *INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN + *CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) + *ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE + *POSSIBILITY OF SUCH DAMAGE. + * + **************************************************************************************************/ +/*! \file + \brief +*/ + +#pragma once + +#include + +#include +#include + +#include + +///////////////////////////////////////////////////////////////////////////////////////////////// + +namespace cutlass { +namespace epilogue { +namespace threadblock { + +// +// Parameters struct for PredicatedTileIterator +// + +template +struct CustomPredicatedTileIteratorParams { + using Dim = typename natten::cuda::fna::GetDim::type; + + using Index = int32_t; + using LongIndex = int64_t; + + // + // Data members + // + + Dim stride; ///< stride in bytes between rows + + // LongIndex increment_row; ///< increment quantity (in bytes) to + // advance when moving between rows LongIndex increment_group; ///< + // increment quantity (in bytes) to advance when moving to the next group + // LongIndex increment_cluster; ///< increment quantity (in bytes) to + // advance when moving to the next cluster + + // LongIndex advance_row; ///< amount to add to move to the next + // 'row' position LongIndex advance_group; ///< amount to add to move + // to the next 'group' position LongIndex advance_cluster; ///< amount to + // add to move to the next 'cluster' position LongIndex advance_tile; ///< + // amount to add to move to the next 'tile' + + // + // Methods + // + + CUTLASS_HOST_DEVICE + Status initialize( + Dim stride_, + Index elem, + Dim extent_row, + OutputTileThreadMapDesc thread_map) { + stride = stride_ * elem; + + // increment_row = (stride * + // natten::cuda::fna::map_index_to_coord(thread_map.delta.row, + // extent_row)).sum(); + + // increment_group = (stride * + // natten::cuda::fna::map_index_to_coord(thread_map.delta.group, extent_row) + // - stride * natten::cuda::fna::map_index_to_coord(thread_map.delta.row * + // (thread_map.iterations.row - 1), extent_row)).sum(); + + // increment_cluster = (stride * + // natten::cuda::fna::map_index_to_coord(thread_map.delta.cluster, + // extent_row) + // - stride * natten::cuda::fna::map_index_to_coord(thread_map.delta.group + // * (thread_map.iterations.group - 1), extent_row) + // - stride * natten::cuda::fna::map_index_to_coord(thread_map.delta.row * + // (thread_map.iterations.row - 1), extent_row)).sum(); + + // advance_row = (stride * + // natten::cuda::fna::map_index_to_coord(thread_map.shape.row, + // extent_row)).sum(); + + // advance_group = + // (stride * + // natten::cuda::fna::map_index_to_coord((thread_map.shape.group - 1) * + // thread_map.shape.row * thread_map.count.row, extent_row)).sum(); + // + // advance_cluster = ( + // stride * + // natten::cuda::fna::map_index_to_coord(thread_map.count.group * + // thread_map.shape.group * + // thread_map.count.row * + // thread_map.shape.row, extent_row)).sum(); + // + // advance_tile =( + // stride * + // natten::cuda::fna::map_index_to_coord(thread_map.shape.group * + // thread_map.shape.row * + // thread_map.shape.cluster * + // thread_map.shape.tile, extent_row)).sum(); + + return Status::kSuccess; + } + + CUTLASS_HOST_DEVICE + CustomPredicatedTileIteratorParams( + Dim stride, + Index elem, + Dim extent_row, + OutputTileThreadMapDesc thread_map) { + initialize(stride, elem, extent_row, thread_map); + } +}; + +} // namespace threadblock +} // namespace epilogue +} // namespace cutlass + +//////////////////////////////////////////////////////////////////////////////// diff --git a/csrc/include/natten/cuda/fna/fna_forward.cuh b/csrc/include/natten/cuda/fna/fna_forward.cuh new file mode 100644 index 0000000..063b8b3 --- /dev/null +++ b/csrc/include/natten/cuda/fna/fna_forward.cuh @@ -0,0 +1,183 @@ +/*************************************************************************************************** + * Copyright (c) 2022-2024 Ali Hassani. + * + * Fused Neighborhood Attention kernels are heavily based on the + * memory-efficient attention kernels from the xFormers project by Meta + * Platforms, Inc. + * + * Copyright (c) Facebook, Inc. and its affiliates + * + * BSD 3-Clause License + * + * Redistribution and use in source and binary forms, with or without + * modification, are permitted provided that the following conditions are met: + * + * 1. Redistributions of source code must retain the above copyright + * notice, this list of conditions and the following disclaimer. + * + * 2. Redistributions in binary form must reproduce the above copyright + * notice, this list of conditions and the following disclaimer in the + * documentation and/or other materials provided with the distribution. + * + * 3. Neither the names of Facebook, Deepmind Technologies, NYU, NEC + * Laboratories America and IDIAP Research Institute nor the names of its + * contributors may be used to endorse or promote products derived from this + * software without specific prior written permission. + * + * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" + * AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE + * IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE + * ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE + * LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR + * CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF + * SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS + * INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN + * CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) + * ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE + * POSSIBILITY OF SUCH DAMAGE. + */ + +#include +#include +#include + +#include + +#include + +namespace natten { +namespace cuda { +namespace fna { + +template +void fna_forward_generic( + const int cc, + const size_t max_smem, + cudaStream_t stream, + void* query_ptr, + void* key_ptr, + void* value_ptr, + void* out_ptr, + void* rpb_ptr, + int32_t batch_size, + IntTuple spatial_extent, + int32_t heads, + int32_t dim, + int32_t dim_value, + IntTuple kernel_size, + IntTuple dilation, + BooleanTuple is_causal, + float attn_scale, + void* logsumexp_ptr, + IntTuple query_tile_shape, + IntTuple key_tile_shape) { + static constexpr auto kRank = + std::tuple_size::value; + using Dim = typename GetDim::type; + + bool has_rpb = rpb_ptr != nullptr; + bool compute_logsumexp = logsumexp_ptr != nullptr; + bool kernel_launched = false; + auto launchKernel = [&](auto _k, auto kernel_fn) { + using Kernel = decltype(_k); + using scalar_t = typename Kernel::scalar_t; + (void)_k; + + if (kernel_launched) { + return; + } + + if (dim_value > Kernel::kMaxK || dim > Kernel::kMaxK) { + return; + } + + if (natten::flatten(query_tile_shape) != Kernel::kQueriesPerBlock || + natten::flatten(key_tile_shape) != Kernel::kKeysPerBlock) { + return; + } + + // Alignment + if ((dim % Kernel::kAlignmentQ) || (dim % Kernel::kAlignmentK) || + (dim_value % Kernel::kAlignmentV)) { + return; + } + // Uses too much smem + size_t smem_bytes = sizeof(typename Kernel::SharedStorage); + if (smem_bytes > max_smem) { + return; + } + + kernel_launched = true; + + typename Kernel::Params p; + p.query_ptr = (scalar_t*)query_ptr; + p.key_ptr = (scalar_t*)key_ptr; + p.value_ptr = (scalar_t*)value_ptr; + p.rpb_ptr = (scalar_t*)rpb_ptr; + p.logsumexp_ptr = compute_logsumexp + ? (typename Kernel::lse_scalar_t*)logsumexp_ptr + : nullptr; + void* accum_ptr = nullptr; + if (Kernel::kNeedsOutputAccumulatorBuffer) { + using AccumType = typename Kernel::output_accum_t; + // TODO: let PT reserve space for accum_ptr and LSE. + // cudaMalloc-ing can break CUDA graphs, and probably does not + // guarantee the space is allocated on the same device (when multiple + // devices are present). + // cudaMallocAsync takes in the stream, which --should-- solve both + // issues. + // cudaMalloc(&accum_ptr, batch_size * natten::flatten(spatial_extent) * + // heads * dim_value * sizeof(AccumType)); + cudaMallocAsync( + &accum_ptr, + batch_size * natten::flatten(spatial_extent) * heads * dim_value * + sizeof(AccumType), + stream); + p.output_accum_ptr = (AccumType*)accum_ptr; + } else { + p.output_accum_ptr = nullptr; + } + p.output_ptr = (typename Kernel::output_t*)out_ptr; + + p.num_heads = heads; + p.head_dim = dim; + p.head_dim_value = dim_value; + p.num_queries = tuple_to_na_dim(spatial_extent); + p.num_batches = batch_size; + + p.scale = attn_scale; + + p.kernel_size = tuple_to_na_dim(kernel_size); + p.dilation = tuple_to_na_dim(dilation); + + p.query_tile_shape = tuple_to_na_dim(query_tile_shape); + p.key_tile_shape = tuple_to_na_dim(key_tile_shape); + + if (smem_bytes > 0xc000) { + auto err = cudaFuncSetAttribute( + kernel_fn, cudaFuncAttributeMaxDynamicSharedMemorySize, smem_bytes); + NATTEN_CHECK( + err != cudaErrorInvalidValue, + "This GPU does not have enough shared-memory."); + NATTEN_CUDA_CHECK(err); + } + auto blocks = p.getBlocksGrid(); + Kernel::check_supported(p); + kernel_fn<<>>(p); + if (Kernel::kNeedsOutputAccumulatorBuffer) { + //NATTEN_CHECK( + // accum_ptr != nullptr, "Expected accum_ptr to be set, got nullptr."); + cudaFreeAsync(accum_ptr, stream); + } + }; + + DISPATCH_FNA_FORWARD_KERNEL(kRank, cc, T, is_causal, has_rpb, launchKernel); + NATTEN_CHECK( + kernel_launched, + "Could not find a compatible fused neighborhood attention kernel."); + NATTEN_CUDA_CHECK(cudaGetLastError()); +} + +} // namespace fna +} // namespace cuda +} // namespace natten diff --git a/csrc/include/natten/cuda/fna/gemm/custom_mma.h b/csrc/include/natten/cuda/fna/gemm/custom_mma.h new file mode 100644 index 0000000..f5671ae --- /dev/null +++ b/csrc/include/natten/cuda/fna/gemm/custom_mma.h @@ -0,0 +1,139 @@ +/* + * Copied from xFormers (https://github.com/facebookresearch/xformers/) and + * edited + * + * Copyright (c) Meta Platforms, Inc. and affiliates. + * All rights reserved. + * + * BSD 3-Clause License + * + * Redistribution and use in source and binary forms, with or without + * modification, are permitted provided that the following conditions are met: + * + * 1. Redistributions of source code must retain the above copyright + * notice, this list of conditions and the following disclaimer. + * + * 2. Redistributions in binary form must reproduce the above copyright + * notice, this list of conditions and the following disclaimer in the + * documentation and/or other materials provided with the distribution. + * + * 3. Neither the names of Facebook, Deepmind Technologies, NYU, NEC + * Laboratories America and IDIAP Research Institute nor the names of its + * contributors may be used to endorse or promote products derived from this + * software without specific prior written permission. + * + * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" + * AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE + * IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE + * ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE + * LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR + * CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF + * SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS + * INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN + * CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) + * ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE + * POSSIBILITY OF SUCH DAMAGE. + */ + +#pragma once + +#include +#include + +#include +#include + +namespace natten { +namespace cuda { +namespace fna { + +template +struct MakeCustomMma; + +template < + typename Shape, + typename IteratorA, + typename SmemIteratorA, + cutlass::arch::CacheOperation::Kind CacheOpA, + typename IteratorB, + typename SmemIteratorB, + cutlass::arch::CacheOperation::Kind CacheOpB, + typename ElementC, + typename LayoutC, + typename Policy, + int Stages, + cutlass::gemm::SharedMemoryClearOption SharedMemoryClear, + int kMaxK> +struct MakeCustomMma< + cutlass::gemm::threadblock::MmaMultistage< + Shape, + IteratorA, + SmemIteratorA, + CacheOpA, + IteratorB, + SmemIteratorB, + CacheOpB, + ElementC, + LayoutC, + Policy, + Stages, + SharedMemoryClear>, + kMaxK> { + // Reduce the number of stages if we don't need that many + static int constexpr kStages = + kMaxK == cutlass::platform::numeric_limits::max() + ? Stages + : cutlass::const_min( + Stages, + (kMaxK + int(Shape::kK) - 1) / int(Shape::kK)); + using Mma = cutlass::gemm::threadblock::CustomMmaMultistage< + Shape, + IteratorA, + SmemIteratorA, + CacheOpA, + IteratorB, + SmemIteratorB, + CacheOpB, + ElementC, + LayoutC, + Policy, + kStages, + SharedMemoryClear, + kMaxK>; +}; + +template < + typename Shape, + typename IteratorA, + typename SmemIteratorA, + typename IteratorB, + typename SmemIteratorB, + typename ElementC, + typename LayoutC, + typename Policy, + int kMaxK> +struct MakeCustomMma< + cutlass::gemm::threadblock::MmaPipelined< + Shape, + IteratorA, + SmemIteratorA, + IteratorB, + SmemIteratorB, + ElementC, + LayoutC, + Policy>, + kMaxK> { + using Mma = cutlass::gemm::threadblock::CustomMmaPipelined< + Shape, + IteratorA, + SmemIteratorA, + IteratorB, + SmemIteratorB, + ElementC, + LayoutC, + Policy>; +}; + +} // namespace fna +} // namespace cuda +} // namespace natten diff --git a/csrc/include/natten/cuda/fna/gemm/custom_mma_base.h b/csrc/include/natten/cuda/fna/gemm/custom_mma_base.h new file mode 100644 index 0000000..62d55bd --- /dev/null +++ b/csrc/include/natten/cuda/fna/gemm/custom_mma_base.h @@ -0,0 +1,189 @@ +/* + * Copied from xFormers (https://github.com/facebookresearch/xformers/) + * + * Copyright (c) Meta Platforms, Inc. and affiliates. + * All rights reserved. + */ +/*************************************************************************************************** + * Copyright (c) 2017 - 2023 NVIDIA CORPORATION & AFFILIATES. All rights + *reserved. SPDX-License-Identifier: BSD-3-Clause + * + * Redistribution and use in source and binary forms, with or without + * modification, are permitted provided that the following conditions are met: + * + * 1. Redistributions of source code must retain the above copyright notice, + *this list of conditions and the following disclaimer. + * + * 2. Redistributions in binary form must reproduce the above copyright notice, + * this list of conditions and the following disclaimer in the documentation + * and/or other materials provided with the distribution. + * + * 3. Neither the name of the copyright holder nor the names of its + * contributors may be used to endorse or promote products derived from + * this software without specific prior written permission. + * + * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" + * AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE + * IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE + *ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE + *LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR + *CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF + *SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS + *INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN + *CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) + *ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE + *POSSIBILITY OF SUCH DAMAGE. + * + **************************************************************************************************/ +/*! \file + \brief Template for a double-buffered threadblock-scoped GEMM kernel. +*/ + +#pragma once + +#include "cutlass/aligned_buffer.h" +#include "cutlass/arch/memory.h" +#include "cutlass/array.h" +#include "cutlass/cutlass.h" +#include "cutlass/gemm/gemm.h" +#include "cutlass/gemm/threadblock/mma_base.h" +#include "cutlass/matrix_shape.h" +#include "cutlass/numeric_types.h" + +//////////////////////////////////////////////////////////////////////////////// + +namespace cutlass { +namespace gemm { +namespace threadblock { + +//////////////////////////////////////////////////////////////////////////////// + +/// Structure to compute the matrix product targeting CUDA cores and SIMT math +/// instructions. +template < + /// Size of the Gemm problem - concept: gemm::GemmShape<> + typename Shape_, + /// Policy describing tuning details (concept: MmaPolicy) + typename Policy_, + /// Number of stages, + int Stages, + /// Used for partial specialization + typename Enable = bool> +class CustomMmaBase { + public: + ///< Size of the Gemm problem - concept: gemm::GemmShape<> + using Shape = Shape_; + + ///< Policy describing tuning details + using Policy = Policy_; + + // + // Dependent types + // + + /// Warp-level Mma + using Operator = typename Policy::Operator; + + /// Shape describing the overall GEMM computed from shared memory + /// by each warp. + using WarpGemm = typename Policy::Operator::Shape; + + /// Shape describing the number of warps filling the CTA + using WarpCount = GemmShape< + Shape::kM / WarpGemm::kM, + Shape::kN / WarpGemm::kN, + Shape::kK / WarpGemm::kK>; + + /// Number of warp-level GEMM oeprations + static int const kWarpGemmIterations = + (WarpGemm::kK / Operator::Policy::MmaShape::kK); + + /// Number of stages + static int const kStages = Stages; + + // + // Nested structs + // + + /// Shared storage object needed by threadblock-scoped GEMM + template + struct OperandSharedStorage { + AlignedBuffer buffer; + using TensorRef = TensorRef; + + CUTLASS_DEVICE + static OperandLayout Layout() { + return OperandLayout::packed({OperandShape::kRow, OperandShape::kColumn}); + } + + /// Returns a TensorRef to the operand + CUTLASS_HOST_DEVICE + TensorRef ref() { + return TensorRef{buffer.data(), Layout()}; + } + }; + + /// Shape of the A matrix operand in shared memory + using ShapeA = MatrixShape< + Shape::kM + Policy::SmemPaddingA::kRow, + Shape::kK * kStages + Policy::SmemPaddingA::kColumn>; + + /// Shape of the B matrix operand in shared memory + using ShapeB = MatrixShape< + Shape::kK * kStages + Policy::SmemPaddingB::kRow, + Shape::kN + Policy::SmemPaddingB::kColumn>; + + using SharedStorageA = OperandSharedStorage< + typename Operator::ElementA, + ShapeA, + typename Operator::LayoutA>; + using SharedStorageB = OperandSharedStorage< + typename Operator::ElementB, + ShapeB, + typename Operator::LayoutB>; + using TensorRefA = typename SharedStorageA::TensorRef; + using TensorRefB = typename SharedStorageB::TensorRef; + + struct SharedStorage { + /// Buffer for A operand + SharedStorageA operand_A; + + /// Buffer for B operand + SharedStorageB operand_B; + }; + + protected: + // + // Data members + // + + /// Iterator to load a warp-scoped tile of A operand from shared memory + typename Operator::IteratorA warp_tile_iterator_A_; + + /// Iterator to load a warp-scoped tile of B operand from shared memory + typename Operator::IteratorB warp_tile_iterator_B_; + + public: + /// Construct from tensor references + CUTLASS_DEVICE + CustomMmaBase( + ///< Shared storage needed for internal use by threadblock-scoped GEMM + SharedStorageA& shared_storageA, + SharedStorageB& shared_storageB, + ///< ID within the threadblock + int thread_idx, + ///< ID of warp + int warp_idx, + ///< ID of each thread within a warp + int lane_idx) + : warp_tile_iterator_A_(shared_storageA.ref(), lane_idx), + warp_tile_iterator_B_(shared_storageB.ref(), lane_idx) {} +}; + +///////////////////////////////////////////////////////////////////////////////////////////////// + +} // namespace threadblock +} // namespace gemm +} // namespace cutlass + +///////////////////////////////////////////////////////////////////////////////////////////////// diff --git a/csrc/include/natten/cuda/fna/gemm/custom_mma_multistage.h b/csrc/include/natten/cuda/fna/gemm/custom_mma_multistage.h new file mode 100644 index 0000000..c4870b7 --- /dev/null +++ b/csrc/include/natten/cuda/fna/gemm/custom_mma_multistage.h @@ -0,0 +1,765 @@ +/* + * Copied from xFormers (https://github.com/facebookresearch/xformers/) + * + * Copyright (c) Meta Platforms, Inc. and affiliates. + * All rights reserved. + */ +/*************************************************************************************************** + * Copyright (c) 2017 - 2023 NVIDIA CORPORATION & AFFILIATES. All rights + *reserved. SPDX-License-Identifier: BSD-3-Clause + * + * Redistribution and use in source and binary forms, with or without + * modification, are permitted provided that the following conditions are met: + * + * 1. Redistributions of source code must retain the above copyright notice, + *this list of conditions and the following disclaimer. + * + * 2. Redistributions in binary form must reproduce the above copyright notice, + * this list of conditions and the following disclaimer in the documentation + * and/or other materials provided with the distribution. + * + * 3. Neither the name of the copyright holder nor the names of its + * contributors may be used to endorse or promote products derived from + * this software without specific prior written permission. + * + * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" + * AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE + * IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE + *ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE + *LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR + *CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF + *SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS + *INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN + *CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) + *ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE + *POSSIBILITY OF SUCH DAMAGE. + * + **************************************************************************************************/ +/*! \file + \brief Template for a double-buffered threadblock-scoped GEMM kernel. +*/ + +#pragma once + +#include "cutlass/aligned_buffer.h" +#include "cutlass/arch/cache_operation.h" +#include "cutlass/arch/memory.h" +#include "cutlass/array.h" +#include "cutlass/cutlass.h" +#include "cutlass/gemm/gemm.h" +#include "cutlass/matrix_shape.h" +#include "cutlass/numeric_types.h" + +#include "custom_mma_base.h" + +///////////////////////////////////////////////////////////////////////////////////////////////// + +namespace cutlass { +namespace gemm { +namespace threadblock { + +///////////////////////////////////////////////////////////////////////////////////////////////// + +/// Structure to compute the matrix product targeting CUDA cores and SIMT math +/// instructions. +template < + /// Size of the Gemm problem - concept: gemm::GemmShape<> + typename Shape_, + /// Iterates over tiles of A operand in global memory + // (concept: ReadableTileIterator | ForwardTileIterator | + // MaskedTileIterator) + typename IteratorA_, + /// Iterates over tiles of A operand in shared memory + /// (concept: WriteableTileIterator | RandomAccessTileIterator) + typename SmemIteratorA_, + /// Cache operation for operand A + cutlass::arch::CacheOperation::Kind CacheOpA, + /// Iterates over tiles of B operand in global memory + // (concept: ReadableTileIterator | ForwardTileIterator | + // MaskedTileIterator) + typename IteratorB_, + /// Iterates over tiles of B operand in shared memory + /// (concept: WriteableTileIterator | RandomAccessTileIterator) + typename SmemIteratorB_, + /// Cache operation for operand B + cutlass::arch::CacheOperation::Kind CacheOpB, + /// Data type of accumulator matrix + typename ElementC_, + /// Data type of accumulator matrix + typename LayoutC_, + /// Policy describing tuning details (concept: MmaPolicy) + typename Policy_, + /// Number of stages, + int Stages, + /// Use zfill or predicate for out-of-bound cp.async + SharedMemoryClearOption SharedMemoryClear = SharedMemoryClearOption::kNone, + /// Upper boundon the K dimension + int kMaxK = cutlass::platform::numeric_limits::max(), + /// Used for partial specialization + typename Enable = bool> +class CustomMmaMultistage : public CustomMmaBase { + public: + ///< Base class + using Base = CustomMmaBase; + ///< Size of the Gemm problem - concept: gemm::GemmShape<> + using Shape = Shape_; + ///< Iterates over tiles of A operand in global memory + using IteratorA = IteratorA_; + ///< Iterates over tiles of B operand in global memory + using IteratorB = IteratorB_; + ///< Data type of accumulator matrix + using ElementC = ElementC_; + ///< Layout of accumulator matrix + using LayoutC = LayoutC_; + ///< Policy describing tuning details + using Policy = Policy_; + + using SmemIteratorA = SmemIteratorA_; + using SmemIteratorB = SmemIteratorB_; + + static cutlass::arch::CacheOperation::Kind const kCacheOpA = CacheOpA; + static cutlass::arch::CacheOperation::Kind const kCacheOpB = CacheOpB; + + // + // Dependent types + // + + /// Fragment of accumulator tile + using FragmentC = typename Policy::Operator::FragmentC; + + /// Warp-level Mma + using Operator = typename Policy::Operator; + + /// Minimum architecture is Sm80 to support cp.async + using ArchTag = arch::Sm80; + + /// Complex transform on A operand + static ComplexTransform const kTransformA = Operator::kTransformA; + + /// Complex transform on B operand + static ComplexTransform const kTransformB = Operator::kTransformB; + + /// Internal structure exposed for introspection. + struct Detail { + static_assert( + Base::kWarpGemmIterations > 1, + "The pipelined structure requires at least two warp-level " + "GEMM operations."); + + /// Number of cp.async instructions to load one stage of operand A + static int const AsyncCopyIterationsPerStageA = + IteratorA::ThreadMap::Iterations::kCount; + + /// Number of cp.async instructions to load one stage of operand B + static int const AsyncCopyIterationsPerStageB = + IteratorB::ThreadMap::Iterations::kCount; + + /// Number of stages + static int const kStages = Stages; + + /// Number of cp.async instructions to load on group of operand A + static int const kAccessesPerGroupA = + (AsyncCopyIterationsPerStageA + Base::kWarpGemmIterations - 1) / + Base::kWarpGemmIterations; + + /// Number of cp.async instructions to load on group of operand B + static int const kAccessesPerGroupB = + (AsyncCopyIterationsPerStageB + Base::kWarpGemmIterations - 1) / + Base::kWarpGemmIterations; + }; + + static bool const kSmemContainsEntireMat = kMaxK <= Shape::kK * Stages; + static constexpr int kNumStagesConcurrentLoad = + kSmemContainsEntireMat ? Stages : Stages - 1; + + private: + using WarpLoadedFragmentA = typename Operator::FragmentA; + using WarpLoadedFragmentB = typename Operator::FragmentB; + using WarpTransformedFragmentA = typename Operator::TransformedFragmentA; + using WarpTransformedFragmentB = typename Operator::TransformedFragmentB; + + private: + // + // Data members + // + + /// Iterator to write threadblock-scoped tile of A operand to shared memory + SmemIteratorA smem_iterator_A_; + + /// Iterator to write threadblock-scoped tile of B operand to shared memory + SmemIteratorB smem_iterator_B_; + + bool prologue_done_; + + // Set to `True` to ensure the accumulator will be zero outside the GEMM + // footprint + bool zero_outside_bounds_; + + public: + /// Construct from tensor references + CUTLASS_DEVICE + CustomMmaMultistage( + ///< Shared storage needed for internal use by threadblock-scoped GEMM + typename Base::SharedStorageA& shared_storageA, + typename Base::SharedStorageB& shared_storageB, + ///< ID within the threadblock + int thread_idx, + ///< ID of warp + int warp_idx, + ///< ID of each thread within a warp + int lane_idx) + : Base(shared_storageA, shared_storageB, thread_idx, warp_idx, lane_idx), + smem_iterator_A_(shared_storageA.ref(), thread_idx), + smem_iterator_B_(shared_storageB.ref(), thread_idx), + prologue_done_(false), + zero_outside_bounds_(false) { + // Compute warp location within threadblock tile by mapping the warp_id to + // three coordinates: + // _m: the warp's position within the threadblock along the M dimension + // _n: the warp's position within the threadblock along the N dimension + // _k: the warp's position within the threadblock along the K dimension + + int warp_idx_mn = warp_idx % (Base::WarpCount::kM * Base::WarpCount::kN); + int warp_idx_k = warp_idx / (Base::WarpCount::kM * Base::WarpCount::kN); + + int warp_idx_m = warp_idx_mn % Base::WarpCount::kM; + int warp_idx_n = warp_idx_mn / Base::WarpCount::kM; + + // Add per-warp offsets in units of warp-level tiles + this->warp_tile_iterator_A_.add_tile_offset( + {warp_idx_m, Base::kWarpGemmIterations * warp_idx_k}); + this->warp_tile_iterator_B_.add_tile_offset( + {Base::kWarpGemmIterations * warp_idx_k, warp_idx_n}); + } + CUTLASS_DEVICE + CustomMmaMultistage( + ///< Shared storage needed for internal use by threadblock-scoped GEMM + typename Base::SharedStorage& st, + ///< ID within the threadblock + int thread_idx, + ///< ID of warp + int warp_idx, + ///< ID of each thread within a warp + int lane_idx) + : CustomMmaMultistage( + st.operand_A, + st.operand_B, + thread_idx, + warp_idx, + lane_idx) {} + + CUTLASS_DEVICE + bool set_prologue_done(bool value) { + prologue_done_ = value; + } + + CUTLASS_DEVICE + bool set_zero_outside_bounds(bool value) { + zero_outside_bounds_ = value; + } + + template + CUTLASS_DEVICE static void prologue( + typename Base::SharedStorage& shared_storage, + ///< iterator over A operand in global memory + IteratorA iterator_A, + ///< iterator over B operand in global memory + IteratorB iterator_B, + int thread_idx, + int problem_size_k) { + prologue( + shared_storage.operand_A, + shared_storage.operand_B, + iterator_A, + iterator_B, + thread_idx, + problem_size_k); + } + + template + CUTLASS_DEVICE static void prologue( + typename Base::SharedStorageA& shared_storageA, + typename Base::SharedStorageB& shared_storageB, + ///< iterator over A operand in global memory + IteratorA iterator_A, + ///< iterator over B operand in global memory + IteratorB iterator_B, + int thread_idx, + int problem_size_k) { + SmemIteratorA smem_iterator_A(shared_storageA.ref(), thread_idx); + SmemIteratorB smem_iterator_B(shared_storageB.ref(), thread_idx); + int32_t iter = (problem_size_k + Base::Shape::kK - 1) / Base::Shape::kK; + _prologue( + iterator_A, iterator_B, iter, smem_iterator_A, smem_iterator_B); + } + + CUTLASS_DEVICE + void copy_tiles_and_advance( + IteratorA& iterator_A, + IteratorB& iterator_B, + int group_start_A = 0, + int group_start_B = 0) { + iterator_A.set_iteration_index( + group_start_A * IteratorA::kAccessesPerVector); + this->smem_iterator_A_.set_iteration_index(group_start_A); + + // Async Copy for operand A + CUTLASS_PRAGMA_UNROLL + for (int j = 0; j < Detail::kAccessesPerGroupA; ++j) { + if (group_start_A + j < Detail::AsyncCopyIterationsPerStageA) { + typename IteratorA::AccessType* dst_ptr = + reinterpret_cast( + this->smem_iterator_A_.get()); + + int const kSrcBytes = sizeof_bits::value * + IteratorA::ThreadMap::kElementsPerAccess / + IteratorA::kAccessesPerVector / 8; + + CUTLASS_PRAGMA_UNROLL + for (int v = 0; v < IteratorA::kAccessesPerVector; ++v) { + auto gmem_ptr = iterator_A.get(); + + if (zero_outside_bounds_ || + SharedMemoryClear == SharedMemoryClearOption::kZfill) { + cutlass::arch::cp_async_zfill( + dst_ptr + v, gmem_ptr, iterator_A.valid()); + } else { + cutlass::arch::cp_async( + dst_ptr + v, gmem_ptr, iterator_A.valid()); + } + + ++iterator_A; + } + + ++this->smem_iterator_A_; + } + } + + iterator_B.set_iteration_index( + group_start_B * IteratorB::kAccessesPerVector); + this->smem_iterator_B_.set_iteration_index(group_start_B); + + // Async Copy for operand B + CUTLASS_PRAGMA_UNROLL + for (int j = 0; j < Detail::kAccessesPerGroupB; ++j) { + if (group_start_B + j < Detail::AsyncCopyIterationsPerStageB) { + typename IteratorB::AccessType* dst_ptr = + reinterpret_cast( + this->smem_iterator_B_.get()); + + int const kSrcBytes = sizeof_bits::value * + IteratorB::ThreadMap::kElementsPerAccess / + IteratorB::kAccessesPerVector / 8; + + CUTLASS_PRAGMA_UNROLL + for (int v = 0; v < IteratorB::kAccessesPerVector; ++v) { + auto gmem_ptr = iterator_B.get(); + + if (zero_outside_bounds_ || + SharedMemoryClear == SharedMemoryClearOption::kZfill) { + cutlass::arch::cp_async_zfill( + dst_ptr + v, gmem_ptr, iterator_B.valid()); + } else { + cutlass::arch::cp_async( + dst_ptr + v, gmem_ptr, iterator_B.valid()); + } + + ++iterator_B; + } + ++this->smem_iterator_B_; + } + } + } + + template + CUTLASS_DEVICE static void _prologue( + IteratorA& iterator_A, + IteratorB& iterator_B, + int32_t& gemm_k_iterations, + SmemIteratorA& smem_iterator_A_, + SmemIteratorB& smem_iterator_B_) { + // Issue several complete stages + CUTLASS_PRAGMA_UNROLL + for (int stage = 0; stage < kNumStagesConcurrentLoad; + ++stage, --gemm_k_iterations) { + iterator_A.clear_mask(gemm_k_iterations == 0); + iterator_B.clear_mask(gemm_k_iterations == 0); + + iterator_A.set_iteration_index(0); + smem_iterator_A_.set_iteration_index(0); + + // Async Copy for operand A + CUTLASS_PRAGMA_UNROLL + for (int j = 0; j < Detail::AsyncCopyIterationsPerStageA; ++j) { + typename IteratorA::AccessType* dst_ptr = + reinterpret_cast( + smem_iterator_A_.get()); + + CUTLASS_PRAGMA_UNROLL + for (int v = 0; v < IteratorA::kAccessesPerVector; ++v) { + int const kSrcBytes = + sizeof_bits::value * + IteratorA::ThreadMap::kElementsPerAccess / + IteratorA::kAccessesPerVector / 8; + + int src_bytes = (iterator_A.valid() ? kSrcBytes : 0); + + if (kLoadA) { + cutlass::arch::cp_async_zfill( + dst_ptr + v, iterator_A.get(), iterator_A.valid()); + } + + ++iterator_A; + } + + ++smem_iterator_A_; + } + + iterator_B.set_iteration_index(0); + smem_iterator_B_.set_iteration_index(0); + + // Async Copy for operand B + CUTLASS_PRAGMA_UNROLL + for (int j = 0; j < Detail::AsyncCopyIterationsPerStageB; ++j) { + typename IteratorB::AccessType* dst_ptr = + reinterpret_cast( + smem_iterator_B_.get()); + + CUTLASS_PRAGMA_UNROLL + for (int v = 0; v < IteratorB::kAccessesPerVector; ++v) { + int const kSrcBytes = + sizeof_bits::value * + IteratorB::ThreadMap::kElementsPerAccess / + IteratorB::kAccessesPerVector / 8; + + if (kLoadB) { + cutlass::arch::cp_async_zfill( + dst_ptr + v, iterator_B.get(), iterator_B.valid()); + } + + ++iterator_B; + } + + ++smem_iterator_B_; + } + + // Move to the next stage + iterator_A.add_tile_offset({0, 1}); + iterator_B.add_tile_offset({1, 0}); + + smem_iterator_A_.add_tile_offset({0, 1}); + smem_iterator_B_.add_tile_offset({1, 0}); + + // Defines the boundary of a stage of cp.async. + cutlass::arch::cp_async_fence(); + } + } + + /// Perform a threadblock-scoped matrix multiply-accumulate + CUTLASS_DEVICE + void operator()( + ///< problem size of GEMM + int gemm_k_iterations, + ///< destination accumulator tile + FragmentC& accum, + ///< iterator over A operand in global memory + IteratorA iterator_A, + ///< iterator over B operand in global memory + IteratorB iterator_B, + ///< initial value of accumulator + FragmentC const& src_accum) { + // + // Prologue + // + + if (!prologue_done_) { + _prologue( + iterator_A, + iterator_B, + gemm_k_iterations, + smem_iterator_A_, + smem_iterator_B_); + } else if (!kSmemContainsEntireMat) { + _prologue( + iterator_A, + iterator_B, + gemm_k_iterations, + smem_iterator_A_, + smem_iterator_B_); + } else { + gemm_k_iterations -= kNumStagesConcurrentLoad; + } + + // Perform accumulation in the 'd' output operand + accum = src_accum; + + // + // Clear the remaining tiles of SMEM. This is a functional requirement for + // some kernels so that all accumulator elements outside the GEMM footprint + // are zero. + // + + if (SharedMemoryClear == SharedMemoryClearOption::kClearLastStage) { + /// Iterator to write threadblock-scoped tile of A operand to shared + /// memory + SmemIteratorA last_smem_iterator_A(this->smem_iterator_A_); + + typename IteratorA::AccessType zero_A; + zero_A.clear(); + + last_smem_iterator_A.set_iteration_index(0); + + // Async Copy for operand A + CUTLASS_PRAGMA_UNROLL + for (int j = 0; j < Detail::AsyncCopyIterationsPerStageA; ++j) { + typename IteratorA::AccessType* dst_ptr = + reinterpret_cast( + last_smem_iterator_A.get()); + + *dst_ptr = zero_A; + + ++last_smem_iterator_A; + } + + /// Iterator to write threadblock-scoped tile of B operand to shared + /// memory + SmemIteratorB last_smem_iterator_B(this->smem_iterator_B_); + typename IteratorB::AccessType zero_B; + + zero_B.clear(); + last_smem_iterator_B.set_iteration_index(0); + + // Async Copy for operand B + CUTLASS_PRAGMA_UNROLL + for (int j = 0; j < Detail::AsyncCopyIterationsPerStageB; ++j) { + typename IteratorB::AccessType* dst_ptr = + reinterpret_cast( + last_smem_iterator_B.get()); + + *dst_ptr = zero_B; + + ++last_smem_iterator_B; + } + } + + // Waits until kStages-2 stages have committed. + cutlass::arch::cp_async_wait(); + __syncthreads(); + + // Pair of fragments used to overlap shared memory loads and math + // instructions + WarpLoadedFragmentA warp_loaded_frag_A[2]; + WarpLoadedFragmentB warp_loaded_frag_B[2]; + WarpTransformedFragmentA warp_transformed_frag_A[2]; + WarpTransformedFragmentB warp_transformed_frag_B[2]; + + Operator warp_mma; + + this->warp_tile_iterator_A_.set_kgroup_index(0); + this->warp_tile_iterator_B_.set_kgroup_index(0); + + this->warp_tile_iterator_A_.load(warp_loaded_frag_A[0]); + this->warp_tile_iterator_B_.load(warp_loaded_frag_B[0]); + + ++this->warp_tile_iterator_A_; + ++this->warp_tile_iterator_B_; + + iterator_A.clear_mask(gemm_k_iterations == 0); + iterator_B.clear_mask(gemm_k_iterations == 0); + + int smem_write_stage_idx = Base::kStages - 1; + int smem_read_stage_idx = 0; + + warp_mma.transform( + warp_transformed_frag_A[0], + warp_transformed_frag_B[0], + warp_loaded_frag_A[0], + warp_loaded_frag_B[0]); + + // tf32x3 kernels use staging accumulation. warp_mma uses a temporary + // accumulator and this temporary accumulator is added to the final + // accumulator once in every mainloop iteration. + plus plus_accum; + + FragmentC tmp_accum; + + if (platform::is_same< + typename Operator::MathOperator, + arch::OpMultiplyAddFastF32>::value || + platform::is_same< + typename Operator::MathOperator, + arch::OpMultiplyAddComplexFastF32>::value) { + tmp_accum.clear(); + } + + // + // Mainloop + // + + CUTLASS_GEMM_LOOP + for (; gemm_k_iterations > (-kNumStagesConcurrentLoad);) { + // + // Loop over GEMM K dimension + // + + // Computes a warp-level GEMM on data held in shared memory + // Each "warp_mma_k" refers to a warp-level matrix multiply-accumulate + CUTLASS_PRAGMA_UNROLL + for (int warp_mma_k = 0; warp_mma_k < Base::kWarpGemmIterations; + ++warp_mma_k) { + // Load warp-level tiles from shared memory, wrapping to k offset if + // this is the last group as the case may be. + + this->warp_tile_iterator_A_.set_kgroup_index( + (warp_mma_k + 1) % Base::kWarpGemmIterations); + this->warp_tile_iterator_B_.set_kgroup_index( + (warp_mma_k + 1) % Base::kWarpGemmIterations); + + // In case of a non-circular buffer ("kSmemContainsEntireMat") + // make sure we don't load out of bounds data. + if (!kSmemContainsEntireMat || + gemm_k_iterations > (-kNumStagesConcurrentLoad) || + warp_mma_k < Base::kWarpGemmIterations - 1) { + this->warp_tile_iterator_A_.load( + warp_loaded_frag_A[(warp_mma_k + 1) % 2]); + this->warp_tile_iterator_B_.load( + warp_loaded_frag_B[(warp_mma_k + 1) % 2]); + } + + ++this->warp_tile_iterator_A_; + ++this->warp_tile_iterator_B_; + + if (warp_mma_k > 0) + warp_mma.transform( + warp_transformed_frag_A[warp_mma_k % 2], + warp_transformed_frag_B[warp_mma_k % 2], + warp_loaded_frag_A[warp_mma_k % 2], + warp_loaded_frag_B[warp_mma_k % 2]); + + if (platform::is_same< + typename Operator::MathOperator, + arch::OpMultiplyAddFastF32>::value || + platform::is_same< + typename Operator::MathOperator, + arch::OpMultiplyAddComplexFastF32>::value) { + warp_mma( + tmp_accum, + warp_transformed_frag_A[warp_mma_k % 2], + warp_transformed_frag_B[warp_mma_k % 2], + tmp_accum); + + if (warp_mma_k == 0) { + accum = plus_accum(accum, tmp_accum); + tmp_accum.clear(); + } + } else { + warp_mma( + accum, + warp_transformed_frag_A[warp_mma_k % 2], + warp_transformed_frag_B[warp_mma_k % 2], + accum); + } + + // Issue global->shared copies for the this stage + if (!kSmemContainsEntireMat && + warp_mma_k < Base::kWarpGemmIterations - 1) { + int group_start_iteration_A, group_start_iteration_B; + + group_start_iteration_A = warp_mma_k * Detail::kAccessesPerGroupA; + group_start_iteration_B = warp_mma_k * Detail::kAccessesPerGroupB; + + copy_tiles_and_advance( + iterator_A, + iterator_B, + group_start_iteration_A, + group_start_iteration_B); + } + + if (warp_mma_k + 2 == Base::kWarpGemmIterations) { + if (!kSmemContainsEntireMat) { + int group_start_iteration_A, group_start_iteration_B; + group_start_iteration_A = + (warp_mma_k + 1) * Detail::kAccessesPerGroupA; + group_start_iteration_B = + (warp_mma_k + 1) * Detail::kAccessesPerGroupB; + + copy_tiles_and_advance( + iterator_A, + iterator_B, + group_start_iteration_A, + group_start_iteration_B); + } + + // Inserts a memory fence between stages of cp.async instructions. + cutlass::arch::cp_async_fence(); + + // Waits until kStages-2 stages have committed. + cutlass::arch::cp_async_wait(); + __syncthreads(); + + // Move to the next stage + iterator_A.add_tile_offset({0, 1}); + iterator_B.add_tile_offset({1, 0}); + + this->smem_iterator_A_.add_tile_offset({0, 1}); + this->smem_iterator_B_.add_tile_offset({1, 0}); + + // Add negative offsets to return iterators to the 'start' of the + // circular buffer in shared memory + if (smem_write_stage_idx == (Base::kStages - 1)) { + this->smem_iterator_A_.add_tile_offset({0, -Base::kStages}); + this->smem_iterator_B_.add_tile_offset({-Base::kStages, 0}); + smem_write_stage_idx = 0; + } else { + ++smem_write_stage_idx; + } + + if (!kSmemContainsEntireMat && + smem_read_stage_idx == (Base::kStages - 1)) { + this->warp_tile_iterator_A_.add_tile_offset( + {0, + -Base::kStages * Policy::kPartitionsK * + Base::kWarpGemmIterations}); + this->warp_tile_iterator_B_.add_tile_offset( + {-Base::kStages * Policy::kPartitionsK * + Base::kWarpGemmIterations, + 0}); + smem_read_stage_idx = 0; + } else { + ++smem_read_stage_idx; + } + + --gemm_k_iterations; + iterator_A.clear_mask(gemm_k_iterations == 0); + iterator_B.clear_mask(gemm_k_iterations == 0); + } + + // Do any conversions feeding the first stage at the end of the loop so + // we can start right away on mma instructions + if (warp_mma_k + 1 == Base::kWarpGemmIterations) + warp_mma.transform( + warp_transformed_frag_A[(warp_mma_k + 1) % 2], + warp_transformed_frag_B[(warp_mma_k + 1) % 2], + warp_loaded_frag_A[(warp_mma_k + 1) % 2], + warp_loaded_frag_B[(warp_mma_k + 1) % 2]); + } + } + + if (platform::is_same< + typename Operator::MathOperator, + arch::OpMultiplyAddFastF32>::value || + platform::is_same< + typename Operator::MathOperator, + arch::OpMultiplyAddComplexFastF32>::value) { + accum = plus_accum(accum, tmp_accum); + } + } +}; + +///////////////////////////////////////////////////////////////////////////////////////////////// + +} // namespace threadblock +} // namespace gemm +} // namespace cutlass + +///////////////////////////////////////////////////////////////////////////////////////////////// diff --git a/csrc/include/natten/cuda/fna/gemm/custom_mma_pipelined.h b/csrc/include/natten/cuda/fna/gemm/custom_mma_pipelined.h new file mode 100644 index 0000000..6c8c399 --- /dev/null +++ b/csrc/include/natten/cuda/fna/gemm/custom_mma_pipelined.h @@ -0,0 +1,408 @@ +/* + * Copied from xFormers (https://github.com/facebookresearch/xformers/) + * + * Copyright (c) Meta Platforms, Inc. and affiliates. + * All rights reserved. + */ +/*************************************************************************************************** + * Copyright (c) 2017 - 2023 NVIDIA CORPORATION & AFFILIATES. All rights + *reserved. SPDX-License-Identifier: BSD-3-Clause + * + * Redistribution and use in source and binary forms, with or without + * modification, are permitted provided that the following conditions are met: + * + * 1. Redistributions of source code must retain the above copyright notice, + *this list of conditions and the following disclaimer. + * + * 2. Redistributions in binary form must reproduce the above copyright notice, + * this list of conditions and the following disclaimer in the documentation + * and/or other materials provided with the distribution. + * + * 3. Neither the name of the copyright holder nor the names of its + * contributors may be used to endorse or promote products derived from + * this software without specific prior written permission. + * + * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" + * AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE + * IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE + *ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE + *LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR + *CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF + *SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS + *INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN + *CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) + *ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE + *POSSIBILITY OF SUCH DAMAGE. + * + **************************************************************************************************/ +/*! \file + \brief Template for a double-buffered threadblock-scoped GEMM kernel. +*/ + +#pragma once + +#include "cutlass/aligned_buffer.h" +#include "cutlass/array.h" +#include "cutlass/cutlass.h" +#include "cutlass/numeric_conversion.h" + +#include "cutlass/matrix_shape.h" +#include "cutlass/numeric_types.h" + +#include "custom_mma_base.h" +#include "cutlass/gemm/gemm.h" + +///////////////////////////////////////////////////////////////////////////////////////////////// + +namespace cutlass { +namespace gemm { +namespace threadblock { + +///////////////////////////////////////////////////////////////////////////////////////////////// + +/// Structure to compute the matrix product targeting CUDA cores and SIMT math +/// instructions. +template < + /// Size of the Gemm problem - concept: gemm::GemmShape<> + typename Shape_, + /// Iterates over tiles of A operand in global memory + // (concept: ReadableTileIterator | ForwardTileIterator | + // MaskedTileIterator) + typename IteratorA_, + /// Iterates over tiles of A operand in shared memory + /// (concept: WriteableTileIterator | RandomAccessTileIterator) + typename SmemIteratorA_, + /// Iterates over tiles of B operand in global memory + // (concept: ReadableTileIterator | ForwardTileIterator | + // MaskedTileIterator) + typename IteratorB_, + /// Iterates over tiles of B operand in shared memory + /// (concept: WriteableTileIterator | RandomAccessTileIterator) + typename SmemIteratorB_, + /// Data type of accumulator matrix + typename ElementC_, + /// Data type of accumulator matrix + typename LayoutC_, + /// Policy describing tuning details (concept: MmaPolicy) + typename Policy_, + /// Transformation applied to A operand + typename TransformA_ = NumericArrayConverter< + typename SmemIteratorA_::Element, + typename IteratorA_::Element, + IteratorA_::Fragment::kElements>, + /// + /// Transformation applied to B operand + typename TransformB_ = NumericArrayConverter< + typename SmemIteratorB_::Element, + typename IteratorB_::Element, + IteratorB_::Fragment::kElements>, + /// Used for partial specialization + typename Enable = bool> +class CustomMmaPipelined : public CustomMmaBase { + public: + ///< Base class + using Base = CustomMmaBase; + + using Shape = + Shape_; ///< Size of the Gemm problem - concept: gemm::GemmShape<> + using IteratorA = + IteratorA_; ///< Iterates over tiles of A operand in global memory + using IteratorB = + IteratorB_; ///< Iterates over tiles of B operand in global memory + using ElementC = ElementC_; ///< Data type of accumulator matrix + using LayoutC = LayoutC_; ///< Layout of accumulator matrix + using Policy = Policy_; ///< Policy describing tuning details + + using SmemIteratorA = SmemIteratorA_; + using SmemIteratorB = SmemIteratorB_; + + using TransformA = TransformA_; + using TransformB = TransformB_; + + // + // Dependent types + // + + /// Fragment of operand A loaded from global memory + using FragmentA = typename IteratorA::Fragment; + + /// Fragment of operand B loaded from global memory + using FragmentB = typename IteratorB::Fragment; + + /// Fragment of accumulator tile + using FragmentC = typename Policy::Operator::FragmentC; + + /// Warp-level Mma + using Operator = typename Policy::Operator; + + /// Obtain the arch tag from the warp-level operator + using ArchTag = typename Policy::Operator::ArchTag; + + /// Complex transform on A operand + static ComplexTransform const kTransformA = Operator::kTransformA; + + /// Complex transform on B operand + static ComplexTransform const kTransformB = Operator::kTransformB; + + // staticaly assert kStages for MmaPipelined is two (Double-buffered pipeline) + static_assert( + (Base::kStages == 2), + "MmaPipelined requires kStages set to value 2"); + + static bool const kSmemContainsEntireMat = false; + + private: + using WarpFragmentA = typename Operator::FragmentA; + using WarpFragmentB = typename Operator::FragmentB; + + protected: + /// Iterator to write threadblock-scoped tile of A operand to shared memory + SmemIteratorA smem_iterator_A_; + + /// Iterator to write threadblock-scoped tile of B operand to shared memory + SmemIteratorB smem_iterator_B_; + + public: + /// Construct from tensor references + CUTLASS_DEVICE + CustomMmaPipelined( + typename Base::SharedStorageA& shared_storageA, + typename Base::SharedStorageB& shared_storageB, + int thread_idx, ///< ID within the threadblock + int warp_idx, ///< ID of warp + int lane_idx ///< ID of each thread within a warp + ) + : Base(shared_storageA, shared_storageB, thread_idx, warp_idx, lane_idx), + smem_iterator_A_(shared_storageA.ref(), thread_idx), + smem_iterator_B_(shared_storageB.ref(), thread_idx) { + // Compute warp location within threadblock tile by mapping the warp_id to + // three coordinates: + // _m: the warp's position within the threadblock along the M dimension + // _n: the warp's position within the threadblock along the N dimension + // _k: the warp's position within the threadblock along the K dimension + + int warp_idx_mn = warp_idx % (Base::WarpCount::kM * Base::WarpCount::kN); + int warp_idx_k = warp_idx / (Base::WarpCount::kM * Base::WarpCount::kN); + + int warp_idx_m = warp_idx_mn % Base::WarpCount::kM; + int warp_idx_n = warp_idx_mn / Base::WarpCount::kM; + + // Add per-warp offsets in units of warp-level tiles + this->warp_tile_iterator_A_.add_tile_offset( + {warp_idx_m, Base::kWarpGemmIterations * warp_idx_k}); + this->warp_tile_iterator_B_.add_tile_offset( + {Base::kWarpGemmIterations * warp_idx_k, warp_idx_n}); + } + CUTLASS_DEVICE + CustomMmaPipelined( + ///< Shared storage needed for internal use by threadblock-scoped GEMM + typename Base::SharedStorage& st, + ///< ID within the threadblock + int thread_idx, + ///< ID of warp + int warp_idx, + ///< ID of each thread within a warp + int lane_idx) + : CustomMmaPipelined( + st.operand_A, + st.operand_B, + thread_idx, + warp_idx, + lane_idx) {} + + CUTLASS_DEVICE + bool set_prologue_done(bool value) { + // NOT IMPLEMENTED FOR PIPELINED + } + + CUTLASS_DEVICE + bool set_zero_outside_bounds(bool value) { + // NOT NEEDED FOR PIPELINED + // shared memory will always be zero-filled + } + + template + CUTLASS_DEVICE static void prologue( + typename Base::SharedStorage& shared_storage, + ///< iterator over A operand in global memory + IteratorA iterator_A, + ///< iterator over B operand in global memory + IteratorB iterator_B, + int thread_idx, + int problem_size_k) { + prologue( + shared_storage.operand_A, + shared_storage.operand_B, + iterator_A, + iterator_B, + thread_idx, + problem_size_k); + } + + template + CUTLASS_DEVICE static void prologue( + typename Base::SharedStorageA& shared_storageA, + typename Base::SharedStorageB& shared_storageB, + ///< iterator over A operand in global memory + IteratorA iterator_A, + ///< iterator over B operand in global memory + IteratorB iterator_B, + int thread_idx, + int problem_size_k) { + // NOT IMPLEMENTED FOR PIPELINED + } + + /// Perform a threadblock-scoped matrix multiply-accumulate + CUTLASS_DEVICE + void operator()( + int gemm_k_iterations, ///< number of iterations of the mainloop + FragmentC& accum, ///< destination accumulator tile + IteratorA iterator_A, ///< iterator over A operand in global memory + IteratorB iterator_B, ///< iterator over B operand in global memory + FragmentC const& src_accum, ///< source accumulator tile + TransformA transform_A = + TransformA(), ///< transformation applied to A fragment + TransformB transform_B = + TransformB()) { ///< transformation applied to B fragment + + // + // Prologue + // + + // Perform accumulation in the 'd' output operand + accum = src_accum; + + FragmentA tb_frag_A; + FragmentB tb_frag_B; + + tb_frag_A.clear(); + tb_frag_B.clear(); + + // The last kblock is loaded in the prolog + iterator_A.load(tb_frag_A); + iterator_B.load(tb_frag_B); + + ++iterator_A; + ++iterator_B; + + this->smem_iterator_A_.store(transform_A(tb_frag_A)); + this->smem_iterator_B_.store(transform_B(tb_frag_B)); + + ++this->smem_iterator_A_; + ++this->smem_iterator_B_; + + __syncthreads(); + + // Pair of fragments used to overlap shared memory loads and math + // instructions + WarpFragmentA warp_frag_A[2]; + WarpFragmentB warp_frag_B[2]; + + this->warp_tile_iterator_A_.set_kgroup_index(0); + this->warp_tile_iterator_B_.set_kgroup_index(0); + + this->warp_tile_iterator_A_.load(warp_frag_A[0]); + this->warp_tile_iterator_B_.load(warp_frag_B[0]); + + ++this->warp_tile_iterator_A_; + ++this->warp_tile_iterator_B_; + + Operator warp_mma; + + int smem_write_stage_idx = 1; + + // Avoid reading out of bounds + iterator_A.clear_mask(gemm_k_iterations <= 1); + iterator_B.clear_mask(gemm_k_iterations <= 1); + + // Issue loads during the first warp-level matrix multiply-add *AFTER* + // issuing shared memory loads (which have the tightest latency + // requirement). + + // + // Mainloop + // + + // Note: The main loop does not support Base::kWarpGemmIterations == 2. + CUTLASS_GEMM_LOOP + for (; gemm_k_iterations > 0; --gemm_k_iterations) { + // + // Loop over GEMM K dimension + // + + CUTLASS_PRAGMA_UNROLL + for (int warp_mma_k = 0; warp_mma_k < Base::kWarpGemmIterations; + ++warp_mma_k) { + // Load warp-level tiles from shared memory, wrapping to k offset if + // this is the last group as the case may be. + + if (warp_mma_k == Base::kWarpGemmIterations - 1) { + // Write fragments to shared memory + this->smem_iterator_A_.store(transform_A(tb_frag_A)); + + this->smem_iterator_B_.store(transform_B(tb_frag_B)); + + __syncthreads(); + + ++this->smem_iterator_A_; + ++this->smem_iterator_B_; + + // Add negative offsets to return iterators to the 'start' of the + // circular buffer in shared memory + if (smem_write_stage_idx == 1) { + this->smem_iterator_A_.add_tile_offset({0, -Base::kStages}); + this->smem_iterator_B_.add_tile_offset({-Base::kStages, 0}); + } else { + this->warp_tile_iterator_A_.add_tile_offset( + {0, + -Base::kStages * Policy::kPartitionsK * + Base::kWarpGemmIterations}); + this->warp_tile_iterator_B_.add_tile_offset( + {-Base::kStages * Policy::kPartitionsK * + Base::kWarpGemmIterations, + 0}); + } + + smem_write_stage_idx ^= 1; + } + + this->warp_tile_iterator_A_.set_kgroup_index( + (warp_mma_k + 1) % Base::kWarpGemmIterations); + this->warp_tile_iterator_B_.set_kgroup_index( + (warp_mma_k + 1) % Base::kWarpGemmIterations); + + this->warp_tile_iterator_A_.load(warp_frag_A[(warp_mma_k + 1) % 2]); + this->warp_tile_iterator_B_.load(warp_frag_B[(warp_mma_k + 1) % 2]); + + ++this->warp_tile_iterator_A_; + ++this->warp_tile_iterator_B_; + + if (warp_mma_k == 0) { + iterator_A.load(tb_frag_A); + iterator_B.load(tb_frag_B); + + ++iterator_A; + ++iterator_B; + + // Avoid reading out of bounds if this was the last loop iteration + iterator_A.clear_mask(gemm_k_iterations <= 2); + iterator_B.clear_mask(gemm_k_iterations <= 2); + } + + warp_mma( + accum, + warp_frag_A[warp_mma_k % 2], + warp_frag_B[warp_mma_k % 2], + accum); + } + } + } +}; + +///////////////////////////////////////////////////////////////////////////////////////////////// + +} // namespace threadblock +} // namespace gemm +} // namespace cutlass + +///////////////////////////////////////////////////////////////////////////////////////////////// diff --git a/csrc/include/natten/cuda/fna/gemm/find_default_mma.h b/csrc/include/natten/cuda/fna/gemm/find_default_mma.h new file mode 100644 index 0000000..ac57788 --- /dev/null +++ b/csrc/include/natten/cuda/fna/gemm/find_default_mma.h @@ -0,0 +1,272 @@ +/*************************************************************************************************** + * Copyright (c) 2022-2024 Ali Hassani. + * + * Fused Neighborhood Attention kernels are heavily based on the + * memory-efficient attention kernels from the xFormers project by Meta + * Platforms, Inc. + * + * Copyright (c) Facebook, Inc. and its affiliates + * + * BSD 3-Clause License + * + * Redistribution and use in source and binary forms, with or without + * modification, are permitted provided that the following conditions are met: + * + * 1. Redistributions of source code must retain the above copyright + * notice, this list of conditions and the following disclaimer. + * + * 2. Redistributions in binary form must reproduce the above copyright + * notice, this list of conditions and the following disclaimer in the + * documentation and/or other materials provided with the distribution. + * + * 3. Neither the names of Facebook, Deepmind Technologies, NYU, NEC + * Laboratories America and IDIAP Research Institute nor the names of its + * contributors may be used to endorse or promote products derived from this + * software without specific prior written permission. + * + * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" + * AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE + * IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE + * ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE + * LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR + * CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF + * SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS + * INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN + * CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) + * ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE + * POSSIBILITY OF SUCH DAMAGE. + */ +/*! \file + \brief Cutlass provides helper template functions to figure out the right + datastructures to instanciate to run a GEMM with various parameters (see + `cutlass/gemm/threadblock/default_mma.h`). However, due to template + instantiation priority rules, it will only create an MmaMultiStage with + kStages=3 (otherwise creates an MmePipelined - which is not compatible with + FastF32). kStages=3 uses too much shared memory and we want to use kStages=2, + so we just copy-pasted some code from `default_mma.h` and + `default_mma_core.h` files and wrapped this template to allow our usecase. + + This is really only for the FastF32 case - aka using TensorCores with fp32. +*/ + +#pragma once + +#include +#include +#include +#include +#include + +#include +#include + +#include + +namespace cutlass { +namespace gemm { +namespace threadblock { + +template < + /// Neighborhood attention spatial rank; must be 0 (self attention), 1, 2, + /// or 3 + int NADim, + /// Element type for A matrix operand + typename ElementA, + /// Layout type for A matrix operand + typename LayoutA, + /// Access granularity of A matrix in units of elements + int kAlignmentA, + /// Element type for B matrix operand + typename ElementB, + /// Layout type for B matrix operand + typename LayoutB, + /// Access granularity of B matrix in units of elements + int kAlignmentB, + /// Element type for internal accumulation + typename ElementAccumulator, + /// Layout type for C and D matrix operand + typename LayoutC, + /// Operator class tag + typename OperatorClass, + /// Tag indicating architecture to tune for + typename ArchTag, + /// Threadblock-level tile size (concept: GemmShape) + typename ThreadblockShape, + /// Warp-level tile size (concept: GemmShape) + typename WarpShape, + /// Instruction-level tile size (concept: GemmShape) + typename InstructionShape, + /// Number of stages used in the pipelined mainloop + int Stages, + /// Operation perfomed by GEMM + typename Operator, + typename Enable_ = void> +struct FindDefaultMma { + static_assert(NADim >= 1 && NADim < 4); + static constexpr bool AccumulatorsInRowMajor = false; + static constexpr SharedMemoryClearOption SharedMemoryClear = + SharedMemoryClearOption::kNone; + using DefaultMma_ = cutlass::gemm::threadblock::DefaultMma< + ElementA, + LayoutA, + kAlignmentA, + ElementB, + LayoutB, + kAlignmentB, + ElementAccumulator, + LayoutC, + OperatorClass, + ArchTag, + ThreadblockShape, + WarpShape, + InstructionShape, + Stages, + Operator, + AccumulatorsInRowMajor, + SharedMemoryClear>; + struct DefaultMma : DefaultMma_ { + using MmaCore_ = typename DefaultMma_::MmaCore; + using IteratorA_ = typename DefaultMma_::IteratorA; + using IteratorB_ = typename DefaultMma_::IteratorB; + using ThreadblockMma_ = typename DefaultMma_::ThreadblockMma; + + // struct ThreadblockMma : ThreadblockMma_ { + using IteratorA = + cutlass::transform::threadblock::CustomPredicatedTileIterator< + NADim, + typename IteratorA_::Shape, + typename IteratorA_::Element, + typename IteratorA_::Layout, + IteratorA_::kAdvanceRank, + typename IteratorA_::ThreadMap, + kAlignmentA>; + + using IteratorB = + cutlass::transform::threadblock::CustomPredicatedTileIterator< + NADim, + typename IteratorB_::Shape, + typename IteratorB_::Element, + typename IteratorB_::Layout, + IteratorB_::kAdvanceRank, + typename IteratorB_::ThreadMap, + kAlignmentB>; + //}; + + // using IteratorA = typename ThreadblockMma::IteratorA; + // using IteratorB = typename ThreadblockMma::IteratorA; + + using ThreadblockMma = typename natten::cuda::fna:: + ReplaceMmaIterators::Mma; + }; +}; + +/// Specialization for sm80 / FastF32 / multistage with kStages=2 +template < + /// Neighborhood attention spatial rank; must be 0 (self attention), 1, 2, + /// or 3 + int NADim, + /// Element type for A matrix operand + typename ElementA_, + /// Layout type for A matrix operand + typename LayoutA_, + /// Access granularity of A matrix in units of elements + int kAlignmentA, + /// Element type for B matrix operand + typename ElementB_, + /// Layout type for B matrix operand + typename LayoutB_, + /// Access granularity of B matrix in units of elements + int kAlignmentB, + /// Element type for internal accumulation + typename ElementAccumulator, + /// Threadblock-level tile size (concept: GemmShape) + typename ThreadblockShape, + /// Warp-level tile size (concept: GemmShape) + typename WarpShape, + /// Instruction-level tile size (concept: GemmShape) + typename InstructionShape, + int kStages, + typename Operator> +struct FindDefaultMma< + NADim, + ElementA_, + LayoutA_, + kAlignmentA, + ElementB_, + LayoutB_, + kAlignmentB, + ElementAccumulator, + layout::RowMajor, + arch::OpClassTensorOp, + arch::Sm80, + ThreadblockShape, + WarpShape, + InstructionShape, + kStages, + Operator, + typename cutlass::platform::enable_if<(kAlignmentA > 1)>::type> { + static_assert(NADim >= 1 && NADim < 4); + using LayoutC = layout::RowMajor; + using OperatorClass = arch::OpClassTensorOp; + using ArchTag = arch::Sm80; + + using DefaultMma_ = cutlass::gemm::threadblock::DefaultMma< + ElementA_, + LayoutA_, + kAlignmentA, + ElementB_, + LayoutB_, + kAlignmentB, + ElementAccumulator, + LayoutC, + OperatorClass, + ArchTag, + ThreadblockShape, + WarpShape, + InstructionShape, + 3, + Operator>; + struct DefaultMma : DefaultMma_ { + using MmaCore_ = typename DefaultMma_::MmaCore; + using IteratorA_ = typename DefaultMma_::IteratorA; + using IteratorB_ = typename DefaultMma_::IteratorB; + + using IteratorA = + cutlass::transform::threadblock::CustomPredicatedTileAccessIterator< + NADim, + typename IteratorA_::Shape, + typename IteratorA_::Element, + typename IteratorA_::Layout, + IteratorA_::kAdvanceRank, + typename IteratorA_::ThreadMap, + typename DefaultMma_::AccessTypeA>; + + using IteratorB = + cutlass::transform::threadblock::CustomPredicatedTileAccessIterator< + NADim, + typename IteratorB_::Shape, + typename IteratorB_::Element, + typename IteratorB_::Layout, + IteratorB_::kAdvanceRank, + typename IteratorB_::ThreadMap, + typename DefaultMma_::AccessTypeB>; + + // Define the threadblock-scoped multistage matrix multiply + using ThreadblockMma = cutlass::gemm::threadblock::MmaMultistage< + typename MmaCore_::Shape, + IteratorA, + typename MmaCore_::SmemIteratorA, + MmaCore_::kCacheOpA, + IteratorB, + typename MmaCore_::SmemIteratorB, + MmaCore_::kCacheOpB, + ElementAccumulator, + LayoutC, + typename MmaCore_::MmaPolicy, + kStages>; + }; +}; + +} // namespace threadblock +} // namespace gemm +} // namespace cutlass diff --git a/csrc/include/natten/cuda/fna/gemm/mma_accum_lambda_iterator.h b/csrc/include/natten/cuda/fna/gemm/mma_accum_lambda_iterator.h new file mode 100644 index 0000000..7deb5d4 --- /dev/null +++ b/csrc/include/natten/cuda/fna/gemm/mma_accum_lambda_iterator.h @@ -0,0 +1,393 @@ +/*************************************************************************************************** + * Copyright (c) 2022-2024 Ali Hassani. + * + * Fused Neighborhood Attention kernels are heavily based on the + * memory-efficient attention kernels from the xFormers project by Meta + * Platforms, Inc. + * + * Copyright (c) Facebook, Inc. and its affiliates + * + * BSD 3-Clause License + * + * Redistribution and use in source and binary forms, with or without + * modification, are permitted provided that the following conditions are met: + * + * 1. Redistributions of source code must retain the above copyright + * notice, this list of conditions and the following disclaimer. + * + * 2. Redistributions in binary form must reproduce the above copyright + * notice, this list of conditions and the following disclaimer in the + * documentation and/or other materials provided with the distribution. + * + * 3. Neither the names of Facebook, Deepmind Technologies, NYU, NEC + * Laboratories America and IDIAP Research Institute nor the names of its + * contributors may be used to endorse or promote products derived from this + * software without specific prior written permission. + * + * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" + * AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE + * IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE + * ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE + * LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR + * CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF + * SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS + * INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN + * CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) + * ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE + * POSSIBILITY OF SUCH DAMAGE. + */ + +#pragma once + +#include +#include +#include +#include +#include + +/* +TensorCores have different accumulator layouts. +This file provides a class to easily map the accumulator +i-th element with the corresponding matrix row/col. +*/ + +template +struct AccumLambdaIteratorSm80 { + static_assert( + cutlass::platform:: + is_same::value, + "only RowMajor is supported"); + + using Policy = typename T::Policy; + using InstructionShape = typename T::InstructionShape; + using OpDelta = typename T::OpDelta; + using Shape = typename T::Shape; + static int const kElementsPerAccess = InstructionShape::kN / 4; + static int const kRowsPerTile = 8; + static int const kAccumulatorRows = InstructionShape::kM / kRowsPerTile; + + static int const kRowIters = Policy::MmaIterations::kRow * kAccumulatorRows; + + static cutlass::MatrixCoord CUTLASS_DEVICE get_lane_offset( + int8_t lane_id, + int8_t warp_id, + typename T::TensorCoord const& tile_offset) { + int quad = (lane_id >> 2); + int lane_in_quad = (lane_id & 3); + return cutlass::MatrixCoord( + quad + tile_offset.row() * Shape::kRow, + lane_in_quad * kElementsPerAccess + + tile_offset.column() * Shape::kColumn); + } + + template + CUTLASS_DEVICE static void iterateRows( + cutlass::MatrixCoord& lane_offset, + FA beginRow, + FB op, + FC endRow) { + // See cutlass/gemm/warp/mma_tensor_op_tile_iterator.h + CUTLASS_PRAGMA_UNROLL + for (int mma_m = 0; mma_m < Policy::MmaIterations::kRow; ++mma_m) { + CUTLASS_PRAGMA_UNROLL + for (int row = 0; row < kAccumulatorRows; ++row) { + int accum_m = mma_m * InstructionShape::kM * OpDelta::kRow + + row * kRowsPerTile + lane_offset.row(); + beginRow(accum_m); + + CUTLASS_PRAGMA_UNROLL + for (int mma_n = 0; mma_n < Policy::MmaIterations::kColumn; ++mma_n) { + int mma_accum_start = kAccumulatorRows * kElementsPerAccess * + (mma_n * Policy::MmaIterations::kRow + mma_m); + CUTLASS_PRAGMA_UNROLL + for (int col = 0; col < kElementsPerAccess; ++col) { + int accum_n = mma_n * InstructionShape::kN * OpDelta::kColumn + + col + lane_offset.column(); + int idx = mma_accum_start + row * kElementsPerAccess + col; + op(accum_m, accum_n, idx); + } + } + + endRow(accum_m); + } + } + } + + template + CUTLASS_DEVICE static bool reduceSameRow(int lane_id, DT& myValue, F fn) { + // In each warp, 4 threads will work on the same row + // - the ones with the same `quad` + auto otherV = __shfl_xor_sync(0xffffffff, myValue, 1); + myValue = fn(myValue, otherV); + otherV = __shfl_xor_sync(0xffffffff, myValue, 2); + myValue = fn(myValue, otherV); + int lane_in_quad = (lane_id & 3); + return lane_in_quad == 0; + } +}; + +template +struct AccumLambdaIteratorSm70 { + static_assert( + cutlass::platform:: + is_same::value, + "only RowMajor is supported"); + + using Policy = typename T::Policy; + using InstructionShape = typename T::InstructionShape; + using OpDelta = typename T::OpDelta; + using Shape = typename T::Shape; + using Element = accum_t; + + static int const kElementsPerPartial = 4; + using EleShapePerPatial = typename cutlass::platform::conditional< + cutlass::platform::is_same::value, + cutlass::MatrixShape<2, 2>, + cutlass::MatrixShape<1, 4>>::type; + static int const kElementsPerMma = 8; + static int const kAccumulatorPatials = 2; + using QuadShapePerPatialMma = cutlass::MatrixShape<4, 4>; + + static int const kRowIters = Policy::TileIterations::kRow * + Policy::MmaIterations::kRow * EleShapePerPatial::kRow; + + static cutlass::MatrixCoord CUTLASS_DEVICE get_lane_offset( + int8_t lane_id, + int8_t warp_id, + typename T::TensorCoord const& tile_offset) { + int quad = (lane_id >> 2); + int lane_in_quad = (lane_id & 3); + int accum_m, accum_n; + + if (cutlass::platform::is_same::value) { + // (quad[2],quad[0])+lane_in_quad[0] + accum_m = (((quad & 0x4) >> 1) + (quad & 0x1)) * 8 + (lane_in_quad & 1); + // (quad[1])+lane_in_quad[1] + accum_n = + ((quad >> 1) & 0x1) * kElementsPerPartial * kAccumulatorPatials + + (lane_in_quad & 2); + } else { + accum_m = (((quad & 0x4) >> 1) + (quad & 0x1)) * 8 + + lane_in_quad; // (quad[2],quad[0]) + accum_n = ((quad >> 1) & 0x1) * kElementsPerPartial * kAccumulatorPatials; + } + return cutlass::MatrixCoord( + accum_m + tile_offset.row() * Shape::kRow, + accum_n + tile_offset.column() * Shape::kColumn); + } + + template + CUTLASS_DEVICE static bool reduceSameRow(int lane_id, DT& myValue, F fn) { + static_assert( + cutlass::platform::is_same::value, + "update to support non-float accum"); + // https://docs.nvidia.com/cuda/parallel-thread-execution/index.html#warp-level-matrix-fragment-mma-884-f16 + // T0 & T2 share same line within a quad + auto otherV = __shfl_xor_sync(0xffffffff, myValue, 1 << 1); + myValue = fn(myValue, otherV); + // quad 0 and quad 2 are on the same lines + otherV = __shfl_xor_sync(0xffffffff, myValue, 1 << 3); + myValue = fn(myValue, otherV); + return (lane_id & ((1 << 1) | (1 << 3))) == 0; + } + + template + CUTLASS_DEVICE static void iterateRows( + cutlass::MatrixCoord& lane_offset, + FA beginRow, + FB op, + FC endRow) { + CUTLASS_PRAGMA_UNROLL + for (int tile_m = 0; tile_m < Policy::TileIterations::kRow; ++tile_m) { + CUTLASS_PRAGMA_UNROLL + for (int mma_m = 0; mma_m < Policy::MmaIterations::kRow; ++mma_m) { + CUTLASS_PRAGMA_UNROLL + for (int m = 0; m < EleShapePerPatial::kRow; ++m) { + int accum_m = tile_m * Policy::InterleavedTile::kRow + + mma_m * QuadShapePerPatialMma::kRow + m * 2 + lane_offset.row(); + beginRow(accum_m); + + CUTLASS_PRAGMA_UNROLL + for (int tile_n = 0; tile_n < Policy::TileIterations::kColumn; + ++tile_n) { + CUTLASS_PRAGMA_UNROLL + for (int mma_n = 0; mma_n < Policy::MmaIterations::kColumn; + ++mma_n) { + CUTLASS_PRAGMA_UNROLL + for (int p = 0; p < kAccumulatorPatials; ++p) { + CUTLASS_PRAGMA_UNROLL + for (int n = 0; n < EleShapePerPatial::kColumn; ++n) { + int mma_accum_start = + (((tile_n * Policy::TileIterations::kRow + tile_m) * + Policy::MmaIterations::kColumn + + mma_n) * + Policy::MmaIterations::kRow + + mma_m) * + kElementsPerMma; + int accum_n = tile_n * Policy::InterleavedTile::kColumn + + mma_n * QuadShapePerPatialMma::kColumn + + p * Policy::InterleavedTile::kColumn / 2 + n + + lane_offset.column(); + int idx = mma_accum_start + p * kElementsPerPartial + + m * EleShapePerPatial::kColumn + n; + op(accum_m, accum_n, idx); + } + } + } + } + endRow(accum_m); + } + } + } + } +}; + +template +struct AccumLambdaIteratorSimt { + using Policy = typename T::Policy; + using Iterations = typename T::Iterations; + using Element = typename T::Element; + using Delta = typename T::Delta; + using Shape = typename T::Shape; + static_assert( + cutlass::platform:: + is_same::value, + "only RowMajor is supported"); + + static int const kRowIters = Iterations::kRow * Policy::LaneMmaShape::kM; + + template + CUTLASS_DEVICE static bool reduceSameRow(int lane_id, DT& myValue, F fn) { + CUTLASS_PRAGMA_UNROLL + for (int bit = 1; bit < Policy::WarpShape::kColumn; bit *= 2) { + auto otherV = __shfl_xor_sync(0xffffffff, myValue, bit); + myValue = fn(myValue, otherV); + } + return (lane_id & (Policy::WarpShape::kColumn - 1)) == 0; + } + + template + CUTLASS_DEVICE static void iterateRows( + cutlass::MatrixCoord& lane_offset, + FA beginRow, + FB op, + FC endRow) { + CUTLASS_PRAGMA_UNROLL + for (int mma_m = 0; mma_m < Iterations::kRow; ++mma_m) { + CUTLASS_PRAGMA_UNROLL + for (int m = 0; m < Policy::LaneMmaShape::kM; ++m) { + int accum_m = mma_m * Delta::kRow + m + lane_offset.row(); + beginRow(accum_m); + + CUTLASS_PRAGMA_UNROLL + for (int mma_n = 0; mma_n < Iterations::kColumn; ++mma_n) { + int accum_n = + mma_n * Policy::WarpShape::kColumn * Policy::LaneMmaShape::kN + + lane_offset.column(); + CUTLASS_PRAGMA_UNROLL + for (int n = 0; n < Policy::LaneMmaShape::kN; ++n) { + int idx = n + + Policy::LaneMmaShape::kN * + (mma_n + + Iterations::kColumn * + (m + mma_m * Policy::LaneMmaShape::kM)); + op(accum_m, accum_n + n, idx); + } + } + endRow(accum_m); + } + } + } + + static cutlass::MatrixCoord CUTLASS_DEVICE get_lane_offset( + int8_t lane_id, + int8_t warp_id, + typename T::TensorCoord const& tile_offset) { + static_assert( + cutlass::platform::is_same< + typename Policy::LaneLayout, + cutlass::layout::RowMajorInterleaved<1>>::value, + ""); + typename Policy::LaneLayout lane_layout = Policy::get_lane_layout(); + + cutlass::MatrixCoord lane_offset = lane_layout.inverse(lane_id) * + cutlass::MatrixCoord(Policy::LaneMmaShape::kM, + Policy::LaneMmaShape::kN); + return lane_offset + + tile_offset * cutlass::MatrixCoord(Shape::kRow, Shape::kColumn); + } +}; + +template +struct DefaultMmaAccumLambdaIterator; + +// Simt +template +struct DefaultMmaAccumLambdaIterator< + cutlass::gemm::warp::MmaSimtTileIterator< + S, + cutlass::gemm::Operand::kC, + accum_t, + cutlass::layout::RowMajor, + P, + 1, + 1>, + accum_t, + kWarpSize> { + using WarpIterator = typename cutlass::gemm::warp::MmaSimtTileIterator< + S, + cutlass::gemm::Operand::kC, + accum_t, + cutlass::layout::RowMajor, + P, + 1, + 1>; + using Iterator = AccumLambdaIteratorSimt; +}; + +// TensorOp - Volta +template +struct DefaultMmaAccumLambdaIterator< + cutlass::gemm::warp::MmaVoltaTensorOpAccumulatorTileIterator< + S1, + accum_t, + cutlass::layout::RowMajor, + S2, + cutlass::MatrixShape<1, 1>>, + accum_t, + kWarpSize> { + using WarpIterator = + typename cutlass::gemm::warp::MmaVoltaTensorOpAccumulatorTileIterator< + S1, + accum_t, + cutlass::layout::RowMajor, + S2, + cutlass::MatrixShape<1, 1>>; + using Iterator = AccumLambdaIteratorSm70; +}; + +// TensorOp - Sm75+ +template < + typename S1, + typename S2, + typename S3, + typename accum_t, + int kWarpSize> +struct DefaultMmaAccumLambdaIterator< + cutlass::gemm::warp::MmaTensorOpAccumulatorTileIterator< + S1, + accum_t, + cutlass::layout::RowMajor, + S2, + S3>, + accum_t, + kWarpSize> { + using WarpIterator = + typename cutlass::gemm::warp::MmaTensorOpAccumulatorTileIterator< + S1, + accum_t, + cutlass::layout::RowMajor, + S2, + S3>; + using Iterator = AccumLambdaIteratorSm80; +}; diff --git a/csrc/include/natten/cuda/fna/gemm/mma_from_smem.h b/csrc/include/natten/cuda/fna/gemm/mma_from_smem.h new file mode 100644 index 0000000..8502497 --- /dev/null +++ b/csrc/include/natten/cuda/fna/gemm/mma_from_smem.h @@ -0,0 +1,1966 @@ +/* + * Copied from xFormers (https://github.com/facebookresearch/xformers/) and + * edited. + * + * Copyright (c) Meta Platforms, Inc. and affiliates. + * All rights reserved. + */ +/*************************************************************************************************** + * Copyright (c) 2017 - 2023 NVIDIA CORPORATION & AFFILIATES. All rights + *reserved. SPDX-License-Identifier: BSD-3-Clause + * + * Redistribution and use in source and binary forms, with or without + * modification, are permitted provided that the following conditions are met: + * + * 1. Redistributions of source code must retain the above copyright notice, + *this list of conditions and the following disclaimer. + * + * 2. Redistributions in binary form must reproduce the above copyright notice, + * this list of conditions and the following disclaimer in the documentation + * and/or other materials provided with the distribution. + * + * 3. Neither the name of the copyright holder nor the names of its + * contributors may be used to endorse or promote products derived from + * this software without specific prior written permission. + * + * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" + * AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE + * IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE + *ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE + *LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR + *CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF + *SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS + *INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN + *CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) + *ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE + *POSSIBILITY OF SUCH DAMAGE. + * + **************************************************************************************************/ +/*! \file + \brief Tools and utils to store a GEMM output in shmem, and to use that + output as operandA for another GEMM back-to-back +*/ + +#pragma once + +#include "cutlass/aligned_buffer.h" +#include "cutlass/arch/memory.h" +#include "cutlass/array.h" +#include "cutlass/cutlass.h" +#include "cutlass/epilogue/thread/linear_combination.h" +#include "cutlass/epilogue/threadblock/default_epilogue_simt.h" +#include "cutlass/epilogue/threadblock/default_epilogue_tensor_op.h" +#include "cutlass/epilogue/threadblock/default_epilogue_volta_tensor_op.h" +#include "cutlass/functional.h" +#include "cutlass/gemm/gemm.h" +#include "cutlass/gemm/warp/mma_tensor_op_fragment_iterator.h" +#include "cutlass/matrix_shape.h" +#include "cutlass/numeric_conversion.h" +#include "cutlass/numeric_types.h" +#include "cutlass/platform/platform.h" +#include "cutlass/transform/threadblock/vector_iterator.h" + +#include "../epilogue/epilogue_thread_apply_logsumexp.h" +#include "../gemm/mma_accum_lambda_iterator.h" +#include "../gemm_kernel_utils.h" +#include "../iterators/default_warp_iterator_from_smem.h" +#include "../iterators/make_residual_last.h" +#include "../iterators/transpose_warp_iterator.h" +#include "../iterators/warp_iterator_from_smem.h" +#include "cutlass/epilogue/threadblock/epilogue_smem_accumulator.h" +#include "cutlass/gemm/threadblock/mma_base.h" +#include "cutlass/gemm/threadblock/mma_multistage.h" +#include "cutlass/gemm/threadblock/mma_pipelined.h" +#include "cutlass/gemm/warp/mma_tensor_op_tile_access_iterator.h" + +namespace cutlass { +namespace gemm { +namespace threadblock { + +/// Shared storage object needed by accumulator +/// From 13_two_tensor_op_fusion/threadblock/b2b_mma_base_smem_accumulator.h +template < + typename Shape_, + typename Element_, + typename Layout_, + typename Padding_> +class AccumulatorSharedStorage { + public: + // + // Type definitions + // + using Shape = Shape_; + using Element = Element_; + using Layout = Layout_; + using Padding = Padding_; + + /// Tensor reference to the accumulator + using TensorRefAccum = cutlass::TensorRef; + + /// Shape of the accumulator matrix in shared memory + using ShapeAccum = cutlass:: + MatrixShape; + + public: + // + // Data members + // + + /// Buffer for accumulator + cutlass::AlignedBuffer accum; + + public: + // + // Methods + // + + /// Returns a layout object for the Accum matrix + CUTLASS_DEVICE + static Layout LayoutAccum() { + return Layout::packed({ShapeAccum::kRow, ShapeAccum::kColumn}); + } + + /// Returns a TensorRef to the Accumulator + CUTLASS_HOST_DEVICE + TensorRefAccum accum_ref() { + return TensorRefAccum{accum.data(), LayoutAccum()}; + } +}; + +//////////////////////////////////////////////////////////////////////////////// +// Taken from +// https://github.com/NVIDIA/cutlass/blob/master/examples/13_two_tensor_op_fusion/threadblock/b2b_mma_base_smem_accumulator.h +//////////////////////////////////////////////////////////////////////////////// + +/// Structure to compute the matrix product targeting CUDA cores and SIMT math +/// instructions. +template < + /// Size of the Gemm problem - concept: gemm::GemmShape<> + typename Shape_, + // Maximum K dimension - also the dimension of the shared-memory + // holding `OperandA` + int kMaxK_, + /// Policy describing tuning details (concept: MmaPolicy) + typename Policy_, + /// Number of stages, + int Stages, + /// Layout in shared-memory of operand A + typename SmemLayoutA, + /// Used for partial specialization + typename Enable = bool> +class MmaBaseFromSharedMemory { + public: + ///< Size of the Gemm problem - concept: gemm::GemmShape<> + using Shape = Shape_; + static constexpr int kMaxK = kMaxK_; + + ///< Policy describing tuning details + using Policy = Policy_; + + // + // Dependent types + // + + /// Warp-level Mma + using Operator = typename Policy::Operator; + + /// Shape describing the overall GEMM computed from shared memory + /// by each warp. + using WarpGemm = typename Policy::Operator::Shape; + + /// Shape describing the number of warps filling the CTA + using WarpCount = GemmShape< + Shape::kM / WarpGemm::kM, + Shape::kN / WarpGemm::kN, + Shape::kK / WarpGemm::kK>; + using WarpCount1 = WarpCount; + + /// Number of warp-level GEMM oeprations + static int const kWarpGemmIterations = + (WarpGemm::kK / Operator::Policy::MmaShape::kK); + static int const kWarpGemmIterations1 = kWarpGemmIterations; + + /// Number of stages + static int const kStages = Stages; + + /// If this is true, we fill the entire shmem buffer at start + /// and don't need to iterate through it in a circular fashion + static bool const kSmemContainsEntireB = kMaxK <= Shape::kK * kStages; + + /// Tensor reference to the A operand + using TensorRefA = TensorRef; + + /// Tensor reference to the B operand + using TensorRefB = + TensorRef; + + // + // Nested structs + // + + /// Shared storage object needed by threadblock-scoped GEMM + class SharedStorage { + public: + // + // Type definitions + // + + /// Shape of the B matrix operand in shared memory + using ShapeB = MatrixShape< + Shape::kK * kStages + Policy::SmemPaddingB::kRow, + Shape::kN + Policy::SmemPaddingB::kColumn>; + + public: + // + // Data members + // + + /// Buffer for B operand + AlignedBuffer operand_B; + + public: + // + // Methods + // + + /// Returns a layout object for the B matrix + CUTLASS_HOST_DEVICE + static typename Operator::LayoutB LayoutB() { + return Operator::LayoutB::packed({ShapeB::kRow, ShapeB::kColumn}); + } + + /// Returns a TensorRef to the B operand + CUTLASS_HOST_DEVICE + TensorRefB operand_B_ref() { + return TensorRefB{operand_B.data(), LayoutB()}; + } + }; + + protected: + // + // Data members + // + + // /// Iterator to load a warp-scoped tile of A operand from shared memory + // typename Operator::IteratorA warp_tile_iterator_A_; + + /// Iterator to load a warp-scoped tile of B operand from shared memory + typename Operator::IteratorB warp_tile_iterator_B_; + + public: + /// Construct from tensor references + CUTLASS_DEVICE + MmaBaseFromSharedMemory( + ///< Shared storage needed for internal use by threadblock-scoped GEMM + TensorRefB& b_tile, + ///< ID within the threadblock + int thread_idx, + ///< ID of warp + int warp_idx, + ///< ID of each thread within a warp + int lane_idx) + : warp_tile_iterator_B_(b_tile, lane_idx) {} +}; + +namespace { + +// has necessary trait compliance with WarpIteratorFromSmem but doesn't do +// anything, can be default initialized, and uses fragment that takes up +// (almost) no space. this warp iterator is selected at compile time when +// elementwise on-the-fly scaling for operand A is disabled, in which case +// operations related to loading scale factors for operand A get wiped out by +// the compiler. +template +class NoOpWarpIteratorScale { + public: + // in pipelined+multistage MMA implementations we keep an array of fragments. + // if we aren't using scaling we don't want to waste registers on fragments + // of scale elements, so ideally this would be sized 0. + // Since arrays of zero-sized objects are not allowed, using size as 1. + // The compiler will most likely wipe it out anyways. + using Fragment = cutlass::Array; + + CUTLASS_HOST_DEVICE + NoOpWarpIteratorScale() {} + + CUTLASS_HOST_DEVICE + NoOpWarpIteratorScale(TensorRef const&, int) {} + + CUTLASS_HOST_DEVICE + NoOpWarpIteratorScale& add_tile_offset( + typename TensorRef::TensorCoord const&) { + return *this; + } + + CUTLASS_HOST_DEVICE + NoOpWarpIteratorScale& operator++() { + return *this; + } + + CUTLASS_DEVICE + void load(Fragment&) const {} +}; + +// if scaling is enabled, performs fragment elementwise multiplication between +// fragment and its scaling factor. +template +class FragmentElementwiseScaler; + +// specialization for scaling being enabled. +template +class FragmentElementwiseScaler { + public: + // cast scale_frag to correct type then apply elementwise to fragment + CUTLASS_DEVICE + static Fragment apply(Fragment frag, FragmentScale const& scale_frag) { + Fragment converted_scale_frag = cutlass::NumericArrayConverter< + typename Fragment::Element, + typename FragmentScale::Element, + FragmentScale::kElements>()(scale_frag); + return cutlass::multiplies()(frag, converted_scale_frag); + } +}; + +// specialization for scaling being disabled. doesn't do anything and should +// just get wiped out by the compiler. +template +class FragmentElementwiseScaler { + public: + CUTLASS_DEVICE + static Fragment apply(Fragment frag, FragmentScale const&) { + return frag; + } +}; +} // namespace + +//////////////////////////////////////////////////////////////////////////////// +// Taken from +// https://github.com/NVIDIA/cutlass/blob/master/examples/13_two_tensor_op_fusion/threadblock/b2b_mma_pipelined_smem_accumulator.h +//////////////////////////////////////////////////////////////////////////////// + +/// Structure to compute the matrix product targeting CUDA cores and SIMT math +/// instructions. +template < + /// Size of the Gemm problem - concept: gemm::GemmShape<> + typename Shape_, + // BEGIN smem + /// Iterates over the intermediate accumulator tile in shared memory + typename WarpIteratorA_, + /// whether or not to perform elementwise multiplication of A + // by another matrix (A_scale) that is also kept in shared memory prior + // to matmul A @ B + bool ScaleOperandA_, + /// Max GEMM problem size in K dimension + int MaxK, + /// Iterates over tiles of B operand in global memory + // (concept: ReadableTileIterator | ForwardTileIterator | + // MaskedTileIterator) + typename IteratorB_, + /// Iterates over tiles of B operand in shared memory + /// (concept: WriteableTileIterator | RandomAccessTileIterator) + typename SmemIteratorB_, + /// Data type of accumulator matrix + typename ElementC_, + /// Data type of accumulator matrix + typename LayoutC_, + /// Policy describing tuning details (concept: MmaPolicy) + typename Policy_, + /// Transformation applied to B operand + typename TransformB_ = NumericArrayConverter< + typename SmemIteratorB_::Element, + typename IteratorB_::Element, + IteratorB_::Fragment::kElements>, + /// Used for partial specialization + typename Enable = bool> +class MmaPipelinedFromSharedMemory : public MmaBaseFromSharedMemory< + Shape_, + MaxK, + Policy_, + 2, + typename WarpIteratorA_::Layout> { + public: + ///< Base class + using Base = MmaBaseFromSharedMemory< + Shape_, + MaxK, + Policy_, + 2, + typename WarpIteratorA_::Layout>; + + using Shape = + Shape_; ///< Size of the Gemm problem - concept: gemm::GemmShape<> + static constexpr bool ScaleOperandA = ScaleOperandA_; + + using WarpIteratorA = WarpIteratorA_; + ///< loads fragments of A_scale from shared memory if operand A scaling is + ///< enabled. otherwise no-op. + using WarpIteratorAScale = typename cutlass::platform::conditional< + ScaleOperandA, + WarpIteratorA, + NoOpWarpIteratorScale>::type; + + using IteratorB = + IteratorB_; ///< Iterates over tiles of B operand in global memory + using ElementC = ElementC_; ///< Data type of accumulator matrix + using LayoutC = LayoutC_; ///< Layout of accumulator matrix + using Policy = Policy_; ///< Policy describing tuning details + + using SmemIteratorB = SmemIteratorB_; + + using TransformB = TransformB_; + + // + // Dependent types + // + + /// Fragment of operand B loaded from global memory + using FragmentB = typename IteratorB::Fragment; + + /// Fragment of accumulator tile + using FragmentC = typename Policy::Operator::FragmentC; + + /// Warp-level Mma + using Operator = typename Policy::Operator; + + /// Obtain the arch tag from the warp-level operator + using ArchTag = typename Policy::Operator::ArchTag; + + /// Complex transform on B operand + static ComplexTransform const kTransformB = Operator::kTransformB; + + // staticaly assert kStages for MmaPipelined is two (Double-buffered pipeline) + static_assert( + (Base::kStages == 2), + "MmaPipelined requires kStages set to value 2"); + + private: + using WarpFragmentA = typename Operator::FragmentA; + + /// fragment type of OperandA elementwise scaling matrix. (almost) empty + /// if operand A scaling is disabled. + using WarpFragmentAScale = typename WarpIteratorAScale::Fragment; + + using WarpFragmentB = typename Operator::FragmentB; + + /// applies scaling factor to operand A fragment if operand A scaling is + /// enabled. otherwise no-op. + using FragmentAScaler = FragmentElementwiseScaler< + WarpFragmentA, + WarpFragmentAScale, + ScaleOperandA>; + + protected: + // /// Iterator to write threadblock-scoped tile of A operand to shared memory + // SmemIteratorA smem_iterator_A_; + + /// Iterator to write threadblock-scoped tile of B operand to shared memory + SmemIteratorB smem_iterator_B_; + + /// Iterator to load a warp-scoped tile of A operand from intermediate + /// accumulator tile + WarpIteratorA warp_tile_iterator_A_; + + /// Iterator to load a warp-scoped tile of A_scale from intermediate + /// accumulator tile (only used if ScaleOperandA_ is true) + WarpIteratorAScale warp_tile_iterator_A_scale_; + + public: + /// constructor for MMA with operand A scaling enabled. + CUTLASS_DEVICE + MmaPipelinedFromSharedMemory( + typename Base::TensorRefA a, // Operand A in shared memory + typename Base::TensorRefA a_scale, // Operand A_scale in shared memory + typename Base::TensorRefB + b_staging, // staging memory for loading tiles of B + int thread_idx, + int warp_idx, + int lane_idx) + : Base(b_staging, thread_idx, warp_idx, lane_idx), + warp_tile_iterator_A_(a, lane_idx), + warp_tile_iterator_A_scale_(a_scale, lane_idx), + smem_iterator_B_(b_staging, thread_idx) { + // Compute warp location within threadblock tile by mapping the warp_id to + // three coordinates: + // _m: the warp's position within the threadblock along the M dimension + // _n: the warp's position within the threadblock along the N dimension + // _k: the warp's position within the threadblock along the K dimension + int warp_idx_mn = warp_idx % (Base::WarpCount::kM * Base::WarpCount::kN); + int warp_idx_k = warp_idx / (Base::WarpCount::kM * Base::WarpCount::kN); + int warp_idx_m = warp_idx_mn % Base::WarpCount::kM; + int warp_idx_n = warp_idx_mn / Base::WarpCount::kM; + + // Add per-warp offsets in units of warp-level tiles + this->warp_tile_iterator_A_.add_tile_offset( + {warp_idx_m, Base::kWarpGemmIterations * warp_idx_k}); + this->warp_tile_iterator_A_scale_.add_tile_offset( + {warp_idx_m, Base::kWarpGemmIterations * warp_idx_k}); + this->warp_tile_iterator_B_.add_tile_offset( + {Base::kWarpGemmIterations * warp_idx_k, warp_idx_n}); + } + + /// Construct from tensor references + CUTLASS_DEVICE + MmaPipelinedFromSharedMemory( + typename Base::TensorRefA a, ///< Operand A in shared memory + typename Base::TensorRefB b_staging, ///< staging memory for loading B + int thread_idx, ///< ID within the threadblock + int warp_idx, ///< ID of warp + int lane_idx) ///< ID of each thread within a warp + : Base(b_staging, thread_idx, warp_idx, lane_idx), + warp_tile_iterator_A_(a, lane_idx), + smem_iterator_B_(b_staging, thread_idx) { + // Compute warp location within threadblock tile by mapping the warp_id to + // three coordinates: + // _m: the warp's position within the threadblock along the M dimension + // _n: the warp's position within the threadblock along the N dimension + // _k: the warp's position within the threadblock along the K dimension + + int warp_idx_mn = warp_idx % (Base::WarpCount::kM * Base::WarpCount::kN); + int warp_idx_k = warp_idx / (Base::WarpCount::kM * Base::WarpCount::kN); + + int warp_idx_m = warp_idx_mn % Base::WarpCount::kM; + int warp_idx_n = warp_idx_mn / Base::WarpCount::kM; + + // Add per-warp offsets in units of warp-level tiles + this->warp_tile_iterator_A_.add_tile_offset( + {warp_idx_m, Base::kWarpGemmIterations * warp_idx_k}); + this->warp_tile_iterator_B_.add_tile_offset( + {Base::kWarpGemmIterations * warp_idx_k, warp_idx_n}); + } + + // For API compatibility with MmaMultistageFromSharedMemory + // but not supported as it worsens perf: older gpus < sm80 don't + // support async tranfers and have to waste registers + CUTLASS_DEVICE + void set_prologue_done(bool value) {} + CUTLASS_DEVICE + static void prologue( + typename Base::SharedStorage& shared_storage, + IteratorB iterator_B1, + int thread_idx, + int problem_size_0_n) {} + + CUTLASS_DEVICE + static void drain_cp_asyncs() {} + + /// Perform a threadblock-scoped matrix multiply-accumulate + CUTLASS_DEVICE + void operator()( + int gemm_k_iterations, ///< number of iterations of the mainloop + FragmentC& accum, ///< destination accumulator tile + // IteratorA iterator_A, ///< iterator over A + // operand in global memory + IteratorB iterator_B, ///< iterator over B operand in global memory + FragmentC const& src_accum, ///< source accumulator tile + // TransformA transform_A = TransformA(), ///< transformation + // applied to A fragment + TransformB transform_B = + TransformB()) { ///< transformation applied to B fragment + + // + // Prologue + // + + // Perform accumulation in the 'd' output operand + accum = src_accum; + + FragmentB tb_frag_B; + + tb_frag_B.clear(); + + // The last kblock is loaded in the prolog + iterator_B.set_residual_tile(gemm_k_iterations == 1); + iterator_B.load(tb_frag_B); + + ++iterator_B; + + this->smem_iterator_B_.store(transform_B(tb_frag_B)); + + ++this->smem_iterator_B_; + + __syncthreads(); + + // remember that WarpFragmentAScale and WarpIteratorAScale are empty/no-op + // if scaling is disabled. + + // Pair of fragments used to overlap shared memory loads and math + // instructions + WarpFragmentA warp_frag_A[2]; + WarpFragmentAScale warp_frag_A_scale[2]; + WarpFragmentB warp_frag_B[2]; + warp_frag_A[0].clear(); + warp_frag_A_scale[0].clear(); + warp_frag_B[0].clear(); + + this->warp_tile_iterator_B_.set_kgroup_index(0); + + this->warp_tile_iterator_A_.load(warp_frag_A[0]); + this->warp_tile_iterator_A_scale_.load(warp_frag_A_scale[0]); + this->warp_tile_iterator_B_.load(warp_frag_B[0]); + + ++this->warp_tile_iterator_A_; + ++this->warp_tile_iterator_A_scale_; + ++this->warp_tile_iterator_B_; + + Operator warp_mma; + + int smem_write_stage_idx = 1; + + // Avoid reading out of bounds + iterator_B.set_residual_tile(gemm_k_iterations == 2); + iterator_B.clear_mask(gemm_k_iterations <= 1); + + // Issue loads during the first warp-level matrix multiply-add *AFTER* + // issuing shared memory loads (which have the tightest latency + // requirement). + + // + // Mainloop + // + + // Note: The main loop does not support Base::kWarpGemmIterations == 2. + CUTLASS_GEMM_LOOP + for (; gemm_k_iterations > 0; --gemm_k_iterations) { + // + // Loop over GEMM K dimension + // + + CUTLASS_PRAGMA_UNROLL + for (int warp_mma_k = 0; warp_mma_k < Base::kWarpGemmIterations; + ++warp_mma_k) { + // Load warp-level tiles from shared memory, wrapping to k offset if + // this is the last group as the case may be. + bool hasNext = true; + + if (warp_mma_k == Base::kWarpGemmIterations - 1) { + if (gemm_k_iterations > 1) { + // Write fragments to shared memory + this->smem_iterator_B_.store(transform_B(tb_frag_B)); + } + + __syncthreads(); + + ++this->smem_iterator_B_; + + // Add negative offsets to return iterators to the 'start' of the + // circular buffer in shared memory SMEM: Don't reset iterator A, as + // we are continuing our iteration at this point + if (smem_write_stage_idx == 1) { + this->smem_iterator_B_.add_tile_offset({-Base::kStages, 0}); + } else { + this->warp_tile_iterator_B_.add_tile_offset( + {-Base::kStages * Policy::kPartitionsK * + Base::kWarpGemmIterations, + 0}); + } + + smem_write_stage_idx ^= 1; + hasNext = gemm_k_iterations > 1; + } + + // Only read the next if we need to + if (hasNext) { + this->warp_tile_iterator_B_.set_kgroup_index( + (warp_mma_k + 1) % Base::kWarpGemmIterations); + + this->warp_tile_iterator_A_.load(warp_frag_A[(warp_mma_k + 1) % 2]); + this->warp_tile_iterator_A_scale_.load( + warp_frag_A_scale[(warp_mma_k + 1) % 2]); + this->warp_tile_iterator_B_.load(warp_frag_B[(warp_mma_k + 1) % 2]); + + ++this->warp_tile_iterator_A_; + ++this->warp_tile_iterator_A_scale_; + ++this->warp_tile_iterator_B_; + + if (warp_mma_k == 0) { + iterator_B.load(tb_frag_B); + + ++iterator_B; + + // Avoid reading out of bounds if this was the last loop iteration + iterator_B.set_residual_tile(gemm_k_iterations == 3); + iterator_B.clear_mask(gemm_k_iterations <= 2); + } + } + + warp_mma( + accum, + FragmentAScaler::apply( + warp_frag_A[warp_mma_k % 2], warp_frag_A_scale[warp_mma_k % 2]), + warp_frag_B[warp_mma_k % 2], + accum); + } + } + } +}; + +//////////////////////////////////////////////////////////////////////////////// +// Taken from +// https://github.com/NVIDIA/cutlass/blob/master/examples/13_two_tensor_op_fusion/threadblock/b2b_mma_multistage_smem_accumulator.h +//////////////////////////////////////////////////////////////////////////////// + +/// Structure to compute the matrix product targeting CUDA cores and SIMT math +/// instructions. +template < + /// Size of the Gemm problem - concept: gemm::GemmShape<> + typename Shape1_, + /// Iterates over the intermediate accumulator tile in shared memory + typename WarpIteratorA1_, + /// whether or not to perform elementwise multiplication of A + // by another matrix (A_scale) that is also kept in shared memory prior + // to matmul A @ B + bool ScaleOperandA_, + /// Iterates over tiles of B operand in global memory + // (concept: ReadableTileIterator | ForwardTileIterator | + // MaskedTileIterator) + typename IteratorB1_, + /// Iterates over tiles of B operand in shared memory + /// (concept: WriteableTileIterator | RandomAccessTileIterator) + typename SmemIteratorB1_, + /// Cache operation for operand B + cutlass::arch::CacheOperation::Kind CacheOpB1, + /// Data type of accumulator matrix + typename ElementC_, + /// Data type of accumulator matrix + typename LayoutC_, + /// Policy describing tuning details (concept: MmaPolicy) + typename Policy1_, + /// Number of stages, + int Stages_, + int kMaxK_, + /// Used for partial specialization + typename Enable = bool> +class MmaMultistageFromSharedMemory : public MmaBaseFromSharedMemory< + Shape1_, + kMaxK_, + Policy1_, + Stages_, + typename WarpIteratorA1_::Layout> { + public: + ///< Base class + using Base = MmaBaseFromSharedMemory< + Shape1_, + kMaxK_, + Policy1_, + Stages_, + typename WarpIteratorA1_::Layout>; + + ///< Size of the Gemm problem - concept: gemm::GemmShape<> + using Shape1 = Shape1_; + ///< Iterates over tiles of B operand in global memory + using IteratorB1 = IteratorB1_; + using IteratorB = IteratorB1; + ///< Policy describing tuning details + using Policy1 = Policy1_; + + using SmemIteratorB1 = SmemIteratorB1_; + using WarpIteratorA1 = WarpIteratorA1_; ///< Iterates over the intermediate + ///< accumulator tile in shared memory + static constexpr bool ScaleOperandA = ScaleOperandA_; + + ///< warp level iterator over A_scale matrix tile kept in shared memory. + ///< if elementwise A scaling is disabled then everything this does is no-op. + using WarpIteratorAScale = typename cutlass::platform::conditional< + ScaleOperandA, + WarpIteratorA1, + NoOpWarpIteratorScale>::type; + ///< Data type of accumulator matrix + using ElementC = ElementC_; + ///< Layout of accumulator matrix + using LayoutC = LayoutC_; + + static cutlass::arch::CacheOperation::Kind const kCacheOpB1 = CacheOpB1; + static constexpr bool kSmemContainsEntireB = Base::kSmemContainsEntireB; + + // + // Dependent types + // + + /// Fragment of accumulator tile + using FragmentC1 = typename Policy1::Operator::FragmentC; + using FragmentC = FragmentC1; + + /// Warp-level Mma + using Operator1 = typename Policy1::Operator; + + /// Minimum architecture is Sm80 to support cp.async + using ArchTag = arch::Sm80; + + /// Complex transform on B operand + static ComplexTransform const kTransformB1 = Operator1::kTransformB; + + /// Internal structure exposed for introspection. + struct Detail { + static_assert( + Base::kWarpGemmIterations1 > 1, + "The pipelined structure requires at least two warp-level " + "GEMM operations."); + + /// Number of cp.async instructions to load one stage of operand B + static int const TBLoadIterationsB1 = + IteratorB1::ThreadMap::Iterations::kCount; + + /// Number of cp.async instructions to load on group of operand B + static int const kAccessesPerGroupB1 = + (TBLoadIterationsB1 + Base::kWarpGemmIterations1 - 1) / + Base::kWarpGemmIterations1; + }; + + static constexpr int kNumStagesConcurrentLoad = + kSmemContainsEntireB ? Base::kStages : Base::kStages - 1; + + private: + using WarpLoadedFragmentA1 = typename Operator1::FragmentA; + /// fragment of OperandA scale matrix. if operand A scaling is disabled this + /// is (almost) empty. + using WarpLoadedFragmentA1Scale = typename WarpIteratorAScale::Fragment; + using WarpLoadedFragmentB1 = typename Operator1::FragmentB; + using WarpTransformedFragmentA1 = typename Operator1::TransformedFragmentA; + using WarpTransformedFragmentB1 = typename Operator1::TransformedFragmentB; + + /// applies elementwise scaling to fragment of A. if operand A scaling is + /// disabled this is a no-op. + using FragmentAScaler = FragmentElementwiseScaler< + WarpLoadedFragmentA1, + WarpLoadedFragmentA1Scale, + ScaleOperandA>; + + private: + // + // Data members + // + + /// Iterator to load a warp-scoped tile of A1 operand from intermediate + /// accumulator tile + WarpIteratorA1 warp_tile_iterator_A1_; + + /// Iterator to load a warp-scoped tile of A1_scale operand from shared memory + /// if operand A scaling is disabled everything this does is a no-op. + WarpIteratorAScale warp_tile_iterator_A1_scale_; + + /// Iterator to write threadblock-scoped tile of B operand to shared memory + SmemIteratorB1 smem_iterator_B1_; + + bool prologue_done_; + + public: + /// constructor for MMA with operand A scaling enabled. + CUTLASS_DEVICE + MmaMultistageFromSharedMemory( + typename Base::TensorRefA a, + typename Base::TensorRefA a_scale, + typename Base::TensorRefB b_tile, + int thread_idx, + int warp_idx, + int lane_idx) + : Base(b_tile, thread_idx, warp_idx, lane_idx), + warp_tile_iterator_A1_(a, lane_idx), + warp_tile_iterator_A1_scale_(a_scale, lane_idx), + smem_iterator_B1_(b_tile, thread_idx), + prologue_done_(false) { + // Compute warp location within threadblock tile by mapping the warp_id to + // three coordinates: + // _m: the warp's position within the threadblock along the M dimension + // _n: the warp's position within the threadblock along the N dimension + // _k: the warp's position within the threadblock along the K dimension + int warp_idx_mn_1 = + warp_idx % (Base::WarpCount1::kM * Base::WarpCount1::kN); + int warp_idx_k_1 = warp_idx / (Base::WarpCount1::kM * Base::WarpCount1::kN); + int warp_idx_m_1 = warp_idx_mn_1 % Base::WarpCount1::kM; + int warp_idx_n_1 = warp_idx_mn_1 / Base::WarpCount1::kM; + + // Add per-warp offsets in units of warp-level tiles + warp_tile_iterator_A1_.add_tile_offset( + {warp_idx_m_1, Base::kWarpGemmIterations1 * warp_idx_k_1}); + warp_tile_iterator_A1_scale_.add_tile_offset( + {warp_idx_m_1, Base::kWarpGemmIterations1 * warp_idx_k_1}); + this->warp_tile_iterator_B_.add_tile_offset( + {Base::kWarpGemmIterations1 * warp_idx_k_1, warp_idx_n_1}); + } + + /// Construct from tensor references + CUTLASS_DEVICE + MmaMultistageFromSharedMemory( + typename Base::TensorRefA a, + typename Base::TensorRefB b_tile, + ///< ID within the threadblock + int thread_idx, + ///< ID of warp + int warp_idx, + ///< ID of each thread within a warp + int lane_idx) + : Base(b_tile, thread_idx, warp_idx, lane_idx), + warp_tile_iterator_A1_(a, lane_idx), + smem_iterator_B1_(b_tile, thread_idx), + prologue_done_(false) { + // Compute warp location within threadblock tile by mapping the warp_id to + // three coordinates: + // _m: the warp's position within the threadblock along the M dimension + // _n: the warp's position within the threadblock along the N dimension + // _k: the warp's position within the threadblock along the K dimension + + int warp_idx_mn_1 = + warp_idx % (Base::WarpCount1::kM * Base::WarpCount1::kN); + int warp_idx_k_1 = warp_idx / (Base::WarpCount1::kM * Base::WarpCount1::kN); + + int warp_idx_m_1 = warp_idx_mn_1 % Base::WarpCount1::kM; + int warp_idx_n_1 = warp_idx_mn_1 / Base::WarpCount1::kM; + + // Add per-warp offsets in units of warp-level tiles + warp_tile_iterator_A1_.add_tile_offset( + {warp_idx_m_1, Base::kWarpGemmIterations1 * warp_idx_k_1}); + this->warp_tile_iterator_B_.add_tile_offset( + {Base::kWarpGemmIterations1 * warp_idx_k_1, warp_idx_n_1}); + } + + CUTLASS_DEVICE + void set_prologue_done(bool value) { + prologue_done_ = value; + } + + CUTLASS_DEVICE + static void prologue( + typename Base::SharedStorage& shared_storage, + IteratorB iterator_B1, + int thread_idx, + int problem_size_0_n) { + SmemIteratorB1 smem_iterator_B1(shared_storage.operand_B_ref(), thread_idx); + _prologue( + iterator_B1, + (problem_size_0_n + Base::Shape::kK - 1) / Base::Shape::kK, + smem_iterator_B1); + } + + CUTLASS_DEVICE + static void drain_cp_asyncs() { + // commit and drain all pending and predicated cp.async pnz from the GEMM + // mainloop + cutlass::arch::cp_async_fence(); + cutlass::arch::cp_async_wait<0>(); + __syncthreads(); + } + + CUTLASS_DEVICE + void copy_tiles_and_advance_1( + IteratorB1& iterator_B1, + int group_start_B1 = 0) { + iterator_B1.set_iteration_index( + group_start_B1 * IteratorB1::kAccessesPerVector); + this->smem_iterator_B1_.set_iteration_index(group_start_B1); + + // Load for operand B + CUTLASS_PRAGMA_UNROLL + for (int j = 0; j < Detail::kAccessesPerGroupB1; ++j) { + if (group_start_B1 + j < Detail::TBLoadIterationsB1) { + typename IteratorB1::AccessType* dst_ptr = + reinterpret_cast( + this->smem_iterator_B1_.get()); + + int const kSrcBytes = sizeof_bits::value * + IteratorB1::ThreadMap::kElementsPerAccess / + IteratorB1::kAccessesPerVector / 8; + + CUTLASS_PRAGMA_UNROLL + for (int v = 0; v < IteratorB1::kAccessesPerVector; ++v) { + auto gmem_ptr = iterator_B1.get(); + + cutlass::arch::cp_async_zfill( + dst_ptr + v, gmem_ptr, iterator_B1.valid()); + + ++iterator_B1; + } + ++this->smem_iterator_B1_; + } + } + } + + CUTLASS_DEVICE + static void _prologue( + IteratorB& iterator_B1, + int32_t gemm_k_iterations_1, + SmemIteratorB1& smem_iterator_B1_) { + // Issue several complete stages + CUTLASS_PRAGMA_UNROLL + for (int stage = 0; stage < kNumStagesConcurrentLoad; + ++stage, --gemm_k_iterations_1) { + iterator_B1.set_residual_tile(gemm_k_iterations_1 == 1); + iterator_B1.clear_mask(gemm_k_iterations_1 == 0); + + iterator_B1.set_iteration_index(0); + smem_iterator_B1_.set_iteration_index(0); + + // Load for operand B + CUTLASS_PRAGMA_UNROLL + for (int j = 0; j < Detail::TBLoadIterationsB1; ++j) { + typename IteratorB1::AccessType* dst_ptr = + reinterpret_cast( + smem_iterator_B1_.get()); + + CUTLASS_PRAGMA_UNROLL + for (int v = 0; v < IteratorB1::kAccessesPerVector; ++v) { + int const kSrcBytes = + sizeof_bits::value * + IteratorB1::ThreadMap::kElementsPerAccess / + IteratorB1::kAccessesPerVector / 8; + + cutlass::arch::cp_async_zfill( + dst_ptr + v, iterator_B1.get(), iterator_B1.valid()); + + ++iterator_B1; + } + + ++smem_iterator_B1_; + } + + // Move to the next stage + iterator_B1.add_tile_offset({1, 0}); + + smem_iterator_B1_.add_tile_offset({1, 0}); + + // Defines the boundary of a stage of cp.async. + cutlass::arch::cp_async_fence(); + } + iterator_B1.set_residual_tile(gemm_k_iterations_1 == 1); + iterator_B1.clear_mask(gemm_k_iterations_1 == 0); + } + + /// Perform a threadblock-scoped matrix multiply-accumulate + CUTLASS_DEVICE + void operator()( + ///< problem size of GEMM + int gemm_k_iterations_1_, + ///< destination accumulator tile + FragmentC1& accum, + ///< iterator over B1 operand in global memory + IteratorB1 iterator_B1, + ///< initial value of accumulator + FragmentC1 const& src_accum) { + // 2nd Gemm + + // + // Prologue + // + // Perform accumulation in the 'd' output operand + accum = src_accum; + + if (!prologue_done_) { + _prologue(iterator_B1, gemm_k_iterations_1_, smem_iterator_B1_); + } else if (!kSmemContainsEntireB) { + // Restore the iterators increments + + int gemm_k_iterations_1 = gemm_k_iterations_1_; + // Issue several complete stages + CUTLASS_PRAGMA_UNROLL + for (int stage = 0; stage < kNumStagesConcurrentLoad; + ++stage, --gemm_k_iterations_1) { + iterator_B1.set_iteration_index(0); + this->smem_iterator_B1_.set_iteration_index(0); + + // Load for operand B + CUTLASS_PRAGMA_UNROLL + for (int j = 0; j < Detail::TBLoadIterationsB1; ++j) { + CUTLASS_PRAGMA_UNROLL + for (int v = 0; v < IteratorB1::kAccessesPerVector; ++v) { + ++iterator_B1; + } + ++this->smem_iterator_B1_; + } + iterator_B1.add_tile_offset({1, 0}); + this->smem_iterator_B1_.add_tile_offset({1, 0}); + } + iterator_B1.set_residual_tile(gemm_k_iterations_1 <= 1); + iterator_B1.clear_mask(gemm_k_iterations_1 <= 0); + } + + // DEPBAR+SYNC + cutlass::arch::cp_async_wait(); + __syncthreads(); + + // remember that WarpFragmentAScale and WarpIteratorAScale are no-op/empty + // if scaling is disabled. + + // Pair of fragments used to overlap shared memory loads and math + // instructions + WarpLoadedFragmentA1 warp_loaded_frag_A1[2]; + WarpLoadedFragmentA1Scale warp_loaded_frag_A1_scale[2]; + WarpLoadedFragmentB1 warp_loaded_frag_B1[2]; + WarpTransformedFragmentA1 warp_transformed_frag_A1[2]; + WarpTransformedFragmentB1 warp_transformed_frag_B1[2]; + + Operator1 warp_mma1; + + warp_tile_iterator_A1_.load(warp_loaded_frag_A1[0]); + ++warp_tile_iterator_A1_; + + warp_tile_iterator_A1_scale_.load(warp_loaded_frag_A1_scale[0]); + ++warp_tile_iterator_A1_scale_; + + this->warp_tile_iterator_B_.set_kgroup_index(0); + this->warp_tile_iterator_B_.load(warp_loaded_frag_B1[0]); + ++this->warp_tile_iterator_B_; + + int smem_write_stage_idx = Base::kStages - 1; + int smem_read_stage_idx = 0; + + warp_mma1.transform( + warp_transformed_frag_A1[0], + warp_transformed_frag_B1[0], + FragmentAScaler::apply( + warp_loaded_frag_A1[0], warp_loaded_frag_A1_scale[0]), + warp_loaded_frag_B1[0]); + + // tf32x3 kernels use staging accumulation. warp_mma uses a temporary + // accumulator and this temporary accumulator is added to the final + // accumulator once in every mainloop iteration. + plus plus_accum; + + FragmentC1 tmp_accum; + + if (platform::is_same< + typename Operator1::MathOperator, + arch::OpMultiplyAddFastF32>::value || + platform::is_same< + typename Operator1::MathOperator, + arch::OpMultiplyAddComplexFastF32>::value) { + tmp_accum.clear(); + } + + // + // Mainloop + // + + CUTLASS_PRAGMA_UNROLL + for (int gemm_k_iterations_1 = gemm_k_iterations_1_ - (Base::kStages - 1); + gemm_k_iterations_1 > (-Base::kStages + 1); + gemm_k_iterations_1--) { + // + // Loop over GEMM K dimension + // + + // Computes a warp-level GEMM on data held in shared memory + // Each "warp_mma_k" refers to a warp-level matrix multiply-accumulate + CUTLASS_PRAGMA_UNROLL + for (int warp_mma_k = 0; warp_mma_k < Base::kWarpGemmIterations1; + ++warp_mma_k) { + // Load warp-level tile from accumulator fragment (A) + // or shared memory (operand B) + this->warp_tile_iterator_B_.set_kgroup_index( + (warp_mma_k + 1) % Base::kWarpGemmIterations1); + // skip warp tile loading for the last kgroup (we are out of the buf) + if (gemm_k_iterations_1 > (-Base::kStages + 2) || + warp_mma_k < Base::kWarpGemmIterations1 - 1) { + warp_tile_iterator_A1_.load( + warp_loaded_frag_A1[(warp_mma_k + 1) % 2]); + warp_tile_iterator_A1_scale_.load( + warp_loaded_frag_A1_scale[(warp_mma_k + 1) % 2]); + this->warp_tile_iterator_B_.load( + warp_loaded_frag_B1[(warp_mma_k + 1) % 2]); + } + ++warp_tile_iterator_A1_; + ++warp_tile_iterator_A1_scale_; + ++this->warp_tile_iterator_B_; + + if (warp_mma_k > 0) + warp_mma1.transform( + warp_transformed_frag_A1[warp_mma_k % 2], + warp_transformed_frag_B1[warp_mma_k % 2], + FragmentAScaler::apply( + warp_loaded_frag_A1[warp_mma_k % 2], + warp_loaded_frag_A1_scale[warp_mma_k % 2]), + warp_loaded_frag_B1[warp_mma_k % 2]); + + if (platform::is_same< + typename Operator1::MathOperator, + arch::OpMultiplyAddFastF32>::value || + platform::is_same< + typename Operator1::MathOperator, + arch::OpMultiplyAddComplexFastF32>::value) { + warp_mma1( + tmp_accum, + warp_transformed_frag_A1[warp_mma_k % 2], + warp_transformed_frag_B1[warp_mma_k % 2], + tmp_accum); + + if (warp_mma_k == 0) { + accum = plus_accum(accum, tmp_accum); + tmp_accum.clear(); + } + } else { + warp_mma1( + accum, + warp_transformed_frag_A1[warp_mma_k % 2], + warp_transformed_frag_B1[warp_mma_k % 2], + accum); + } + + // Issue global->shared copies for the this stage + if (warp_mma_k < Base::kWarpGemmIterations1 - 1) { + int group_start_iteration_B1; + + group_start_iteration_B1 = warp_mma_k * Detail::kAccessesPerGroupB1; + + if (!kSmemContainsEntireB) { + copy_tiles_and_advance_1(iterator_B1, group_start_iteration_B1); + } + } + + if (warp_mma_k + 2 == Base::kWarpGemmIterations1) { + int group_start_iteration_B1; + group_start_iteration_B1 = + (warp_mma_k + 1) * Detail::kAccessesPerGroupB1; + + if (!kSmemContainsEntireB) { + copy_tiles_and_advance_1(iterator_B1, group_start_iteration_B1); + } + + // Inserts a memory fence between stages of cp.async instructions. + cutlass::arch::cp_async_fence(); + + // Waits until kStages-2 stages have committed. + arch::cp_async_wait(); + __syncthreads(); + + // Move to the next stage + iterator_B1.add_tile_offset({1, 0}); + + this->smem_iterator_B1_.add_tile_offset({1, 0}); + + // Add negative offsets to return iterators to the 'start' of the + // circular buffer in shared memory + if (!kSmemContainsEntireB) { + if (smem_write_stage_idx == (Base::kStages - 1)) { + this->smem_iterator_B1_.add_tile_offset({-Base::kStages, 0}); + smem_write_stage_idx = 0; + } else { + ++smem_write_stage_idx; + } + + if (smem_read_stage_idx == (Base::kStages - 1)) { + this->warp_tile_iterator_B_.add_tile_offset( + {-Base::kStages * Policy1::kPartitionsK * + Base::kWarpGemmIterations1, + 0}); + smem_read_stage_idx = 0; + } else { + ++smem_read_stage_idx; + } + } + + iterator_B1.set_residual_tile(gemm_k_iterations_1 == 2); + iterator_B1.clear_mask(gemm_k_iterations_1 == 1); + } + + // Do any conversions feeding the first stage at the end of the loop so + // we can start right away on mma instructions + if (warp_mma_k + 1 == Base::kWarpGemmIterations1) + warp_mma1.transform( + warp_transformed_frag_A1[(warp_mma_k + 1) % 2], + warp_transformed_frag_B1[(warp_mma_k + 1) % 2], + FragmentAScaler::apply( + warp_loaded_frag_A1[(warp_mma_k + 1) % 2], + warp_loaded_frag_A1_scale[(warp_mma_k + 1) % 2]), + warp_loaded_frag_B1[(warp_mma_k + 1) % 2]); + } + } + + if (platform::is_same< + typename Operator1::MathOperator, + arch::OpMultiplyAddFastF32>::value || + platform::is_same< + typename Operator1::MathOperator, + arch::OpMultiplyAddComplexFastF32>::value) { + accum = plus_accum(accum, tmp_accum); + } + } +}; + +// Converts a "regular" Mma into their counterpart from shared memory +template < + int NADim, + typename Mma_, + int kMaxK, + typename WarpIteratorA_, + /// whether or not to apply elementwise multiplication of operand A by + /// another matrix in shared memory before usage in A @ B + bool kScaleOperandA, + bool kTransposeA = false> +struct DefaultMmaFromSharedMemory; + +// Mma pipelined +template < + int NADim, + /// Size of the Gemm problem - concept: gemm::GemmShape<> + typename Shape_, + /// Iterates over tiles of A operand in global memory + // (concept: ReadableTileIterator | ForwardTileIterator | + // MaskedTileIterator) + typename IteratorA_, + /// Iterates over tiles of A operand in shared memory + /// (concept: WriteableTileIterator | RandomAccessTileIterator) + typename SmemIteratorA_, + typename WarpIteratorA_, + /// Iterates over tiles of B operand in global memory + // (concept: ReadableTileIterator | ForwardTileIterator | + // MaskedTileIterator) + typename IteratorB_, + /// Iterates over tiles of B operand in shared memory + /// (concept: WriteableTileIterator | RandomAccessTileIterator) + typename SmemIteratorB_, + /// Data type of accumulator matrix + typename ElementC_, + /// Data type of accumulator matrix + typename LayoutC_, + /// Policy describing tuning details (concept: MmaPolicy) + typename Policy_, + /// Transformation applied to A operand + typename TransformA_, + /// Transformation applied to B operand + typename TransformB_, + // Max MMA problem size K + int kMaxK, + /// whether or not to apply elementwise multiplication of operand A by + /// another matrix in shared memory before usage in A @ B + bool kScaleOperandA, + bool kTransposeA> +struct DefaultMmaFromSharedMemory< + NADim, + MmaPipelined< + Shape_, + IteratorA_, + SmemIteratorA_, + IteratorB_, + SmemIteratorB_, + ElementC_, + LayoutC_, + Policy_, + TransformA_, + TransformB_>, + kMaxK, + WarpIteratorA_, + kScaleOperandA, + kTransposeA> { + using RegularMma = MmaPipelined< + Shape_, + IteratorA_, + SmemIteratorA_, + IteratorB_, + SmemIteratorB_, + ElementC_, + LayoutC_, + Policy_, + TransformA_, + TransformB_>; + + using WarpShape = typename Policy_::Operator::Shape; + using InstructionShape = typename Policy_::Operator::InstructionShape; + using ArchMmaOperator = typename Policy_::Operator; + + static constexpr bool kIsTransposedA = false; + using WarpIteratorA = WarpIteratorA_; + using IteratorB = typename cutlass::transform::threadblock:: + MakeIteratorResidualLast::Iterator; + + using Mma = typename cutlass::gemm::threadblock::MmaPipelinedFromSharedMemory< + Shape_, + WarpIteratorA, + kScaleOperandA, + kMaxK, + IteratorB, + SmemIteratorB_, + ElementC_, + LayoutC_, + Policy_>; +}; + +template < + int NADim, + /// Size of the Gemm problem - concept: gemm::GemmShape<> + typename Shape_, + /// Iterates over tiles of A operand in global memory + // (concept: ReadableTileIterator | ForwardTileIterator | + // MaskedTileIterator) + typename IteratorA_, + /// Iterates over tiles of A operand in shared memory + /// (concept: WriteableTileIterator | RandomAccessTileIterator) + typename SmemIteratorA_, + typename WarpIteratorA_, + /// Cache operation for operand A + cutlass::arch::CacheOperation::Kind CacheOpA, + /// Iterates over tiles of B operand in global memory + // (concept: ReadableTileIterator | ForwardTileIterator | + // MaskedTileIterator) + typename IteratorB_, + /// Iterates over tiles of B operand in shared memory + /// (concept: WriteableTileIterator | RandomAccessTileIterator) + typename SmemIteratorB_, + /// Cache operation for operand B + cutlass::arch::CacheOperation::Kind CacheOpB, + /// Data type of accumulator matrix + typename ElementC_, + /// Data type of accumulator matrix + typename LayoutC_, + /// Policy describing tuning details (concept: MmaPolicy) + typename Policy_, + /// Number of stages, + int Stages, + /// Use zfill or predicate for out-of-bound cp.async + SharedMemoryClearOption SharedMemoryClear, + int kMaxK, + /// whether or not to apply elementwise multiplication of operand A by + /// another matrix in shared memory before usage in A @ B + bool kScaleOperandA, + bool kTransposeA> +struct DefaultMmaFromSharedMemory< + NADim, + MmaMultistage< + Shape_, + IteratorA_, + SmemIteratorA_, + CacheOpA, + IteratorB_, + SmemIteratorB_, + CacheOpB, + ElementC_, + LayoutC_, + Policy_, + Stages, + SharedMemoryClear>, + kMaxK, + WarpIteratorA_, + kScaleOperandA, + kTransposeA> { + using RegularMma = MmaMultistage< + Shape_, + IteratorA_, + SmemIteratorA_, + CacheOpA, + IteratorB_, + SmemIteratorB_, + CacheOpB, + ElementC_, + LayoutC_, + Policy_, + Stages, + SharedMemoryClear>; + + using WarpShape = typename Policy_::Operator::Shape; + using InstructionShape = typename Policy_::Operator::InstructionShape; + using WarpIteratorTranspose = TransposeWarpIterator; + static constexpr bool kIsTransposedA = + WarpIteratorTranspose::kSupportsTranspose && kTransposeA; + using WarpIteratorA = typename platform::conditional< + kIsTransposedA, + typename WarpIteratorTranspose::Iterator, + WarpIteratorA_>::type; + + // Reduce the number of stages if we don't need that many + static int constexpr kStagesMax = + (kMaxK + int(Shape_::kK) - 1) / int(Shape_::kK); + static int constexpr kStages = cutlass::const_min(Stages, kStagesMax); + + using IteratorB = typename cutlass::transform::threadblock:: + MakeIteratorResidualLast::Iterator; + using Mma = + typename cutlass::gemm::threadblock::MmaMultistageFromSharedMemory< + Shape_, + WarpIteratorA, + kScaleOperandA, + IteratorB, + SmemIteratorB_, + RegularMma::kCacheOpB, + ElementC_, + LayoutC_, + Policy_, + kStages, + kMaxK>; +}; + +///////////////////////////////////////////////////////////////////////////////////////////////// + +template < + typename IteratorC, + typename Operator, + typename scalar_t, + typename WarpShape_, + typename ThreadblockShape_> +struct B2bGemm; + +// Tensor Cores >= Sm75 specialization (Ampere ...) +template < /// Size of the matrix to load (concept: MatrixShape) + typename Shape_, + /// Element type + typename Element_, + /// Layout of operand in memory + typename Layout_, + /// Shape of one matrix product operation (concept: MatrixShape) + typename InstructionShape_, + /// Interval between adjacent *MMA instructions (in units of MMA + /// instructions, concept: MatrixShape) + typename OpDelta_, + typename Operator, + typename scalar_t, + typename WarpShape_, + typename ThreadblockShape_> +struct B2bGemm< + cutlass::gemm::warp::MmaTensorOpAccumulatorTileIterator< + Shape_, + Element_, + Layout_, + InstructionShape_, + OpDelta_>, + Operator, + scalar_t, + WarpShape_, + ThreadblockShape_> { + using IteratorC = + typename cutlass::gemm::warp::MmaTensorOpAccumulatorTileIterator< + Shape_, + Element_, + Layout_, + InstructionShape_, + OpDelta_>; + using FragmentC = typename IteratorC::Fragment; + using InstructionShape = InstructionShape_; + using WarpShape = WarpShape_; + using ThreadblockShape = ThreadblockShape_; + using accum_t = Element_; + using lse_scalar_t = float; + + using SmemAccumulatorLayout = cutlass::layout::RowMajor; + + // Iterator to load accumulators (results of matmul in registers) + using FragmentIteratorAccumulator = + cutlass::epilogue::warp::FragmentIteratorTensorOp< + WarpShape, + InstructionShape, + accum_t, + typename Operator::Policy::Operator::FragmentC, + cutlass::layout::RowMajor>; + + // Iterator to store to shared-memory + using SmemIteratorD0 = typename cutlass::epilogue::warp::TileIteratorTensorOp< + WarpShape, + InstructionShape, + scalar_t, // accum_t, + SmemAccumulatorLayout>; + using AccumulatorSharedStorage = + cutlass::gemm::threadblock::AccumulatorSharedStorage< + ThreadblockShape, + typename SmemIteratorD0::Element, + typename SmemIteratorD0::TensorLayout, + typename SmemIteratorD0::Padding>; + // We need to provide an operation for the epilogue. Let's create an + // operation that does nothing (ScaleType::Nothing), just converts + // from accum_t (float) -> scalar_t (can be half) + using OutputOpNoOp = cutlass::epilogue::thread::LinearCombination< + typename SmemIteratorD0::Element, // ElementOutput + FragmentIteratorAccumulator::Fragment::kElements, + accum_t, // ElementAccumulator + typename SmemIteratorD0::Element, // ElementCompute + cutlass::epilogue::thread::ScaleType::Nothing>; + using Epilogue = cutlass::epilogue::threadblock::EpilogueSmemAccumulator< + SmemIteratorD0, + FragmentIteratorAccumulator, + SmemIteratorD0, // ScaleBiasIterator - not used + OutputOpNoOp>; + + // Epilogue 2: with LSE (for backwards pass) + static int const kElementsPerAccess = 2; // TODO: Why 2? + using IteratorAccumulatorLSE = + cutlass::transform::threadblock::VectorIterator< + cutlass::transform::threadblock::PredicatedVectorAccessIterator< + // Shape + cutlass::MatrixShape, + // WarpShape + cutlass::MatrixShape, + lse_scalar_t, + cutlass::layout::RowMajor, + kElementsPerAccess>>; + using EpilogueOpApplyLSE = cutlass::epilogue::thread::ApplyLogSumExp< + scalar_t, // ElementOutput_ + lse_scalar_t, // ElementLSE_ + accum_t, // ElementAccumulator_ + accum_t, // ElementCompute_ + 128 / cutlass::sizeof_bits::value + // FragmentIteratorAccumulator::Fragment::kElements + // InstructionShape::kM * InstructionShape::kN / 32 + >; + using EpilogueWithLSE = + cutlass::epilogue::threadblock::EpilogueSmemAccumulator< + SmemIteratorD0, + FragmentIteratorAccumulator, + IteratorAccumulatorLSE, + EpilogueOpApplyLSE>; + + static void CUTLASS_DEVICE accumToSmem( + AccumulatorSharedStorage& shared_storage, + FragmentC const& accum, + int lane_id, + cutlass::MatrixCoord const& tile_coords) { + SmemIteratorD0 smem_iterator_attn(shared_storage.accum_ref(), lane_id); + smem_iterator_attn.add_tile_offset( + tile_coords * + cutlass::MatrixCoord{ + SmemIteratorD0::TileIterations::kRow, + SmemIteratorD0::TileIterations::kColumn}); + Epilogue epilogue; + epilogue(OutputOpNoOp({}), smem_iterator_attn, accum); + } + + static void CUTLASS_DEVICE accumApplyLSEToSmem( + AccumulatorSharedStorage& shared_storage, + FragmentC& accum, + lse_scalar_t const* lse, + int32_t lse_extents, + int thread_id, + int warp_id, + int lane_id, + cutlass::MatrixCoord const& tile_coords) { + constexpr int32_t kAlignLSE = 32; + IteratorAccumulatorLSE iterator_lse( + lse, + {(int32_t)0, (int32_t)ceil_div(lse_extents, kAlignLSE) * kAlignLSE}, + thread_id, + warp_id, + cutlass::MatrixCoord{0, 0} // offset + ); + + SmemIteratorD0 smem_iterator_attn(shared_storage.accum_ref(), lane_id); + smem_iterator_attn.add_tile_offset( + tile_coords * + cutlass::MatrixCoord{ + SmemIteratorD0::TileIterations::kRow, + SmemIteratorD0::TileIterations::kColumn}); + EpilogueWithLSE epilogue; + EpilogueOpApplyLSE minus_lse_exp({}); + epilogue( + minus_lse_exp, + smem_iterator_attn, + accum, + // scale - unused + iterator_lse, + // bias + iterator_lse); + } +}; + +// Volta Specialization +// only supported for f16 +template +struct B2bGemm< + cutlass::gemm::warp::MmaVoltaTensorOpAccumulatorTileIterator< + cutlass::MatrixShape<32, 32>, + float, + cutlass::layout::RowMajor, + cutlass::gemm::GemmShape<16, 16, 4>, + cutlass::MatrixShape<1, 1>>, + Operator, + cutlass::half_t, + WarpShape_, + ThreadblockShape_> { + using IteratorC = + cutlass::gemm::warp::MmaVoltaTensorOpAccumulatorTileIterator< + cutlass::MatrixShape<32, 32>, + float, + cutlass::layout::RowMajor, + cutlass::gemm::GemmShape<16, 16, 4>, + cutlass::MatrixShape<1, 1>>; + using scalar_t = cutlass::half_t; + using accum_t = IteratorC::Element; + using WarpShape = WarpShape_; + using ThreadblockShape = ThreadblockShape_; + using FragmentC = IteratorC::Fragment; + using lse_scalar_t = float; + + // Storage in shared-memory for Q.Kt + using SmemAccumulatorLayout = + cutlass::layout::RowMajorVoltaTensorOpMultiplicandCrosswise<16, 32>; + using AccumulatorSharedStorage = + cutlass::gemm::threadblock::AccumulatorSharedStorage< + ThreadblockShape, + scalar_t, + SmemAccumulatorLayout, + cutlass::MatrixShape<0, 0> // Padding + >; + using TensorRef = cutlass::TensorRef; + using Policy = typename IteratorC::Policy; + using Element = accum_t; + // Those are MmaVoltaTensorOpAccumulatorTileIterator private fields + // Let's copy their values + static int const kElementsPerPartial = 4; + using EleShapePerPatial = typename cutlass::platform::conditional< + cutlass::platform::is_same::value, + cutlass::MatrixShape<2, 2>, + cutlass::MatrixShape<1, 4>>::type; + static int const kElementsPerMma = 8; + static int const kAccumulatorPatials = 2; + using QuadShapePerPatialMma = cutlass::MatrixShape<4, 4>; + + static void CUTLASS_DEVICE accumToSmem( + AccumulatorSharedStorage& shared_storage, + FragmentC const& accum, + int lane_id, + cutlass::MatrixCoord const& tile_coords) { + // ctor - from MmaVoltaTensorOpAccumulatorTileIterator + TensorRef ref_(shared_storage.accum_ref()); + int quad = (lane_id >> 2); + int lane_in_quad = (lane_id & 3); + int accum_m, accum_n; + + if (cutlass::platform::is_same::value) { + // (quad[2],quad[0])+lane_in_quad[0] + accum_m = (((quad & 0x4) >> 1) + (quad & 0x1)) * 8 + (lane_in_quad & 1); + // (quad[1])+lane_in_quad[1] + accum_n = + ((quad >> 1) & 0x1) * kElementsPerPartial * kAccumulatorPatials + + (lane_in_quad & 2); + } else { + accum_m = (((quad & 0x4) >> 1) + (quad & 0x1)) * 8 + + lane_in_quad; // (quad[2],quad[0]) + accum_n = ((quad >> 1) & 0x1) * kElementsPerPartial * kAccumulatorPatials; + } + cutlass::MatrixCoord lane_offset(accum_m, accum_n); + + // Tile offset + ref_.add_coord_offset( + tile_coords * + cutlass::MatrixCoord( + {IteratorC::Shape::kRow, IteratorC::Shape::kColumn})); + + using AccessType = cutlass::Array; + + // store - from MmaVoltaTensorOpAccumulatorTileIterator + CUTLASS_PRAGMA_UNROLL + for (int tile_n = 0; tile_n < Policy::TileIterations::kColumn; ++tile_n) { + CUTLASS_PRAGMA_UNROLL + for (int tile_m = 0; tile_m < Policy::TileIterations::kRow; ++tile_m) { + CUTLASS_PRAGMA_UNROLL + for (int mma_n = 0; mma_n < Policy::MmaIterations::kColumn; ++mma_n) { + CUTLASS_PRAGMA_UNROLL + for (int mma_m = 0; mma_m < Policy::MmaIterations::kRow; ++mma_m) { + int mma_accum_start = + (((tile_n * Policy::TileIterations::kRow + tile_m) * + Policy::MmaIterations::kColumn + + mma_n) * + Policy::MmaIterations::kRow + + mma_m) * + kElementsPerMma; + + CUTLASS_PRAGMA_UNROLL + for (int p = 0; p < kAccumulatorPatials; ++p) { + CUTLASS_PRAGMA_UNROLL + for (int m = 0; m < EleShapePerPatial::kRow; ++m) { + int accum_m = tile_m * Policy::InterleavedTile::kRow + + mma_m * QuadShapePerPatialMma::kRow + m * 2; + int accum_n = tile_n * Policy::InterleavedTile::kColumn + + mma_n * QuadShapePerPatialMma::kColumn + + p * Policy::InterleavedTile::kColumn / 2; + int r = (accum_m + lane_offset.row()); + AccessType to_store; + CUTLASS_PRAGMA_UNROLL + for (int n = 0; n < EleShapePerPatial::kColumn; ++n) { + int idx = mma_accum_start + p * kElementsPerPartial + + m * EleShapePerPatial::kColumn + n; + int c = (accum_n + n + lane_offset.column()); + to_store[n] = scalar_t(accum[idx]); + } + int c = (accum_n + lane_offset.column()); + assert(r < 32); + assert(c < 32); + *reinterpret_cast( + ref_.data() + ref_.offset({r, c})) = to_store; + } + } + } + } + } + } + } + + static void CUTLASS_DEVICE accumApplyLSEToSmem( + AccumulatorSharedStorage& shared_storage, + typename IteratorC::Fragment& accum, + lse_scalar_t const* lse, + int lse_extent, + int thread_id, + int warp_id, + int lane_id, + cutlass::MatrixCoord const& tile_coords) { + // Non-optimized way to apply LSE to registers + // NOTE: accum is attn.T + // TODO: Optimize for each architecture + static constexpr int WarpSize = 32; + using AccumLambdaIterator = + typename DefaultMmaAccumLambdaIterator:: + Iterator; + auto lane_offset = + AccumLambdaIterator::get_lane_offset(lane_id, warp_id, tile_coords); + + cutlass::Array lse_prefetched; + lse_prefetched.clear(); + int rowIdx = 0; + int colIdx = 0; + AccumLambdaIterator::iterateRows( + lane_offset, + [&](int accum_m) { + ++rowIdx; + colIdx = 0; + }, + [&](int accum_m, int accum_n, int idx) { + if (rowIdx == 1) { + lse_prefetched[colIdx] = accum_n < lse_extent + ? lse[accum_n] + : platform::numeric_limits::infinity(); + } + accum[idx] = expf(accum[idx] - lse_prefetched[colIdx]); + ++colIdx; + }, + [&](int accum_m) {}); + accumToSmem(shared_storage, accum, lane_id, tile_coords); + } +}; + +// Simt Specialization +// for f32 on Sm70-Sm75 and f16/f32 below + +template < + typename Operator, + typename OperatorPolicy, + typename scalar_t, + typename WarpShape_, + typename ThreadblockShape_> +struct B2bGemm< + cutlass::gemm::warp::MmaSimtTileIterator< + cutlass::MatrixShape<32, 32>, + cutlass::gemm::Operand::kC, + float, + cutlass::layout::RowMajor, + OperatorPolicy, + 1, + 1>, + Operator, + scalar_t, + WarpShape_, + ThreadblockShape_> { + using IteratorC = cutlass::gemm::warp::MmaSimtTileIterator< + cutlass::MatrixShape<32, 32>, + cutlass::gemm::Operand::kC, + float, + cutlass::layout::RowMajor, + OperatorPolicy, + 1, + 1>; + using accum_t = typename IteratorC::Element; + using WarpShape = WarpShape_; + using ThreadblockShape = ThreadblockShape_; + using FragmentC = typename IteratorC::Fragment; + using lse_scalar_t = float; + + // Storage in shared-memory for Q.Kt + using AccumulatorSharedStorage = + cutlass::gemm::threadblock::AccumulatorSharedStorage< + ThreadblockShape, + scalar_t, + cutlass::layout::ColumnMajor, + cutlass::MatrixShape<0, 0> // Padding + >; + + static void CUTLASS_DEVICE accumToSmem( + AccumulatorSharedStorage& shared_storage, + FragmentC const& accum, + int lane_id, + cutlass::MatrixCoord const& tile_coords) { + using Policy = typename IteratorC::Policy; + using Element = typename IteratorC::Element; + using Iterations = typename IteratorC::Iterations; + using Delta = typename IteratorC::Delta; + + auto ref_ = shared_storage.accum_ref(); + // ctor - MmaSimtTileIterator + // compute offset based on thread ID and lane layout + typename Policy::LaneLayout lane_layout = Policy::get_lane_layout(); + + MatrixCoord lane_offset = lane_layout.inverse(lane_id) * + MatrixCoord(Policy::LaneMmaShape::kM, Policy::LaneMmaShape::kN); + + ref_.add_coord_offset(lane_offset); + + // Tile offset + ref_.add_coord_offset( + tile_coords * + cutlass::MatrixCoord( + {IteratorC::Shape::kRow, IteratorC::Shape::kColumn})); + + // store - MmaSimtTileIterator + CUTLASS_PRAGMA_UNROLL + for (int mma_n = 0; mma_n < Iterations::kColumn; ++mma_n) { + CUTLASS_PRAGMA_UNROLL + for (int n = 0; n < Policy::LaneMmaShape::kN; ++n) { + CUTLASS_PRAGMA_UNROLL + for (int mma_m = 0; mma_m < Iterations::kRow; ++mma_m) { + CUTLASS_PRAGMA_UNROLL + for (int m = 0; m < Policy::LaneMmaShape::kM; ++m) { + int r = + Policy::LaneMmaShape::kM * (mma_m * Policy::WarpShape::kRow) + + m; + int c = mma_n * Delta::kColumn + n; + int idx = n + + Policy::LaneMmaShape::kN * + (mma_n + + Iterations::kColumn * + (m + mma_m * Policy::LaneMmaShape::kM)); + ref_.at({r, c}) = scalar_t(accum[idx]); + } + } + } + } + } + + static void CUTLASS_DEVICE accumApplyLSEToSmem( + AccumulatorSharedStorage& shared_storage, + typename IteratorC::Fragment& accum, + lse_scalar_t const* lse, + int lse_extent, + int thread_id, + int warp_id, + int lane_id, + cutlass::MatrixCoord const& tile_coords) { + // Non-optimized way to apply LSE to registers + // NOTE: accum is attn.T + // TODO: Optimize for each architecture + static constexpr int WarpSize = 32; + using AccumLambdaIterator = + typename DefaultMmaAccumLambdaIterator:: + Iterator; + auto lane_offset = + AccumLambdaIterator::get_lane_offset(lane_id, warp_id, tile_coords); + + cutlass::Array lse_prefetched; + lse_prefetched.clear(); + int rowIdx = 0; + int colIdx = 0; + AccumLambdaIterator::iterateRows( + lane_offset, + [&](int accum_m) { + ++rowIdx; + colIdx = 0; + }, + [&](int accum_m, int accum_n, int idx) { + if (rowIdx == 1) { + lse_prefetched[colIdx] = accum_n < lse_extent + ? lse[accum_n] + : platform::numeric_limits::infinity(); + } + accum[idx] = expf(accum[idx] - lse_prefetched[colIdx]); + ++colIdx; + }, + [&](int accum_m) {}); + accumToSmem(shared_storage, accum, lane_id, tile_coords); + } +}; + +} // namespace threadblock +} // namespace gemm +} // namespace cutlass + +///////////////////////////////////////////////////////////////////////////////////////////////// diff --git a/csrc/include/natten/cuda/fna/gemm/replace_mma_iterators.h b/csrc/include/natten/cuda/fna/gemm/replace_mma_iterators.h new file mode 100644 index 0000000..cab443e --- /dev/null +++ b/csrc/include/natten/cuda/fna/gemm/replace_mma_iterators.h @@ -0,0 +1,123 @@ +/*************************************************************************************************** + * Copyright (c) 2022-2024 Ali Hassani. + * + * Permission is hereby granted, free of charge, to any person obtaining a copy + * of this software and associated documentation files (the "Software"), to deal + * in the Software without restriction, including without limitation the rights + * to use, copy, modify, merge, publish, distribute, sublicense, and/or sell + * copies of the Software, and to permit persons to whom the Software is + * furnished to do so, subject to the following conditions: + * + * The above copyright notice and this permission notice shall be included in + *all copies or substantial portions of the Software. + * + * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, + * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE + * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER + * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, + * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE + * SOFTWARE. + * + **************************************************************************************************/ + +#pragma once + +#include +#include + +namespace natten { +namespace cuda { +namespace fna { + +// Technically we can easily replace iterators with a derived +// class, but `MakeCustomMma` dispatches based on Mma classes, +// which makes it really difficult to add arbitrary derived classes, +// so to avoid that, we just replace the Mma iterators by creating +// new Mma instantiations instead of using inheritance. +template +struct ReplaceMmaIterators; + +template < + typename Shape, + typename IteratorA, + typename SmemIteratorA, + cutlass::arch::CacheOperation::Kind CacheOpA, + typename IteratorB, + typename SmemIteratorB, + cutlass::arch::CacheOperation::Kind CacheOpB, + typename ElementC, + typename LayoutC, + typename Policy, + int kStages, + cutlass::gemm::SharedMemoryClearOption SharedMemoryClear, + typename NewIteratorA, + typename NewIteratorB> +struct ReplaceMmaIterators< + cutlass::gemm::threadblock::MmaMultistage< + Shape, + IteratorA, + SmemIteratorA, + CacheOpA, + IteratorB, + SmemIteratorB, + CacheOpB, + ElementC, + LayoutC, + Policy, + kStages, + SharedMemoryClear>, + NewIteratorA, + NewIteratorB> { + using Mma = cutlass::gemm::threadblock::MmaMultistage< + Shape, + NewIteratorA, + SmemIteratorA, + CacheOpA, + NewIteratorB, + SmemIteratorB, + CacheOpB, + ElementC, + LayoutC, + Policy, + kStages, + SharedMemoryClear>; +}; + +template < + typename Shape, + typename IteratorA, + typename SmemIteratorA, + typename IteratorB, + typename SmemIteratorB, + typename ElementC, + typename LayoutC, + typename Policy, + typename NewIteratorA, + typename NewIteratorB> +struct ReplaceMmaIterators< + cutlass::gemm::threadblock::MmaPipelined< + Shape, + IteratorA, + SmemIteratorA, + IteratorB, + SmemIteratorB, + ElementC, + LayoutC, + Policy>, + NewIteratorA, + NewIteratorB> { + using Mma = cutlass::gemm::threadblock::MmaPipelined< + Shape, + NewIteratorA, + SmemIteratorA, + NewIteratorB, + SmemIteratorB, + ElementC, + LayoutC, + Policy>; +}; + +} // namespace fna +} // namespace cuda +} // namespace natten diff --git a/csrc/include/natten/cuda/fna/gemm_kernel_utils.h b/csrc/include/natten/cuda/fna/gemm_kernel_utils.h new file mode 100644 index 0000000..fef0499 --- /dev/null +++ b/csrc/include/natten/cuda/fna/gemm_kernel_utils.h @@ -0,0 +1,186 @@ +/*************************************************************************************************** + * Copyright (c) 2022-2024 Ali Hassani. + * + * Fused Neighborhood Attention kernels are heavily based on the + * memory-efficient attention kernels from the xFormers project by Meta + * Platforms, Inc. + * + * Copyright (c) Facebook, Inc. and its affiliates + * + * BSD 3-Clause License + * + * Redistribution and use in source and binary forms, with or without + * modification, are permitted provided that the following conditions are met: + * + * 1. Redistributions of source code must retain the above copyright + * notice, this list of conditions and the following disclaimer. + * + * 2. Redistributions in binary form must reproduce the above copyright + * notice, this list of conditions and the following disclaimer in the + * documentation and/or other materials provided with the distribution. + * + * 3. Neither the names of Facebook, Deepmind Technologies, NYU, NEC + * Laboratories America and IDIAP Research Institute nor the names of its + * contributors may be used to endorse or promote products derived from this + * software without specific prior written permission. + * + * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" + * AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE + * IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE + * ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE + * LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR + * CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF + * SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS + * INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN + * CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) + * ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE + * POSSIBILITY OF SUCH DAMAGE. + */ + +#pragma once + +#include + +#include + +#define DISPATCH_BOOL(BOOL_V, BOOL_NAME, F) \ + { \ + if (BOOL_V) { \ + constexpr bool BOOL_NAME = true; \ + F(); \ + } else { \ + constexpr bool BOOL_NAME = false; \ + F(); \ + } \ + } + +#define CHECK_ALIGNED_PTR(PTR, ALIGNMENT) \ + NATTEN_CHECK(uint64_t(PTR) % ALIGNMENT == 0, #PTR " is not correctly aligned") + +namespace gemm_kernel_utils { + +template +constexpr CUTLASS_HOST_DEVICE integer ceil_div(integer n, integer m) { + return (n + m - 1) / m; +} + +template +constexpr CUTLASS_HOST_DEVICE integer align_up(integer n, integer m) { + return ((n + m - 1) / m) * m; +} + +//////////////////////////////////////////////////////////////////////////////// +// Determine the type of GEMM we do (TensorCores or not, Shapes ...) +// TODO: Maybe we could rely on Cutlass's DefaultGemm templates +//////////////////////////////////////////////////////////////////////////////// + +// Fallback to Simt (FMA on cuda cores) if not in a special case below +template +struct DefaultGemmType { + static constexpr int ThreadK = 8; + static constexpr int WarpK = 8; + static constexpr int kMinimumAlignment = 1; + using InstructionShape = cutlass::gemm::GemmShape<1, 1, 1>; + using OpClass = cutlass::arch::OpClassSimt; + using Operator = cutlass::arch::OpMultiplyAdd; +}; + +// Specialization for tensorcores with f32 +template +struct DefaultGemmType< + ArchTag, + float, + typename cutlass::platform::enable_if< + ArchTag::kMinComputeCapability >= 80>::type> { + static constexpr int ThreadK = 32; + static constexpr int WarpK = 32; + static constexpr int kMinimumAlignment = 4; + using OpClass = cutlass::arch::OpClassTensorOp; + using InstructionShape = cutlass::gemm::GemmShape<16, 8, 8>; + using Operator = cutlass::arch::OpMultiplyAddFastF32; +}; + +// Specialization for tensorcores with f16/bf16 - Sm75+ +template +struct DefaultGemmType< + ArchTag, + scalar_t, + typename cutlass::platform::enable_if< + ArchTag::kMinComputeCapability >= 75 && + cutlass::sizeof_bits::value == 16>::type> { + static constexpr int ThreadK = 32; + static constexpr int WarpK = 32; + static constexpr int kMinimumAlignment = 4; + using OpClass = cutlass::arch::OpClassTensorOp; + using InstructionShape = cutlass::gemm::GemmShape<16, 8, 8>; + using Operator = cutlass::arch::OpMultiplyAdd; +}; + +// Specialization for tensorcores with f16 - Volta +template <> +struct DefaultGemmType { + static constexpr int ThreadK = 32; + static constexpr int WarpK = 32; + static constexpr int kMinimumAlignment = 2; + using OpClass = cutlass::arch::OpClassTensorOp; + using InstructionShape = cutlass::gemm::GemmShape<8, 8, 4>; + using Operator = cutlass::arch::OpMultiplyAdd; +}; + +// Enables to do +// `auto x = kCondition ? fa(arg) : fb(arg)` +// when `fa` and `fb` have different types +template +struct call_conditional; + +template +struct call_conditional { + template + static CUTLASS_HOST_DEVICE auto apply(TA ta, TB tb, Arg arg) + -> decltype(ta(arg)) { + return ta(arg); + } +}; + +template +struct call_conditional { + template + static CUTLASS_HOST_DEVICE auto apply(TA ta, TB tb, Arg arg) + -> decltype(tb(arg)) { + return tb(arg); + } +}; + +//////////////////////////////////////////////////////////////////////////////// +// Mark a variable as warp-uniform - enables some compiler optimizations +// The cheapest way to do it is just to broadcast it from lane 0 +//////////////////////////////////////////////////////////////////////////////// + +template +CUTLASS_DEVICE T warp_uniform(T value) { + struct { + union { + T value; + uint32_t asInt; + }; + } p; + p.value = value; + p.asInt = __shfl_sync(0xffffffff, (unsigned)p.asInt, 0); + return p.value; +} + +template +CUTLASS_DEVICE T* warp_uniform(T* ptr) { + struct { + union { + T* ptr; + uint32_t asInt[2]; + }; + } p; + p.ptr = ptr; + p.asInt[0] = warp_uniform(p.asInt[0]); + p.asInt[1] = warp_uniform(p.asInt[1]); + return p.ptr; +} + +} // namespace gemm_kernel_utils diff --git a/csrc/include/natten/cuda/fna/iterators/default_warp_iterator_from_smem.h b/csrc/include/natten/cuda/fna/iterators/default_warp_iterator_from_smem.h new file mode 100644 index 0000000..a7256e4 --- /dev/null +++ b/csrc/include/natten/cuda/fna/iterators/default_warp_iterator_from_smem.h @@ -0,0 +1,149 @@ +/* + * Copied from xFormers (https://github.com/facebookresearch/xformers/) + * + * Copyright (c) Meta Platforms, Inc. and affiliates. + * All rights reserved. + */ +/*************************************************************************************************** + * Copyright (c) 2017 - 2023 NVIDIA CORPORATION & AFFILIATES. All rights + *reserved. SPDX-License-Identifier: BSD-3-Clause + * + * Redistribution and use in source and binary forms, with or without + * modification, are permitted provided that the following conditions are met: + * + * 1. Redistributions of source code must retain the above copyright notice, + *this list of conditions and the following disclaimer. + * + * 2. Redistributions in binary form must reproduce the above copyright notice, + * this list of conditions and the following disclaimer in the documentation + * and/or other materials provided with the distribution. + * + * 3. Neither the name of the copyright holder nor the names of its + * contributors may be used to endorse or promote products derived from + * this software without specific prior written permission. + * + * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" + * AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE + * IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE + *ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE + *LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR + *CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF + *SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS + *INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN + *CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) + *ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE + *POSSIBILITY OF SUCH DAMAGE. + * + **************************************************************************************************/ +/*! \file + \brief Instanciates the right WarpIterator to read from shared memory + The class `DefaultWarpIteratorAFromSharedMemory` is useful when reading + data dumped with `B2bGemm::accumToSmem`. +*/ + +#pragma once + +#include "cutlass/cutlass.h" +#include "cutlass/gemm/warp/mma_tensor_op_tile_access_iterator.h" +#include "cutlass/platform/platform.h" + +#include "warp_iterator_from_smem.h" + +namespace cutlass { +namespace gemm { +namespace threadblock { + +template < + typename WarpShape, + typename InstructionShape, + typename RegularWarpIterator, + typename Policy, + typename Enable = void> +struct DefaultWarpIteratorAFromSharedMemory {}; + +// TensorOp - Ampere half +template +struct DefaultWarpIteratorAFromSharedMemory< + cutlass::gemm::GemmShape<32, 32, 32>, + cutlass::gemm::GemmShape<16, 8, kInstrK>, + RegularWarpIterator, + Policy, + typename platform::enable_if<( + sizeof_bits::value == 16 && + Policy::Operator::Policy::OpDelta::kRow == 1)>::type> { + using OpDelta = typename Policy::Operator::Policy::OpDelta; + using WarpShape = cutlass::MatrixShape<32, 32>; + using InstructionShape = cutlass::gemm::GemmShape<16, 8, kInstrK>; + + using WarpIterator = cutlass::gemm::warp::WarpIteratorFromSmem< + cutlass::gemm::Operand::kA, + typename RegularWarpIterator::Element, + cutlass::MatrixShape>; +}; + +// TensorOp - Ampere f32 +template +struct DefaultWarpIteratorAFromSharedMemory< + WarpShape, + cutlass::gemm::GemmShape<16, 8, 8>, + RegularWarpIterator, + Policy, + typename platform::enable_if<( + sizeof_bits::value != 16 || + Policy::Operator::Policy::OpDelta::kRow != 1)>::type> { + using InstructionShape = cutlass::gemm::GemmShape<16, 8, 8>; + static constexpr auto kWarpSize = 32; + using OpDelta = typename Policy::Operator::Policy::OpDelta; + + using WarpIterator = + cutlass::gemm::warp::MmaTensorOpMultiplicandTileAccessIterator< + cutlass::MatrixShape, + cutlass::gemm::Operand::kA, + typename RegularWarpIterator::Element, + cutlass::layout::RowMajor, + cutlass::MatrixShape, + OpDelta::kRow, + kWarpSize>; +}; + +// TensorOp - Volta +template +struct DefaultWarpIteratorAFromSharedMemory< + WarpShape, + cutlass::gemm::GemmShape<16, 16, 4>, + RegularWarpIterator, + Policy> { + using InstructionShape = cutlass::gemm::GemmShape<16, 16, 4>; + static constexpr auto kWarpSize = 32; + using OpDelta = typename Policy::Operator::Policy::OpDelta; + + using WarpIterator = + cutlass::gemm::warp::MmaVoltaTensorOpMultiplicandTileIterator< + cutlass::MatrixShape<32, 32>, // MatrixShape, + cutlass::gemm::Operand::kA, + typename RegularWarpIterator::Element, + cutlass::layout::RowMajorVoltaTensorOpMultiplicandCrosswise<16, 32>, + cutlass::MatrixShape<16, 4>, + OpDelta::kRow, + kWarpSize>; +}; + +// Simt +template +struct DefaultWarpIteratorAFromSharedMemory< + WarpShape, + cutlass::gemm::GemmShape<1, 1, 1>, + RegularWarpIterator, + Policy> { + using InstructionShape = cutlass::gemm::GemmShape<1, 1, 1>; + static constexpr auto kWarpSize = 32; + + // We just use the same iterator, as we reproduced the same shared-memory + // schema. Just modify it to handle non-complete tiles. + using WarpIterator = RegularWarpIterator; +}; + +} // namespace threadblock +} // namespace gemm +} // namespace cutlass diff --git a/csrc/include/natten/cuda/fna/iterators/epilogue_predicated_tile_iterator.h b/csrc/include/natten/cuda/fna/iterators/epilogue_predicated_tile_iterator.h new file mode 100644 index 0000000..1b7b827 --- /dev/null +++ b/csrc/include/natten/cuda/fna/iterators/epilogue_predicated_tile_iterator.h @@ -0,0 +1,760 @@ +/* + * Copied from xFormers (https://github.com/facebookresearch/xformers/) + * + * NOTE: this is only used in the backward pass kernel. + * + * Copyright (c) Meta Platforms, Inc. and affiliates. + * All rights reserved. + */ +/*************************************************************************************************** + * Copyright (c) 2017 - 2023 NVIDIA CORPORATION & AFFILIATES. All rights + *reserved. SPDX-License-Identifier: BSD-3-Clause + * + * Redistribution and use in source and binary forms, with or without + * modification, are permitted provided that the following conditions are met: + * + * 1. Redistributions of source code must retain the above copyright notice, + *this list of conditions and the following disclaimer. + * + * 2. Redistributions in binary form must reproduce the above copyright notice, + * this list of conditions and the following disclaimer in the documentation + * and/or other materials provided with the distribution. + * + * 3. Neither the name of the copyright holder nor the names of its + * contributors may be used to endorse or promote products derived from + * this software without specific prior written permission. + * + * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" + * AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE + * IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE + *ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE + *LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR + *CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF + *SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS + *INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN + *CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) + *ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE + *POSSIBILITY OF SUCH DAMAGE. + * + **************************************************************************************************/ +/*! \file + \brief Epilogue iterator that supports prefetching + + Mostly copied from "cutlass/epilogue/threadblock/predicated_tile_iterator.h" +*/ + +#pragma once + +#include "cutlass/arch/arch.h" +#include "cutlass/arch/memory.h" +#include "cutlass/array.h" +#include "cutlass/cutlass.h" +#include "cutlass/epilogue/threadblock/output_tile_thread_map.h" +#include "cutlass/epilogue/threadblock/predicated_tile_iterator_params.h" +#include "cutlass/layout/matrix.h" +#include "cutlass/layout/tensor.h" +#include "cutlass/matrix_shape.h" +#include "cutlass/numeric_types.h" +#include "cutlass/tensor_ref.h" +#include "cutlass/transform/pitch_linear_thread_map.h" + +//////////////////////////////////////////////////////////////////////////////// + +namespace cutlass { + +//////////////////////////////////////////////////////////////////////////////// + +namespace epilogue { +namespace threadblock { + +//////////////////////////////////////////////////////////////////////////////// + +/// Tile iterator used to load and store output tile from global memory in +/// epilogue. +/// +/// Satisfies: ReadableTileIterator | PredicatedTileIterator | +/// ForwardTileIterator +/// +template < + typename ThreadMap_, ///< Thread map (conept: OutputTileThreadMap) + typename Element_, ///< Element data type + bool ScatterD = false, ///< Scatter D operand or not + bool UseCUDAStore = false> +class PredicatedTileIteratorPrefetch { + public: + using ThreadMap = ThreadMap_; + using Shape = typename ThreadMap::Shape; + + using Element = Element_; + + using Layout = layout::RowMajor; + using TensorRef = TensorRef; + using ConstTensorRef = typename TensorRef::ConstTensorRef; + + using Index = typename Layout::Index; + using LongIndex = typename Layout::LongIndex; + using TensorCoord = MatrixCoord; + + static int const kElementsPerAccess = ThreadMap::kElementsPerAccess; + static int const kThreads = ThreadMap::kThreads; + static int const kIterations = ThreadMap::Count::kTile; + + static_assert( + ThreadMap::Iterations::kRow > 0, + "ThreadMap::Iterations::kRow must be > 0"); + static_assert( + ThreadMap::Iterations::kGroup > 0, + "ThreadMap::Iterations::kGroup must be > 0"); + static_assert( + ThreadMap::Iterations::kCluster > 0, + "ThreadMap::Iterations::kCluster must be > 0"); + static_assert( + ThreadMap::Iterations::kColumn > 0, + "ThreadMap::Iterations::kColumn must be > 0"); + + /// Fragment object + using Fragment = Array< + Element, + ThreadMap::Iterations::kColumn * ThreadMap::Iterations::kRow * + ThreadMap::Iterations::kGroup * ThreadMap::Iterations::kCluster * + ThreadMap::kElementsPerAccess>; + + /// Memory access size + using AccessType = AlignedArray; + + // + // Parameters struct + // + + /// Uses a non-template class + struct Params : PredicatedTileIteratorParams { + using Base = PredicatedTileIteratorParams; + + CUTLASS_HOST_DEVICE + Params() {} + + CUTLASS_HOST_DEVICE + Params(Layout const& layout) + : PredicatedTileIteratorParams( + layout.stride(0) * int(sizeof(AccessType)) / kElementsPerAccess, + make_OutputTileThreadMapDesc()) {} + + CUTLASS_HOST_DEVICE + Params(Base const& base) : Base(base) {} + }; + + /// Mask object + struct Mask { + static int const kCount = ThreadMap::Iterations::kColumn; + + /// Predicate state + bool predicates[kCount]; + + // + // Mask + // + CUTLASS_HOST_DEVICE + Mask() { + enable(); + } + + ///< Efficiently disables all accesses guarded by mask + CUTLASS_HOST_DEVICE void clear() { + CUTLASS_PRAGMA_UNROLL + for (int i = 0; i < kCount; ++i) { + predicates[i] = false; + } + } + + ///< CUTLASS_HOST_DEVICE enables all accesses guarded by mask + CUTLASS_DEVICE void enable() { + CUTLASS_PRAGMA_UNROLL + for (int i = 0; i < kCount; ++i) { + predicates[i] = true; + } + } + }; + + private: + // + // Data members + // + + /// Parameters structure containing reference and precomputed state. + PredicatedTileIteratorParams params_; + + /// Byte-level pointer + uint8_t* byte_pointer_; + + /// Array of boolean values to contain steady-state predicates + Mask mask_; + + /// Extent of the matrix tile in rows + Index extent_row_; + + /// Extent of the matrix tile in rows + Index extent_column_; + + /// A thread's starting row position (assuming steady-state predicates have + /// been computed) + Index thread_start_row_; + + /// A thread's starting column + Index thread_start_column_; + + /// Internal state counter + int state_[3]; + + /// Scatter indices + int const* indices_; + + // + // Static asserts about internal strides + // + + static_assert(sizeof(extent_row_) == 4, "Expected 32b extents"); + static_assert(sizeof(thread_start_row_) == 4, "Expected 32b extents"); + static_assert( + sizeof(PredicatedTileIteratorParams::stride) == 8, + "Expected 64b strides"); + + private: + // + // Methods + // + + public: + // + // Methods + // + + /// Constructor + CUTLASS_DEVICE + PredicatedTileIteratorPrefetch( + PredicatedTileIteratorParams const& params, + Element* pointer, + TensorCoord extent, + int thread_idx, + TensorCoord threadblock_offset = TensorCoord(), + int const* indices = nullptr) + : params_(params), indices_(indices) { + TensorCoord thread_offset = + ThreadMap::initial_offset(thread_idx) + threadblock_offset; + + extent_row_ = extent.row(); + extent_column_ = extent.column(); + + thread_start_row_ = thread_offset.row(); + thread_start_column_ = thread_offset.column(); + + // Initialize predicates + CUTLASS_PRAGMA_UNROLL + for (int c = 0; c < ThreadMap::Iterations::kColumn; ++c) { + mask_.predicates[c] = + ((thread_offset.column() + ThreadMap::Delta::kColumn * c) < + extent.column()); + } + + // Null pointer performs no accesses + if (!pointer) { + mask_.clear(); + } + + if (ScatterD && !indices) { + mask_.clear(); + } + + // Initialize pointer + byte_pointer_ = reinterpret_cast(pointer) + + LongIndex(thread_offset.row()) * LongIndex(params_.stride) + + LongIndex(thread_offset.column()) * sizeof(AccessType) / + kElementsPerAccess; + + if (ScatterD) { + byte_pointer_ = reinterpret_cast(pointer) + + LongIndex(thread_offset.column()) * sizeof(AccessType) / + kElementsPerAccess; + } + + // Initialize internal state counter + state_[0] = state_[1] = state_[2] = 0; + } + + /// Adds a pointer offset in units of Element + CUTLASS_HOST_DEVICE + void add_pointer_offset(LongIndex pointer_offset) { + byte_pointer_ += pointer_offset * sizeof_bits::value / 8; + } + + CUTLASS_DEVICE + void prefetch_all() { + CUTLASS_PRAGMA_UNROLL + for (int iter = 0; iter < kIterations; ++iter) { + prefetch(); + ++(*this); + } + } + + CUTLASS_DEVICE + void prefetch() { + uint8_t* byte_pointer = byte_pointer_; + + CUTLASS_PRAGMA_UNROLL + for (int cluster = 0; cluster < ThreadMap::Iterations::kCluster; + ++cluster) { + CUTLASS_PRAGMA_UNROLL + for (int group = 0; group < ThreadMap::Iterations::kGroup; ++group) { + CUTLASS_PRAGMA_UNROLL + for (int row = 0; row < ThreadMap::Iterations::kRow; ++row) { + int row_offset = row * ThreadMap::Delta::kRow + + group * ThreadMap::Delta::kGroup + + cluster * ThreadMap::Delta::kCluster; + + AccessType* memory_pointer = + reinterpret_cast(byte_pointer); + + CUTLASS_PRAGMA_UNROLL + for (int column = 0; column < ThreadMap::Iterations::kColumn; + ++column) { + // on windows using unsigned long here gives the error + // error: asm operand type size(4) does not match + // type/size implied by constraint 'l' + uint64_t addr = (uint64_t)( + (void*)&memory_pointer + [column * ThreadMap::Delta::kColumn / kElementsPerAccess]); + asm volatile("prefetch.global.L1 [ %1 ];" : "=l"(addr) : "l"(addr)); + } + + if (row + 1 < ThreadMap::Iterations::kRow) { + if (!ScatterD) { + byte_pointer += params_.increment_row; + } + } + } + + if (group + 1 < ThreadMap::Iterations::kGroup) { + byte_pointer += params_.increment_group; + } + } + + if (cluster + 1 < ThreadMap::Iterations::kCluster) { + byte_pointer += params_.increment_cluster; + } + } + } + + /// Loads a fragment from memory + CUTLASS_DEVICE + void load_with_byte_offset(Fragment& frag, int64_t byte_offset) const { + uint8_t* byte_pointer = byte_pointer_; + AccessType* frag_ptr = reinterpret_cast(&frag); + + CUTLASS_PRAGMA_UNROLL + for (int cluster = 0; cluster < ThreadMap::Iterations::kCluster; + ++cluster) { + CUTLASS_PRAGMA_UNROLL + for (int group = 0; group < ThreadMap::Iterations::kGroup; ++group) { + CUTLASS_PRAGMA_UNROLL + for (int row = 0; row < ThreadMap::Iterations::kRow; ++row) { + int frag_row_idx = + (row + + ThreadMap::Iterations::kRow * + (group + ThreadMap::Iterations::kGroup * cluster)); + + int row_offset = row * ThreadMap::Delta::kRow + + group * ThreadMap::Delta::kGroup + + cluster * ThreadMap::Delta::kCluster; + + bool row_guard = ((row_offset + thread_start_row_) < extent_row_); + + AccessType* memory_pointer = + reinterpret_cast(byte_pointer + byte_offset); + + if (ScatterD && row_guard) { + assert(indices_); + + memory_pointer = reinterpret_cast( + byte_pointer + byte_offset + + LongIndex(indices_[row_offset + thread_start_row_]) * + LongIndex(params_.stride)); + } + + CUTLASS_PRAGMA_UNROLL + for (int column = 0; column < ThreadMap::Iterations::kColumn; + ++column) { + bool guard = row_guard && mask_.predicates[column]; + + cutlass::arch::global_load( + frag_ptr + [frag_row_idx * ThreadMap::Iterations::kColumn + column], + (void*)&memory_pointer + [column * ThreadMap::Delta::kColumn / kElementsPerAccess], + guard); + } + + if (row + 1 < ThreadMap::Iterations::kRow) { + if (!ScatterD) { + byte_pointer += params_.increment_row; + } + } + } + + if (group + 1 < ThreadMap::Iterations::kGroup) { + byte_pointer += params_.increment_group; + } + } + + if (cluster + 1 < ThreadMap::Iterations::kCluster) { + byte_pointer += params_.increment_cluster; + } + } + } + + /// Loads a fragment from memory + CUTLASS_DEVICE + void load(Fragment& frag) const { + load_with_byte_offset(frag, 0); + } + + /// Stores a fragment to memory + CUTLASS_DEVICE + void store_with_byte_offset(Fragment const& frag, int64_t byte_offset) const { + uint8_t* byte_pointer = byte_pointer_; + AccessType const* frag_ptr = reinterpret_cast(&frag); + + CUTLASS_PRAGMA_UNROLL + for (int cluster = 0; cluster < ThreadMap::Iterations::kCluster; + ++cluster) { + CUTLASS_PRAGMA_UNROLL + for (int group = 0; group < ThreadMap::Iterations::kGroup; ++group) { + CUTLASS_PRAGMA_UNROLL + for (int row = 0; row < ThreadMap::Iterations::kRow; ++row) { + int frag_row_idx = + (row + + ThreadMap::Iterations::kRow * + (group + ThreadMap::Iterations::kGroup * cluster)); + + int row_offset = row * ThreadMap::Delta::kRow + + group * ThreadMap::Delta::kGroup + + cluster * ThreadMap::Delta::kCluster; + + bool row_guard = ((row_offset + thread_start_row_) < extent_row_); + + AccessType* memory_pointer = + reinterpret_cast(byte_pointer + byte_offset); + + if (ScatterD && row_guard) { + assert(indices_); + + memory_pointer = reinterpret_cast( + byte_pointer + byte_offset + + LongIndex(indices_[row_offset + thread_start_row_]) * + LongIndex(params_.stride)); + } + + CUTLASS_PRAGMA_UNROLL + for (int column = 0; column < ThreadMap::Iterations::kColumn; + ++column) { + bool guard = row_guard && mask_.predicates[column]; + + if (UseCUDAStore) { + if (guard) { + memory_pointer + [column * ThreadMap::Delta::kColumn / kElementsPerAccess] = + frag_ptr + [frag_row_idx * ThreadMap::Iterations::kColumn + + column]; + } + } else { + cutlass::arch::global_store( + frag_ptr + [frag_row_idx * ThreadMap::Iterations::kColumn + column], + (void*)&memory_pointer + [column * ThreadMap::Delta::kColumn / kElementsPerAccess], + guard); + } + } + + if (row + 1 < ThreadMap::Iterations::kRow) { + if (!ScatterD) { + byte_pointer += params_.increment_row; + } + } + } + + if (group + 1 < ThreadMap::Iterations::kGroup) { + byte_pointer += params_.increment_group; + } + } + + if (cluster + 1 < ThreadMap::Iterations::kCluster) { + byte_pointer += params_.increment_cluster; + } + } + } + + /// Stores a fragment to memory + CUTLASS_DEVICE + void store(Fragment const& frag) const { + store_with_byte_offset(frag, 0); + } + + /// Loads a fragment from memory + CUTLASS_DEVICE + void downsample_load_with_byte_offset( + Fragment& frag, + int64_t byte_offset, + int convolution_P, + int convolution_Q, + int add_P, + int add_Q, + int problem_N) const { + uint8_t* byte_pointer = byte_pointer_; + AccessType* frag_ptr = reinterpret_cast(&frag); + + CUTLASS_PRAGMA_UNROLL + for (int cluster = 0; cluster < ThreadMap::Iterations::kCluster; + ++cluster) { + CUTLASS_PRAGMA_UNROLL + for (int group = 0; group < ThreadMap::Iterations::kGroup; ++group) { + CUTLASS_PRAGMA_UNROLL + for (int row = 0; row < ThreadMap::Iterations::kRow; ++row) { + int frag_row_idx = + (row + + ThreadMap::Iterations::kRow * + (group + ThreadMap::Iterations::kGroup * cluster)); + + int row_offset = row * ThreadMap::Delta::kRow + + group * ThreadMap::Delta::kGroup + + cluster * ThreadMap::Delta::kCluster; + + bool row_guard = ((row_offset + thread_start_row_) < extent_row_); + + int output_row = row_offset + thread_start_row_; + int output_N = output_row / (convolution_P * convolution_Q); + int output_PQ = output_row % (convolution_P * convolution_Q); + int output_P = output_PQ / convolution_Q; + int output_Q = output_PQ % convolution_Q; + + int input_row = output_N * 2 * convolution_P * 2 * convolution_Q + + (2 * output_P + add_P) * 2 * convolution_Q + 2 * output_Q + add_Q; + + int64_t byte_offset = + (input_row - output_row) * problem_N * sizeof(float); + + AccessType* memory_pointer = + reinterpret_cast(byte_pointer + byte_offset); + + CUTLASS_PRAGMA_UNROLL + for (int column = 0; column < ThreadMap::Iterations::kColumn; + ++column) { + bool guard = row_guard && mask_.predicates[column]; + + cutlass::arch::global_load( + frag_ptr + [frag_row_idx * ThreadMap::Iterations::kColumn + column], + (void*)&memory_pointer + [column * ThreadMap::Delta::kColumn / kElementsPerAccess], + guard); + } + + if (row + 1 < ThreadMap::Iterations::kRow) { + byte_pointer += params_.increment_row; + } + } + + if (group + 1 < ThreadMap::Iterations::kGroup) { + byte_pointer += params_.increment_group; + } + } + + if (cluster + 1 < ThreadMap::Iterations::kCluster) { + byte_pointer += params_.increment_cluster; + } + } + } + + /// Loads a fragment from memory + CUTLASS_DEVICE + void upsample_load_with_byte_offset( + Fragment& frag, + int64_t byte_offset, + int convolution_P, + int convolution_Q, + int add_P, + int add_Q, + int problem_N) const { + uint8_t* byte_pointer = byte_pointer_; + AccessType* frag_ptr = reinterpret_cast(&frag); + + CUTLASS_PRAGMA_UNROLL + for (int cluster = 0; cluster < ThreadMap::Iterations::kCluster; + ++cluster) { + CUTLASS_PRAGMA_UNROLL + for (int group = 0; group < ThreadMap::Iterations::kGroup; ++group) { + CUTLASS_PRAGMA_UNROLL + for (int row = 0; row < ThreadMap::Iterations::kRow; ++row) { + int frag_row_idx = + (row + + ThreadMap::Iterations::kRow * + (group + ThreadMap::Iterations::kGroup * cluster)); + + int row_offset = row * ThreadMap::Delta::kRow + + group * ThreadMap::Delta::kGroup + + cluster * ThreadMap::Delta::kCluster; + + bool row_guard = ((row_offset + thread_start_row_) < extent_row_); + + int output_row = row_offset + thread_start_row_; + int output_N = output_row / (convolution_P * convolution_Q); + int output_PQ = output_row % (convolution_P * convolution_Q); + int output_P = output_PQ / convolution_Q; + int output_Q = output_PQ % convolution_Q; + int row_add_P = add_P; + int row_add_Q = add_Q; + if (output_P > convolution_P - 2) + row_add_P = 0; + if (output_Q > convolution_Q - 2) + row_add_Q = 0; + + int input_row = output_N * (convolution_P / 2) * (convolution_Q / 2) + + ((output_P + row_add_P) / 2) * (convolution_Q / 2) + + (output_Q + row_add_Q) / 2; + + int64_t byte_offset = + (input_row - output_row) * problem_N * sizeof(float); + + AccessType* memory_pointer = + reinterpret_cast(byte_pointer + byte_offset); + + CUTLASS_PRAGMA_UNROLL + for (int column = 0; column < ThreadMap::Iterations::kColumn; + ++column) { + bool guard = row_guard && mask_.predicates[column]; + + cutlass::arch::global_load( + frag_ptr + [frag_row_idx * ThreadMap::Iterations::kColumn + column], + (void*)&memory_pointer + [column * ThreadMap::Delta::kColumn / kElementsPerAccess], + guard); + } + + if (row + 1 < ThreadMap::Iterations::kRow) { + byte_pointer += params_.increment_row; + } + } + + if (group + 1 < ThreadMap::Iterations::kGroup) { + byte_pointer += params_.increment_group; + } + } + + if (cluster + 1 < ThreadMap::Iterations::kCluster) { + byte_pointer += params_.increment_cluster; + } + } + } + + CUTLASS_DEVICE + MatrixCoord thread_start() const { + return MatrixCoord(thread_start_row_, thread_start_column_); + } + + /// Need to get the thread start row from the tile iterator + CUTLASS_DEVICE + int32_t thread_start_row() const { + return thread_start_row_; + } + + /// Need to get the thread start row from the tile iterator + CUTLASS_DEVICE + int32_t thread_start_column() const { + return thread_start_column_; + } + + /// Extent of the matrix in rows + CUTLASS_DEVICE + Index extent_row() const { + return extent_row_; + } + + /// Extent of the matrix in columns + CUTLASS_DEVICE + Index extent_column() const { + return extent_column_; + } + + /// Advances to the next position to load or store + CUTLASS_HOST_DEVICE + PredicatedTileIteratorPrefetch& operator++() { + ++state_[0]; + + if (!ScatterD) { + byte_pointer_ += params_.advance_row; + } + + thread_start_row_ += ThreadMap::Shape::kRow; + + if (state_[0] == ThreadMap::Count::kRow) { + state_[0] = 0; + ++state_[1]; + byte_pointer_ += params_.advance_group; + + thread_start_row_ += (ThreadMap::Shape::kGroup - 1) * + ThreadMap::Shape::kRow * ThreadMap::Count::kRow; + + if (state_[1] == ThreadMap::Count::kGroup) { + state_[1] = 0; + ++state_[2]; + byte_pointer_ += params_.advance_cluster; + + thread_start_row_ += ThreadMap::Count::kGroup * + ThreadMap::Shape::kGroup * ThreadMap::Count::kRow * + ThreadMap::Shape::kRow; + + if (state_[2] == ThreadMap::Count::kCluster) { + state_[2] = 0; + byte_pointer_ += params_.advance_tile; + } + } + } + + return *this; + } + + ///< Efficiently disables all accesses guarded by mask + CUTLASS_DEVICE void clear_mask() { + mask_.clear(); + } + + ///< Efficiently enables all accesses guarded by mask + CUTLASS_DEVICE void enable_mask() { + mask_.enable(); + } + + ///< Sets the mask + CUTLASS_DEVICE void get_mask(Mask& mask) const { + mask = mask_; + } + + ///< Sets the mask + CUTLASS_DEVICE void set_mask(Mask const& mask) { + mask_ = mask; + } +}; + +template +struct MakePrefetchableIterator { + using Iterator = PredicatedTileIteratorPrefetch< + typename IT::ThreadMap, + typename IT::Element>; +}; + +/////////////////////////////////////////////////////////////////////////////// + +} // namespace threadblock +} // namespace epilogue +} // namespace cutlass + +//////////////////////////////////////////////////////////////////////////////// diff --git a/csrc/include/natten/cuda/fna/iterators/make_residual_last.h b/csrc/include/natten/cuda/fna/iterators/make_residual_last.h new file mode 100644 index 0000000..69abf4c --- /dev/null +++ b/csrc/include/natten/cuda/fna/iterators/make_residual_last.h @@ -0,0 +1,108 @@ +/*************************************************************************************************** + * Copyright (c) 2022-2024 Ali Hassani. + * + * Fused Neighborhood Attention kernels are heavily based on the + * memory-efficient attention kernels from the xFormers project by Meta + * Platforms, Inc. + * + * Copyright (c) Facebook, Inc. and its affiliates + * + * BSD 3-Clause License + * + * Redistribution and use in source and binary forms, with or without + * modification, are permitted provided that the following conditions are met: + * + * 1. Redistributions of source code must retain the above copyright + * notice, this list of conditions and the following disclaimer. + * + * 2. Redistributions in binary form must reproduce the above copyright + * notice, this list of conditions and the following disclaimer in the + * documentation and/or other materials provided with the distribution. + * + * 3. Neither the names of Facebook, Deepmind Technologies, NYU, NEC + * Laboratories America and IDIAP Research Institute nor the names of its + * contributors may be used to endorse or promote products derived from this + * software without specific prior written permission. + * + * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" + * AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE + * IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE + * ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE + * LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR + * CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF + * SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS + * INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN + * CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) + * ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE + * POSSIBILITY OF SUCH DAMAGE. + */ + +#pragma once + +#include "predicated_tile_access_iterator_residual_last.h" +#include "predicated_tile_iterator_residual_last.h" + +namespace cutlass { +namespace transform { +namespace threadblock { + +template +struct MakeIteratorResidualLast; + +template < + int NADim, + typename Shape, + typename Element, + typename Layout, + int AdvanceRank, + typename ThreadMap, + int AccessSize> +struct MakeIteratorResidualLast< + NADim, + PredicatedTileIterator< + Shape, + Element, + Layout, + AdvanceRank, + ThreadMap, + AccessSize>> { + using Iterator = PredicatedTileIteratorResidualLast< + NADim, + Shape, + Element, + Layout, + AdvanceRank, + ThreadMap, + AccessSize>; +}; + +template < + int NADim, + typename Shape, + typename Element, + typename Layout, + int AdvanceRank, + typename ThreadMap, + typename AccessType> +struct MakeIteratorResidualLast< + NADim, + PredicatedTileAccessIterator< + Shape, + Element, + Layout, + AdvanceRank, + ThreadMap, + AccessType>> { + using Iterator = PredicatedTileAccessIteratorResidualLast< + NADim, + Shape, + Element, + Layout, + AdvanceRank, + ThreadMap, + AccessType>; +}; + +} // namespace threadblock +} // namespace transform +} // namespace cutlass diff --git a/csrc/include/natten/cuda/fna/iterators/predicated_tile_access_iterator.h b/csrc/include/natten/cuda/fna/iterators/predicated_tile_access_iterator.h new file mode 100644 index 0000000..795cc9a --- /dev/null +++ b/csrc/include/natten/cuda/fna/iterators/predicated_tile_access_iterator.h @@ -0,0 +1,1662 @@ +/* + * Copied from CUTLASS (https://github.com/NVIDIA/cutlass/) and edited. + */ +/*************************************************************************************************** + * Copyright (c) 2017 - 2024 NVIDIA CORPORATION & AFFILIATES. All rights + *reserved. SPDX-License-Identifier: BSD-3-Clause + * + * Redistribution and use in source and binary forms, with or without + * modification, are permitted provided that the following conditions are met: + * + * 1. Redistributions of source code must retain the above copyright notice, + *this list of conditions and the following disclaimer. + * + * 2. Redistributions in binary form must reproduce the above copyright notice, + * this list of conditions and the following disclaimer in the documentation + * and/or other materials provided with the distribution. + * + * 3. Neither the name of the copyright holder nor the names of its + * contributors may be used to endorse or promote products derived from + * this software without specific prior written permission. + * + * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" + * AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE + * IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE + *ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE + *LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR + *CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF + *SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS + *INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN + *CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) + *ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE + *POSSIBILITY OF SUCH DAMAGE. + * + **************************************************************************************************/ +/*! \file + \brief Templates calculating the address and predicates to the load of tiles + from pitch-linear rank=2 tensors. + + This iterator uses masks to guard out-of-bounds accesses. The first tile + this iterator visits maybe partial, then the remaining tiles are complete. + So, we only need to compute the predicates twice, once before the first tile + and once for the remaining full tiles which can share the same predicates. + + A precomputed "Params" object minimizes the amount of state that must be + stored in registers, and integer addition is used to advance the pointer + through memory. +*/ + +#pragma once + +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include + +#include + +//////////////////////////////////////////////////////////////////////////////// + +//////////////////////////////////////////////////////////////////////////////// + +namespace cutlass { +namespace transform { +namespace threadblock { + +template +struct CustomPredicatedTileAccessIteratorParams { + using Dim = typename natten::cuda::fna::GetDim::type; + + using Index = int32_t; + using LongIndex = + typename std::conditional::type; + + // + // Data members + // + /// stride of pitch-linear layout (units of Element) + Dim stride_; + /// amount (in byte) to increment pointer to move to next access along + /// strided dimension + LongIndex inc_strided_; + /// amount (in byte) to increment pointer from last access to first access + /// of next tile + LongIndex inc_next_; + /// amount (in byte) to increment pointer from first access of current tile + /// to first access of next tile + LongIndex inc_advance_; + + // + // Methods + // + + CUTLASS_HOST_DEVICE + Status initialize( + Dim stride, + Dim extent_row, + PredicatedTileAccessIteratorDesc desc) { + stride_ = stride; + + if constexpr (NADim == 1) { + inc_strided_ = (LongIndex(stride_.x) * desc.threadmap_delta.strided()) * + desc.element_size_bits / 8; + + if (desc.advance_rank) { + // advance along strided dimension + inc_advance_ = desc.threadblock_shape.strided() * LongIndex(stride_.x) * + desc.element_size_bits / 8; + } else { + // advance along contiguous dimension + inc_advance_ = + desc.threadblock_shape.contiguous() * desc.element_size_bits / 8; + } + + inc_next_ = inc_advance_ - + LongIndex(desc.threadmap_iterations.strided() - 1) * + desc.threadmap_delta.strided() * LongIndex(stride_.x) * + desc.element_size_bits / 8; + // inc_strided_ = + // (natten::cuda::fna::map_index_to_coord(desc.threadmap_delta.strided(), + // extent_row) * stride_).sum() * + // desc.element_size_bits / 8; + //// inc_strided_ = (LongIndex(stride_) * desc.threadmap_delta.strided()) + ///* / desc.element_size_bits / 8; + + // if (desc.advance_rank) { + // // advance along strided dimension + // inc_advance_ = (natten::cuda::fna::map_index_to_coord( + // desc.threadblock_shape.strided(), extent_row) * stride_).sum() * + // desc.element_size_bits / 8; + // // inc_advance_ = + // // desc.threadblock_shape.strided() * LongIndex(stride_) * + // desc.element_size_bits / 8; + //} else { + // // advance along contiguous dimension + // inc_advance_ = desc.threadblock_shape.contiguous() * + // desc.element_size_bits / 8; + //} + + // inc_next_ = inc_advance_ - + // (natten::cuda::fna::map_index_to_coord((desc.threadmap_iterations.strided() + // - 1) * + // desc.threadmap_delta.strided(), + // extent_row) * stride_).sum() * + // desc.element_size_bits / 8; + //// inc_next_ = inc_advance_ - + ///LongIndex(desc.threadmap_iterations.strided() - 1) * / + ///desc.threadmap_delta.strided() * LongIndex(stride_) * / + ///desc.element_size_bits / 8; + } else { + // inc_strided_ = LongIndex(desc.threadmap_delta.strided()); + + // if (desc.advance_rank) { + // // advance along strided dimension + // inc_advance_ = LongIndex(desc.threadblock_shape.strided()); + // } else { + // // advance along contiguous dimension + // inc_advance_ = LongIndex(desc.threadblock_shape.contiguous()); + // } + + // inc_next_ = inc_advance_ - + // LongIndex(desc.threadmap_iterations.strided() - 1) * + // desc.threadmap_delta.strided(); + } + + return Status::kSuccess; + } + + CUTLASS_HOST_DEVICE + CustomPredicatedTileAccessIteratorParams( + Dim stride, + Dim extent_row, + PredicatedTileAccessIteratorDesc desc) { + initialize(stride, extent_row, desc); + } +}; + +//////////////////////////////////////////////////////////////////////////////// + +/// CustomPredicatedTileAccessIteratorPredicates +/// +template < + int NADim, + typename Shape_, + typename Element_, + typename Layout_, + int AdvanceRank, + typename ThreadMap_, + typename AccessType_> +class CustomPredicatedTileAccessIteratorPredicates { + public: + static_assert(NADim >= 1 && NADim < 4); + using Dim = typename natten::cuda::fna::GetDim::type; + + using Shape = Shape_; + using Element = Element_; + using Layout = Layout_; + static int const kAdvanceRank = AdvanceRank; + using ThreadMap = ThreadMap_; + using AccessType = AccessType_; + + using Index = typename Layout::Index; + using LongIndex = typename Layout::LongIndex; + + using TensorCoord = typename Layout::TensorCoord; + + static int const kAccessesPerVector = + ThreadMap::kElementsPerAccess / AccessType::kElements; + + static_assert( + !(ThreadMap::kElementsPerAccess % AccessType::kElements), + "Vectors implied by the thread map must be divisible by the access type."); + + static int const kPredicatesPerByte = 4; + static int const kPredicatesPerWord = 4 * kPredicatesPerByte; + + static int const kPredicateCount = + ThreadMap::Iterations::kCount * kAccessesPerVector; + + /// Number of 32b words containing predicates + static int const kPredicateByteCount = + (kPredicateCount + kPredicatesPerByte - 1) / kPredicatesPerByte; + static int const kPredicateWordCount = (kPredicateByteCount + 3) / 4; + + static unsigned const kPredicateMask = (1u << kPredicatesPerByte) - 1u; + + static_assert(kPredicateWordCount <= 4, "Too many predicates."); + + /// Predicate vector stores mask to guard accesses + using Mask = Array; + + // private: + /// Guard predicates + uint32_t predicates_[kPredicateWordCount]; + + /// Size of tensor + Dim extent_row_dim; + TensorCoord extent_; + + /// Initial offset for each thread + TensorCoord thread_offset_; + + /// Offset to the first steady-state tile + TensorCoord residue_offset_; + + /// Iteration along vectors implied by the thread map + int iteration_vector_; + + /// Iteration in the contiguous dimension + int iteration_contiguous_; + + /// Iteration in the strided dimension + int iteration_strided_; + + public: + /// Computes predicates based on internally tracked per-thread offset. + CUTLASS_DEVICE + void compute_predicates_( + /// Extent of the matrix window + TensorCoord extent, + /// optionally, simplify predicate calculation during 'steady state' phase + bool is_steady_state = false) { + CUTLASS_PRAGMA_UNROLL + for (int i = 0; i < kPredicateWordCount; ++i) { + predicates_[i] = 0u; + } + + CUTLASS_PRAGMA_UNROLL + for (int access_idx = 0; + access_idx < ThreadMap::Iterations::kCount * kAccessesPerVector; + ++access_idx) { + int s = access_idx / + (ThreadMap::Iterations::kContiguous * kAccessesPerVector); + + int access_residual = access_idx % + (ThreadMap::Iterations::kContiguous * kAccessesPerVector); + + int c = access_residual / kAccessesPerVector; + int v = access_residual % kAccessesPerVector; + + TensorCoord iteration_coord( + c * ThreadMap::Delta::kContiguous + v * AccessType::kElements, + s * ThreadMap::Delta::kStrided); + + TensorCoord coord = thread_offset_ + iteration_coord; + + bool guard; + + // Dim coord_row; + // if constexpr (kAdvanceRank == 0) { + // coord_row = natten::cuda::fna::map_index_to_coord(coord.strided(), + // extent_.strided()); + //} else { + // coord_row = natten::cuda::fna::map_index_to_coord(coord.contiguous(), + // natten::cuda::fna::MapTileSizeToNd::value); + //} + + // if (is_steady_state) { + // guard = coord.strided() < extent.strided(); + // } else { + ////if constexpr (kAdvanceRank == 0) { + // guard = (coord.strided() < extent.strided() && + // coord.contiguous() < extent.contiguous()); + ////} else { + //// guard = (coord.strided() < extent_col && + //// coord_row < extent_row); + ////} + //} + if (is_steady_state) { + if (kAdvanceRank == 0) { + guard = (coord.strided() < extent.strided()); + } else { + guard = (coord.contiguous() < extent.contiguous()); + } + } else { + guard = + (coord.strided() < extent.strided() && + coord.contiguous() < extent.contiguous()); + } + + int pred_idx = + v + kAccessesPerVector * (c + ThreadMap::Iterations::kContiguous * s); + + int word_idx = pred_idx / kPredicatesPerWord; + int residual = pred_idx % kPredicatesPerWord; + int byte_idx = residual / kPredicatesPerByte; + int bit_idx = residual % kPredicatesPerByte; + + predicates_[word_idx] |= (unsigned(guard) << (byte_idx * 8 + bit_idx)); + } + } + + CUTLASS_HOST_DEVICE + void set_predicates(int thread_id, TensorCoord const& threadblock_offset) { + TensorCoord residue_extent; + // int32_t residue_extent_row = 0; + // int32_t residue_extent_col = 0; + // if constexpr (kAdvanceRank == 0) { + // // Column-major right handside operand (only instance is K in QK^T). + // // extent_[0] == K (dim per head) == extent_.contiguous() + // // extent_.contiguous() == extent_[0] == extent_.contiguous() + // // + // // Row-major left handside operand (only instance is Q in QK^T) + // // extent_[0] == K (dim per head) == extent_.contiguous() + // // extent_.contiguous() == extent_[0] == extent_.contiguous() + // typename TensorCoord::Index residue_size = (extent_.contiguous() - + // threadblock_offset.contiguous()) % Shape::kContiguous; if + // (!residue_size) { + // residue_size = Shape::kContiguous; + // } + // residue_offset_ = make_Coord(residue_size, 0); + // + // residue_extent.strided() = extent_.strided(); + // residue_extent.contiguous() = min(extent_.contiguous(), + // threadblock_offset.contiguous() + residue_size); + //} else { + // // Row-major right-handside operand (only instance is V in PV) + // // extent_[1] == M == extent_.strided() + // // extent_.contiguous() == extent_[0] == extent_.contiguous() + // // extent_.strided() == extent_[1] == extent_.strided() + // // + // typename TensorCoord::Index residue_size = (extent_.strided() - + // threadblock_offset.strided()) % Shape::kStrided; if (!residue_size) { + // residue_size = Shape::kStrided; + // } + // residue_offset_ = make_Coord(0, residue_size); + + // residue_extent.strided() = min(threadblock_offset.strided() + + // residue_size, extent_.strided()); residue_extent.contiguous() = + // extent_.contiguous(); + //} + if (kAdvanceRank) { + typename TensorCoord::Index residue_size = + (extent_[kAdvanceRank] - threadblock_offset.strided()) % + Shape::kStrided; + if (!residue_size) { + residue_size = Shape::kStrided; + } + + residue_offset_ = make_Coord(0, residue_size); + residue_extent = make_Coord( + extent_.contiguous(), + min(threadblock_offset.strided() + residue_size, extent_.strided())); + } else { + typename TensorCoord::Index residue_size = + (extent_[kAdvanceRank] - threadblock_offset.contiguous()) % + Shape::kContiguous; + if (!residue_size) { + residue_size = Shape::kContiguous; + } + + residue_offset_ = make_Coord(residue_size, 0); + + residue_extent = make_Coord( + min(extent_.contiguous(), + threadblock_offset.contiguous() + residue_size), + extent_.strided()); + } + + // Per-thread offset in logical coordinates of tensor + thread_offset_ = threadblock_offset + ThreadMap::initial_offset(thread_id); + + compute_predicates_(residue_extent, false); + + set_iteration_index(0); + } + + /// Default constructor + CustomPredicatedTileAccessIteratorPredicates() = default; + + /// Constructs a TileIterator from its precomputed state, threadblock offset, + /// and thread ID + CUTLASS_HOST_DEVICE + CustomPredicatedTileAccessIteratorPredicates( + /// Extent of tensor (rows) + Dim extent_row, + /// Extent of tensor (columns) + int32_t extent_col) + : extent_row_dim(extent_row), extent_(extent_col, extent_row.prod32()) {} + + /// Overrides the internal iteration index + CUTLASS_HOST_DEVICE + void set_iteration_index(int index) { + iteration_vector_ = index % kAccessesPerVector; + int residual_access = index / kAccessesPerVector; + + iteration_contiguous_ = + residual_access % ThreadMap::Iterations::kContiguous; + iteration_strided_ = residual_access / ThreadMap::Iterations::kContiguous; + } + + /// Increment and return an instance to self. + CUTLASS_HOST_DEVICE + CustomPredicatedTileAccessIteratorPredicates& operator++() { + return *this; + } + + /// Clears the predicate set efficiently + CUTLASS_HOST_DEVICE + void clear_mask(bool enable = true) { + CUTLASS_PRAGMA_UNROLL + for (int i = 0; i < kPredicateWordCount; ++i) { + predicates_[i] = enable ? 0u : predicates_[i]; + } + } + + /// Clears the predicate set efficiently + CUTLASS_HOST_DEVICE + void enable_mask() { + CUTLASS_PRAGMA_UNROLL + for (int i = 0; i < kPredicateWordCount; ++i) { + predicates_[i] = 0xffffffff; + } + } + + /// Sets the predicate mask, overriding value stored in predicate iterator + CUTLASS_HOST_DEVICE + void set_mask(Mask const& mask) { + CUTLASS_PRAGMA_UNROLL + for (int i = 0; i < kPredicateWordCount; ++i) { + predicates_[i] = mask[i]; + } + } + + /// Gets the mask + CUTLASS_HOST_DEVICE + void get_mask(Mask& mask) { + CUTLASS_PRAGMA_UNROLL + for (int i = 0; i < kPredicateWordCount; ++i) { + mask[i] = predicates_[i]; + } + } + + /// Returns whether access is valid or not + CUTLASS_HOST_DEVICE + bool valid() const { + int pred_idx = iteration_vector_ + + kAccessesPerVector * + (iteration_contiguous_ + + iteration_strided_ * ThreadMap::Iterations::kContiguous); + + int word_idx = pred_idx / kPredicatesPerWord; + int residual = pred_idx % kPredicatesPerWord; + int byte_idx = residual / kPredicatesPerByte; + int bit_idx = residual % kPredicatesPerByte; + + bool pred = (predicates_[word_idx] & (1u << (byte_idx * 8 + bit_idx))) != 0; + return pred; + } +}; + +//////////////////////////////////////////////////////////////////////////////// + +/// CustomPredicatedTileAccessIterator +/// +template < + int NADim, + typename Shape, + typename Element, + typename Layout, + int AdvanceRank, + typename ThreadMap, + typename AccessType> +class CustomPredicatedTileAccessIterator; + +//////////////////////////////////////////////////////////////////////////////// + +/// CustomPredicatedTileAccessIterator for possibly multiple axes (NA1D, 2D, 3D) +template < + int NADim, + typename Shape_, + typename Element_, + int AdvanceRank, + typename ThreadMap_, + typename AccessType_> +class CustomPredicatedTileAccessIterator< + NADim, + Shape_, + Element_, + layout::PitchLinear, + AdvanceRank, + ThreadMap_, + AccessType_> { + public: + static_assert(NADim >= 1 && NADim < 4); + static_assert( + AdvanceRank == 0 || AdvanceRank == 1, + "Specialization for pitch-linear iterator may along advance along the " + "contiguous(rank=0) or strided(rank=1) dimension."); + + using Dim = typename natten::cuda::fna::GetDim::type; + + using Shape = Shape_; + using Element = Element_; + using Layout = layout::PitchLinear; + static int const kAdvanceRank = AdvanceRank; + using ThreadMap = ThreadMap_; + using AccessType = AccessType_; + + using Index = typename Layout::Index; + using LongIndex = typename Layout::LongIndex; + + using TensorRef = TensorRef; + using TensorView = TensorView; + using TensorCoord = typename Layout::TensorCoord; + + using Pointer = Element*; + using NonConstPointer = typename platform::remove_const::type*; + + using UnderlyingPredicates = CustomPredicatedTileAccessIteratorPredicates< + NADim, + Shape, + Element, + Layout, + AdvanceRank, + ThreadMap, + AccessType>; + + static int const kAccessesPerVector = + ThreadMap::kElementsPerAccess / AccessType::kElements; + + static_assert( + !(ThreadMap::kElementsPerAccess % AccessType::kElements), + "Vectors implied by the thread map must be divisible by the access type."); + + using Mask = typename UnderlyingPredicates::Mask; + + /// Uses a non-template class + struct Params : CustomPredicatedTileAccessIteratorParams { + using Base = CustomPredicatedTileAccessIteratorParams; + + /// Default constructor + Params() = default; + + /// Construct the Params object given a pitch-linear tensor's layout + CUTLASS_HOST_DEVICE + Params(Dim stride, Dim extent_row) + : Base( + stride, + extent_row, + MakePredicatedTileAccessIteratorDesc< + Shape, + Element, + Layout, + kAdvanceRank, + ThreadMap>()()) {} + + CUTLASS_HOST_DEVICE + Params(Base const& base) : Base(base) {} + }; + + private: + /// Internal pointer type permits fast address arithmetic + using BytePointer = char*; + + private: + // + // Data members + // + + UnderlyingPredicates the_predicates; + + /// Parameters object with precomputed internal state + Params params_; + + /// Internal pointer to first access of tile + BytePointer pointer_; + TensorCoord coord_offset_; + + /// Used for out-of-order visitation + bool is_residue_tile_; + + private: + /// Computes predicates based on internally tracked per-thread offset. + CUTLASS_DEVICE + void compute_predicates_( + /// Extent of the matrix window + TensorCoord extent, + /// optionally, simplify predicate calculation during 'steady state' phase + bool is_steady_state = false) { + the_predicates.compute_predicates_(extent, is_steady_state); + } + + public: + /// Default constructor + CustomPredicatedTileAccessIterator() = default; + + /// Constructs a TileIterator from its precomputed state, threadblock offset, + /// and thread ID + CUTLASS_HOST_DEVICE + CustomPredicatedTileAccessIterator( + /// Precomputed parameters object + Params const& params, + /// Pointer to start of tensor + Pointer pointer, + /// Extent of tensor (rows) + Dim extent_row, + /// Extent of tensor (columns) + int32_t extent_col, + /// ID of each participating thread + int thread_id, + /// Initial offset of threadblock + TensorCoord const& threadblock_offset) + : params_(params), + pointer_(reinterpret_cast( + const_cast(pointer))), + the_predicates(extent_row, extent_col), + is_residue_tile_(true) { + the_predicates.set_predicates(thread_id, threadblock_offset); + + // update internal pointers + coord_offset_ = the_predicates.thread_offset_; + // add_pointer_offset(the_predicates.thread_offset_.strided(), + // the_predicates.thread_offset_.contiguous()); + } + + /// Construct a CustomPredicatedTileAccessIterator with zero threadblock + /// offset + CUTLASS_HOST_DEVICE + CustomPredicatedTileAccessIterator( + /// Precomputed parameters object + Params const& params, + /// Pointer to start of tensor + Pointer pointer, + /// Extent of tensor (rows) + Dim extent_row, + /// Extent of tensor (columns) + int32_t extent_col, + ///< ID of each participating thread + int thread_id) + : CustomPredicatedTileAccessIterator( + params, + pointer, + extent_row, + extent_col, + thread_id, + make_Coord(0, 0)) {} + + /// Overrides the internal iteration index + CUTLASS_HOST_DEVICE + void set_iteration_index(int index) { + the_predicates.set_iteration_index(index); + } + + CUTLASS_HOST_DEVICE + void add_pointer_offset(int32_t row_offset, int32_t col_offset) { + coord_offset_.strided() += row_offset; + coord_offset_.contiguous() += col_offset; + } + + CUTLASS_HOST_DEVICE + void add_pointer_offset(LongIndex linear_offset) { + printf("FATAL!"); + // if constexpr (kAdvanceRank) { + // auto col_offset = linear_offset % Shape::kStrided; + // auto row_offset = linear_offset / Shape::kStrided; + // add_pointer_offset(row_offset, (int32_t)col_offset); + // } else { + // auto col_offset = linear_offset % Shape::kContiguous; + // auto row_offset = linear_offset / Shape::kContiguous; + // add_pointer_offset(row_offset, (int32_t)col_offset); + // } + } + + /// Advances an iterator along logical dimensions of matrix in units of whole + /// tiles + CUTLASS_DEVICE + void add_tile_offset(TensorCoord const& tile_offset) { + if (is_residue_tile_) { + the_predicates.thread_offset_ += the_predicates.residue_offset_; + + the_predicates.compute_predicates_(the_predicates.extent_, true); + + // add_pointer_offset(the_predicates.residue_offset_.strided(), + // the_predicates.residue_offset_.contiguous()); + + coord_offset_.strided() = the_predicates.thread_offset_.strided() + + Shape::kStrided * (tile_offset.strided() - kAdvanceRank); + coord_offset_.contiguous() = the_predicates.thread_offset_.contiguous() + + Shape::kContiguous * (tile_offset.contiguous() - (1 - kAdvanceRank)); + + // if (kAdvanceRank) { + // add_pointer_offset( + // params_.inc_advance_ * (tile_offset.strided() - 1) + + // Shape::kContiguous * tile_offset.contiguous()); + // // pointer_ += params_.inc_advance_ * LongIndex(tile_offset.strided() + // - 1); + // // pointer_ += Shape::kContiguous * tile_offset.contiguous() * + // sizeof_bits::value / 8; + // } else { + // // Advance along contiguous dimension + // add_pointer_offset( + // params_.inc_advance_ * (tile_offset.contiguous() - 1) + + // Shape::kStrided * tile_offset.strided()); + // // pointer_ += params_.inc_advance_ * + // LongIndex(tile_offset.contiguous() - 1); + // // pointer_ += Shape::kStrided * tile_offset.strided() * + // sizeof_bits::value / 8; + // } + } else { + add_pointer_offset( + Shape::kStrided * tile_offset.strided(), + Shape::kContiguous * tile_offset.contiguous()); + // if (kAdvanceRank) { + // add_pointer_offset( + // params_.inc_advance_ * tile_offset.strided() + + // Shape::kContiguous * tile_offset.contiguous()); + // // pointer_ += params_.inc_advance_ * + // LongIndex(tile_offset.strided()); + // // pointer_ += Shape::kContiguous * tile_offset.contiguous(); + // } else { + // // Advance along contiguous dimension + // add_pointer_offset( + // params_.inc_advance_ * tile_offset.contiguous() + + // Shape::kStrided * tile_offset.strided()); + // // pointer_ += params_.inc_advance_ * + // LongIndex(tile_offset.contiguous()); + // // pointer_ += Shape::kStrided * tile_offset.strided(); + // } + } + + is_residue_tile_ = false; + } + + CUTLASS_HOST_DEVICE + LongIndex get_pointer_offset() const { + LongIndex coord_contig = coord_offset_.contiguous() + + the_predicates.iteration_contiguous_ * ThreadMap::Delta::kContiguous; + LongIndex coord_strided = + (natten::cuda::fna::map_index_to_coord( + coord_offset_.strided() + + the_predicates.iteration_strided_ * ThreadMap::Delta::kStrided, + the_predicates.extent_row_dim) * + params_.stride_) + .sum(); + return ((coord_strided + coord_contig) * sizeof_bits::value) / 8; + } + + /// Returns a pointer + CUTLASS_HOST_DEVICE + AccessType* get() const { + if (!valid()) { + return nullptr; + } + return reinterpret_cast(pointer_ + get_pointer_offset()) + + the_predicates.iteration_vector_; + } + + /// Increment and return an instance to self. + CUTLASS_HOST_DEVICE + CustomPredicatedTileAccessIterator& operator++() { + the_predicates.operator++(); + + ++the_predicates.iteration_vector_; + if (the_predicates.iteration_vector_ < kAccessesPerVector) { + return *this; + } + + the_predicates.iteration_vector_ = 0; + ++the_predicates.iteration_contiguous_; + + if (the_predicates.iteration_contiguous_ < + ThreadMap::Iterations::kContiguous) { + return *this; + } + + // Enter here only if (iteration_contiguous_ == + // ThreadMap::Iteration::kContiguous) + the_predicates.iteration_contiguous_ = 0; + ++the_predicates.iteration_strided_; + + if (the_predicates.iteration_strided_ < ThreadMap::Iterations::kStrided) { + // add_pointer_offset(params_.inc_strided_, 0); + // pointer_ += params_.inc_strided_; + + return *this; + } + + // Enter here only if (iteration_stride_ == ThreadMap::Iteration::kStrided) + // which means we enter the next tile. + the_predicates.iteration_strided_ = 0; + + // // advance to next tile + // add_pointer_offset(params_.inc_next_, 0); + // // pointer_ += params_.inc_next_; + + // //if constexpr (kAdvanceRank) { + // add_pointer_offset(-1 * params_.inc_advance_); + // //} else { + // // // along the contiguous axis. + // // add_pointer_offset(0, -1 * params_.inc_advance_); + // //} + // // now return to start tile - if the iterator is subsequently advanced, + // this + // // subtraction as well as the subsequent integer addition are both elided + // by + // // the compiler. + // // pointer_ -= params_.inc_advance_; + + return *this; + } + + /// Increment and return an instance to self. + CUTLASS_HOST_DEVICE + CustomPredicatedTileAccessIterator operator++(int) { + CustomPredicatedTileAccessIterator self(*this); + operator++(); + return self; + } + + /// Clears the predicate set efficiently + CUTLASS_HOST_DEVICE + void clear_mask(bool enable = true) { + the_predicates.clear_mask(enable); + } + + /// Clears the predicate set efficiently + CUTLASS_HOST_DEVICE + void enable_mask() { + the_predicates.enable_mask(); + } + + /// Sets the predicate mask, overriding value stored in predicate iterator + CUTLASS_HOST_DEVICE + void set_mask(Mask const& mask) { + the_predicates.set_mask(mask); + } + + /// Gets the mask + CUTLASS_HOST_DEVICE + void get_mask(Mask& mask) { + the_predicates.get_mask(mask); + } + + /// Returns whether access is valid or not + CUTLASS_HOST_DEVICE + bool valid() const { + return the_predicates.valid(); + } +}; + +// Specialization for NA1D, where we only have one stride, +// and therefore the typical pitch-linear layout, and won't have +// to manually convert offsets on every get(). +template < + typename Shape_, + typename Element_, + int AdvanceRank, + typename ThreadMap_, + typename AccessType_> +class CustomPredicatedTileAccessIterator< + 1, + Shape_, + Element_, + layout::PitchLinear, + AdvanceRank, + ThreadMap_, + AccessType_> { + public: + static constexpr int NADim = 1; + static_assert( + AdvanceRank == 0 || AdvanceRank == 1, + "Specialization for pitch-linear iterator may along advance along the " + "contiguous(rank=0) or strided(rank=1) dimension."); + + using Dim = typename natten::cuda::fna::GetDim::type; + + using Shape = Shape_; + using Element = Element_; + using Layout = layout::PitchLinear; + static int const kAdvanceRank = AdvanceRank; + using ThreadMap = ThreadMap_; + using AccessType = AccessType_; + + using Index = typename Layout::Index; + using LongIndex = typename Layout::LongIndex; + + using TensorRef = TensorRef; + using TensorView = TensorView; + using TensorCoord = typename Layout::TensorCoord; + + using Pointer = Element*; + using NonConstPointer = typename platform::remove_const::type*; + + using UnderlyingPredicates = CustomPredicatedTileAccessIteratorPredicates< + NADim, + Shape, + Element, + Layout, + AdvanceRank, + ThreadMap, + AccessType>; + + static int const kAccessesPerVector = + ThreadMap::kElementsPerAccess / AccessType::kElements; + + static_assert( + !(ThreadMap::kElementsPerAccess % AccessType::kElements), + "Vectors implied by the thread map must be divisible by the access type."); + + using Mask = typename UnderlyingPredicates::Mask; + + /// Uses a non-template class + struct Params : CustomPredicatedTileAccessIteratorParams { + using Base = CustomPredicatedTileAccessIteratorParams; + + /// Default constructor + Params() = default; + + /// Construct the Params object given a pitch-linear tensor's layout + CUTLASS_HOST_DEVICE + Params(Dim stride, Dim extent_row) + : Base( + stride, + extent_row, + MakePredicatedTileAccessIteratorDesc< + Shape, + Element, + Layout, + kAdvanceRank, + ThreadMap>()()) {} + + CUTLASS_HOST_DEVICE + Params(Base const& base) : Base(base) {} + }; + + private: + /// Internal pointer type permits fast address arithmetic + using BytePointer = char*; + + private: + // + // Data members + // + + UnderlyingPredicates the_predicates; + + /// Parameters object with precomputed internal state + Params params_; + + /// Internal pointer to first access of tile + BytePointer pointer_; + + /// Used for out-of-order visitation + bool is_residue_tile_; + + private: + /// Computes predicates based on internally tracked per-thread offset. + CUTLASS_DEVICE + void compute_predicates_( + /// Extent of the matrix window + Dim extent_row, + int32_t extent_col, + /// optionally, simplify predicate calculation during 'steady state' phase + bool is_steady_state = false) { + the_predicates.compute_predicates_(extent_row, extent_col, is_steady_state); + } + + public: + /// Default constructor + CustomPredicatedTileAccessIterator() = default; + + /// Constructs a TileIterator from its precomputed state, threadblock offset, + /// and thread ID + CUTLASS_HOST_DEVICE + CustomPredicatedTileAccessIterator( + /// Precomputed parameters object + Params const& params, + /// Pointer to start of tensor + Pointer pointer, + /// Extent of tensor (rows) + Dim extent_row, + /// Extent of tensor (columns) + int32_t extent_col, + /// ID of each participating thread + int thread_id, + /// Initial offset of threadblock + TensorCoord const& threadblock_offset) + : params_(params), + pointer_(reinterpret_cast( + const_cast(pointer))), + the_predicates(extent_row, extent_col), + is_residue_tile_(true) { + the_predicates.set_predicates(thread_id, threadblock_offset); + + // update internal pointers + auto offset = (natten::cuda::fna::map_index_to_coord( + the_predicates.thread_offset_.strided(), + the_predicates.extent_row_dim) * + params_.stride_) + .sum() + + the_predicates.thread_offset_.contiguous(); + + add_pointer_offset(offset); + } + + /// Construct a CustomPredicatedTileAccessIterator with zero threadblock + /// offset + CUTLASS_HOST_DEVICE + CustomPredicatedTileAccessIterator( + /// Precomputed parameters object + Params const& params, + /// Pointer to start of tensor + Pointer pointer, + /// Extent of tensor (rows) + Dim extent_row, + /// Extent of tensor (columns) + int32_t extent_col, + ///< ID of each participating thread + int thread_id) + : CustomPredicatedTileAccessIterator( + params, + pointer, + extent_row, + extent_col, + thread_id, + make_Coord(0, 0)) {} + + /// Overrides the internal iteration index + CUTLASS_HOST_DEVICE + void set_iteration_index(int index) { + the_predicates.set_iteration_index(index); + } + + /// Adds a pointer offset in units of Element + CUTLASS_HOST_DEVICE + void add_pointer_offset(LongIndex pointer_offset) { + pointer_ += sizeof_bits::value * pointer_offset / 8; + } + + /// Advances an iterator along logical dimensions of matrix in units of whole + /// tiles + CUTLASS_DEVICE + void add_tile_offset(TensorCoord const& tile_offset) { + if (is_residue_tile_) { + the_predicates.thread_offset_ += the_predicates.residue_offset_; + + the_predicates.compute_predicates_(the_predicates.extent_, true); + + auto offset = (natten::cuda::fna::map_index_to_coord( + the_predicates.residue_offset_.strided(), + the_predicates.extent_row_dim) * + params_.stride_) + .sum() + + the_predicates.residue_offset_.contiguous(); + + add_pointer_offset(offset); + + if (kAdvanceRank) { + pointer_ += params_.inc_advance_ * LongIndex(tile_offset.strided() - 1); + pointer_ += Shape::kContiguous * tile_offset.contiguous() * + sizeof_bits::value / 8; + } else { + pointer_ += + params_.inc_advance_ * LongIndex(tile_offset.contiguous() - 1); + pointer_ += Shape::kStrided * tile_offset.strided() * + sizeof_bits::value / 8; + } + } else { + if (kAdvanceRank) { + pointer_ += params_.inc_advance_ * LongIndex(tile_offset.strided()); + pointer_ += Shape::kContiguous * tile_offset.contiguous(); + } else { + pointer_ += params_.inc_advance_ * LongIndex(tile_offset.contiguous()); + pointer_ += Shape::kStrided * tile_offset.strided(); + } + } + + is_residue_tile_ = false; + } + + /// Returns a pointer + CUTLASS_HOST_DEVICE + AccessType* get() const { + return reinterpret_cast( + pointer_ + + the_predicates.iteration_contiguous_ * + (ThreadMap::Delta::kContiguous * + sizeof_bits::value) / + 8) + + the_predicates.iteration_vector_; + } + + /// Increment and return an instance to self. + CUTLASS_HOST_DEVICE + CustomPredicatedTileAccessIterator& operator++() { + the_predicates.operator++(); + + ++the_predicates.iteration_vector_; + if (the_predicates.iteration_vector_ < kAccessesPerVector) { + return *this; + } + + the_predicates.iteration_vector_ = 0; + ++the_predicates.iteration_contiguous_; + + if (the_predicates.iteration_contiguous_ < + ThreadMap::Iterations::kContiguous) { + return *this; + } + + // Enter here only if (iteration_contiguous_ == + // ThreadMap::Iteration::kContiguous) + the_predicates.iteration_contiguous_ = 0; + ++the_predicates.iteration_strided_; + + if (the_predicates.iteration_strided_ < ThreadMap::Iterations::kStrided) { + pointer_ += params_.inc_strided_; + + return *this; + } + + // Enter here only if (iteration_stride_ == ThreadMap::Iteration::kStrided) + // which means we enter the next tile. + the_predicates.iteration_strided_ = 0; + + // advance to next tile + pointer_ += params_.inc_next_; + + // now return to start tile - if the iterator is subsequently advanced, this + // subtraction as well as the subsequent integer addition are both elided by + // the compiler. + pointer_ -= params_.inc_advance_; + + return *this; + } + + /// Increment and return an instance to self. + CUTLASS_HOST_DEVICE + CustomPredicatedTileAccessIterator operator++(int) { + CustomPredicatedTileAccessIterator self(*this); + operator++(); + return self; + } + + /// Clears the predicate set efficiently + CUTLASS_HOST_DEVICE + void clear_mask(bool enable = true) { + the_predicates.clear_mask(enable); + } + + /// Clears the predicate set efficiently + CUTLASS_HOST_DEVICE + void enable_mask() { + the_predicates.enable_mask(); + } + + /// Sets the predicate mask, overriding value stored in predicate iterator + CUTLASS_HOST_DEVICE + void set_mask(Mask const& mask) { + the_predicates.set_mask(mask); + } + + /// Gets the mask + CUTLASS_HOST_DEVICE + void get_mask(Mask& mask) { + the_predicates.get_mask(mask); + } + + /// Returns whether access is valid or not + CUTLASS_HOST_DEVICE + bool valid() const { + return the_predicates.valid(); + } +}; + +//////////////////////////////////////////////////////////////////////////////// + +/// Specialization of CustomPredicatedTileAccessIterator for column-major data. +/// +/// Satisfies: ForwardTileIteratorConcept | +/// ReadableContiguousTileIteratorConcept | +/// WriteableContiguousTileIteratorConcept | +/// MaskedTileIteratorConcept +/// +template < + int NADim, + typename Shape_, + typename Element_, + int AdvanceRank, + typename ThreadMap_, + typename AccessType_> +class CustomPredicatedTileAccessIterator< + NADim, + Shape_, + Element_, + layout::ColumnMajor, + AdvanceRank, + ThreadMap_, + AccessType_> { + public: + static_assert(NADim >= 1 && NADim < 4); + static_assert( + AdvanceRank == 0 || AdvanceRank == 1, + "Specialization for pitch-linear iterator may along advance along the " + "contiguous(rank=0) or strided(rank=1) dimension."); + + using Dim = typename natten::cuda::fna::GetDim::type; + + using Shape = Shape_; + using Element = Element_; + using Layout = layout::ColumnMajor; + static int const kAdvanceRank = AdvanceRank; + using ThreadMap = ThreadMap_; + using AccessType = AccessType_; + + using Index = typename Layout::Index; + using LongIndex = typename Layout::LongIndex; + + using TensorRef = TensorRef; + using TensorView = TensorView; + using TensorCoord = typename Layout::TensorCoord; + + using Pointer = Element*; + using NonConstPointer = typename platform::remove_const::type*; + + using UnderlyingIterator = CustomPredicatedTileAccessIterator< + NADim, + layout::PitchLinearShape, + Element, + layout::PitchLinear, + (kAdvanceRank == 0 ? 0 : 1), + ThreadMap, + AccessType>; + + /// Predicate vector stores mask to guard accesses + using Mask = typename UnderlyingIterator::Mask; + + static int const kAccessesPerVector = UnderlyingIterator::kAccessesPerVector; + + /// Parameters object is precomputed state and is host-constructible + class Params { + private: + friend CustomPredicatedTileAccessIterator; + + /// Parameters object + typename UnderlyingIterator::Params params_; + + public: + /// Default constructor + Params() = default; + + /// Construct the Params object given a pitch-linear tensor's layout + CUTLASS_HOST_DEVICE + Params(Dim stride, Dim extent_row) : params_(stride, extent_row){}; + + /// Construct the Params object given a pitch-linear tensor's layout + CUTLASS_HOST_DEVICE + Params(typename UnderlyingIterator::Params::Base const& base) + : params_(base) {} + }; + + private: + // + // Data members + // + + /// Underlying pitch-linear tile iterator + UnderlyingIterator iterator_; + + public: + /// Default constructor + CustomPredicatedTileAccessIterator() = default; + + /// Constructs a TileIterator from its precomputed state, threadblock offset, + /// and thread ID + CUTLASS_HOST_DEVICE + CustomPredicatedTileAccessIterator( + ///< Precomputed parameters object + Params const& params, + ///< Pointer to start of tensor + Pointer pointer, + /// Extent of tensor (rows) + Dim extent_row, + /// Extent of tensor (columns) + int32_t extent_col, + ///< ID of each participating thread + int thread_id, + ///< Initial offset of threadblock + TensorCoord const& threadblock_offset) + : iterator_( + params.params_, + pointer, + extent_row, + extent_col, + thread_id, + layout::PitchLinearCoord( + threadblock_offset.row(), + threadblock_offset.column())) {} + + /// Construct a CustomPredicatedTileAccessIterator with zero threadblock + /// offset + CUTLASS_HOST_DEVICE + CustomPredicatedTileAccessIterator( + Params const& params, ///< Precomputed parameters object + Pointer pointer, ///< Pointer to start of tensor + Dim extent_row, ///< Extent of tensor (rows) + int32_t extent_col, ///< Extent of tensor (columns) + int thread_id ///< ID of each participating thread + ) + : CustomPredicatedTileAccessIterator( + params, + pointer, + extent_row, + extent_col, + thread_id, + make_Coord(0, 0)) {} + + /// Overrides the internal iteration index + CUTLASS_HOST_DEVICE + void set_iteration_index(int index) { + iterator_.set_iteration_index(index); + } + + /// Adds a pointer offset in units of Element + CUTLASS_HOST_DEVICE + void add_pointer_offset(LongIndex pointer_offset) { + iterator_.add_pointer_offset(pointer_offset); + } + + /// Advances an iterator along logical dimensions of matrix in units of whole + /// tiles + CUTLASS_HOST_DEVICE + void add_tile_offset(TensorCoord const& tile_offset) { + iterator_.add_tile_offset({tile_offset.row(), tile_offset.column()}); + } + + /// Returns a pointer + CUTLASS_HOST_DEVICE + AccessType* get() const { + return reinterpret_cast(iterator_.get()); + } + + /// Advances to the next tile in memory. + /// + /// The first time this method is called, predicates are updated, and the + /// iterator's internal pointer is reverted to the first "steady state" tile. + /// Subsequent calls are lightweight and must only update the internal + /// pointer. + CUTLASS_HOST_DEVICE + CustomPredicatedTileAccessIterator& operator++() { + ++iterator_; + return *this; + } + + /// Advances to the next tile in memory. + /// + /// The first time this method is called, predicates are updated, and the + /// iterator's internal pointer is reverted to the first "steady state" tile. + /// Subsequent calls are lightweight and must only update the internal + /// pointer. + CUTLASS_HOST_DEVICE + CustomPredicatedTileAccessIterator operator++(int) { + CustomPredicatedTileAccessIterator self(*this); + operator++(); + return self; + } + + /// Clears the predicate set efficiently + CUTLASS_HOST_DEVICE + void clear_mask(bool enable = true) { + iterator_.clear_mask(enable); + } + + /// Clears the predicate set efficiently + CUTLASS_HOST_DEVICE + void enable_mask() { + iterator_.enable_mask(); + } + + /// Sets the predicate mask, overriding value stored in predicate iterator + CUTLASS_HOST_DEVICE + void set_mask(Mask const& mask) { + iterator_.set_mask(mask); + } + + /// Gets the mask + CUTLASS_HOST_DEVICE + void get_mask(Mask& mask) { + iterator_.get_mask(mask); + } + + /// Returns whether access is valid or not + CUTLASS_HOST_DEVICE + bool valid() { + return iterator_.valid(); + } +}; + +//////////////////////////////////////////////////////////////////////////////// + +/// Specialization of CustomPredicatedTileAccessIterator for row-major data. +/// +/// Satisfies: ForwardTileIteratorConcept | +/// ReadableContiguousTileIteratorConcept | +/// WriteableContiguousTileIteratorConcept | +/// MaskedTileIteratorConcept +/// +template < + int NADim, + typename Shape_, + typename Element_, + int AdvanceRank, + typename ThreadMap_, + typename AccessType_> +class CustomPredicatedTileAccessIterator< + NADim, + Shape_, + Element_, + layout::RowMajor, + AdvanceRank, + ThreadMap_, + AccessType_> { + public: + static_assert(NADim >= 1 && NADim < 4); + static_assert( + AdvanceRank == 0 || AdvanceRank == 1, + "Specialization for pitch-linear iterator may along advance along the " + "contiguous(rank=0) or strided(rank=1) dimension."); + + using Dim = typename natten::cuda::fna::GetDim::type; + + using Shape = Shape_; + using Element = Element_; + using Layout = layout::RowMajor; + static int const kAdvanceRank = AdvanceRank; + using ThreadMap = ThreadMap_; + using AccessType = AccessType_; + + using Index = typename Layout::Index; + using LongIndex = typename Layout::LongIndex; + + using TensorRef = TensorRef; + using TensorView = TensorView; + using TensorCoord = typename Layout::TensorCoord; + + using Pointer = Element*; + using NonConstPointer = typename platform::remove_const::type*; + + using UnderlyingIterator = CustomPredicatedTileAccessIterator< + NADim, + layout::PitchLinearShape, + Element, + layout::PitchLinear, + (kAdvanceRank == 0 ? 1 : 0), + ThreadMap, + AccessType>; + + static int const kAccessesPerVector = UnderlyingIterator::kAccessesPerVector; + + /// Predicate vector stores mask to guard accesses + using Mask = typename UnderlyingIterator::Mask; + + /// Parameters object is precomputed state and is host-constructible + class Params { + private: + friend CustomPredicatedTileAccessIterator; + + /// Parameters object + typename UnderlyingIterator::Params params_; + + public: + /// Default constructor + Params() = default; + + /// Construct the Params object given a pitch-linear tensor's layout + CUTLASS_HOST_DEVICE + Params(Dim stride, Dim extent_row) : params_(stride, extent_row){}; + + /// Construct the Params object given a pitch-linear tensor's layout + CUTLASS_HOST_DEVICE + Params(typename UnderlyingIterator::Params::Base const& base) + : params_(base) {} + }; + + private: + // + // Data members + // + + /// Underlying pitch-linear tile iterator + UnderlyingIterator iterator_; + + public: + /// Default constructor + CustomPredicatedTileAccessIterator() = default; + + /// Constructs a TileIterator from its precomputed state, threadblock offset, + /// and thread ID + CUTLASS_HOST_DEVICE + CustomPredicatedTileAccessIterator( + ///< Precomputed parameters object + Params const& params, + ///< Pointer to start of tensor + Pointer pointer, + /// Extent of tensor (rows) + Dim extent_row, + /// Extent of tensor (columns) + int32_t extent_col, + ///< ID of each participating thread + int thread_id, + ///< Initial offset of threadblock + TensorCoord const& threadblock_offset) + : iterator_( + params.params_, + pointer, + extent_row, + extent_col, + thread_id, + layout::PitchLinearCoord( + threadblock_offset.column(), + threadblock_offset.row())) {} + + /// Construct a CustomPredicatedTileAccessIterator with zero threadblock + /// offset + CUTLASS_HOST_DEVICE + CustomPredicatedTileAccessIterator( + Params const& params, ///< Precomputed parameters object + Pointer pointer, ///< Pointer to start of tensor + Dim extent_row, ///< Extent of tensor (rows) + int32_t extent_col, ///< Extent of tensor (columns) + int thread_id ///< ID of each participating thread + ) + : CustomPredicatedTileAccessIterator( + params, + pointer, + extent_row, + extent_col, + thread_id, + make_Coord(0, 0)) {} + + /// Overrides the internal iteration index + CUTLASS_HOST_DEVICE + void set_iteration_index(int index) { + iterator_.set_iteration_index(index); + } + + /// Adds a pointer offset in units of Element + CUTLASS_HOST_DEVICE + void add_pointer_offset(LongIndex pointer_offset) { + iterator_.add_pointer_offset(pointer_offset); + } + + /// Advances an iterator along logical dimensions of matrix in units of whole + /// tiles + CUTLASS_HOST_DEVICE + void add_tile_offset(TensorCoord const& tile_offset) { + iterator_.add_tile_offset({tile_offset.column(), tile_offset.row()}); + } + + /// Returns a pointer + CUTLASS_HOST_DEVICE + AccessType* get() const { + return reinterpret_cast(iterator_.get()); + } + + /// Advances to the next tile in memory. + /// + /// The first time this method is called, predicates are updated, and the + /// iterator's internal pointer is reverted to the first "steady state" tile. + /// Subsequent calls are lightweight and must only update the internal + /// pointer. + CUTLASS_HOST_DEVICE + CustomPredicatedTileAccessIterator& operator++() { + ++iterator_; + return *this; + } + + /// Advances to the next tile in memory. + /// + /// The first time this method is called, predicates are updated, and the + /// iterator's internal pointer is reverted to the first "steady state" tile. + /// Subsequent calls are lightweight and must only update the internal + /// pointer. + CUTLASS_HOST_DEVICE + CustomPredicatedTileAccessIterator operator++(int) { + CustomPredicatedTileAccessIterator self(*this); + operator++(); + return self; + } + + /// Clears the predicate set efficiently + CUTLASS_HOST_DEVICE + void clear_mask(bool enable = true) { + iterator_.clear_mask(enable); + } + + /// Clears the predicate set efficiently + CUTLASS_HOST_DEVICE + void enable_mask() { + iterator_.enable_mask(); + } + + /// Sets the predicate mask, overriding value stored in predicate iterator + CUTLASS_HOST_DEVICE + void set_mask(Mask const& mask) { + iterator_.set_mask(mask); + } + + /// Gets the mask + CUTLASS_HOST_DEVICE + void get_mask(Mask& mask) { + iterator_.get_mask(mask); + } + + /// Returns whether access is valid or not + CUTLASS_HOST_DEVICE + bool valid() { + return iterator_.valid(); + } +}; + +} // namespace threadblock +} // namespace transform +} // namespace cutlass + +//////////////////////////////////////////////////////////////////////////////// diff --git a/csrc/include/natten/cuda/fna/iterators/predicated_tile_access_iterator_residual_last.h b/csrc/include/natten/cuda/fna/iterators/predicated_tile_access_iterator_residual_last.h new file mode 100644 index 0000000..07f59aa --- /dev/null +++ b/csrc/include/natten/cuda/fna/iterators/predicated_tile_access_iterator_residual_last.h @@ -0,0 +1,976 @@ +/* + * Copied from xFormers (https://github.com/facebookresearch/xformers/) and + * edited. + * + * Copyright (c) Meta Platforms, Inc. and affiliates. + * All rights reserved. + */ +/*************************************************************************************************** + * Copyright (c) 2017 - 2023 NVIDIA CORPORATION & AFFILIATES. All rights + *reserved. SPDX-License-Identifier: BSD-3-Clause + * + * Redistribution and use in source and binary forms, with or without + * modification, are permitted provided that the following conditions are met: + * + * 1. Redistributions of source code must retain the above copyright notice, + *this list of conditions and the following disclaimer. + * + * 2. Redistributions in binary form must reproduce the above copyright notice, + * this list of conditions and the following disclaimer in the documentation + * and/or other materials provided with the distribution. + * + * 3. Neither the name of the copyright holder nor the names of its + * contributors may be used to endorse or promote products derived from + * this software without specific prior written permission. + * + * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" + * AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE + * IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE + *ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE + *LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR + *CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF + *SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS + *INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN + *CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) + *ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE + *POSSIBILITY OF SUCH DAMAGE. + * + **************************************************************************************************/ +/*! \file + \brief Templates calculating the address and predicates to the load of tiles + from pitch-linear rank=2 tensors. + + This iterator uses masks to guard out-of-bounds accesses. The first tile + this iterator visits maybe partial, then the remaining tiles are complete. + So, we only need to compute the predicates twice, once before the first tile + and once for the remaining full tiles which can share the same predicates. + + A precomputed "Params" object minimizes the amount of state that must be + stored in registers, and integer addition is used to advance the pointer + through memory. +*/ + +#pragma once + +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include + +#include +#include + +//////////////////////////////////////////////////////////////////////////////// + +//////////////////////////////////////////////////////////////////////////////// + +namespace cutlass { +namespace transform { +namespace threadblock { + +//////////////////////////////////////////////////////////////////////////////// + +/// PredicatedTileAccessIteratorResidualLast +/// +template < + int NADim, + typename Shape, + typename Element, + typename Layout, + int AdvanceRank, + typename ThreadMap, + typename AccessType> +class PredicatedTileAccessIteratorResidualLast; + +//////////////////////////////////////////////////////////////////////////////// + +/// PredicatedTileAccessIteratorResidualLast for possibly multiple axes (NA1D, +/// 2D, 3D) +template < + int NADim, + typename Shape_, + typename Element_, + int AdvanceRank, + typename ThreadMap_, + typename AccessType_> +class PredicatedTileAccessIteratorResidualLast< + NADim, + Shape_, + Element_, + layout::PitchLinear, + AdvanceRank, + ThreadMap_, + AccessType_> { + public: + static_assert(NADim >= 1 && NADim < 4); + static_assert( + AdvanceRank == 0 || AdvanceRank == 1, + "Specialization for pitch-linear iterator may along advance along the " + "contiguous(rank=0) or strided(rank=1) dimension."); + + using Dim = typename natten::cuda::fna::GetDim::type; + + using Shape = Shape_; + using Element = Element_; + using Layout = layout::PitchLinear; + static int const kAdvanceRank = AdvanceRank; + using ThreadMap = ThreadMap_; + using AccessType = AccessType_; + + using Index = typename Layout::Index; + using LongIndex = typename Layout::LongIndex; + + using TensorRef = TensorRef; + using TensorView = TensorView; + using TensorCoord = typename Layout::TensorCoord; + + using Pointer = Element*; + using NonConstPointer = typename platform::remove_const::type*; + + using UnderlyingPredicates = CustomPredicatedTileAccessIteratorPredicates< + NADim, + Shape, + Element, + Layout, + AdvanceRank, + ThreadMap, + AccessType>; + + static int const kAccessesPerVector = + ThreadMap::kElementsPerAccess / AccessType::kElements; + + static_assert( + !(ThreadMap::kElementsPerAccess % AccessType::kElements), + "Vectors implied by the thread map must be divisible by the access type."); + + using Mask = typename UnderlyingPredicates::Mask; + + /// Uses a non-template class + struct Params : CustomPredicatedTileAccessIteratorParams { + using Base = CustomPredicatedTileAccessIteratorParams; + + // Default ctor + CUTLASS_HOST_DEVICE + Params() {} + + /// Construct the Params object given a pitch-linear tensor's layout + CUTLASS_HOST_DEVICE + Params(Dim stride, Dim extent_row) + : Base( + stride, + extent_row, + MakePredicatedTileAccessIteratorDesc< + Shape, + Element, + Layout, + kAdvanceRank, + ThreadMap>()()) {} + + CUTLASS_HOST_DEVICE + Params(Base const& base) : Base(base) {} + }; + + private: + /// Internal pointer type permits fast address arithmetic + using BytePointer = char*; + + private: + // + // Data members + // + + UnderlyingPredicates the_predicates; + Mask residual_tile_mask; + + /// Parameters object with precomputed internal state + Params params_; + + /// Internal pointer to first access of tile + BytePointer pointer_; + TensorCoord coord_offset_; + + public: + /// Constructs a TileIterator from its precomputed state, threadblock offset, + /// and thread ID + CUTLASS_HOST_DEVICE + PredicatedTileAccessIteratorResidualLast( + /// Precomputed parameters object + Params const& params, + /// Pointer to start of tensor + Pointer pointer, + /// Extent of tensor (rows) + Dim extent_row, + /// Extent of tensor (columns) + int32_t extent_col, + /// ID of each participating thread + int thread_id, + /// Initial offset of threadblock + TensorCoord const& threadblock_offset) + : params_(params), + pointer_(reinterpret_cast( + const_cast(pointer))), + the_predicates(extent_row, extent_col) { + the_predicates.set_predicates(thread_id, threadblock_offset); + the_predicates.get_mask(residual_tile_mask); + + // Working around a weird compiler bug happening on P100 for the backward. + // I've seen together: the_predicates.predicates_[0] = 14 (instead of 15) + // residual_tile_mask[0] = 15 (correct) + // + // Adding prints when the value is calculated (in `compute_predicates_`) + // sometimes removes the bug. The consequence is that we skip some + // element of a tensor, leading to wrong results + // Setting `compute_predicates_`'s second argument (`is_steady_state`) to + // true also seems to get rid of the bug - at the cost of twice as many + // comparisons. +#if !defined(__CUDA_ARCH__) || (__CUDA_ARCH__ >= 700) + constexpr bool kWorkAroundCompilerBug = false; +#else + constexpr bool kWorkAroundCompilerBug = true; +#endif + the_predicates.compute_predicates_( + the_predicates.extent_, true && !kWorkAroundCompilerBug); + + // update internal pointers + coord_offset_ = the_predicates.thread_offset_; + // add_pointer_offset(the_predicates.thread_offset_.strided(), + // the_predicates.thread_offset_.contiguous()); + } + + /// Construct a PredicatedTileAccessIteratorResidualLast with zero threadblock + /// offset + CUTLASS_HOST_DEVICE + PredicatedTileAccessIteratorResidualLast( + /// Precomputed parameters object + Params const& params, + /// Pointer to start of tensor + Pointer pointer, + /// Extent of tensor (rows) + Dim extent_row, + /// Extent of tensor (columns) + int32_t extent_col, + ///< ID of each participating thread + int thread_id) + : PredicatedTileAccessIteratorResidualLast( + params, + pointer, + extent_row, + extent_col, + thread_id, + make_Coord(0, 0)) {} + + /// Overrides the internal iteration index + CUTLASS_HOST_DEVICE + void set_iteration_index(int index) { + the_predicates.set_iteration_index(index); + } + + CUTLASS_HOST_DEVICE + void set_residual_tile(bool is_residual_tile) { + if (is_residual_tile) { + the_predicates.set_mask(residual_tile_mask); + } + } + + CUTLASS_HOST_DEVICE + void add_pointer_offset(int32_t row_offset, int32_t col_offset) { + coord_offset_.strided() += row_offset; + coord_offset_.contiguous() += col_offset; + } + + CUTLASS_HOST_DEVICE + void add_pointer_offset(LongIndex linear_offset) { + printf("FATAL!"); + // if constexpr (kAdvanceRank) { + // auto col_offset = linear_offset % Shape::kStrided; + // auto row_offset = linear_offset / Shape::kStrided; + // add_pointer_offset((int32_t)row_offset, (int32_t)col_offset); + // } else { + // auto col_offset = linear_offset % Shape::kContiguous; + // auto row_offset = linear_offset / Shape::kContiguous; + // add_pointer_offset((int32_t)row_offset, (int32_t)col_offset); + // } + } + + /// Advances an iterator along logical dimensions of matrix in units of whole + /// tiles + CUTLASS_DEVICE + void add_tile_offset(TensorCoord const& tile_offset) { + add_pointer_offset( + Shape::kStrided * tile_offset.strided(), + Shape::kContiguous * tile_offset.contiguous()); + // if (kAdvanceRank) { + // add_pointer_offset( + // params_.inc_advance_ * (tile_offset.strided()) + + // Shape::kContiguous * tile_offset.contiguous()); + // // pointer_ += params_.inc_advance_ * LongIndex(tile_offset.strided()); + // // pointer_ += Shape::kContiguous * tile_offset.contiguous(); + // } else { + // // Advance along contiguous dimension + // add_pointer_offset( + // params_.inc_advance_ * (tile_offset.contiguous()) + + // Shape::kStrided * tile_offset.strided()); + // // pointer_ += params_.inc_advance_ * + // LongIndex(tile_offset.contiguous()); + // // pointer_ += Shape::kStrided * tile_offset.strided(); + // } + } + + CUTLASS_HOST_DEVICE + LongIndex get_pointer_offset() const { + LongIndex coord_contig = coord_offset_.contiguous() + + the_predicates.iteration_contiguous_ * ThreadMap::Delta::kContiguous; + LongIndex coord_strided = + (natten::cuda::fna::map_index_to_coord( + coord_offset_.strided() + + the_predicates.iteration_strided_ * ThreadMap::Delta::kStrided, + the_predicates.extent_row_dim) * + params_.stride_) + .sum(); + return ((coord_strided + coord_contig) * sizeof_bits::value) / 8; + } + + /// Returns a pointer + CUTLASS_HOST_DEVICE + AccessType* get() const { + if (!valid()) { + return nullptr; + } + return reinterpret_cast(pointer_ + get_pointer_offset()) + + the_predicates.iteration_vector_; + } + + /// Increment and return an instance to self. + CUTLASS_HOST_DEVICE + PredicatedTileAccessIteratorResidualLast& operator++() { + the_predicates.operator++(); + + ++the_predicates.iteration_vector_; + if (the_predicates.iteration_vector_ < kAccessesPerVector) { + return *this; + } + + the_predicates.iteration_vector_ = 0; + ++the_predicates.iteration_contiguous_; + + if (the_predicates.iteration_contiguous_ < + ThreadMap::Iterations::kContiguous) { + return *this; + } + + // Enter here only if (iteration_contiguous_ == + // ThreadMap::Iteration::kContiguous) + the_predicates.iteration_contiguous_ = 0; + ++the_predicates.iteration_strided_; + + if (the_predicates.iteration_strided_ < ThreadMap::Iterations::kStrided) { + // add_pointer_offset(params_.inc_strided_); + // pointer_ += params_.inc_strided_; + + return *this; + } + + // Enter here only if (iteration_stride_ == ThreadMap::Iteration::kStrided) + // which means we enter the next tile. + the_predicates.iteration_strided_ = 0; + + //// advance to next tile + // add_pointer_offset(params_.inc_next_); + //// pointer_ += params_.inc_next_; + + // add_pointer_offset(-1 * params_.inc_advance_); + ////if constexpr (kAdvanceRank) { + //// add_pointer_offset(-1 * params_.inc_advance_, 0); + ////} else { + //// // along the contiguous axis. + //// add_pointer_offset(0, -1 * params_.inc_advance_); + ////} + //// now return to start tile - if the iterator is subsequently advanced, + ///this / subtraction as well as the subsequent integer addition are both + ///elided by / the compiler. / pointer_ -= params_.inc_advance_; + + return *this; + } + + /// Increment and return an instance to self. + CUTLASS_HOST_DEVICE + PredicatedTileAccessIteratorResidualLast operator++(int) { + PredicatedTileAccessIteratorResidualLast self(*this); + operator++(); + return self; + } + + /// Clears the predicate set efficiently + CUTLASS_HOST_DEVICE + void clear_mask(bool enable = true) { + the_predicates.clear_mask(enable); + } + + /// Clears the predicate set efficiently + CUTLASS_HOST_DEVICE + void enable_mask() { + the_predicates.enable_mask(); + } + + /// Sets the predicate mask, overriding value stored in predicate iterator + CUTLASS_HOST_DEVICE + void set_mask(Mask const& mask) { + the_predicates.set_mask(mask); + } + + /// Gets the mask + CUTLASS_HOST_DEVICE + void get_mask(Mask& mask) { + the_predicates.get_mask(mask); + } + + /// Returns whether access is valid or not + CUTLASS_HOST_DEVICE + bool valid() const { + return the_predicates.valid(); + } +}; + +// Specialization for NA1D, where we only have one stride, +// and therefore the typical pitch-linear layout, and won't have +// to manually convert offsets on every get(). +template < + typename Shape_, + typename Element_, + int AdvanceRank, + typename ThreadMap_, + typename AccessType_> +class PredicatedTileAccessIteratorResidualLast< + 1, + Shape_, + Element_, + layout::PitchLinear, + AdvanceRank, + ThreadMap_, + AccessType_> { + public: + static constexpr int NADim = 1; + static_assert( + AdvanceRank == 0 || AdvanceRank == 1, + "Specialization for pitch-linear iterator may along advance along the " + "contiguous(rank=0) or strided(rank=1) dimension."); + + using Dim = typename natten::cuda::fna::GetDim::type; + + using Shape = Shape_; + using Element = Element_; + using Layout = layout::PitchLinear; + static int const kAdvanceRank = AdvanceRank; + using ThreadMap = ThreadMap_; + using AccessType = AccessType_; + + using Index = typename Layout::Index; + using LongIndex = typename Layout::LongIndex; + + using TensorRef = TensorRef; + using TensorView = TensorView; + using TensorCoord = typename Layout::TensorCoord; + + using Pointer = Element*; + using NonConstPointer = typename platform::remove_const::type*; + + using UnderlyingPredicates = CustomPredicatedTileAccessIteratorPredicates< + NADim, + Shape, + Element, + Layout, + AdvanceRank, + ThreadMap, + AccessType>; + + static int const kAccessesPerVector = + ThreadMap::kElementsPerAccess / AccessType::kElements; + + static_assert( + !(ThreadMap::kElementsPerAccess % AccessType::kElements), + "Vectors implied by the thread map must be divisible by the access type."); + + using Mask = typename UnderlyingPredicates::Mask; + + /// Uses a non-template class + struct Params : CustomPredicatedTileAccessIteratorParams { + using Base = CustomPredicatedTileAccessIteratorParams; + + // Default ctor + CUTLASS_HOST_DEVICE + Params() {} + + /// Construct the Params object given a pitch-linear tensor's layout + CUTLASS_HOST_DEVICE + Params(Dim stride, Dim extent_row) + : Base( + stride, + extent_row, + MakePredicatedTileAccessIteratorDesc< + Shape, + Element, + Layout, + kAdvanceRank, + ThreadMap>()()) {} + + CUTLASS_HOST_DEVICE + Params(Base const& base) : Base(base) {} + }; + + private: + /// Internal pointer type permits fast address arithmetic + using BytePointer = char*; + + private: + // + // Data members + // + + UnderlyingPredicates the_predicates; + Mask residual_tile_mask; + + /// Parameters object with precomputed internal state + Params params_; + + /// Internal pointer to first access of tile + BytePointer pointer_; + + public: + /// Constructs a TileIterator from its precomputed state, threadblock offset, + /// and thread ID + CUTLASS_HOST_DEVICE + PredicatedTileAccessIteratorResidualLast( + /// Precomputed parameters object + Params const& params, + /// Pointer to start of tensor + Pointer pointer, + /// Extent of tensor (rows) + Dim extent_row, + /// Extent of tensor (columns) + int32_t extent_col, + /// ID of each participating thread + int thread_id, + /// Initial offset of threadblock + TensorCoord const& threadblock_offset) + : params_(params), + pointer_(reinterpret_cast( + const_cast(pointer))), + the_predicates(extent_row, extent_col) { + the_predicates.set_predicates(thread_id, threadblock_offset); + the_predicates.get_mask(residual_tile_mask); + + // Working around a weird compiler bug happening on P100 for the backward. + // I've seen together: the_predicates.predicates_[0] = 14 (instead of 15) + // residual_tile_mask[0] = 15 (correct) + // + // Adding prints when the value is calculated (in `compute_predicates_`) + // sometimes removes the bug. The consequence is that we skip some + // element of a tensor, leading to wrong results + // Setting `compute_predicates_`'s second argument (`is_steady_state`) to + // true also seems to get rid of the bug - at the cost of twice as many + // comparisons. +#if !defined(__CUDA_ARCH__) || (__CUDA_ARCH__ >= 700) + constexpr bool kWorkAroundCompilerBug = false; +#else + constexpr bool kWorkAroundCompilerBug = true; +#endif + the_predicates.compute_predicates_( + the_predicates.extent_, true && !kWorkAroundCompilerBug); + + // update internal pointers + auto offset = (natten::cuda::fna::map_index_to_coord( + the_predicates.thread_offset_.strided(), extent_row) * + params_.stride_) + .sum() + + the_predicates.thread_offset_.contiguous(); + + add_pointer_offset(offset); + } + + /// Construct a PredicatedTileAccessIteratorResidualLast with zero threadblock + /// offset + CUTLASS_HOST_DEVICE + PredicatedTileAccessIteratorResidualLast( + /// Precomputed parameters object + Params const& params, + /// Pointer to start of tensor + Pointer pointer, + /// Extent of tensor (rows) + Dim extent_row, + /// Extent of tensor (columns) + int32_t extent_col, + ///< ID of each participating thread + int thread_id) + : PredicatedTileAccessIteratorResidualLast( + params, + pointer, + extent_row, + extent_col, + thread_id, + make_Coord(0, 0)) {} + + /// Overrides the internal iteration index + CUTLASS_HOST_DEVICE + void set_iteration_index(int index) { + the_predicates.set_iteration_index(index); + } + + CUTLASS_HOST_DEVICE + void set_residual_tile(bool is_residual_tile) { + if (is_residual_tile) { + the_predicates.set_mask(residual_tile_mask); + } + } + + /// Adds a pointer offset in units of Element + CUTLASS_HOST_DEVICE + void add_pointer_offset(LongIndex pointer_offset) { + pointer_ += sizeof_bits::value * pointer_offset / 8; + } + + /// Advances an iterator along logical dimensions of matrix in units of whole + /// tiles + CUTLASS_DEVICE + void add_tile_offset(TensorCoord const& tile_offset) { + if (kAdvanceRank) { + pointer_ += params_.inc_advance_ * LongIndex(tile_offset.strided()); + pointer_ += Shape::kContiguous * tile_offset.contiguous(); + } else { + pointer_ += params_.inc_advance_ * LongIndex(tile_offset.contiguous()); + pointer_ += Shape::kStrided * tile_offset.strided(); + } + } + + /// Returns a pointer + CUTLASS_HOST_DEVICE + AccessType* get() const { + return reinterpret_cast( + pointer_ + + the_predicates.iteration_contiguous_ * + (ThreadMap::Delta::kContiguous * + sizeof_bits::value) / + 8) + + the_predicates.iteration_vector_; + } + + /// Increment and return an instance to self. + CUTLASS_HOST_DEVICE + PredicatedTileAccessIteratorResidualLast& operator++() { + the_predicates.operator++(); + + ++the_predicates.iteration_vector_; + if (the_predicates.iteration_vector_ < kAccessesPerVector) { + return *this; + } + + the_predicates.iteration_vector_ = 0; + ++the_predicates.iteration_contiguous_; + + if (the_predicates.iteration_contiguous_ < + ThreadMap::Iterations::kContiguous) { + return *this; + } + + // Enter here only if (iteration_contiguous_ == + // ThreadMap::Iteration::kContiguous) + the_predicates.iteration_contiguous_ = 0; + ++the_predicates.iteration_strided_; + + if (the_predicates.iteration_strided_ < ThreadMap::Iterations::kStrided) { + pointer_ += params_.inc_strided_; + + return *this; + } + + // Enter here only if (iteration_stride_ == ThreadMap::Iteration::kStrided) + // which means we enter the next tile. + the_predicates.iteration_strided_ = 0; + + // advance to next tile + pointer_ += params_.inc_next_; + + // now return to start tile - if the iterator is subsequently advanced, + // this subtraction as well as the subsequent integer addition are both + // elided by the compiler. + pointer_ -= params_.inc_advance_; + + return *this; + } + + /// Increment and return an instance to self. + CUTLASS_HOST_DEVICE + PredicatedTileAccessIteratorResidualLast operator++(int) { + PredicatedTileAccessIteratorResidualLast self(*this); + operator++(); + return self; + } + + /// Clears the predicate set efficiently + CUTLASS_HOST_DEVICE + void clear_mask(bool enable = true) { + the_predicates.clear_mask(enable); + } + + /// Clears the predicate set efficiently + CUTLASS_HOST_DEVICE + void enable_mask() { + the_predicates.enable_mask(); + } + + /// Sets the predicate mask, overriding value stored in predicate iterator + CUTLASS_HOST_DEVICE + void set_mask(Mask const& mask) { + the_predicates.set_mask(mask); + } + + /// Gets the mask + CUTLASS_HOST_DEVICE + void get_mask(Mask& mask) { + the_predicates.get_mask(mask); + } + + /// Returns whether access is valid or not + CUTLASS_HOST_DEVICE + bool valid() const { + return the_predicates.valid(); + } +}; + +//////////////////////////////////////////////////////////////////////////////// + +/// Specialization of PredicatedTileAccessIteratorResidualLast for row-major +/// data. +/// +/// Satisfies: ForwardTileIteratorConcept | +/// ReadableContiguousTileIteratorConcept | +/// WriteableContiguousTileIteratorConcept | +/// MaskedTileIteratorConcept +/// +template < + int NADim, + typename Shape_, + typename Element_, + int AdvanceRank, + typename ThreadMap_, + typename AccessType_> +class PredicatedTileAccessIteratorResidualLast< + NADim, + Shape_, + Element_, + layout::RowMajor, + AdvanceRank, + ThreadMap_, + AccessType_> { + public: + static_assert(NADim >= 1 && NADim < 4); + static_assert( + AdvanceRank == 0 || AdvanceRank == 1, + "Specialization for pitch-linear iterator may along advance along the " + "contiguous(rank=0) or strided(rank=1) dimension."); + + using Dim = typename natten::cuda::fna::GetDim::type; + + using Shape = Shape_; + using Element = Element_; + using Layout = layout::RowMajor; + static int const kAdvanceRank = AdvanceRank; + using ThreadMap = ThreadMap_; + using AccessType = AccessType_; + + using Index = typename Layout::Index; + using LongIndex = typename Layout::LongIndex; + + using TensorRef = TensorRef; + using TensorView = TensorView; + using TensorCoord = typename Layout::TensorCoord; + + using Pointer = Element*; + using NonConstPointer = typename platform::remove_const::type*; + + using UnderlyingIterator = PredicatedTileAccessIteratorResidualLast< + NADim, + layout::PitchLinearShape, + Element, + layout::PitchLinear, + (kAdvanceRank == 0 ? 1 : 0), + ThreadMap, + AccessType>; + + static int const kAccessesPerVector = UnderlyingIterator::kAccessesPerVector; + + /// Predicate vector stores mask to guard accesses + using Mask = typename UnderlyingIterator::Mask; + + /// Parameters object is precomputed state and is host-constructible + class Params { + private: + friend PredicatedTileAccessIteratorResidualLast; + + /// Parameters object + typename UnderlyingIterator::Params params_; + + public: + /// Default ctor + CUTLASS_HOST_DEVICE + Params() {} + + /// Construct the Params object given a pitch-linear tensor's layout + CUTLASS_HOST_DEVICE + Params(Dim stride, Dim extent_row) : params_(stride, extent_row){}; + + /// Construct the Params object given a pitch-linear tensor's layout + CUTLASS_HOST_DEVICE + Params(typename UnderlyingIterator::Params::Base const& base) + : params_(base) {} + }; + + private: + // + // Data members + // + + /// Underlying pitch-linear tile iterator + UnderlyingIterator iterator_; + + public: + /// Constructs a TileIterator from its precomputed state, threadblock offset, + /// and thread ID + CUTLASS_HOST_DEVICE + PredicatedTileAccessIteratorResidualLast( + ///< Precomputed parameters object + Params const& params, + ///< Pointer to start of tensor + Pointer pointer, + /// Extent of tensor (rows) + Dim extent_row, + /// Extent of tensor (columns) + int32_t extent_col, + ///< ID of each participating thread + int thread_id, + ///< Initial offset of threadblock + TensorCoord const& threadblock_offset) + : iterator_( + params.params_, + pointer, + extent_row, + extent_col, + thread_id, + layout::PitchLinearCoord( + threadblock_offset.column(), + threadblock_offset.row())) {} + + /// Construct a PredicatedTileAccessIteratorResidualLast with zero threadblock + /// offset + CUTLASS_HOST_DEVICE + PredicatedTileAccessIteratorResidualLast( + Params const& params, ///< Precomputed parameters object + Pointer pointer, ///< Pointer to start of tensor + Dim extent_row, ///< Extent of tensor (rows) + int32_t extent_col, ///< Extent of tensor (columns) + int thread_id ///< ID of each participating thread + ) + : PredicatedTileAccessIteratorResidualLast( + params, + pointer, + extent_row, + extent_col, + thread_id, + make_Coord(0, 0)) {} + + /// Overrides the internal iteration index + CUTLASS_HOST_DEVICE + void set_iteration_index(int index) { + iterator_.set_iteration_index(index); + } + + CUTLASS_HOST_DEVICE + void set_residual_tile(bool enable) { + iterator_.set_residual_tile(enable); + } + + /// Adds a pointer offset in units of Element + CUTLASS_HOST_DEVICE + void add_pointer_offset(LongIndex pointer_offset) { + iterator_.add_pointer_offset(pointer_offset); + } + + /// Advances an iterator along logical dimensions of matrix in units of whole + /// tiles + CUTLASS_HOST_DEVICE + void add_tile_offset(TensorCoord const& tile_offset) { + iterator_.add_tile_offset({tile_offset.column(), tile_offset.row()}); + } + + /// Returns a pointer + CUTLASS_HOST_DEVICE + AccessType* get() const { + return reinterpret_cast(iterator_.get()); + } + + /// Advances to the next tile in memory. + /// + /// The first time this method is called, predicates are updated, and the + /// iterator's internal pointer is reverted to the first "steady state" tile. + /// Subsequent calls are lightweight and must only update the internal + /// pointer. + CUTLASS_HOST_DEVICE + PredicatedTileAccessIteratorResidualLast& operator++() { + ++iterator_; + return *this; + } + + /// Advances to the next tile in memory. + /// + /// The first time this method is called, predicates are updated, and the + /// iterator's internal pointer is reverted to the first "steady state" tile. + /// Subsequent calls are lightweight and must only update the internal + /// pointer. + CUTLASS_HOST_DEVICE + PredicatedTileAccessIteratorResidualLast operator++(int) { + PredicatedTileAccessIteratorResidualLast self(*this); + operator++(); + return self; + } + + /// Clears the predicate set efficiently + CUTLASS_HOST_DEVICE + void clear_mask(bool enable = true) { + iterator_.clear_mask(enable); + } + + /// Clears the predicate set efficiently + CUTLASS_HOST_DEVICE + void enable_mask() { + iterator_.enable_mask(); + } + + /// Sets the predicate mask, overriding value stored in predicate iterator + CUTLASS_HOST_DEVICE + void set_mask(Mask const& mask) { + iterator_.set_mask(mask); + } + + /// Gets the mask + CUTLASS_HOST_DEVICE + void get_mask(Mask& mask) { + iterator_.get_mask(mask); + } + + /// Returns whether access is valid or not + CUTLASS_HOST_DEVICE + bool valid() { + return iterator_.valid(); + } +}; + +} // namespace threadblock +} // namespace transform +} // namespace cutlass + +//////////////////////////////////////////////////////////////////////////////// diff --git a/csrc/include/natten/cuda/fna/iterators/predicated_tile_iterator.h b/csrc/include/natten/cuda/fna/iterators/predicated_tile_iterator.h new file mode 100644 index 0000000..a22a49b --- /dev/null +++ b/csrc/include/natten/cuda/fna/iterators/predicated_tile_iterator.h @@ -0,0 +1,831 @@ +/* + * Copied from CUTLASS (https://github.com/NVIDIA/cutlass/) and edited. + */ +/*************************************************************************************************** + * Copyright (c) 2017 - 2023 NVIDIA CORPORATION & AFFILIATES. All rights + *reserved. SPDX-License-Identifier: BSD-3-Clause + * + * Redistribution and use in source and binary forms, with or without + * modification, are permitted provided that the following conditions are met: + * + * 1. Redistributions of source code must retain the above copyright notice, + *this list of conditions and the following disclaimer. + * + * 2. Redistributions in binary form must reproduce the above copyright notice, + * this list of conditions and the following disclaimer in the documentation + * and/or other materials provided with the distribution. + * + * 3. Neither the name of the copyright holder nor the names of its + * contributors may be used to endorse or promote products derived from + * this software without specific prior written permission. + * + * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" + * AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE + * IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE + *ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE + *LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR + *CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF + *SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS + *INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN + *CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) + *ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE + *POSSIBILITY OF SUCH DAMAGE. + * + **************************************************************************************************/ +/*! \file + \brief Templates implementing loading of tiles from pitch-linear rank=2 + tensors. + + This iterator uses masks to guard out-of-bounds accesses. The first tile + this iterator visits maybe partial, then the remaining tiles are complete. + So, we only need to compute the predicates twice, once before the first tile + and once for the remaining full tiles which can share the same predicates. + + A precomputed "Params" object minimizes the amount of state that must be + stored in registers, and integer addition is used to advance the pointer + through memory. +*/ + +#pragma once + +#include + +#include +#include + +//////////////////////////////////////////////////////////////////////////////// + +namespace cutlass { +namespace transform { +namespace threadblock { + +template < + int NADim, + typename Shape, + typename Element, + typename Layout, + int AdvanceRank, + typename ThreadMap, + int AccessSize = ThreadMap::kElementsPerAccess> +class CustomPredicatedTileIterator; + +//////////////////////////////////////////////////////////////////////////////// + +template < + int NADim, + typename Shape_, + typename Element_, + int AdvanceRank, + typename ThreadMap_, + int AccessSize> +class CustomPredicatedTileIterator< + NADim, + Shape_, + Element_, + layout::PitchLinear, + AdvanceRank, + ThreadMap_, + AccessSize> { + public: + static_assert(NADim >= 1 && NADim < 4); + static_assert( + AdvanceRank == 0 || AdvanceRank == 1, + "Specialization for pitch-linear iterator may advance along the " + "contiguous(rank=0) or strided(rank=1) dimension."); + + using Dim = typename natten::cuda::fna::GetDim::type; + + using Shape = Shape_; + using Element = Element_; + using Layout = layout::PitchLinear; + static int const kAdvanceRank = AdvanceRank; + using ThreadMap = ThreadMap_; + + using Index = typename Layout::Index; + using LongIndex = typename Layout::LongIndex; + + using TensorRef = TensorRef; + using TensorView = TensorView; + using TensorCoord = typename Layout::TensorCoord; + + using Pointer = Element*; + using NonConstPointer = typename platform::remove_const::type*; + + /// Type used for internal memory accesses + using AccessType = AlignedArray< + Element, + AccessSize, + (AccessSize * sizeof_bits::value / 8)>; + + /// Underlying iterator to compute the addresses + using TileAccessIterator = CustomPredicatedTileAccessIterator< + NADim, + Shape, + Element, + Layout, + kAdvanceRank, + ThreadMap, + AccessType>; + + static int const kAccessesPerVector = TileAccessIterator::kAccessesPerVector; + + /// Fragment object to be loaded or stored + using Fragment = cutlass::Array< + Element, + ThreadMap::Iterations::kCount * ThreadMap::kElementsPerAccess>; + + /// Predicate vector stores mask to guard accesses + using Mask = typename TileAccessIterator::Mask; + + /// Parameters object is precomputed state and is host-constructible + class Params { + public: + using Base = typename TileAccessIterator::Params::Base; + + friend CustomPredicatedTileIterator; + + private: + /// Parameters object + typename TileAccessIterator::Params params_; + + public: + /// Construct the Params object given a pitch-linear tensor's layout + CUTLASS_HOST_DEVICE + Params(Dim stride, Dim extent_row) : params_(stride, extent_row) {} + + /// Default constructor + Params() = default; + + CUTLASS_HOST_DEVICE + Params(Base const& base) : params_(base) {} + }; + + private: + /// Internal pointer type permits fast address arithmetic + using BytePointer = char*; + + private: + // + // Data members + // + + /// Data member to the tile access iterator + TileAccessIterator address_iterator_; + + public: + /// Default constructor + CustomPredicatedTileIterator() = default; + + /// Constructs a TileIterator from its precomputed state, threadblock offset, + /// and thread ID + CUTLASS_HOST_DEVICE + CustomPredicatedTileIterator( + /// Precomputed parameters object + Params const& params, + /// Pointer to start of tensor + Pointer pointer, + /// Extent of tensor (rows) + Dim extent_row, + /// Extent of tensor (columns) + int32_t extent_col, + /// ID of each participating thread + int thread_id, + /// Initial offset of threadblock + TensorCoord const& threadblock_offset) + : address_iterator_( + params.params_, + pointer, + extent_row, + extent_col, + thread_id, + threadblock_offset) {} + + /// Construct a CustomPredicatedTileIterator with zero threadblock offset + CUTLASS_HOST_DEVICE + CustomPredicatedTileIterator( + Params const& params, ///< Precomputed parameters object + Pointer pointer, ///< Pointer to start of tensor + Dim extent_row, ///< Extent of tensor (rows) + int32_t extent_col, ///< Extent of tensor (columns) + int thread_id ///< ID of each participating thread + ) + : CustomPredicatedTileIterator( + params, + pointer, + extent_row, + extent_col, + thread_id, + make_Coord(0, 0)) {} + + /// Adds a pointer offset in units of Element + CUTLASS_HOST_DEVICE + void add_pointer_offset(LongIndex pointer_offset) { + address_iterator_.add_pointer_offset(pointer_offset); + } + + /// Advances to the next tile in memory. + /// + /// The first time this method is called, predicates are updated, and the + /// iterator's internal pointer is reverted to the first "steady state" tile. + /// Subsequent calls are lightweight and must only update the internal + /// pointer. + CUTLASS_HOST_DEVICE + CustomPredicatedTileIterator& operator++() { + if (kAdvanceRank) + address_iterator_.add_tile_offset({0, 1}); + else + address_iterator_.add_tile_offset({1, 0}); + + return *this; + } + + /// Advances to the next tile in memory. + /// + /// The first time this method is called, predicates are updated, and the + /// iterator's internal pointer is reverted to the first "steady state" tile. + /// Subsequent calls are lightweight and must only update the internal + /// pointer. + CUTLASS_HOST_DEVICE + CustomPredicatedTileIterator operator++(int) { + CustomPredicatedTileIterator self(*this); + operator++(); + return self; + } + + /// Clears the predicate set efficiently + CUTLASS_HOST_DEVICE + void clear_mask(bool enable = true) { + address_iterator_.clear_mask(enable); + } + + /// Clears the predicate set efficiently + CUTLASS_HOST_DEVICE + void enable_mask() { + address_iterator_.enable_mask(); + } + + /// Sets the predicate mask, overriding value stored in predicate iterator + CUTLASS_HOST_DEVICE + void set_mask(Mask const& mask) { + address_iterator_.set_mask(mask); + } + + /// Gets the mask + CUTLASS_HOST_DEVICE + void get_mask(Mask& mask) { + address_iterator_.get_mask(mask); + } + + CUTLASS_DEVICE + void load_with_pointer_offset(Fragment& frag, Index pointer_offset) { + load_with_byte_offset( + frag, pointer_offset * sizeof_bits::value / 8); + } + + CUTLASS_DEVICE + void load_with_byte_offset(Fragment& frag, LongIndex byte_offset) { + AccessType* frag_ptr = reinterpret_cast(&frag); + + CUTLASS_PRAGMA_UNROLL + for (int s = 0; s < ThreadMap::Iterations::kStrided; ++s) { + CUTLASS_PRAGMA_UNROLL + for (int c = 0; c < ThreadMap::Iterations::kContiguous; ++c) { + CUTLASS_PRAGMA_UNROLL + for (int v = 0; v < kAccessesPerVector; ++v) { + int idx = v + + kAccessesPerVector * (c + s * ThreadMap::Iterations::kContiguous); + + address_iterator_.set_iteration_index(idx); + char const* byte_ptr = + reinterpret_cast(address_iterator_.get()) + + byte_offset; + + AccessType const* access_ptr = + reinterpret_cast(byte_ptr); + + cutlass::arch::global_load( + frag_ptr[idx], access_ptr, address_iterator_.valid()); + + ++address_iterator_; + } + } + } + } + + /// Loads a fragment from memory + CUTLASS_DEVICE + void load(Fragment& frag) { + load_with_byte_offset(frag, 0); + } + + /// Store a fragment to memory + CUTLASS_DEVICE + void store_with_pointer_offset(Fragment const& frag, Index pointer_offset) { + store_with_byte_offset( + frag, pointer_offset * sizeof_bits::value / 8); + } + + /// Store a fragment to memory + CUTLASS_DEVICE + void store_with_byte_offset(Fragment const& frag, LongIndex byte_offset) { + address_iterator_.set_iteration_index(0); + AccessType const* frag_ptr = reinterpret_cast(&frag); + + CUTLASS_PRAGMA_UNROLL + for (int s = 0; s < ThreadMap::Iterations::kStrided; ++s) { + CUTLASS_PRAGMA_UNROLL + for (int c = 0; c < ThreadMap::Iterations::kContiguous; ++c) { + CUTLASS_PRAGMA_UNROLL + for (int v = 0; v < kAccessesPerVector; ++v) { + int idx = v + + kAccessesPerVector * (c + s * ThreadMap::Iterations::kContiguous); + + char* byte_ptr = + reinterpret_cast(address_iterator_.get()) + byte_offset; + AccessType* access_ptr = reinterpret_cast(byte_ptr); + + if (address_iterator_.valid()) { + *access_ptr = frag_ptr[idx]; + } + ++address_iterator_; + } + } + } + } + + /// Store a fragment to memory + CUTLASS_DEVICE + void store(Fragment const& frag) { + store_with_byte_offset(frag, 0); + } +}; + +//////////////////////////////////////////////////////////////////////////////// + +/// Specialization of CustomPredicatedTileIterator for column-major data. +/// +/// Satisfies: ForwardTileIteratorConcept | +/// ReadableContiguousTileIteratorConcept | +/// WriteableContiguousTileIteratorConcept | +/// MaskedTileIteratorConcept +/// +template < + int NADim, + typename Shape_, + typename Element_, + int AdvanceRank, + typename ThreadMap_, + int AccessSize> +class CustomPredicatedTileIterator< + NADim, + Shape_, + Element_, + layout::ColumnMajor, + AdvanceRank, + ThreadMap_, + AccessSize> { + public: + static_assert(NADim >= 1 && NADim < 4); + static_assert( + AdvanceRank == 0 || AdvanceRank == 1, + "Specialization for pitch-linear iterator may along advance along the " + "contiguous(rank=0) or strided(rank=1) dimension."); + + using Dim = typename natten::cuda::fna::GetDim::type; + + using Shape = Shape_; + using Element = Element_; + using Layout = layout::ColumnMajor; + static int const kAdvanceRank = AdvanceRank; + using ThreadMap = ThreadMap_; + + using Index = typename Layout::Index; + using LongIndex = typename Layout::LongIndex; + + using TensorRef = TensorRef; + using TensorView = TensorView; + using TensorCoord = typename Layout::TensorCoord; + + using Pointer = Element*; + using NonConstPointer = typename platform::remove_const::type*; + + using UnderlyingIterator = CustomPredicatedTileIterator< + NADim, + layout::PitchLinearShape, + Element, + layout::PitchLinear, + (kAdvanceRank == 0 ? 0 : 1), + ThreadMap, + AccessSize>; + + using AccessType = typename UnderlyingIterator::AccessType; + + /// Fragment object to be loaded or stored + using Fragment = cutlass::Array< + Element, + ThreadMap::Iterations::kCount * ThreadMap::kElementsPerAccess>; + + /// Predicate vector stores mask to guard accesses + using Mask = typename UnderlyingIterator::Mask; + + /// Parameters object is precomputed state and is host-constructible + class Params { + private: + friend CustomPredicatedTileIterator; + + /// Parameters object + typename UnderlyingIterator::Params params_; + + public: + /// Default constructor + Params() = default; + + /// Construct the Params object given a pitch-linear tensor's layout + CUTLASS_HOST_DEVICE + Params(Dim stride, Dim extent_row) : params_(stride, extent_row) {} + + CUTLASS_HOST_DEVICE + Params(typename UnderlyingIterator::Params::Base const& base) + : params_(base) {} + }; + + private: + // + // Data members + // + + /// Underlying pitch-linear tile iterator + UnderlyingIterator iterator_; + + public: + /// Default constructor + CustomPredicatedTileIterator() = default; + + /// Constructs a TileIterator from its precomputed state, threadblock offset, + /// and thread ID + CUTLASS_HOST_DEVICE + CustomPredicatedTileIterator( + Params const& params, ///< Precomputed parameters object + Pointer pointer, ///< Pointer to start of tensor + Dim extent_row, ///< Extent of tensor (rows) + int32_t extent_col, ///< Extent of tensor (columns) + int thread_id, ///< ID of each participating thread + TensorCoord const& threadblock_offset ///< Initial offset of threadblock + ) + : iterator_( + params.params_, + pointer, + extent_row, + extent_col, + thread_id, + layout::PitchLinearCoord( + threadblock_offset.row(), + threadblock_offset.column())) {} + + /// Construct a CustomPredicatedTileIterator with zero threadblock offset + CUTLASS_HOST_DEVICE + CustomPredicatedTileIterator( + Params const& params, ///< Precomputed parameters object + Pointer pointer, ///< Pointer to start of tensor + Dim extent_row, ///< Extent of tensor (rows) + int32_t extent_col, ///< Extent of tensor (columns) + int thread_id ///< ID of each participating thread + ) + : CustomPredicatedTileIterator( + params, + pointer, + extent_row, + extent_col, + thread_id, + make_Coord(0, 0)) {} + + /// Adds a pointer offset in units of Element + CUTLASS_HOST_DEVICE + void add_pointer_offset(LongIndex pointer_offset) { + iterator_.add_pointer_offset(pointer_offset); + } + + /// Advances to the next tile in memory. + /// + /// The first time this method is called, predicates are updated, and the + /// iterator's internal pointer is reverted to the first "steady state" tile. + /// Subsequent calls are lightweight and must only update the internal + /// pointer. + CUTLASS_HOST_DEVICE + CustomPredicatedTileIterator& operator++() { + ++iterator_; + return *this; + } + + /// Advances to the next tile in memory. + /// + /// The first time this method is called, predicates are updated, and the + /// iterator's internal pointer is reverted to the first "steady state" tile. + /// Subsequent calls are lightweight and must only update the internal + /// pointer. + CUTLASS_HOST_DEVICE + CustomPredicatedTileIterator operator++(int) { + CustomPredicatedTileIterator self(*this); + operator++(); + return self; + } + + /// Clears the predicate set efficiently + CUTLASS_HOST_DEVICE + void clear_mask(bool enable = true) { + iterator_.clear_mask(enable); + } + + /// Clears the predicate set efficiently + CUTLASS_HOST_DEVICE + void enable_mask() { + iterator_.enable_mask(); + } + + /// Sets the predicate mask, overriding value stored in predicate iterator + CUTLASS_HOST_DEVICE + void set_mask(Mask const& mask) { + iterator_.set_mask(mask); + } + + /// Gets the mask + CUTLASS_HOST_DEVICE + void get_mask(Mask& mask) { + iterator_.get_mask(mask); + } + + /// Loads a fragment from memory + CUTLASS_DEVICE + void load_with_pointer_offset(Fragment& frag, Index pointer_offset) { + iterator_.load_with_pointer_offset(frag, pointer_offset); + } + + /// Loads a fragment from memory + CUTLASS_DEVICE + void load_with_byte_offset(Fragment& frag, LongIndex byte_offset) { + iterator_.load_with_byte_offset(frag, byte_offset); + } + + /// Loads a fragment from memory + CUTLASS_DEVICE + void load(Fragment& frag) { + load_with_pointer_offset(frag, 0); + } + + /// Store a fragment to memory + CUTLASS_DEVICE + void store_with_pointer_offset(Fragment const& frag, Index pointer_offset) { + iterator_.store_with_pointer_offset(frag, pointer_offset); + } + + /// Store a fragment to memory + CUTLASS_DEVICE + void store_with_byte_offset(Fragment const& frag, LongIndex byte_offset) { + iterator_.store_with_byte_offset(frag, byte_offset); + } + + /// Store a fragment to memory + CUTLASS_DEVICE + void store(Fragment const& frag) { + store_with_pointer_offset(frag, 0); + } +}; + +//////////////////////////////////////////////////////////////////////////////// + +/// Specialization of CustomPredicatedTileIterator for row-major data. +/// +/// Satisfies: ForwardTileIteratorConcept | +/// ReadableContiguousTileIteratorConcept | +/// WriteableContiguousTileIteratorConcept | +/// MaskedTileIteratorConcept +/// +template < + int NADim, + typename Shape_, + typename Element_, + int AdvanceRank, + typename ThreadMap_, + int AccessSize> +class CustomPredicatedTileIterator< + NADim, + Shape_, + Element_, + layout::RowMajor, + AdvanceRank, + ThreadMap_, + AccessSize> { + public: + static_assert(NADim >= 1 && NADim < 4); + static_assert( + AdvanceRank == 0 || AdvanceRank == 1, + "Specialization for pitch-linear iterator may along advance along the " + "contiguous(rank=0) or strided(rank=1) dimension."); + + using Dim = typename natten::cuda::fna::GetDim::type; + + using Shape = Shape_; + using Element = Element_; + using Layout = layout::RowMajor; + static int const kAdvanceRank = AdvanceRank; + using ThreadMap = ThreadMap_; + + using Index = typename Layout::Index; + using LongIndex = typename Layout::LongIndex; + + using TensorRef = TensorRef; + using TensorView = TensorView; + using TensorCoord = typename Layout::TensorCoord; + + using Pointer = Element*; + using NonConstPointer = typename platform::remove_const::type*; + + using UnderlyingIterator = CustomPredicatedTileIterator< + NADim, + layout::PitchLinearShape, + Element, + layout::PitchLinear, + (kAdvanceRank == 0 ? 1 : 0), + ThreadMap, + AccessSize>; + + using AccessType = typename UnderlyingIterator::AccessType; + + /// Fragment object to be loaded or stored + using Fragment = cutlass::Array< + Element, + ThreadMap::Iterations::kCount * ThreadMap::kElementsPerAccess>; + + /// Predicate vector stores mask to guard accesses + using Mask = typename UnderlyingIterator::Mask; + + /// Parameters object is precomputed state and is host-constructible + class Params { + private: + friend CustomPredicatedTileIterator; + + /// Parameters object + typename UnderlyingIterator::Params params_; + + public: + /// Default constructor + Params() = default; + + /// Construct the Params object given a pitch-linear tensor's layout + CUTLASS_HOST_DEVICE + Params(Dim stride, Dim extent_row) : params_(stride, extent_row) {} + + CUTLASS_HOST_DEVICE + Params(typename UnderlyingIterator::Params::Base const& base) + : params_(base) {} + }; + + private: + // + // Data members + // + + /// Underlying pitch-linear tile iterator + UnderlyingIterator iterator_; + + public: + /// Default constructor + CustomPredicatedTileIterator() = default; + + /// Constructs a TileIterator from its precomputed state, threadblock offset, + /// and thread ID + CUTLASS_HOST_DEVICE + CustomPredicatedTileIterator( + Params const& params, ///< Precomputed parameters object + Pointer pointer, ///< Pointer to start of tensor + Dim extent_row, ///< Extent of tensor (rows) + int32_t extent_col, ///< Extent of tensor (columns) + int thread_id, ///< ID of each participating thread + TensorCoord const& threadblock_offset ///< Initial offset of threadblock + ) + : iterator_( + params.params_, + pointer, + extent_row, + extent_col, + thread_id, + layout::PitchLinearCoord( + threadblock_offset.column(), + threadblock_offset.row())) {} + + /// Construct a CustomPredicatedTileIterator with zero threadblock offset + CUTLASS_HOST_DEVICE + CustomPredicatedTileIterator( + Params const& params, ///< Precomputed parameters object + Pointer pointer, ///< Pointer to start of tensor + Dim extent_row, ///< Extent of tensor (rows) + int32_t extent_col, ///< Extent of tensor (columns) + int thread_id ///< ID of each participating thread + ) + : CustomPredicatedTileIterator( + params, + pointer, + extent_row, + extent_col, + thread_id, + make_Coord(0, 0)) {} + + /// Adds a pointer offset in units of Element + CUTLASS_HOST_DEVICE + void add_pointer_offset(LongIndex pointer_offset) { + iterator_.add_pointer_offset(pointer_offset); + } + + /// Advances to the next tile in memory. + /// + /// The first time this method is called, predicates are updated, and the + /// iterator's internal pointer is reverted to the first "steady state" tile. + /// Subsequent calls are lightweight and must only update the internal + /// pointer. + CUTLASS_HOST_DEVICE + CustomPredicatedTileIterator& operator++() { + ++iterator_; + return *this; + } + + /// Advances to the next tile in memory. + /// + /// The first time this method is called, predicates are updated, and the + /// iterator's internal pointer is reverted to the first "steady state" tile. + /// Subsequent calls are lightweight and must only update the internal + /// pointer. + CUTLASS_HOST_DEVICE + CustomPredicatedTileIterator operator++(int) { + CustomPredicatedTileIterator self(*this); + operator++(); + return self; + } + + /// Clears the predicate set efficiently + CUTLASS_HOST_DEVICE + void clear_mask(bool enable = true) { + iterator_.clear_mask(enable); + } + + /// Clears the predicate set efficiently + CUTLASS_HOST_DEVICE + void enable_mask() { + iterator_.enable_mask(); + } + + /// Sets the predicate mask, overriding value stored in predicate iterator + CUTLASS_HOST_DEVICE + void set_mask(Mask const& mask) { + iterator_.set_mask(mask); + } + + /// Gets the mask + CUTLASS_HOST_DEVICE + void get_mask(Mask& mask) { + iterator_.get_mask(mask); + } + + /// Loads a fragment from memory + CUTLASS_DEVICE + void load_with_pointer_offset(Fragment& frag, Index pointer_offset) { + iterator_.load_with_pointer_offset(frag, pointer_offset); + } + + /// Loads a fragment from memory + CUTLASS_DEVICE + void load_with_byte_offset(Fragment& frag, LongIndex byte_offset) { + iterator_.load_with_byte_offset(frag, byte_offset); + } + + /// Loads a fragment from memory + CUTLASS_DEVICE + void load(Fragment& frag) { + load_with_pointer_offset(frag, 0); + } + + /// Store a fragment to memory + CUTLASS_DEVICE + void store_with_pointer_offset(Fragment const& frag, Index pointer_offset) { + iterator_.store_with_pointer_offset(frag, pointer_offset); + } + + /// Store a fragment to memory + CUTLASS_DEVICE + void store_with_byte_offset(Fragment const& frag, LongIndex byte_offset) { + iterator_.store_with_byte_offset(frag, byte_offset); + } + + /// Store a fragment to memory + CUTLASS_DEVICE + void store(Fragment const& frag) { + store_with_pointer_offset(frag, 0); + } +}; + +//////////////////////////////////////////////////////////////////////////////// + +} // namespace threadblock +} // namespace transform +} // namespace cutlass + +//////////////////////////////////////////////////////////////////////////////// diff --git a/csrc/include/natten/cuda/fna/iterators/predicated_tile_iterator_residual_last.h b/csrc/include/natten/cuda/fna/iterators/predicated_tile_iterator_residual_last.h new file mode 100644 index 0000000..b2a2f54 --- /dev/null +++ b/csrc/include/natten/cuda/fna/iterators/predicated_tile_iterator_residual_last.h @@ -0,0 +1,617 @@ +/* + * Copied from xFormers (https://github.com/facebookresearch/xformers/) and + * edited. + * + * Copyright (c) Meta Platforms, Inc. and affiliates. + * All rights reserved. + */ +/*************************************************************************************************** + * Copyright (c) 2017 - 2023 NVIDIA CORPORATION & AFFILIATES. All rights + *reserved. SPDX-License-Identifier: BSD-3-Clause + * + * Redistribution and use in source and binary forms, with or without + * modification, are permitted provided that the following conditions are met: + * + * 1. Redistributions of source code must retain the above copyright notice, + *this list of conditions and the following disclaimer. + * + * 2. Redistributions in binary form must reproduce the above copyright notice, + * this list of conditions and the following disclaimer in the documentation + * and/or other materials provided with the distribution. + * + * 3. Neither the name of the copyright holder nor the names of its + * contributors may be used to endorse or promote products derived from + * this software without specific prior written permission. + * + * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" + * AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE + * IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE + *ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE + *LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR + *CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF + *SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS + *INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN + *CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) + *ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE + *POSSIBILITY OF SUCH DAMAGE. + * + **************************************************************************************************/ +/*! \file + \brief Templates implementing loading of tiles from pitch-linear rank=2 + tensors. + + This iterator uses masks to guard out-of-bounds accesses. The first tile + this iterator visits maybe partial, then the remaining tiles are complete. + So, we only need to compute the predicates twice, once before the first tile + and once for the remaining full tiles which can share the same predicates. + + A precomputed "Params" object minimizes the amount of state that must be + stored in registers, and integer addition is used to advance the pointer + through memory. +*/ + +#pragma once + +#include + +#include +#include + +//////////////////////////////////////////////////////////////////////////////// + +namespace cutlass { +namespace transform { +namespace threadblock { + +//////////////////////////////////////////////////////////////////////////////// + +template < + int NADim, + typename Shape, + typename Element, + typename Layout, + int AdvanceRank, + typename ThreadMap, + int AccessSize = ThreadMap::kElementsPerAccess> +class PredicatedTileIteratorResidualLast; + +//////////////////////////////////////////////////////////////////////////////// + +/// Specialization of PredicatedTileIteratorResidualLast for pitch-linear data. +/// +/// Satisfies: ForwardTileIteratorConcept | +/// ReadableContiguousTileIteratorConcept | +/// WriteableContiguousTileIteratorConcept | +/// MaskedTileIteratorConcept +/// +template < + int NADim, + typename Shape_, + typename Element_, + int AdvanceRank, + typename ThreadMap_, + int AccessSize> +class PredicatedTileIteratorResidualLast< + NADim, + Shape_, + Element_, + layout::PitchLinear, + AdvanceRank, + ThreadMap_, + AccessSize> { + public: + static_assert(NADim >= 1 && NADim < 4); + static_assert( + AdvanceRank == 0 || AdvanceRank == 1, + "Specialization for pitch-linear iterator may advance along the " + "contiguous(rank=0) or strided(rank=1) dimension."); + + using Dim = typename natten::cuda::fna::GetDim::type; + + using Shape = Shape_; + using Element = Element_; + using Layout = layout::PitchLinear; + static int const kAdvanceRank = AdvanceRank; + using ThreadMap = ThreadMap_; + + using Index = typename Layout::Index; + using LongIndex = typename Layout::LongIndex; + + using TensorRef = TensorRef; + using TensorView = TensorView; + using TensorCoord = typename Layout::TensorCoord; + + using Pointer = Element*; + using NonConstPointer = typename platform::remove_const::type*; + + /// Type used for internal memory accesses + using AccessType = AlignedArray< + Element, + AccessSize, + (AccessSize * sizeof_bits::value / 8)>; + + /// Underlying iterator to compute the addresses + using TileAccessIterator = PredicatedTileAccessIteratorResidualLast< + NADim, + Shape, + Element, + Layout, + kAdvanceRank, + ThreadMap, + AccessType>; + + static int const kAccessesPerVector = TileAccessIterator::kAccessesPerVector; + + /// Fragment object to be loaded or stored + using Fragment = cutlass::Array< + Element, + ThreadMap::Iterations::kCount * ThreadMap::kElementsPerAccess>; + + /// Predicate vector stores mask to guard accesses + using Mask = typename TileAccessIterator::Mask; + + /// Parameters object is precomputed state and is host-constructible + class Params { + public: + using Base = typename TileAccessIterator::Params::Base; + + friend PredicatedTileIteratorResidualLast; + + private: + /// Parameters object + typename TileAccessIterator::Params params_; + + public: + /// Construct the Params object given a pitch-linear tensor's layout + CUTLASS_HOST_DEVICE + Params(Dim stride, Dim extent_row) : params_(stride, extent_row) {} + + CUTLASS_HOST_DEVICE + Params() {} + + CUTLASS_HOST_DEVICE + Params(Base const& base) : params_(base) {} + }; + + private: + /// Internal pointer type permits fast address arithmetic + using BytePointer = char*; + + private: + // + // Data members + // + + /// Data member to the tile access iterator + TileAccessIterator address_iterator_; + + public: + /// Constructs a TileIterator from its precomputed state, threadblock offset, + /// and thread ID + CUTLASS_HOST_DEVICE + PredicatedTileIteratorResidualLast( + /// Precomputed parameters object + Params const& params, + /// Pointer to start of tensor + Pointer pointer, + /// Extent of tensor (rows) + Dim extent_row, + /// Extent of tensor (columns) + int32_t extent_col, + /// ID of each participating thread + int thread_id, + /// Initial offset of threadblock + TensorCoord const& threadblock_offset) + : address_iterator_( + params.params_, + pointer, + extent_row, + extent_col, + thread_id, + threadblock_offset) {} + + /// Construct a PredicatedTileIteratorResidualLast with zero threadblock + /// offset + CUTLASS_HOST_DEVICE + PredicatedTileIteratorResidualLast( + Params const& params, ///< Precomputed parameters object + Pointer pointer, ///< Pointer to start of tensor + Dim extent_row, ///< Extent of tensor (rows) + int32_t extent_col, ///< Extent of tensor (columns) + int thread_id ///< ID of each participating thread + ) + : PredicatedTileIteratorResidualLast( + params, + pointer, + extent_row, + extent_col, + thread_id, + make_Coord(0, 0)) {} + + /// Adds a pointer offset in units of Element + CUTLASS_HOST_DEVICE + void add_pointer_offset(LongIndex pointer_offset) { + address_iterator_.add_pointer_offset(pointer_offset); + } + + /// Advances to the next tile in memory. + /// + /// The first time this method is called, predicates are updated, and the + /// iterator's internal pointer is reverted to the first "steady state" tile. + /// Subsequent calls are lightweight and must only update the internal + /// pointer. + CUTLASS_HOST_DEVICE + PredicatedTileIteratorResidualLast& operator++() { + if (kAdvanceRank) + address_iterator_.add_tile_offset({0, 1}); + else + address_iterator_.add_tile_offset({1, 0}); + + return *this; + } + + /// Advances to the next tile in memory. + /// + /// The first time this method is called, predicates are updated, and the + /// iterator's internal pointer is reverted to the first "steady state" tile. + /// Subsequent calls are lightweight and must only update the internal + /// pointer. + CUTLASS_HOST_DEVICE + PredicatedTileIteratorResidualLast operator++(int) { + PredicatedTileIteratorResidualLast self(*this); + operator++(); + return self; + } + + /// Clears the predicate set efficiently + CUTLASS_HOST_DEVICE + void clear_mask(bool enable = true) { + address_iterator_.clear_mask(enable); + } + + CUTLASS_HOST_DEVICE + void set_residual_tile(bool enable) { + address_iterator_.set_residual_tile(enable); + } + + /// Clears the predicate set efficiently + CUTLASS_HOST_DEVICE + void enable_mask() { + address_iterator_.enable_mask(); + } + + /// Sets the predicate mask, overriding value stored in predicate iterator + CUTLASS_HOST_DEVICE + void set_mask(Mask const& mask) { + address_iterator_.set_mask(mask); + } + + /// Gets the mask + CUTLASS_HOST_DEVICE + void get_mask(Mask& mask) { + address_iterator_.get_mask(mask); + } + + CUTLASS_DEVICE + void load_with_pointer_offset(Fragment& frag, Index pointer_offset) { + load_with_byte_offset( + frag, pointer_offset * sizeof_bits::value / 8); + } + + CUTLASS_DEVICE + void load_with_byte_offset(Fragment& frag, LongIndex byte_offset) { + AccessType* frag_ptr = reinterpret_cast(&frag); + + CUTLASS_PRAGMA_UNROLL + for (int s = 0; s < ThreadMap::Iterations::kStrided; ++s) { + CUTLASS_PRAGMA_UNROLL + for (int c = 0; c < ThreadMap::Iterations::kContiguous; ++c) { + CUTLASS_PRAGMA_UNROLL + for (int v = 0; v < kAccessesPerVector; ++v) { + int idx = v + + kAccessesPerVector * (c + s * ThreadMap::Iterations::kContiguous); + + address_iterator_.set_iteration_index(idx); + char const* byte_ptr = + reinterpret_cast(address_iterator_.get()) + + byte_offset; + + AccessType const* access_ptr = + reinterpret_cast(byte_ptr); + + cutlass::arch::global_load( + frag_ptr[idx], access_ptr, address_iterator_.valid()); + + ++address_iterator_; + } + } + } + } + + /// Loads a fragment from memory + CUTLASS_DEVICE + void load(Fragment& frag) { + load_with_byte_offset(frag, 0); + } + + /// Store a fragment to memory + CUTLASS_DEVICE + void store_with_pointer_offset(Fragment const& frag, Index pointer_offset) { + store_with_byte_offset( + frag, pointer_offset * sizeof_bits::value / 8); + } + + /// Store a fragment to memory + CUTLASS_DEVICE + void store_with_byte_offset(Fragment const& frag, LongIndex byte_offset) { + address_iterator_.set_iteration_index(0); + AccessType const* frag_ptr = reinterpret_cast(&frag); + + CUTLASS_PRAGMA_UNROLL + for (int s = 0; s < ThreadMap::Iterations::kStrided; ++s) { + CUTLASS_PRAGMA_UNROLL + for (int c = 0; c < ThreadMap::Iterations::kContiguous; ++c) { + CUTLASS_PRAGMA_UNROLL + for (int v = 0; v < kAccessesPerVector; ++v) { + int idx = v + + kAccessesPerVector * (c + s * ThreadMap::Iterations::kContiguous); + + char* byte_ptr = + reinterpret_cast(address_iterator_.get()) + byte_offset; + AccessType* access_ptr = reinterpret_cast(byte_ptr); + + if (address_iterator_.valid()) { + *access_ptr = frag_ptr[idx]; + } + ++address_iterator_; + } + } + } + } + + /// Store a fragment to memory + CUTLASS_DEVICE + void store(Fragment const& frag) { + store_with_byte_offset(frag, 0); + } +}; + +//////////////////////////////////////////////////////////////////////////////// + +/// Specialization of PredicatedTileIteratorResidualLast for row-major data. +/// +/// Satisfies: ForwardTileIteratorConcept | +/// ReadableContiguousTileIteratorConcept | +/// WriteableContiguousTileIteratorConcept | +/// MaskedTileIteratorConcept +/// +template < + int NADim, + typename Shape_, + typename Element_, + int AdvanceRank, + typename ThreadMap_, + int AccessSize> +class PredicatedTileIteratorResidualLast< + NADim, + Shape_, + Element_, + layout::RowMajor, + AdvanceRank, + ThreadMap_, + AccessSize> { + public: + static_assert(NADim >= 1 && NADim < 4); + static_assert( + AdvanceRank == 0 || AdvanceRank == 1, + "Specialization for pitch-linear iterator may along advance along the " + "contiguous(rank=0) or strided(rank=1) dimension."); + + using Dim = typename natten::cuda::fna::GetDim::type; + + using Shape = Shape_; + using Element = Element_; + using Layout = layout::RowMajor; + static int const kAdvanceRank = AdvanceRank; + using ThreadMap = ThreadMap_; + + using Index = typename Layout::Index; + using LongIndex = typename Layout::LongIndex; + + using TensorRef = TensorRef; + using TensorView = TensorView; + using TensorCoord = typename Layout::TensorCoord; + + using Pointer = Element*; + using NonConstPointer = typename platform::remove_const::type*; + + using UnderlyingIterator = PredicatedTileIteratorResidualLast< + NADim, + layout::PitchLinearShape, + Element, + layout::PitchLinear, + (kAdvanceRank == 0 ? 1 : 0), + ThreadMap, + AccessSize>; + + using AccessType = typename UnderlyingIterator::AccessType; + + /// Fragment object to be loaded or stored + using Fragment = cutlass::Array< + Element, + ThreadMap::Iterations::kCount * ThreadMap::kElementsPerAccess>; + + /// Predicate vector stores mask to guard accesses + using Mask = typename UnderlyingIterator::Mask; + + /// Parameters object is precomputed state and is host-constructible + class Params { + private: + friend PredicatedTileIteratorResidualLast; + + /// Parameters object + typename UnderlyingIterator::Params params_; + + public: + CUTLASS_HOST_DEVICE + Params() {} + + /// Construct the Params object given a pitch-linear tensor's layout + CUTLASS_HOST_DEVICE + Params(Dim stride, Dim extent_row) : params_(stride, extent_row) {} + + CUTLASS_HOST_DEVICE + Params(typename UnderlyingIterator::Params::Base const& base) + : params_(base) {} + }; + + private: + // + // Data members + // + + /// Underlying pitch-linear tile iterator + UnderlyingIterator iterator_; + + public: + /// Constructs a TileIterator from its precomputed state, threadblock offset, + /// and thread ID + CUTLASS_HOST_DEVICE + PredicatedTileIteratorResidualLast( + Params const& params, ///< Precomputed parameters object + Pointer pointer, ///< Pointer to start of tensor + Dim extent_row, ///< Extent of tensor (rows) + int32_t extent_col, ///< Extent of tensor (columns) + int thread_id, ///< ID of each participating thread + TensorCoord const& threadblock_offset ///< Initial offset of threadblock + ) + : iterator_( + params.params_, + pointer, + extent_row, + extent_col, + thread_id, + layout::PitchLinearCoord( + threadblock_offset.column(), + threadblock_offset.row())) {} + + /// Construct a PredicatedTileIteratorResidualLast with zero threadblock + /// offset + CUTLASS_HOST_DEVICE + PredicatedTileIteratorResidualLast( + Params const& params, ///< Precomputed parameters object + Pointer pointer, ///< Pointer to start of tensor + Dim extent_row, ///< Extent of tensor (rows) + int32_t extent_col, ///< Extent of tensor (columns) + int thread_id ///< ID of each participating thread + ) + : PredicatedTileIteratorResidualLast( + params, + pointer, + extent_row, + extent_col, + thread_id, + make_Coord(0, 0)) {} + + /// Adds a pointer offset in units of Element + CUTLASS_HOST_DEVICE + void add_pointer_offset(LongIndex pointer_offset) { + iterator_.add_pointer_offset(pointer_offset); + } + + /// Advances to the next tile in memory. + /// + /// The first time this method is called, predicates are updated, and the + /// iterator's internal pointer is reverted to the first "steady state" tile. + /// Subsequent calls are lightweight and must only update the internal + /// pointer. + CUTLASS_HOST_DEVICE + PredicatedTileIteratorResidualLast& operator++() { + ++iterator_; + return *this; + } + + /// Advances to the next tile in memory. + /// + /// The first time this method is called, predicates are updated, and the + /// iterator's internal pointer is reverted to the first "steady state" tile. + /// Subsequent calls are lightweight and must only update the internal + /// pointer. + CUTLASS_HOST_DEVICE + PredicatedTileIteratorResidualLast operator++(int) { + PredicatedTileIteratorResidualLast self(*this); + operator++(); + return self; + } + + /// Clears the predicate set efficiently + CUTLASS_HOST_DEVICE + void clear_mask(bool enable = true) { + iterator_.clear_mask(enable); + } + + CUTLASS_HOST_DEVICE + void set_residual_tile(bool enable) { + iterator_.set_residual_tile(enable); + } + + /// Clears the predicate set efficiently + CUTLASS_HOST_DEVICE + void enable_mask() { + iterator_.enable_mask(); + } + + /// Sets the predicate mask, overriding value stored in predicate iterator + CUTLASS_HOST_DEVICE + void set_mask(Mask const& mask) { + iterator_.set_mask(mask); + } + + /// Gets the mask + CUTLASS_HOST_DEVICE + void get_mask(Mask& mask) { + iterator_.get_mask(mask); + } + + /// Loads a fragment from memory + CUTLASS_DEVICE + void load_with_pointer_offset(Fragment& frag, Index pointer_offset) { + iterator_.load_with_pointer_offset(frag, pointer_offset); + } + + /// Loads a fragment from memory + CUTLASS_DEVICE + void load_with_byte_offset(Fragment& frag, LongIndex byte_offset) { + iterator_.load_with_byte_offset(frag, byte_offset); + } + + /// Loads a fragment from memory + CUTLASS_DEVICE + void load(Fragment& frag) { + load_with_pointer_offset(frag, 0); + } + + /// Store a fragment to memory + CUTLASS_DEVICE + void store_with_pointer_offset(Fragment const& frag, Index pointer_offset) { + iterator_.store_with_pointer_offset(frag, pointer_offset); + } + + /// Store a fragment to memory + CUTLASS_DEVICE + void store_with_byte_offset(Fragment const& frag, LongIndex byte_offset) { + iterator_.store_with_byte_offset(frag, byte_offset); + } + + /// Store a fragment to memory + CUTLASS_DEVICE + void store(Fragment const& frag) { + store_with_pointer_offset(frag, 0); + } +}; + +} // namespace threadblock +} // namespace transform +} // namespace cutlass + +//////////////////////////////////////////////////////////////////////////////// diff --git a/csrc/include/natten/cuda/fna/iterators/transpose_warp_iterator.h b/csrc/include/natten/cuda/fna/iterators/transpose_warp_iterator.h new file mode 100644 index 0000000..8a69b7e --- /dev/null +++ b/csrc/include/natten/cuda/fna/iterators/transpose_warp_iterator.h @@ -0,0 +1,60 @@ +/* + * Copied from xFormers (https://github.com/facebookresearch/xformers/) + * + * Copyright (c) Meta Platforms, Inc. and affiliates. + * All rights reserved. + * + * BSD 3-Clause License + * + * Redistribution and use in source and binary forms, with or without + * modification, are permitted provided that the following conditions are met: + * + * 1. Redistributions of source code must retain the above copyright + * notice, this list of conditions and the following disclaimer. + * + * 2. Redistributions in binary form must reproduce the above copyright + * notice, this list of conditions and the following disclaimer in the + * documentation and/or other materials provided with the distribution. + * + * 3. Neither the names of Facebook, Deepmind Technologies, NYU, NEC + * Laboratories America and IDIAP Research Institute nor the names of its + * contributors may be used to endorse or promote products derived from this + * software without specific prior written permission. + * + * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" + * AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE + * IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE + * ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE + * LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR + * CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF + * SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS + * INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN + * CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) + * ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE + * POSSIBILITY OF SUCH DAMAGE. + */ + +#pragma once + +#include "warp_iterator_from_smem.h" + +template +struct TransposeWarpIterator { + using Iterator = char; + static bool constexpr kSupportsTranspose = false; +}; + +template < + /// Operand identity + cutlass::gemm::Operand Operand, + /// Data type of A elements + typename Element, + typename InstructionShape, + bool kTranspose> +struct TransposeWarpIterator< + cutlass::gemm::warp:: + WarpIteratorFromSmem> { + using Iterator = cutlass::gemm::warp:: + WarpIteratorFromSmem; + static bool constexpr kSupportsTranspose = true; +}; diff --git a/csrc/include/natten/cuda/fna/iterators/warp_iterator_from_smem.h b/csrc/include/natten/cuda/fna/iterators/warp_iterator_from_smem.h new file mode 100644 index 0000000..f36aefb --- /dev/null +++ b/csrc/include/natten/cuda/fna/iterators/warp_iterator_from_smem.h @@ -0,0 +1,290 @@ +/* + * Copied from xFormers (https://github.com/facebookresearch/xformers/). + * + * Copyright (c) Meta Platforms, Inc. and affiliates. + * All rights reserved. + */ +/*************************************************************************************************** + * Copyright (c) 2017 - 2023 NVIDIA CORPORATION & AFFILIATES. All rights + *reserved. SPDX-License-Identifier: BSD-3-Clause + * + * Redistribution and use in source and binary forms, with or without + * modification, are permitted provided that the following conditions are met: + * + * 1. Redistributions of source code must retain the above copyright notice, + *this list of conditions and the following disclaimer. + * + * 2. Redistributions in binary form must reproduce the above copyright notice, + * this list of conditions and the following disclaimer in the documentation + * and/or other materials provided with the distribution. + * + * 3. Neither the name of the copyright holder nor the names of its + * contributors may be used to endorse or promote products derived from + * this software without specific prior written permission. + * + * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" + * AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE + * IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE + *ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE + *LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR + *CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF + *SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS + *INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN + *CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) + *ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE + *POSSIBILITY OF SUCH DAMAGE. + * + **************************************************************************************************/ +/*! \file + \brief Inspired from + "cutlass/gemm/warp/mma_tensor_op_tile_access_iterator.h" Loads tiles of GEMM + operands from a RowMajor shared-memory layout into registers to use by A100 + TensorCores. + + The difference with "mma_tensor_op_tile_access_iterator.h" is that: + (1) We use "ldmatrix" to load tiles, rather than manual loads (slightly + faster) (2) We support to transpose the operand (eg read `A.transpose()` when + the shared memory holds `A`) + + This is only implemented for the specific shapes. +*/ +#pragma once + +#include + +//////////////////////////////////////////////////////////////////////////////// +namespace cutlass { +namespace gemm { +namespace warp { + +template < + /// Operand identity + Operand Operand_, + /// Data type of A elements + typename Element_, + typename InstructionShape_, + bool kTranspose = false> +class WarpIteratorFromSmem { + public: + /// Shape of tile to load (concept: MatrixShape) + using Shape = cutlass::MatrixShape<32, 32>; + + /// Operand tag + static Operand const kOperand = Operand_; + static_assert( + kOperand == Operand::kA, + "No support for OperandB at the moment"); + + /// Basic check + static_assert( + kOperand == Operand::kA || kOperand == Operand::kB, + "WarpIteratorFromSmem may only be instantiated for A or B operands to warp-level Mma."); + + /// Element type + using Element = Element_; + static_assert(sizeof_bits::value == 16, "Only supported for half"); + + /// Layout of source tile + using Layout = cutlass::layout::RowMajor; + + /// Shape of one matrix product operation (concept: MatrixShape) + using InstructionShape = InstructionShape_; + static_assert(InstructionShape::kRow == 16, "Only supports 16x8x8 / 16x8x16"); + static_assert( + InstructionShape::kColumn == 8 || InstructionShape::kColumn == 16, + "Only supports 16x8x8 / 16x8x16"); + + /// Delta between *MMA operations (in units of *MMA operations, concept: + /// MatrixShape) + static int const kOpDelta = 1; + + /// Number of participating threads + static int const kThreads = 32; + + /// TensorRef type for loading element from a tensor + using TensorRef = TensorRef; + + /// Index type + using Index = typename TensorRef::Index; + + /// Long Index type + using LongIndex = typename TensorRef::LongIndex; + + /// Coordinate for an element in the tensor + using TensorCoord = typename TensorRef::TensorCoord; + + /// Number of elements accessed per Shared Memory load + static int const kElementsPerAccess = + (sizeof_bits::value >= 32 ? 1 + : 32 / sizeof_bits::value); + + using InstructionCount = MatrixShape< + Shape::kRow / InstructionShape::kRow, + Shape::kColumn / InstructionShape::kColumn>; + + static int const kIterations = (kOperand == Operand::kA) + ? InstructionCount::kColumn + : InstructionCount::kRow; + + public: + // + // Derived quantities + // + + /// Fragment object holding a thread's part of a tile + using Fragment = Array< + Element, + (kOperand == Operand::kA) + ? (Shape::kRow* InstructionShape::kColumn / kThreads) + : (Shape::kColumn* InstructionShape::kRow / kThreads)>; + + /// Memory access type + // using AccessType = AlignedArray; + using AccessType = Array; + + static int constexpr kWarpShapeDivisibleInner = + (kOperand == Operand::kA ? InstructionShape::kColumn + : InstructionShape::kRow); + static int constexpr kAccessesInner = + (kWarpShapeDivisibleInner / kElementsPerAccess) / 4; + // Number of 32bits tiles to load per `ldmatrix` + static int const kTilesPerInstruction = InstructionShape::kRow / 8; + static_assert(kTilesPerInstruction == 2, "Only supports 16x8x16 and 16x8x8"); + + private: + /// Underlying tensor reference + TensorRef ref_; + + /// Origin + MatrixCoord origin_; + + /// Iterations in a tile + int iterations_; + + public: + /// Constructor from TensorRef + CUTLASS_HOST_DEVICE + WarpIteratorFromSmem(TensorRef const& ref, int lane_id) + : WarpIteratorFromSmem(ref, {Shape::kRow, Shape::kColumn}, lane_id) {} + CUTLASS_HOST_DEVICE + WarpIteratorFromSmem(TensorRef const& ref, TensorCoord extent, int lane_id) + : ref_(ref), iterations_(0) { + // See also: + // https://docs.nvidia.com/cuda/archive/11.7.1/parallel-thread-execution/index.html#warp-level-matrix-fragment-mma-1688 + // 16x8x8: kAccessesInner = 1 (1 ldmatrix.x4) + // 16x8x16: kAccessesInner = 2 (2 ldmatrix.x4) + int ldsm_vec_num = (lane_id >> 3); + if (kOperand == Operand::kA) { + origin_ = MatrixCoord(lane_id % 8, 0); + static_assert( + InstructionCount::kRow * kTilesPerInstruction == 4, + "can't use ldmatrix.x4"); + int access_m_idx = ldsm_vec_num % kTilesPerInstruction; + int inner_idx = (ldsm_vec_num / kTilesPerInstruction) % kAccessesInner; + int inst_m_idx = ldsm_vec_num / (kTilesPerInstruction * kAccessesInner); + MatrixCoord offset( + access_m_idx * 8 + inst_m_idx * InstructionShape::kRow, + inner_idx * 4 * kElementsPerAccess); + if (kTranspose) { + offset = MatrixCoord(offset.column(), offset.row()); + } + origin_ += offset; + } else { + // XXX: This is not tested or used + origin_ = MatrixCoord(0, lane_id % 8); + static_assert(InstructionCount::kColumn * kAccessesInner == 4, ""); + CUTLASS_PRAGMA_UNROLL + for (int inst_n_idx = 0; inst_n_idx < InstructionCount::kColumn; + ++inst_n_idx) { + CUTLASS_PRAGMA_UNROLL + for (int inner_idx = 0; inner_idx < kAccessesInner; ++inner_idx) { + int access_idx = inner_idx + kAccessesInner * inst_n_idx; + + MatrixCoord offset( + inner_idx * 4 * kElementsPerAccess, inst_n_idx * 8); + + if (access_idx == ldsm_vec_num) { + if (kTranspose) { + offset = MatrixCoord(offset.column(), offset.row()); + } + origin_ += offset; + } + } + } + } + + ref_.add_coord_offset(origin_); + } + + /// Advances an iterator along logical dimensions of matrix in units of whole + /// tiles + CUTLASS_HOST_DEVICE + WarpIteratorFromSmem& add_tile_offset(TensorCoord const& tile_offset) { + TensorCoord coord_offset( + tile_offset.row() * Shape::kRow, tile_offset.column() * Shape::kColumn); + if (kTranspose) { + coord_offset = TensorCoord{coord_offset.column(), coord_offset.row()}; + } + origin_ += coord_offset; + + ref_.add_coord_offset(coord_offset); + + return *this; + } + + /// Advances the iterator along the advance dimension + CUTLASS_DEVICE + void advance() { + if (kOperand == Operand::kA) { + add_tile_offset({0, 1}); + } else { + add_tile_offset({1, 0}); + } + + iterations_ = 0; + } + + /// increase iterations in a tile + CUTLASS_HOST_DEVICE + WarpIteratorFromSmem& operator++() { + iterations_++; + + if (iterations_ >= kIterations) + advance(); + + return *this; + } + + /// Loads a fragment from memory at the location pointed to by the iterator. + CUTLASS_DEVICE + void load(Fragment& frag) const { + AccessType* access_ptr = reinterpret_cast(&frag); + using LoadLayout = typename platform:: + conditional::type; + + CUTLASS_PRAGMA_UNROLL + for (int access_m_idx = 0; access_m_idx < + (InstructionCount::kRow * kTilesPerInstruction * kAccessesInner) / 4; + ++access_m_idx) { + MatrixCoord offset; + if (kOperand == Operand::kA) { + offset = MatrixCoord( + access_m_idx * 16, iterations_ * InstructionShape::kColumn); + } else { + offset = MatrixCoord(iterations_ * InstructionShape::kRow, 0); + } + if (kTranspose) { + offset = MatrixCoord(offset.column(), offset.row()); + } + cutlass::arch::ldsm( + access_ptr[access_m_idx], ref_.data() + ref_.offset(offset)); + } + } +}; + +//////////////////////////////////////////////////////////////////////////////// + +} // namespace warp +} // namespace gemm +} // namespace cutlass +//////////////////////////////////////////////////////////////////////////////// diff --git a/csrc/include/natten/cuda/fna/kernel_forward.h b/csrc/include/natten/cuda/fna/kernel_forward.h new file mode 100644 index 0000000..8c8fbdb --- /dev/null +++ b/csrc/include/natten/cuda/fna/kernel_forward.h @@ -0,0 +1,1168 @@ +/*************************************************************************************************** + * Copyright (c) 2022-2024 Ali Hassani. + * + * Fused Neighborhood Attention kernels are heavily based on the + * memory-efficient attention kernels from the xFormers project by Meta + * Platforms, Inc. + * + * Copyright (c) Facebook, Inc. and its affiliates + * + * BSD 3-Clause License + * + * Redistribution and use in source and binary forms, with or without + * modification, are permitted provided that the following conditions are met: + * + * 1. Redistributions of source code must retain the above copyright + * notice, this list of conditions and the following disclaimer. + * + * 2. Redistributions in binary form must reproduce the above copyright + * notice, this list of conditions and the following disclaimer in the + * documentation and/or other materials provided with the distribution. + * + * 3. Neither the names of Facebook, Deepmind Technologies, NYU, NEC + * Laboratories America and IDIAP Research Institute nor the names of its + * contributors may be used to endorse or promote products derived from this + * software without specific prior written permission. + * + * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" + * AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE + * IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE + * ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE + * LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR + * CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF + * SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS + * INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN + * CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) + * ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE + * POSSIBILITY OF SUCH DAMAGE. + */ + +#pragma once + +#define CACHE_NEIGHBORHOOD_COORDINATES + +#include +#include +#include +#include +#include +#include +#include +#include +#include + +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include + +#include +#include +#include +#include +#include +#include +#include +#include + +#include + +#include + +namespace natten { +namespace cuda { +namespace fna { + +namespace { +template +constexpr int getWarpsPerSmFw() { + return ( + Arch::kMinComputeCapability >= 80 && + !cutlass::platform::is_same::value + ? 16 + : 12); +} +static CUTLASS_DEVICE float atomicMaxFloat(float* addr, float value) { + // source: https://stackoverflow.com/a/51549250 + return (value >= 0) + ? __int_as_float(atomicMax((int*)addr, __float_as_int(value))) + : __uint_as_float(atomicMin((unsigned int*)addr, __float_as_uint(value))); +} + +} // namespace + +template < + // NA spatial dimension; can be 1 (NA1d), 2 (NA2d), or 3 (NA3d). + int NADim_, + typename CausalMask, + // The datatype of Q/K/V + typename scalar_t_, + // Architecture we are targeting (eg `cutlass::arch::Sm80`) + typename ArchTag, + // If Q/K/V are correctly aligned in memory and we can run a fast kernel + bool isAligned_, + int kQueriesPerBlock_, + int kKeysPerBlock_, + // upperbound on `max(value.shape[-1], query.shape[-1])` + int kMaxK_ = (int)cutlass::platform::numeric_limits::max(), + bool kSupportsRPB_ = false> +struct FusedNeighborhoodAttentionKernel { + static constexpr int NADim = NADim_; + static_assert(NADim >= 1 && NADim < 4, "Only 1D-3D NA are implemented."); + using Dim = typename GetDim::type; + using NAMask = NeighborhoodAttentionMask; + + static constexpr int kKeysPerBlock = kKeysPerBlock_; + static constexpr int kQueriesPerBlock = kQueriesPerBlock_; + static constexpr int kMaxK = kMaxK_; + + // static constexpr Dim QueryTileShape = MapTileSizeToNd::value; static constexpr Dim KeyTileShape = + // MapTileSizeToNd::value; + static constexpr bool kHasCausalDims = CausalMask::AnyCausalDims; + static constexpr bool kSupportsRPB = kSupportsRPB_; + static_assert( + !kSupportsRPB || !kHasCausalDims, + "Causal NA does not support RPB yet."); + + using scalar_t = scalar_t_; + using accum_t = float; + using lse_scalar_t = float; + using output_t = scalar_t; + // Accumulator between 2 iterations + // Using `accum_t` improves perf on f16 at the cost of + // numerical errors + using output_accum_t = accum_t; + static constexpr bool kIsAligned = isAligned_; + static constexpr bool kSingleValueIteration = kMaxK <= kKeysPerBlock; + static constexpr int32_t kAlignLSE = 32; // block size of backward + static constexpr bool kIsHalf = cutlass::sizeof_bits::value == 16; + static constexpr bool kPreloadV = + ArchTag::kMinComputeCapability >= 80 && kIsHalf; + static constexpr bool kKeepOutputInRF = kSingleValueIteration; + static constexpr bool kNeedsOutputAccumulatorBuffer = !kKeepOutputInRF && + !cutlass::platform::is_same::value; + + static_assert(kQueriesPerBlock % 32 == 0, ""); + static_assert(kKeysPerBlock % 32 == 0, ""); + static constexpr int kNumWarpsPerBlock = + kQueriesPerBlock * kKeysPerBlock / (32 * 32); + static constexpr int kWarpSize = 32; + + // Launch bounds + static constexpr int kNumThreads = kWarpSize * kNumWarpsPerBlock; + static constexpr int kMinBlocksPerSm = + getWarpsPerSmFw() / kNumWarpsPerBlock; + + struct Params { + // Input tensors + scalar_t* query_ptr = nullptr; // [..., num_heads, head_dim] + scalar_t* key_ptr = nullptr; // [..., num_heads, head_dim] + scalar_t* value_ptr = nullptr; // [..., num_heads, head_dim_value] + + scalar_t* rpb_ptr = nullptr; // [num_heads, *(kernel_size * 2 - 1)] + + // Output tensors + output_t* output_ptr = + nullptr; // [num_queries_post_partitioning, num_heads, head_dim_value] + // [num_queries_post_partitioning, num_heads, head_dim_value] + output_accum_t* output_accum_ptr = nullptr; + // [num_heads, num_queries_post_partitioning] - can be null + lse_scalar_t* logsumexp_ptr = nullptr; + + // Sliding window. ignored if == 0 + Dim kernel_size; + Dim cta_shape_x; + + // Scale + accum_t scale = 0.0; + + // Dimensions/strides + int32_t head_dim = 0; + int32_t head_dim_value = 0; + Dim num_queries; + Dim num_queries_post_partitioning; + + Dim query_tile_shape; + Dim key_tile_shape; + + Dim q_strideM; + Dim k_strideM; + Dim v_strideM; + Dim o_strideM; + + Dim rpb_stride; + + int32_t num_heads = 0; + int32_t num_batches = 0; + + Dim dilation; + + // Moves pointers to what we should process + // Returns "false" if there is no work to do + CUTLASS_DEVICE bool advance_to_block() { + num_queries_post_partitioning = ceil_div_dim(num_queries, dilation); + + auto batch_id = blockIdx.z; + auto bidxy = blockIdx.y; + auto dilation_size = dilation.prod32(); + auto dilation_dim_idx = (int32_t)bidxy % dilation_size; + auto dilation_idx = map_index_to_coord(dilation_dim_idx, dilation); + auto head_id = bidxy / dilation_size; + + auto q_strideH = head_dim; + auto k_strideH = head_dim; + auto v_strideH = head_dim_value; + auto o_strideH = head_dim_value; + + auto q_heads_dims = num_heads * q_strideH; + auto k_heads_dims = num_heads * k_strideH; + auto v_heads_dims = num_heads * v_strideH; + auto o_heads_dims = num_heads * o_strideH; + + auto q_stride_dilation = compute_stride(num_queries, q_heads_dims); + auto k_stride_dilation = compute_stride(num_queries, k_heads_dims); + auto v_stride_dilation = compute_stride(num_queries, v_heads_dims); + auto o_stride_dilation = compute_stride(num_queries, o_heads_dims); + + q_strideM = q_stride_dilation * dilation; + k_strideM = k_stride_dilation * dilation; + v_strideM = v_stride_dilation * dilation; + o_strideM = o_stride_dilation * dilation; + + auto q_strideB = num_queries.prod() * q_heads_dims; + auto k_strideB = num_queries.prod() * k_heads_dims; + auto v_strideB = num_queries.prod() * v_heads_dims; + auto o_strideB = num_queries.prod() * o_heads_dims; + + maybe_mask_qk_tiles( + num_queries_post_partitioning, num_queries, dilation, dilation_idx); + + cta_shape_x = + ceil_div_dim(num_queries_post_partitioning, query_tile_shape); + + const auto first_query = + map_index_to_coord((int32_t)blockIdx.x, cta_shape_x) * + query_tile_shape; + + // TODO: + // auto lse_dim = gemm_kernel_utils::ceil_div((int32_t)queries_end, + // kAlignLSE) * kAlignLSE; + + // Advance to current batch + query_ptr += batch_id * q_strideB; + key_ptr += batch_id * k_strideB; + value_ptr += batch_id * v_strideB; + output_ptr += batch_id * o_strideB; + if (output_accum_ptr != nullptr) { + output_accum_ptr += batch_id * o_strideB; + } + + // Advance to the current batch / head / first_query + query_ptr += (first_query * q_strideM).sum() + + (dilation_idx * q_stride_dilation).sum() + head_id * q_strideH; + key_ptr += (dilation_idx * k_stride_dilation).sum() + head_id * k_strideH; + value_ptr += + (dilation_idx * v_stride_dilation).sum() + head_id * v_strideH; + output_ptr += (first_query * o_strideM).sum() + + (dilation_idx * o_stride_dilation).sum() + head_id * o_strideH; + + if constexpr (kSupportsRPB) { + if (rpb_ptr != nullptr) { + auto head_rpb_shape = (kernel_size * 2) - 1; + rpb_ptr += head_id * head_rpb_shape.prod(); + rpb_stride = compute_stride(head_rpb_shape, 1); + } + } + + if (output_accum_ptr != nullptr) { + output_accum_ptr += (first_query * o_strideM).sum() + + (dilation_idx * o_stride_dilation).sum() + head_id * o_strideH; + } else { + // Accumulate directly in the destination buffer (eg for f32) + output_accum_ptr = (accum_t*)output_ptr; + } + + if (logsumexp_ptr != nullptr) { + // TODO::: LSE + // // lse[batch_id, head_id, first_query] + // logsumexp_ptr += + // batch_id * lse_dim * num_heads + head_id * lse_dim + first_query; + } + + num_batches = 0; // no longer used after + // dilation = 0; + + // Make sure the compiler knows these variables are the same on all + // the threads of the warp. + // Only worth doing if they could have been modified above. + query_ptr = gemm_kernel_utils::warp_uniform(query_ptr); + key_ptr = gemm_kernel_utils::warp_uniform(key_ptr); + value_ptr = gemm_kernel_utils::warp_uniform(value_ptr); + output_ptr = gemm_kernel_utils::warp_uniform(output_ptr); + output_accum_ptr = gemm_kernel_utils::warp_uniform(output_accum_ptr); + logsumexp_ptr = gemm_kernel_utils::warp_uniform(logsumexp_ptr); + num_heads = gemm_kernel_utils::warp_uniform(num_heads); + return true; + } + + __host__ dim3 getBlocksGrid() const { + return dim3( + ceil_div_dim(ceil_div_dim(num_queries, dilation), query_tile_shape) + .prod32(), + num_heads * dilation.prod32(), + num_batches); + } + + __host__ dim3 getThreadsGrid() const { + return dim3(kWarpSize, kNumWarpsPerBlock, 1); + } + }; + + struct MM0 { + /* + In this first matmul, we compute a block of `Q @ K.T`. + While the calculation result is still hot in registers, we update + `mi`, `m_prime`, `s_prime` in shared-memory, and then store this value + into a shared-memory ("AccumulatorSharedStorage") that is used later as + operand A for the second matmul (see MM1) + */ + using GemmType = gemm_kernel_utils::DefaultGemmType; + + using OpClass = typename GemmType::OpClass; + using DefaultConfig = + typename cutlass::gemm::device::DefaultGemmConfiguration< + OpClass, + ArchTag, + scalar_t, + scalar_t, + scalar_t, // ElementC + accum_t // ElementAccumulator + >; + static constexpr int kAlignmentA = + kIsAligned ? DefaultConfig::kAlignmentA : GemmType::kMinimumAlignment; + static constexpr int kAlignmentB = + kIsAligned ? DefaultConfig::kAlignmentB : GemmType::kMinimumAlignment; + using ThreadblockShape = cutlass::gemm:: + GemmShape; + using WarpShape = cutlass::gemm::GemmShape<32, 32, GemmType::WarpK>; + using DefaultMma = typename cutlass::gemm::threadblock::FindDefaultMma< + NADim, + scalar_t, // ElementA, + cutlass::layout::RowMajor, // LayoutA, + kAlignmentA, + scalar_t, // ElementB, + cutlass::layout::ColumnMajor, // LayoutB, + kAlignmentB, + accum_t, + cutlass::layout::RowMajor, // LayoutC, + OpClass, + ArchTag, // ArchTag + ThreadblockShape, // ThreadblockShape + WarpShape, // WarpShape + typename GemmType::InstructionShape, // InstructionShape + ArchTag::kMinComputeCapability >= 80 && kIsHalf + ? 4 + : DefaultConfig::kStages, + typename GemmType::Operator // Operator + >::DefaultMma; + using MmaCore = typename DefaultMma::MmaCore; + using IteratorA = typename DefaultMma::IteratorA; + using IteratorB = typename DefaultMma::IteratorB; + using DefaultThreadblockMma = typename DefaultMma::ThreadblockMma; + using Mma = typename cutlass::platform::conditional< + kSingleValueIteration, + typename MakeCustomMma::Mma, + DefaultThreadblockMma>::type; + using AccumLambdaIterator = typename DefaultMmaAccumLambdaIterator< + typename Mma::Operator::IteratorC, + accum_t, + kWarpSize>::Iterator; + static_assert( + MmaCore::WarpCount::kM * MmaCore::WarpCount::kN * + MmaCore::WarpCount::kK == + kNumWarpsPerBlock, + ""); + + // Epilogue to store to shared-memory in a format that we can use later for + // the second matmul + using B2bGemm = typename cutlass::gemm::threadblock::B2bGemm< + typename Mma::Operator::IteratorC, + typename Mma::Operator, + scalar_t, + WarpShape, + ThreadblockShape>; + using AccumulatorSharedStorage = typename B2bGemm::AccumulatorSharedStorage; + }; + + struct MM1 { + /** + Second matmul: perform `attn @ V` where `attn` is the attention (not + normalized) and stored in shared memory + */ + using GemmType = gemm_kernel_utils::DefaultGemmType; + + using OpClass = typename GemmType::OpClass; + using DefaultConfig = + typename cutlass::gemm::device::DefaultGemmConfiguration< + OpClass, + ArchTag, + scalar_t, + scalar_t, + output_accum_t, // ElementC + accum_t // ElementAccumulator + >; + static constexpr int kAlignmentA = DefaultConfig::kAlignmentA; // from smem + static constexpr int kAlignmentB = + kIsAligned ? DefaultConfig::kAlignmentB : GemmType::kMinimumAlignment; + using ThreadblockShape = cutlass::gemm:: + GemmShape; + using WarpShape = cutlass::gemm::GemmShape<32, 32, GemmType::WarpK>; + using InstructionShape = typename GemmType::InstructionShape; + + using LayoutB = cutlass::layout::RowMajor; + using DefaultGemm = cutlass::gemm::kernel::DefaultGemm< + scalar_t, // ElementA, + cutlass::layout::RowMajor, // LayoutA, + kAlignmentA, + scalar_t, // ElementB, + LayoutB, // LayoutB, + kAlignmentB, + output_accum_t, + cutlass::layout::RowMajor, // LayoutC, + accum_t, + OpClass, + ArchTag, + ThreadblockShape, + WarpShape, + typename GemmType::InstructionShape, + typename DefaultConfig::EpilogueOutputOp, + void, // ThreadblockSwizzle - not used + ArchTag::kMinComputeCapability >= 80 && kIsHalf + ? 4 + : DefaultConfig::kStages, + false, // SplitKSerial + typename GemmType::Operator>; + + using WarpIteratorA = typename cutlass::gemm::threadblock:: + DefaultWarpIteratorAFromSharedMemory< + typename DefaultGemm::Mma::Policy::Operator::Shape, // WarpShape + typename DefaultGemm::Mma::Policy::Operator::InstructionShape, + typename DefaultGemm::Mma::Policy::Operator::IteratorA, + typename DefaultGemm::Mma::Policy>::WarpIterator; + using DefaultMmaFromSmem = + typename cutlass::gemm::threadblock::DefaultMmaFromSharedMemory< + NADim, + typename DefaultGemm::Mma, + MM0::AccumulatorSharedStorage::Shape::kN, // kMaxK + WarpIteratorA, + false>; // kScaleOperandA + using Mma = typename DefaultMmaFromSmem::Mma; + using IteratorB = typename Mma::IteratorB; + using WarpCount = typename Mma::WarpCount; + static_assert( + WarpCount::kM * WarpCount::kN * WarpCount::kK == kNumWarpsPerBlock, + ""); + + using DefaultEpilogue = typename DefaultGemm::Epilogue; + using OutputTileIterator = + typename cutlass::epilogue::threadblock::CustomPredicatedTileIterator< + NADim, + kQueriesPerBlock, + typename DefaultEpilogue::OutputTileIterator::ThreadMap, + output_t>; + using OutputTileIteratorAccum = + typename cutlass::epilogue::threadblock::CustomPredicatedTileIterator< + NADim, + kQueriesPerBlock, + typename DefaultEpilogue::OutputTileIterator::ThreadMap, + output_accum_t>; + }; + + static constexpr int64_t kAlignmentQ = MM0::kAlignmentA; + static constexpr int64_t kAlignmentK = MM0::kAlignmentB; + static constexpr int64_t kAlignmentV = 1; + + // Shared storage - depends on kernel params + struct ScalingCoefs { + cutlass::Array m_prime; + cutlass::Array s_prime; + cutlass::Array mi; + cutlass::Array out_rescale; + cutlass::Array + addition_storage; + }; + + struct SharedStorageEpilogueAtEnd : ScalingCoefs { + struct SharedStorageAfterMM0 { + // Everything here might be overwritten during MM0 + union { + typename MM0::AccumulatorSharedStorage si; + }; + typename MM1::Mma::SharedStorage mm1; + }; + + union { + typename MM0::Mma::SharedStorage mm0; + SharedStorageAfterMM0 after_mm0; + typename MM1::DefaultEpilogue::SharedStorage epilogue; + }; + + CUTLASS_DEVICE typename MM1::DefaultEpilogue::SharedStorage& + epilogue_shared_storage() { + return epilogue; + } + }; + + struct SharedStorageEpilogueInLoop : ScalingCoefs { + struct SharedStorageAfterMM0 { + // Everything here might be overwritten during MM0 + union { + typename MM0::AccumulatorSharedStorage si; + }; + typename MM1::Mma::SharedStorage mm1; + typename MM1::DefaultEpilogue::SharedStorage epilogue; + }; + + union { + typename MM0::Mma::SharedStorage mm0; + SharedStorageAfterMM0 after_mm0; + }; + + CUTLASS_DEVICE typename MM1::DefaultEpilogue::SharedStorage& + epilogue_shared_storage() { + return after_mm0.epilogue; + } + }; + + using SharedStorage = typename cutlass::platform::conditional< + kSingleValueIteration || kKeepOutputInRF, + SharedStorageEpilogueAtEnd, + SharedStorageEpilogueInLoop>::type; + + static bool __host__ check_supported(Params const& p) { + CHECK_ALIGNED_PTR(p.query_ptr, kAlignmentQ); + CHECK_ALIGNED_PTR(p.key_ptr, kAlignmentK); + CHECK_ALIGNED_PTR(p.value_ptr, kAlignmentV); + NATTEN_CHECK( + p.head_dim % kAlignmentQ == 0, + "query is not correctly aligned (strideM)"); + NATTEN_CHECK( + p.head_dim % kAlignmentK == 0, + "key is not correctly aligned (strideM)"); + NATTEN_CHECK( + p.head_dim_value % kAlignmentV == 0, + "value is not correctly aligned (strideM)"); + return true; + } + + static void CUTLASS_DEVICE attention_kernel(Params& p) { + // In this block, we will only ever: + // - read query[first_query:last_query, :] + // - write to output[first_query:last_query, :] + + extern __shared__ char smem_buffer[]; + SharedStorage& shared_storage = *((SharedStorage*)smem_buffer); + auto& m_prime = shared_storage.m_prime; + auto& s_prime = shared_storage.s_prime; + auto& mi = shared_storage.mi; + auto& out_rescale = shared_storage.out_rescale; + auto first_query = map_index_to_coord((int32_t)blockIdx.x, p.cta_shape_x) * + p.query_tile_shape; + auto problem_size_0_m = fast_min( + p.query_tile_shape, p.num_queries_post_partitioning - first_query); + auto last_query = first_query + problem_size_0_m - 1; + auto problem_size_0_m_int = problem_size_0_m.prod32(); + + static_assert(kQueriesPerBlock < kNumWarpsPerBlock * kWarpSize, ""); + if (thread_id() < kQueriesPerBlock) { + s_prime[thread_id()] = accum_t(0); + out_rescale[thread_id()] = accum_t(1.0); + m_prime[thread_id()] = + -cutlass::platform::numeric_limits::infinity(); + mi[thread_id()] = -cutlass::platform::numeric_limits::infinity(); + } + typename MM1::Mma::FragmentC accum_o; + accum_o.clear(); + + auto createOutputIter = [&](int col) -> typename MM1::OutputTileIterator { + using OutputTileIterator = typename MM1::OutputTileIterator; + return OutputTileIterator( + typename OutputTileIterator::Params{p.o_strideM, problem_size_0_m}, + p.output_ptr, + problem_size_0_m, + p.head_dim_value, + thread_id(), + {0, col}); + }; + + auto createOutputAccumIter = [&](int col) -> + typename MM1::OutputTileIteratorAccum { + using OutputTileIteratorAccum = typename MM1::OutputTileIteratorAccum; + return OutputTileIteratorAccum( + typename OutputTileIteratorAccum::Params{ + p.o_strideM, problem_size_0_m}, + p.output_accum_ptr, + problem_size_0_m, + p.head_dim_value, + thread_id(), + {0, col}); + }; + + int32_t const& problem_size_0_k_int = p.head_dim; + int32_t const& problem_size_1_n_int = p.head_dim_value; + auto gemm_k_iterations = + (problem_size_0_k_int + MM0::Mma::Shape::kK - 1) / MM0::Mma::Shape::kK; + const int64_t nBlockN = kSingleValueIteration + ? 1 + : gemm_kernel_utils::ceil_div( + (int64_t)problem_size_1_n_int, + int64_t(MM1::ThreadblockShape::kN)); + + auto na_mask = NAMask(p.kernel_size, p.num_queries_post_partitioning); + + auto first_key = na_mask.get_window_start(first_query); + auto last_key = na_mask.get_window_end( + last_query, na_mask.get_window_start(last_query)); + + auto my_warp_id = gemm_kernel_utils::warp_uniform(warp_id()); + auto my_lane_id = lane_id(); + + typename MM0::Mma::Operator::IteratorC::TensorCoord iteratorC_tile_offset = + {/*(tb_tile_offset.m() * MM0::Mma::WarpCount::kM) +*/ + (my_warp_id % MM0::Mma::WarpCount::kM), + /*(tb_tile_offset.n() * MM0::Mma::WarpCount::kN) +*/ + (my_warp_id / MM0::Mma::WarpCount::kM)}; + + auto lane_offset = MM0::AccumLambdaIterator::get_lane_offset( + my_lane_id, my_warp_id, iteratorC_tile_offset); + + int warp_idx_mn_0 = my_warp_id % + (MM0::Mma::Base::WarpCount::kM * MM0::Mma::Base::WarpCount::kN); + auto output_tile_coords = cutlass::MatrixCoord{ + warp_idx_mn_0 % MM0::Mma::Base::WarpCount::kM, + warp_idx_mn_0 / MM0::Mma::Base::WarpCount::kM}; + +#ifdef CACHE_NEIGHBORHOOD_COORDINATES + // NA mask info + static constexpr auto kNumRows = MM0::AccumLambdaIterator::kRowIters; + Dim first_cols[kNumRows]; + // Dim last_cols[kNumRows]; + Dim key_bounds[kNumRows]; + Dim rpb_starts[kNumRows]; + MM0::AccumLambdaIterator::iterateRows( + lane_offset, + [&](int accum_m) { + auto row_idx = + map_index_to_coord((int32_t)accum_m, problem_size_0_m) + + first_query; + first_cols[accum_m] = na_mask.get_window_start(row_idx); + // last_cols[accum_m] = na_mask.get_window_end(row_idx, + // first_cols[accum_m]); + key_bounds[accum_m] = + na_mask.get_window_end(row_idx, first_cols[accum_m]) - + first_cols[accum_m]; + if constexpr (kSupportsRPB) { + rpb_starts[accum_m] = na_mask.get_rpb_start(row_idx); + } + }, + [&](int accum_m, int accum_n, int idx) {}, + [&](int accum_m) {}); +#endif + + // Iterate through keys + for (auto iter_key_start = first_key; + is_coord_within_upper_bound(iter_key_start, last_key); + increment_tile(iter_key_start, p.key_tile_shape, last_key)) { + const auto problem_size_0_n = + fast_min(p.key_tile_shape, last_key - iter_key_start); + auto const& problem_size_1_k = problem_size_0_n; + auto problem_size_0_n_int = problem_size_0_n.prod32(); + auto& gemm_1_num_iterations = problem_size_0_n_int; + + auto& last_kv_col = gemm_1_num_iterations; + bool is_first_kv_iter = iter_key_start == first_key; + bool is_last_kv_iter = + is_tile_last(iter_key_start, p.key_tile_shape, last_key); + + auto prologueV = [&](int blockN) { + typename MM1::Mma::IteratorB iterator_V( + typename MM1::IteratorB::Params{p.v_strideM, problem_size_1_k}, + p.value_ptr + (iter_key_start * p.v_strideM).sum(), + problem_size_1_k, + problem_size_1_n_int, + thread_id(), + cutlass::MatrixCoord{0, blockN * MM1::Mma::Shape::kN}); + MM1::Mma::prologue( + shared_storage.after_mm0.mm1, + iterator_V, + thread_id(), + gemm_1_num_iterations); + }; + + __syncthreads(); // Need to have shared memory initialized, and `m_prime` + // updated from end of prev iter + // + // MATMUL: Q.K_t + // + // Computes the block-matrix product of: + // (a) query[first_query:last_query, :] + // with + // (b) key[iter_key_start:iter_key_start + kKeysPerBlock] + // and stores that into `shared_storage.si` + // + + // Construct iterators to A and B operands + typename MM0::IteratorA iterator_A( + typename MM0::IteratorA::Params(p.q_strideM, problem_size_0_m), + p.query_ptr, + problem_size_0_m, + problem_size_0_k_int, + thread_id()); + + typename MM0::IteratorB iterator_B( + typename MM0::IteratorB::Params(p.k_strideM, problem_size_0_n), + p.key_ptr + (iter_key_start * p.k_strideM).sum(), + problem_size_0_n, + problem_size_0_k_int, + thread_id()); + + // Construct thread-scoped matrix multiply + typename MM0::Mma mma( + shared_storage.mm0, thread_id(), my_warp_id, my_lane_id); + + typename MM0::Mma::FragmentC accum; + + accum.clear(); + + // Compute threadblock-scoped matrix multiply-add + mma(gemm_k_iterations, accum, iterator_A, iterator_B, accum); + __syncthreads(); + + if (kPreloadV) { + prologueV(0); + } else { + MM1::Mma::drain_cp_asyncs(); + } + + // Neighborhood Attention masking + // if (p.kernel_size.prod32() > 0) { +#ifndef CACHE_NEIGHBORHOOD_COORDINATES + // Dim first_col, last_col, row_idx; + Dim first_col, key_bound, row_idx; + Dim rpb_start; +#endif + MM0::AccumLambdaIterator::iterateRows( + lane_offset, + [&](int accum_m) { +#ifndef CACHE_NEIGHBORHOOD_COORDINATES + row_idx = map_index_to_coord((int32_t)accum_m, problem_size_0_m) + + first_query; + first_col = na_mask.get_window_start(row_idx); + // last_col = na_mask.get_window_end(row_idx, first_col); + key_bound = na_mask.get_window_end(row_idx, first_col) - first_col; + if constexpr (kSupportsRPB) { + rpb_start = na_mask.get_rpb_start(row_idx); + } +#endif + }, + [&](int accum_m, int accum_n, int idx) { + auto col = map_index_to_coord((int32_t)accum_n, problem_size_0_n) + + iter_key_start; + if constexpr (!kSupportsRPB) { +#ifndef CACHE_NEIGHBORHOOD_COORDINATES + if (!is_coord_within_bounds_nn(col - first_col, key_bound)) { + // if (!is_coord_within_bounds(col, first_col, last_col)) { +#else + // if (!is_coord_within_bounds(col, first_cols[accum_m], + // last_cols[accum_m])) { + if (!is_coord_within_bounds_nn( + col - first_cols[accum_m], key_bounds[accum_m])) { +#endif + accum[idx] = + -cutlass::platform::numeric_limits::infinity(); + } + } else { + // NOTE: Flipped the condition for kernels with RPB so we can + // avoid checking whether the rpb offset goes out of bounds. +#ifndef CACHE_NEIGHBORHOOD_COORDINATES + col = col - first_col; + auto rpb_idx = rpb_start + col; + if (is_coord_within_bounds_nn(col, key_bound)) { +#else + col = col - first_cols[accum_m]; + auto rpb_idx = rpb_starts[accum_m] + col; + if (is_coord_within_bounds_nn(col, key_bounds[accum_m])) { +#endif + accum[idx] = p.scale * accum[idx] + (accum_t)(p.rpb_ptr[(rpb_idx * p.rpb_stride).sum()]); + } else { + accum[idx] = + -cutlass::platform::numeric_limits::infinity(); + } + } + }, + [&](int accum_m) {}); + //} + + // Update `mi` from accum stored in registers + // Also does accum[i] <- exp(accum[i] - mi) + iterative_softmax( + accum_o, + accum, + mi, + m_prime, + s_prime, + out_rescale, + shared_storage.addition_storage, + my_lane_id, + thread_id(), + my_warp_id, + last_kv_col, + is_first_kv_iter, + iteratorC_tile_offset, + kSupportsRPB ? 1.0 : p.scale); + + // Output results to shared-memory + + MM0::B2bGemm::accumToSmem( + shared_storage.after_mm0.si, accum, my_lane_id, output_tile_coords); + + __syncthreads(); + + // + // MATMUL: Attn . V + // Run the matmul `attn @ V` for a block of attn and V. + // `attn` is read from shared memory (in `shared_storage_si`) + // `V` is read from global memory (with iterator_B) + // + + for (int blockN = 0; blockN < nBlockN; ++blockN) { + int gemm_k_iterations = + (gemm_1_num_iterations + MM1::Mma::Shape::kK - 1) / + MM1::Mma::Shape::kK; + + // Compute threadblock-scoped matrix multiply-add and store it in accum + // (in registers) + if (!kPreloadV) { + __syncthreads(); // we share shmem between mma and epilogue + } + + typename MM1::Mma::IteratorB iterator_V( + typename MM1::IteratorB::Params{p.v_strideM, problem_size_1_k}, + p.value_ptr + (iter_key_start * p.v_strideM).sum(), + problem_size_1_k, + problem_size_1_n_int, + thread_id(), + cutlass::MatrixCoord{0, blockN * MM1::Mma::Shape::kN}); + typename MM1::Mma mma_pv( + // operand A: Pij_dropped in shared memory + shared_storage.after_mm0.si.accum_ref(), + // operand B: shared memory staging area for Vj, which is loaded + // from global memory + shared_storage.after_mm0.mm1.operand_B_ref(), + (int)thread_id(), + (int)my_warp_id, + (int)my_lane_id); + mma_pv.set_prologue_done(kPreloadV); + if (!kKeepOutputInRF) { + accum_o.clear(); + } + mma_pv(gemm_k_iterations, accum_o, iterator_V, accum_o); + __syncthreads(); + + if (kPreloadV && !kSingleValueIteration && blockN + 1 < nBlockN) { + prologueV(blockN + 1); + } + + if (!kKeepOutputInRF) { + MM1::Mma::drain_cp_asyncs(); + DISPATCH_BOOL( + is_first_kv_iter, kIsFirst, ([&] { + DISPATCH_BOOL( + is_last_kv_iter, kIsLast, ([&] { + using DefaultEpilogue = typename MM1::DefaultEpilogue; + using DefaultOp = + typename MM1::DefaultConfig::EpilogueOutputOp; + using ElementCompute = typename DefaultOp::ElementCompute; + using EpilogueOutputOp = typename cutlass::epilogue:: + thread::MemoryEfficientAttentionNormalize< + typename cutlass::platform::conditional< + kIsLast, + output_t, + output_accum_t>::type, + output_accum_t, + DefaultOp::kCount, + typename DefaultOp::ElementAccumulator, + ElementCompute, + kIsFirst, + kIsLast, + cutlass::Array>; + using Epilogue = typename cutlass::epilogue::threadblock:: + EpiloguePipelined< + typename DefaultEpilogue::Shape, + typename MM1::Mma::Operator, + DefaultEpilogue::kPartitionsK, + typename cutlass::platform::conditional< + kIsLast, + typename MM1::OutputTileIterator, + typename MM1::OutputTileIteratorAccum>::type, + typename DefaultEpilogue:: + AccumulatorFragmentIterator, + typename DefaultEpilogue::WarpTileIterator, + typename DefaultEpilogue::SharedLoadIterator, + EpilogueOutputOp, + typename DefaultEpilogue::Padding, + DefaultEpilogue::kFragmentsPerIteration, + true, // IterationsUnroll + typename MM1::OutputTileIteratorAccum // Read + // iterator + >; + + int col = blockN * MM1::Mma::Shape::kN; + auto source_iter = createOutputAccumIter(col); + auto dest_iter = gemm_kernel_utils::call_conditional< + kIsLast, + decltype(createOutputIter), + decltype(createOutputAccumIter)>:: + apply(createOutputIter, createOutputAccumIter, col); + EpilogueOutputOp rescale(s_prime, out_rescale); + Epilogue epilogue( + shared_storage.epilogue_shared_storage(), + thread_id(), + my_warp_id, + my_lane_id); + epilogue(rescale, dest_iter, accum_o, source_iter); + })); + })); + if (!kSingleValueIteration) { + __syncthreads(); + } + } + } + __syncthreads(); // we modify `m_prime` after + } + + if (kKeepOutputInRF) { + constexpr bool kIsFirst = true; + constexpr bool kIsLast = true; + using DefaultEpilogue = typename MM1::DefaultEpilogue; + using DefaultOp = typename MM1::DefaultConfig::EpilogueOutputOp; + using ElementCompute = typename DefaultOp::ElementCompute; + using EpilogueOutputOp = + typename cutlass::epilogue::thread::MemoryEfficientAttentionNormalize< + output_t, // output + output_accum_t, // source + DefaultOp::kCount, + typename DefaultOp::ElementAccumulator, // accum + output_accum_t, // compute + kIsFirst, + kIsLast, + cutlass::Array>; + using Epilogue = + typename cutlass::epilogue::threadblock::EpiloguePipelined< + typename DefaultEpilogue::Shape, + typename MM1::Mma::Operator, + DefaultEpilogue::kPartitionsK, + typename MM1::OutputTileIterator, // destination + typename DefaultEpilogue::AccumulatorFragmentIterator, + typename DefaultEpilogue::WarpTileIterator, + typename DefaultEpilogue::SharedLoadIterator, + EpilogueOutputOp, + typename DefaultEpilogue::Padding, + DefaultEpilogue::kFragmentsPerIteration, + true, // IterationsUnroll + typename MM1::OutputTileIteratorAccum // source tile + >; + auto dest_iter = createOutputIter(0); + EpilogueOutputOp rescale(s_prime, out_rescale); + Epilogue epilogue( + shared_storage.epilogue_shared_storage(), + thread_id(), + warp_id(), + lane_id()); + MM1::Mma::drain_cp_asyncs(); + epilogue(rescale, dest_iter, accum_o); + } + + // TODO: + // 7. Calculate logsumexp + // // To make the backward easier, we pad logsumexp with `inf` + // // this avoids a few bound checks, and is not more expensive during fwd + // static_assert(kQueriesPerBlock < kNumWarpsPerBlock * kWarpSize, ""); + // if (p.logsumexp_ptr && thread_id() < kQueriesPerBlock) { + // auto lse_dim = gemm_kernel_utils::ceil_div((int32_t)p.queries_end, + // kAlignLSE) * kAlignLSE; constexpr float kLog2e = 1.4426950408889634074; + // // log_2(e) = M_LOG2E if (thread_id() < p.queries_end) { + // p.logsumexp_ptr[thread_id()] = accum_t(mi[thread_id()] / kLog2e) + + // cutlass::fast_log(accum_t(s_prime[thread_id()])); + // } else if (thread_id() < lse_dim) { + // p.logsumexp_ptr[thread_id()] = + // cutlass::platform::numeric_limits::infinity(); + // } + // } + } + + template + CUTLASS_DEVICE static void iterative_softmax( + typename WarpIteratorC::Fragment& frag_o, // output so far + typename WarpIteratorC::Fragment& frag, + cutlass::Array& mi, + cutlass::Array& m_prime, + cutlass::Array& s_prime, + cutlass::Array& out_rescale, + cutlass::Array& + addition_storage, + int8_t lane_id, + int8_t thread_id, + int8_t warp_id, + int max_col, + bool is_first, + typename WarpIteratorC::TensorCoord const& tile_offset, + float scaling) { + /* Iterates on the accumulator and corresponding position on result matrix + + (1) Update `mi[r]` to the max value of the row `r` + (2) In a second iteration do the following: + (a) accum <- exp(accum - mi) + (b) m_prime <- exp(m_prime - mi) + (c) s_prime <- s_prime * m_prime + sum(accum) + + All of this is done on registers, before we store all of this + on shared memory for the next matmul with Value. + */ + using Fragment = typename WarpIteratorC::Fragment; + using LambdaIterator = typename DefaultMmaAccumLambdaIterator< + WarpIteratorC, + accum_t, + kWarpSize>::Iterator; + // Convert to `accum_t` (rather than double) + constexpr float kLog2e = 1.4426950408889634074; // log_2(e) = M_LOG2E + + static_assert(kQueriesPerBlock % kNumWarpsPerBlock == 0, ""); + static constexpr int kLinesPerWarp = kQueriesPerBlock / kNumWarpsPerBlock; + + frag = cutlass::multiplies()(scaling * kLog2e, frag); + + auto lane_offset = + LambdaIterator::get_lane_offset(lane_id, warp_id, tile_offset); + + // First update `mi` to the max per-row + { + accum_t max; + LambdaIterator::iterateRows( + lane_offset, + [&](int accum_m) { + max = -cutlass::platform::numeric_limits::infinity(); + }, + [&](int accum_m, int accum_n, int idx) { + if (accum_n < max_col) { + max = cutlass::fast_max(max, frag[idx]); + } + }, + [&](int accum_m) { + // Having 4x atomicMax seems faster than reduce within warp + // first... + atomicMaxFloat(&mi[accum_m], max); + }); + } + + // Make sure we all share the update values for `mi` + __syncthreads(); + + // Doing this `exp` is quite expensive. Let's + // split it across the warps + bool restore_mi_to_minus_inf = false; + if (lane_id < kLinesPerWarp) { + int id = warp_id * kLinesPerWarp + lane_id; + auto m_prime_id = m_prime[id]; + auto mi_id = mi[id]; + bool changed = m_prime_id < mi_id; // `false` if both are -inf + if (changed) { + auto m_prime_exp = exp2f(m_prime_id - mi_id); + out_rescale[id] = m_prime_exp; + s_prime[id] *= m_prime_exp; + } else { + out_rescale[id] = 1.0f; + } + } + __syncthreads(); // Update output fragments + if (kKeepOutputInRF && !is_first) { + accum_t line_rescale; + LambdaIterator::iterateRows( + lane_offset, + [&](int accum_m) { line_rescale = out_rescale[accum_m]; }, + [&](int accum_m, int accum_n, int idx) { + frag_o[idx] = frag_o[idx] * line_rescale; + }, + [&](int accum_m) {}); + } + // Update accum_m, accum_n, ... + { + accum_t mi_row, total_row; + LambdaIterator::iterateRows( + lane_offset, + [&](int accum_m) { mi_row = mi[accum_m]; }, + [&](int accum_m, int accum_n, int idx) { + frag[idx] = + (accum_n < max_col) ? exp2f(frag[idx] - mi_row) : accum_t(0.0); + }, + [&](int accum_m) {}); + LambdaIterator::iterateRows( + lane_offset, + [&](int accum_m) { total_row = 0.0; }, + [&](int accum_m, int accum_n, int idx) { total_row += frag[idx]; }, + [&](int accum_m) { + if (LambdaIterator::reduceSameRow( + lane_id, total_row, [](accum_t a, accum_t b) { + return a + b; + })) { + // NOTE: we could atomically add `total_row` to `s_prime`, but + // it's faster (and deterministic) to avoid atomics here + addition_storage + [accum_m + kQueriesPerBlock * tile_offset.column()] = + total_row; + } + }); + } + __syncthreads(); + if (lane_id < kLinesPerWarp) { + int id = warp_id * kLinesPerWarp + lane_id; + accum_t total_row = s_prime[id]; + if (restore_mi_to_minus_inf) { + // Restore `mi`, see above when we set `restore_mi_to_minus_inf=true` + mi[id] = -cutlass::platform::numeric_limits::infinity(); + } else { + m_prime[id] = mi[id]; + } + CUTLASS_PRAGMA_UNROLL + for (int i = 0; i < MM0::MmaCore::WarpCount::kN; ++i) { + total_row += addition_storage[id + kQueriesPerBlock * i]; + } + s_prime[id] = total_row; + } + } + + static CUTLASS_DEVICE int8_t lane_id() { + return threadIdx.x; + } + static CUTLASS_DEVICE int8_t warp_id() { + return threadIdx.y; + } + static CUTLASS_DEVICE int16_t thread_id() { + return threadIdx.x + threadIdx.y * blockDim.x; + } +}; + +} // namespace fna +} // namespace cuda +} // namespace natten diff --git a/csrc/include/natten/cuda/fna/na_utils.cuh b/csrc/include/natten/cuda/fna/na_utils.cuh new file mode 100644 index 0000000..78df6e2 --- /dev/null +++ b/csrc/include/natten/cuda/fna/na_utils.cuh @@ -0,0 +1,1158 @@ +/*************************************************************************************************** + * Copyright (c) 2022-2024 Ali Hassani. + * + * Permission is hereby granted, free of charge, to any person obtaining a copy + * of this software and associated documentation files (the "Software"), to deal + * in the Software without restriction, including without limitation the rights + * to use, copy, modify, merge, publish, distribute, sublicense, and/or sell + * copies of the Software, and to permit persons to whom the Software is + * furnished to do so, subject to the following conditions: + * + * The above copyright notice and this permission notice shall be included in + *all copies or substantial portions of the Software. + * + * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, + * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE + * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER + * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, + * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE + * SOFTWARE. + * + **************************************************************************************************/ +/*! \file + \brief Fused NA (FNA) utils. + Holds data structures and helper functions that are templated by + spatial rank (1d, 2d, 3d), and provide (almost) generic interfaces + for the kernel. +*/ +#pragma once + +#include +#include + +#include + +namespace natten { +namespace cuda { +namespace fna { + +struct NA1dDim { + int32_t x; + + // Default ctor + CUTLASS_HOST_DEVICE constexpr NA1dDim(int32_t x_) : x(x_) {} + + CUTLASS_HOST_DEVICE NA1dDim() : x(0) {} + + CUTLASS_HOST_DEVICE void operator=(const NA1dDim& other) { + x = other.x; + } + + // Operators + CUTLASS_HOST_DEVICE NA1dDim operator+(NA1dDim other) const { + return NA1dDim(x + other.x); + } + + CUTLASS_HOST_DEVICE NA1dDim operator*(NA1dDim other) const { + return NA1dDim(x * other.x); + } + + CUTLASS_HOST_DEVICE NA1dDim operator-(NA1dDim other) const { + return NA1dDim(x - other.x); + } + + CUTLASS_HOST_DEVICE NA1dDim operator/(NA1dDim other) const { + return NA1dDim(x / other.x); + } + + CUTLASS_HOST_DEVICE NA1dDim operator+(int32_t other) const { + return NA1dDim(x + other); + } + + CUTLASS_HOST_DEVICE NA1dDim operator*(int32_t other) const { + return NA1dDim(x * other); + } + + CUTLASS_HOST_DEVICE NA1dDim operator-(int32_t other) const { + return NA1dDim(x - other); + } + + CUTLASS_HOST_DEVICE NA1dDim operator/(int32_t other) const { + return NA1dDim(x / other); + } + + // Cmp + // CUTLASS_HOST_DEVICE bool operator<=(NA1dDim other) const { + // return x <= other.x; + // } + + // CUTLASS_HOST_DEVICE bool operator<(NA1dDim other) const { + // return x < other.x; + // } + + // CUTLASS_HOST_DEVICE bool operator>(NA1dDim other) const { + // return x > other.x; + // } + + // CUTLASS_HOST_DEVICE bool operator>=(NA1dDim other) const { + // return x >= other.x; + // } + + CUTLASS_HOST_DEVICE bool operator==(NA1dDim other) const { + return x == other.x; + } + + // This method is used to determine whether or not the current + // coordinate exceeds the spatial extent. This is used in + // the NA mask (`get_window_start`). + // It should NOT overload the >= operator. + CUTLASS_HOST_DEVICE NA1dDim is_overflowing(NA1dDim extent) const { + return NA1dDim(x >= extent.x); + } + + // CUTLASS_HOST_DEVICE bool operator<=(int32_t other) const { + // return x <= other; + //} + + // CUTLASS_HOST_DEVICE bool operator<(int32_t other) const { + // return x < other; + //} + + // CUTLASS_HOST_DEVICE bool operator>(int32_t other) const { + // return x > other; + //} + + // CUTLASS_HOST_DEVICE bool operator>=(int32_t other) const { + // return x >= other; + //} + + // CUTLASS_HOST_DEVICE bool operator==(int32_t other) const { + // return x == other; + //} + + // Reduce + CUTLASS_HOST_DEVICE int64_t sum() const { + return (int64_t)x; + } + + CUTLASS_HOST_DEVICE int64_t prod() const { + return (int64_t)x; + } + + CUTLASS_HOST_DEVICE int32_t prod32() const { + return x; + } +}; + +struct NA2dDim { + int32_t x; + int32_t y; + + // Default ctor + CUTLASS_HOST_DEVICE constexpr NA2dDim(int32_t x_, int32_t y_) + : x(x_), y(y_) {} + + CUTLASS_HOST_DEVICE NA2dDim() : x(0), y(0) {} + + CUTLASS_HOST_DEVICE void operator=(const NA2dDim& other) { + x = other.x; + y = other.y; + } + + // CUTLASS_HOST_DEVICE NA2dDim(int32_t n): x(n), y(n) {} + + // Operators + CUTLASS_HOST_DEVICE NA2dDim operator+(NA2dDim other) const { + return NA2dDim(x + other.x, y + other.y); + } + + CUTLASS_HOST_DEVICE NA2dDim operator*(NA2dDim other) const { + return NA2dDim(x * other.x, y * other.y); + } + + CUTLASS_HOST_DEVICE NA2dDim operator-(NA2dDim other) const { + return NA2dDim(x - other.x, y - other.y); + } + + CUTLASS_HOST_DEVICE NA2dDim operator/(NA2dDim other) const { + return NA2dDim(x / other.x, y / other.y); + } + + CUTLASS_HOST_DEVICE NA2dDim operator+(int32_t other) const { + return NA2dDim(x + other, y + other); + } + + CUTLASS_HOST_DEVICE NA2dDim operator*(int32_t other) const { + return NA2dDim(x * other, y * other); + } + + CUTLASS_HOST_DEVICE NA2dDim operator-(int32_t other) const { + return NA2dDim(x - other, y - other); + } + + CUTLASS_HOST_DEVICE NA2dDim operator/(int32_t other) const { + return NA2dDim(x / other, y / other); + } + + // Cmp + // CUTLASS_HOST_DEVICE bool operator<=(NA2dDim other) const { + // return x <= other.x && y <= other.y; + //} + + // CUTLASS_HOST_DEVICE bool operator<(NA2dDim other) const { + // return x < other.x && y < other.y; + //} + + // CUTLASS_HOST_DEVICE bool operator>(NA2dDim other) const { + // return x > other.x && y > other.y; + //} + + // CUTLASS_HOST_DEVICE bool operator>=(NA2dDim other) const { + // return x >= other.x && y >= other.y; + //} + + CUTLASS_HOST_DEVICE bool operator==(NA2dDim other) const { + return x == other.x && y == other.y; + } + + // This method is used to determine whether or not the current + // coordinate exceeds the spatial extent. This is used in + // the NA mask (`get_window_start`). + // It should NOT overload the >= operator. + CUTLASS_HOST_DEVICE NA2dDim is_overflowing(NA2dDim extent) const { + return NA2dDim(x >= extent.x, y >= extent.y); + } + + // CUTLASS_HOST_DEVICE bool operator<=(int32_t other) const { + // return x <= other && y <= other; + //} + + // CUTLASS_HOST_DEVICE bool operator<(int32_t other) const { + // return x < other && y < other; + //} + + // CUTLASS_HOST_DEVICE bool operator>(int32_t other) const { + // return x > other && y > other; + //} + + // CUTLASS_HOST_DEVICE bool operator>=(int32_t other) const { + // return x >= other && y >= other; + //} + + // CUTLASS_HOST_DEVICE bool operator==(int32_t other) const { + // return x == other && y == other; + //} + + // Reduce + CUTLASS_HOST_DEVICE int64_t sum() const { + return (int64_t)(x + y); + } + + CUTLASS_HOST_DEVICE int64_t prod() const { + return (int64_t)(x * y); + } + + CUTLASS_HOST_DEVICE int32_t prod32() const { + return x * y; + } +}; + +struct NA3dDim { + int32_t x; + int32_t y; + int32_t z; + + // Default ctor + CUTLASS_HOST_DEVICE constexpr NA3dDim(int32_t x_, int32_t y_, int32_t z_) + : x(x_), y(y_), z(z_) {} + + CUTLASS_HOST_DEVICE NA3dDim() : x(0), y(0), z(0) {} + + CUTLASS_HOST_DEVICE void operator=(const NA3dDim& other) { + x = other.x; + y = other.y; + z = other.z; + } + + // CUTLASS_HOST_DEVICE NA3dDim(int32_t n): x(n), y(n), z(n) {} + + // Operators + CUTLASS_HOST_DEVICE NA3dDim operator+(NA3dDim other) const { + return NA3dDim(x + other.x, y + other.y, z + other.z); + } + + CUTLASS_HOST_DEVICE NA3dDim operator*(NA3dDim other) const { + return NA3dDim(x * other.x, y * other.y, z * other.z); + } + + CUTLASS_HOST_DEVICE NA3dDim operator-(NA3dDim other) const { + return NA3dDim(x - other.x, y - other.y, z - other.z); + } + + CUTLASS_HOST_DEVICE NA3dDim operator/(NA3dDim other) const { + return NA3dDim(x / other.x, y / other.y, z / other.z); + } + + CUTLASS_HOST_DEVICE NA3dDim operator+(int32_t other) const { + return NA3dDim(x + other, y + other, z + other); + } + + CUTLASS_HOST_DEVICE NA3dDim operator*(int32_t other) const { + return NA3dDim(x * other, y * other, z * other); + } + + CUTLASS_HOST_DEVICE NA3dDim operator-(int32_t other) const { + return NA3dDim(x - other, y - other, z - other); + } + + CUTLASS_HOST_DEVICE NA3dDim operator/(int32_t other) const { + return NA3dDim(x / other, y / other, z / other); + } + + // Cmp + // CUTLASS_HOST_DEVICE bool operator<=(NA3dDim other) const { + // return x <= other.x && y <= other.y && z <= other.z; + //} + + // CUTLASS_HOST_DEVICE bool operator<(NA3dDim other) const { + // return x < other.x && y < other.y && z < other.z; + //} + + // CUTLASS_HOST_DEVICE bool operator>(NA3dDim other) const { + // return x > other.x && y > other.y && z > other.z; + //} + + // CUTLASS_HOST_DEVICE bool operator>=(NA3dDim other) const { + // return x >= other.x && y >= other.y && z >= other.z; + //} + + CUTLASS_HOST_DEVICE bool operator==(NA3dDim other) const { + return x == other.x && y == other.y && z == other.z; + } + + // This method is used to determine whether or not the current + // coordinate exceeds the spatial extent. This is used in + // the NA mask (`get_window_start`). + // It should NOT overload the >= operator. + CUTLASS_HOST_DEVICE NA3dDim is_overflowing(NA3dDim extent) const { + return NA3dDim(x >= extent.x, y >= extent.y, z >= extent.z); + } + + // CUTLASS_HOST_DEVICE bool operator<=(int32_t other) const { + // return x <= other && y <= other && z <= other; + //} + + // CUTLASS_HOST_DEVICE bool operator<(int32_t other) const { + // return x < other && y < other && z < other; + //} + + // CUTLASS_HOST_DEVICE bool operator>(int32_t other) const { + // return x > other && y > other && z > other; + //} + + // CUTLASS_HOST_DEVICE bool operator>=(int32_t other) const { + // return x >= other && y >= other && z >= other; + //} + + // CUTLASS_HOST_DEVICE bool operator==(int32_t other) const { + // return x == other && y == other && z == other; + //} + + // Reduce + CUTLASS_HOST_DEVICE int64_t sum() const { + return (int64_t)(x + y + z); + } + + CUTLASS_HOST_DEVICE int64_t prod() const { + return (int64_t)(x * y * z); + } + + CUTLASS_HOST_DEVICE int32_t prod32() const { + return x * y * z; + } +}; + +template +struct GetDim; + +template <> +struct GetDim<1> { + using type = NA1dDim; +}; + +template <> +struct GetDim<2> { + using type = NA2dDim; +}; + +template <> +struct GetDim<3> { + using type = NA3dDim; +}; + +// data structure conversions +template +Dim tuple_to_na_dim(TupleType v); + +template <> +inline NA1dDim tuple_to_na_dim(std::tuple v) { + return NA1dDim(std::get<0>(v)); +} + +template <> +inline NA2dDim tuple_to_na_dim(std::tuple v) { + return NA2dDim(std::get<0>(v), std::get<1>(v)); +} + +template <> +inline NA3dDim tuple_to_na_dim(std::tuple v) { + return NA3dDim(std::get<0>(v), std::get<1>(v), std::get<2>(v)); +} + +//// Factories +// template +// struct MakeDim; +// +// template +// struct MakeDim { +// static constexpr NA1dDim value = {Value}; +//}; +// +// template +// struct MakeDim { +// static constexpr NA2dDim value = {Value, Value}; +//}; +// +// template +// struct MakeDim { +// static constexpr NA3dDim value = {Value, Value, Value}; +//}; + +//// Tile mappings +// template +// struct MapTileSizeToNd; +// +//// Identity mapping for all 1D problems. +// template +// struct MapTileSizeToNd { +// static constexpr NA1dDim value = TileSize; +//}; +// +//// 2D problems +// template <> +// struct MapTileSizeToNd<32, 2> { +// static constexpr NA2dDim value = {2, 16}; +//}; +// +// template <> +// struct MapTileSizeToNd<64, 2> { +// static constexpr NA2dDim value = {4, 16}; +//}; +// +// template <> +// struct MapTileSizeToNd<128, 2> { +// static constexpr NA2dDim value = {8, 16}; +//}; +// +//// 3D problems +// template <> +// struct MapTileSizeToNd<32, 3> { +// static constexpr NA3dDim value = {2, 2, 8}; +//}; +// +// template <> +// struct MapTileSizeToNd<64, 3> { +// static constexpr NA3dDim value = {4, 4, 4}; +//}; +// +// template <> +// struct MapTileSizeToNd<128, 3> { +// static constexpr NA3dDim value = {4, 4, 8}; +//}; + +// template +// struct Divmod; +// +// template <> +// struct Divmod<1> { +// using Dim = typename GetDim<1>::type; +// +// cutlass::FastDivmod x; +// +// CUTLASS_HOST_DEVICE constexpr Divmod(Dim divisor): x((int)divisor.x) {} +// +// CUTLASS_HOST_DEVICE void operator()(Dim "ient, Dim &remainder, Dim +// dividend) const { +// x(quotient.x, remainder.x, dividend.x); +// } +// }; +// +// template <> +// struct Divmod<2> { +// using Dim = typename GetDim<2>::type; +// +// cutlass::FastDivmod x; +// cutlass::FastDivmod y; +// +// CUTLASS_HOST_DEVICE constexpr Divmod(Dim divisor): x((int)divisor.x), +// y((int)divisor.y) {} +// +// CUTLASS_HOST_DEVICE void operator()(Dim "ient, Dim &remainder, Dim +// dividend) const { +// x(quotient.x, remainder.x, dividend.x); +// y(quotient.y, remainder.y, dividend.y); +// } +// }; +// +// template <> +// struct Divmod<3> { +// using Dim = typename GetDim<3>::type; +// +// cutlass::FastDivmod x; +// cutlass::FastDivmod y; +// cutlass::FastDivmod z; +// +// CUTLASS_HOST_DEVICE constexpr Divmod(Dim divisor): x((int)divisor.x), +// y((int)divisor.y) {} +// +// CUTLASS_HOST_DEVICE void operator()(Dim "ient, Dim &remainder, Dim +// dividend) const { +// x(quotient.x, remainder.x, dividend.x); +// y(quotient.y, remainder.y, dividend.y); +// } +// }; + +// template +// struct CoordMapper; +// +// template <> +// struct CoordMapper <1> { +// using Dim = typename GetDim<1>::type; +// +// CUTLASS_HOST_DEVICE CoordMapper(Dim block_shape) {} +// +// CUTLASS_HOST_DEVICE Dim operator()(int32_t block_index) { +// return Dim(block_index); +// } +//}; +// +// template <> +// struct CoordMapper <2> { +// using Dim = typename GetDim<2>::type; +// +// cutlass::FastDivmod divmod_y; +// +// CUTLASS_HOST_DEVICE CoordMapper(Dim block_shape): +// divmod_y((int)block_shape.y) {} +// +// CUTLASS_HOST_DEVICE Dim operator()(int32_t block_index) { +// int index_x, index_y; +// divmod_y(index_x, index_y, block_index); +// return Dim(index_x, index_y); +// } +//}; +// +// template <> +// struct CoordMapper <3> { +// using Dim = typename GetDim<3>::type; +// +// cutlass::FastDivmod divmod_y; +// cutlass::FastDivmod divmod_z; +// +// CUTLASS_HOST_DEVICE CoordMapper(Dim block_shape): +// divmod_y((int)block_shape.y), divmod_z((int)block_shape.z) {} +// +// CUTLASS_HOST_DEVICE Dim operator()(int32_t block_index) { +// int index_x, index_y, index_z, index_xy; +// divmod_z(index_xy, index_z, block_index); +// divmod_z(index_x, index_y, index_xy); +// return Dim(index_x, index_y, index_z); +// } +//}; + +template +CUTLASS_DEVICE Dim map_index_to_coord(int32_t block_index, Dim block_shape); + +template <> +CUTLASS_DEVICE NA1dDim +map_index_to_coord(int32_t block_index, NA1dDim block_shape) { + // 1d is just linear indexing. + return NA1dDim(block_index); +}; + +template <> +CUTLASS_DEVICE NA2dDim +map_index_to_coord(int32_t block_index, NA2dDim block_shape) { + auto index_x = block_index / block_shape.y; + return NA2dDim(index_x, block_index - index_x * block_shape.y); +} + +template <> +CUTLASS_DEVICE NA3dDim +map_index_to_coord(int32_t block_index, NA3dDim block_shape) { + auto index_z = block_index % block_shape.z; + auto index_xy = block_index / block_shape.z; + auto index_y = index_xy % block_shape.y; + auto index_x = index_xy / block_shape.y; + return NA3dDim(index_x, index_y, index_z); +} + +template +CUTLASS_DEVICE int32_t map_coord_to_index(Dim coord, Dim block_shape); + +template <> +CUTLASS_DEVICE int32_t map_coord_to_index(NA1dDim coord, NA1dDim block_shape) { + // 1d is just linear indexing. + return coord.x; +} + +template <> +CUTLASS_DEVICE int32_t map_coord_to_index(NA2dDim coord, NA2dDim block_shape) { + return coord.x * block_shape.y + coord.y; +} + +template <> +CUTLASS_DEVICE int32_t map_coord_to_index(NA3dDim coord, NA3dDim block_shape) { + return coord.x * block_shape.y * block_shape.z + coord.y * block_shape.z + + coord.z; +} + +template +CUTLASS_DEVICE Dim fast_min(Dim lhs, Dim rhs); + +template <> +CUTLASS_DEVICE NA1dDim fast_min(NA1dDim lhs, NA1dDim rhs) { + return NA1dDim(cutlass::fast_min(lhs.x, rhs.x)); +} + +template <> +CUTLASS_DEVICE NA2dDim fast_min(NA2dDim lhs, NA2dDim rhs) { + return NA2dDim( + cutlass::fast_min(lhs.x, rhs.x), cutlass::fast_min(lhs.y, rhs.y)); +} + +template <> +CUTLASS_DEVICE NA3dDim fast_min(NA3dDim lhs, NA3dDim rhs) { + return NA3dDim( + cutlass::fast_min(lhs.x, rhs.x), + cutlass::fast_min(lhs.y, rhs.y), + cutlass::fast_min(lhs.z, rhs.z)); +} + +template +CUTLASS_DEVICE Dim fast_max(Dim lhs, Dim rhs); + +template <> +CUTLASS_DEVICE NA1dDim fast_max(NA1dDim lhs, NA1dDim rhs) { + return NA1dDim(cutlass::fast_max(lhs.x, rhs.x)); +} + +template <> +CUTLASS_DEVICE NA2dDim fast_max(NA2dDim lhs, NA2dDim rhs) { + return NA2dDim( + cutlass::fast_max(lhs.x, rhs.x), cutlass::fast_max(lhs.y, rhs.y)); +} + +template <> +CUTLASS_DEVICE NA3dDim fast_max(NA3dDim lhs, NA3dDim rhs) { + return NA3dDim( + cutlass::fast_max(lhs.x, rhs.x), + cutlass::fast_max(lhs.y, rhs.y), + cutlass::fast_max(lhs.z, rhs.z)); +} + +template +CUTLASS_HOST_DEVICE Dim ceil_div_dim(Dim lhs, Dim rhs) {} + +template <> +CUTLASS_HOST_DEVICE NA1dDim ceil_div_dim(NA1dDim lhs, NA1dDim rhs) { + return NA1dDim(gemm_kernel_utils::ceil_div(lhs.x, rhs.x)); +} + +template <> +CUTLASS_HOST_DEVICE NA2dDim ceil_div_dim(NA2dDim lhs, NA2dDim rhs) { + return NA2dDim( + gemm_kernel_utils::ceil_div(lhs.x, rhs.x), + gemm_kernel_utils::ceil_div(lhs.y, rhs.y)); +} + +template <> +CUTLASS_HOST_DEVICE NA3dDim ceil_div_dim(NA3dDim lhs, NA3dDim rhs) { + return NA3dDim( + gemm_kernel_utils::ceil_div(lhs.x, rhs.x), + gemm_kernel_utils::ceil_div(lhs.y, rhs.y), + gemm_kernel_utils::ceil_div(lhs.z, rhs.z)); +} + +template +CUTLASS_DEVICE void maybe_mask_qk_tiles( + Dim& q_shape, + Dim orig_shape, + Dim dilation, + Dim dilation_idx); + +template <> +CUTLASS_DEVICE void maybe_mask_qk_tiles( + NA1dDim& q_shape, + NA1dDim orig_shape, + NA1dDim dilation, + NA1dDim dilation_idx) { + auto num_tiles_with_fewer_tokens = (q_shape * dilation) - orig_shape; + // TODO: assert num_tiles_with_fewer_tokens >= 0 + if (dilation_idx.x >= dilation.x - num_tiles_with_fewer_tokens.x) { + q_shape.x = q_shape.x - 1; + } +} + +template <> +CUTLASS_DEVICE void maybe_mask_qk_tiles( + NA2dDim& q_shape, + NA2dDim orig_shape, + NA2dDim dilation, + NA2dDim dilation_idx) { + auto num_tiles_with_fewer_tokens = (q_shape * dilation) - orig_shape; + // TODO: assert num_tiles_with_fewer_tokens >= 0 + if (dilation_idx.x >= dilation.x - num_tiles_with_fewer_tokens.x) { + q_shape.x = q_shape.x - 1; + } + if (dilation_idx.y >= dilation.y - num_tiles_with_fewer_tokens.y) { + q_shape.y = q_shape.y - 1; + } +} + +template <> +CUTLASS_DEVICE void maybe_mask_qk_tiles( + NA3dDim& q_shape, + NA3dDim orig_shape, + NA3dDim dilation, + NA3dDim dilation_idx) { + auto num_tiles_with_fewer_tokens = (q_shape * dilation) - orig_shape; + // TODO: assert num_tiles_with_fewer_tokens >= 0 + if (dilation_idx.x >= dilation.x - num_tiles_with_fewer_tokens.x) { + q_shape.x = q_shape.x - 1; + } + if (dilation_idx.y >= dilation.y - num_tiles_with_fewer_tokens.y) { + q_shape.y = q_shape.y - 1; + } + if (dilation_idx.z >= dilation.z - num_tiles_with_fewer_tokens.z) { + q_shape.z = q_shape.z - 1; + } +} + +template +CUTLASS_DEVICE Dim compute_stride(Dim input_shape, int64_t last_stride); + +template <> +CUTLASS_DEVICE NA1dDim +compute_stride(NA1dDim input_shape, int64_t last_stride) { + return NA1dDim(last_stride); +} + +template <> +CUTLASS_DEVICE NA2dDim +compute_stride(NA2dDim input_shape, int64_t last_stride) { + return NA2dDim(input_shape.y * last_stride, last_stride); +} + +template <> +CUTLASS_DEVICE NA3dDim +compute_stride(NA3dDim input_shape, int64_t last_stride) { + return NA3dDim( + input_shape.y * input_shape.z * last_stride, + input_shape.z * last_stride, + last_stride); +} + +template +struct CausalMask; + +template +struct CausalMask { + static constexpr int kRank = 1; + static constexpr bool Dim0 = IsDim0Causal; + + static constexpr bool AnyCausalDims = IsDim0Causal; +}; + +template +struct CausalMask { + static constexpr int kRank = 2; + static constexpr bool Dim0 = IsDim0Causal; + static constexpr bool Dim1 = IsDim1Causal; + + static constexpr bool AnyCausalDims = IsDim0Causal || IsDim1Causal; +}; + +template +struct CausalMask { + static constexpr int kRank = 3; + static constexpr bool Dim0 = IsDim0Causal; + static constexpr bool Dim1 = IsDim1Causal; + static constexpr bool Dim2 = IsDim2Causal; + + static constexpr bool AnyCausalDims = + IsDim0Causal || IsDim1Causal || IsDim2Causal; +}; + +// No causal masking +template +struct GetNonCausalMask; + +template <> +struct GetNonCausalMask<1> { + using type = CausalMask; +}; + +template <> +struct GetNonCausalMask<2> { + using type = CausalMask; +}; + +template <> +struct GetNonCausalMask<3> { + using type = CausalMask; +}; + +// NA Mask +template +struct NeighborhoodAttentionMaskBase { + static constexpr bool DoCausalMasking = DoCausalMasking_; +}; + +// Standard NA +template <> +struct NeighborhoodAttentionMaskBase { + static constexpr bool DoCausalMasking = false; + + int32_t window_size; + int32_t window_radius; + int32_t spatial_extent; + int32_t last_start; + + CUTLASS_DEVICE NeighborhoodAttentionMaskBase( + int32_t kernel_size, + int32_t spatial_extent_) + : window_size(kernel_size), + window_radius(kernel_size / 2), + spatial_extent(spatial_extent_), + last_start(spatial_extent_ - window_radius - 1) {} + + CUTLASS_DEVICE int32_t get_window_start(int32_t index) { + // return cutlass::fast_max(index - window_radius, 0) + + // (index + window_radius >= spatial_extent) * + // (spatial_extent - index - window_radius - 1); + return cutlass::fast_max(index - window_radius, 0) + + cutlass::fast_min(last_start - index, 0); + } + + CUTLASS_DEVICE int32_t get_window_end(int32_t index, int32_t start) { + return start + window_size; + } + + CUTLASS_DEVICE int32_t get_rpb_start(int32_t index) { + return cutlass::fast_max(window_radius + cutlass::fast_max(window_radius - index, 0) - + cutlass::fast_max(index - spatial_extent + window_radius + 1, 0), 0); + } +}; + +// Causal NA +template <> +struct NeighborhoodAttentionMaskBase { + static constexpr bool DoCausalMasking = true; + + int32_t window_size; + int32_t window_radius; + int32_t spatial_extent; + int32_t last_start; + + CUTLASS_DEVICE NeighborhoodAttentionMaskBase( + int32_t kernel_size, + int32_t spatial_extent_) + : window_size(kernel_size), + window_radius(kernel_size / 2), + spatial_extent(spatial_extent_), + last_start(spatial_extent_ - window_radius - 1) {} + + CUTLASS_DEVICE int32_t get_window_start(int32_t index) { + return cutlass::fast_max(index - window_size + 1, 0); + } + + CUTLASS_DEVICE int32_t get_window_end(int32_t index, int32_t start) { + return cutlass::fast_min(index + 1, spatial_extent); + } + + CUTLASS_DEVICE int32_t get_rpb_start(int32_t index) { + printf( + "FATAL: RPB kernels do not support causal masking. Please open an issue."); + asm volatile("brkpt;\n"); + } +}; + +// TODO: figure out an alternative to partial specializations for +// literally every dim. I'm pretty sure there's a better way of handling +// it with less repetition. It might require refactoring this whole +// header file, particularly how NA.dDim structs are defined. + +template +struct NeighborhoodAttentionMask; + +template +struct NeighborhoodAttentionMask { + static_assert(NADim >= 1 && NADim < 4); + static_assert(NADim == CausalMask_::kRank); + + using CausalMask = CausalMask_; +}; + +template +struct NeighborhoodAttentionMask<1, CausalMask_> { + static constexpr int kRank = 1; + + using MaskType0 = NeighborhoodAttentionMaskBase; + using Dim = typename natten::cuda::fna::GetDim::type; + + MaskType0 mask_0; + + CUTLASS_DEVICE NeighborhoodAttentionMask(Dim kernel_size, Dim spatial_extent_) + : mask_0(kernel_size.x, spatial_extent_.x) {} + + CUTLASS_DEVICE Dim get_window_start(Dim index) { + return Dim(mask_0.get_window_start(index.x)); + } + + CUTLASS_DEVICE Dim get_window_end(Dim index, Dim start) { + return Dim(mask_0.get_window_end(index.x, start.x)); + } + + CUTLASS_DEVICE Dim get_rpb_start(Dim index) { + return Dim(mask_0.get_rpb_start(index.x)); + } +}; + +template +struct NeighborhoodAttentionMask<2, CausalMask_> { + static constexpr int kRank = 2; + + using MaskType0 = NeighborhoodAttentionMaskBase; + using MaskType1 = NeighborhoodAttentionMaskBase; + using Dim = typename natten::cuda::fna::GetDim::type; + + MaskType0 mask_0; + MaskType1 mask_1; + + CUTLASS_DEVICE NeighborhoodAttentionMask(Dim kernel_size, Dim spatial_extent_) + : mask_0(kernel_size.x, spatial_extent_.x), + mask_1(kernel_size.y, spatial_extent_.y) {} + + CUTLASS_DEVICE Dim get_window_start(Dim index) { + return Dim( + mask_0.get_window_start(index.x), mask_1.get_window_start(index.y)); + } + + CUTLASS_DEVICE Dim get_window_end(Dim index, Dim start) { + return Dim( + mask_0.get_window_end(index.x, start.x), + mask_1.get_window_end(index.y, start.y)); + } + + CUTLASS_DEVICE Dim get_rpb_start(Dim index) { + return Dim(mask_0.get_rpb_start(index.x), mask_1.get_rpb_start(index.y)); + } +}; + +template +struct NeighborhoodAttentionMask<3, CausalMask_> { + static constexpr int kRank = 3; + + using MaskType0 = NeighborhoodAttentionMaskBase; + using MaskType1 = NeighborhoodAttentionMaskBase; + using MaskType2 = NeighborhoodAttentionMaskBase; + using Dim = typename natten::cuda::fna::GetDim::type; + + MaskType0 mask_0; + MaskType1 mask_1; + MaskType2 mask_2; + + CUTLASS_DEVICE NeighborhoodAttentionMask(Dim kernel_size, Dim spatial_extent_) + : mask_0(kernel_size.x, spatial_extent_.x), + mask_1(kernel_size.y, spatial_extent_.y), + mask_2(kernel_size.z, spatial_extent_.z) {} + + CUTLASS_DEVICE Dim get_window_start(Dim index) { + return Dim( + mask_0.get_window_start(index.x), + mask_1.get_window_start(index.y), + mask_2.get_window_start(index.z)); + } + + CUTLASS_DEVICE Dim get_window_end(Dim index, Dim start) { + return Dim( + mask_0.get_window_end(index.x, start.x), + mask_1.get_window_end(index.y, start.y), + mask_2.get_window_end(index.z, start.z)); + } + + CUTLASS_DEVICE Dim get_rpb_start(Dim index) { + return Dim( + mask_0.get_rpb_start(index.x), + mask_1.get_rpb_start(index.y), + mask_2.get_rpb_start(index.z)); + } +}; + +template +CUTLASS_DEVICE bool is_coord_within_upper_bound(Dim coord, Dim upper_bound); + +template <> +CUTLASS_DEVICE bool is_coord_within_upper_bound( + NA1dDim coord, + NA1dDim upper_bound) { + return coord.x < upper_bound.x; +} + +template <> +CUTLASS_DEVICE bool is_coord_within_upper_bound( + NA2dDim coord, + NA2dDim upper_bound) { + return coord.x < upper_bound.x && coord.y < upper_bound.y; +} + +template <> +CUTLASS_DEVICE bool is_coord_within_upper_bound( + NA3dDim coord, + NA3dDim upper_bound) { + return coord.x < upper_bound.x && coord.y < upper_bound.y && + coord.z < upper_bound.z; +} + +template +CUTLASS_DEVICE bool is_coord_within_bounds( + Dim coord, + Dim lower_bound, + Dim upper_bound); + +template <> +CUTLASS_DEVICE bool is_coord_within_bounds( + NA1dDim coord, + NA1dDim lower_bound, + NA1dDim upper_bound) { + return coord.x >= lower_bound.x && coord.x < upper_bound.x; +} + +template <> +CUTLASS_DEVICE bool is_coord_within_bounds( + NA2dDim coord, + NA2dDim lower_bound, + NA2dDim upper_bound) { + return coord.x >= lower_bound.x && coord.x < upper_bound.x && + coord.y >= lower_bound.y && coord.y < upper_bound.y; +} + +template <> +CUTLASS_DEVICE bool is_coord_within_bounds( + NA3dDim coord, + NA3dDim lower_bound, + NA3dDim upper_bound) { + return coord.x >= lower_bound.x && coord.x < upper_bound.x && + coord.y >= lower_bound.y && coord.y < upper_bound.y && + coord.z >= lower_bound.z && coord.z < upper_bound.z; +} + +template +CUTLASS_DEVICE bool is_coord_within_bounds_nn(Dim coord, Dim upper_bound); + +template <> +CUTLASS_DEVICE bool is_coord_within_bounds_nn( + NA1dDim coord, + NA1dDim upper_bound) { + return coord.x >= 0 && coord.x < upper_bound.x; +} + +template <> +CUTLASS_DEVICE bool is_coord_within_bounds_nn( + NA2dDim coord, + NA2dDim upper_bound) { + return coord.x >= 0 && coord.y >= 0 && coord.x < upper_bound.x && + coord.y < upper_bound.y; +} + +template <> +CUTLASS_DEVICE bool is_coord_within_bounds_nn( + NA3dDim coord, + NA3dDim upper_bound) { + return coord.x >= 0 && coord.y >= 0 && coord.z >= 0 && + coord.x < upper_bound.x && coord.y < upper_bound.y && + coord.z < upper_bound.z; +} + +template +CUTLASS_DEVICE void increment_tile(Dim& tile, Dim inc_value, Dim upper_bound); + +template <> +CUTLASS_DEVICE void increment_tile( + NA1dDim& tile, + NA1dDim inc_value, + NA1dDim upper_bound) { + tile.x += inc_value.x; +} + +template <> +CUTLASS_DEVICE void increment_tile( + NA2dDim& tile, + NA2dDim inc_value, + NA2dDim upper_bound) { + tile.y = tile.y + inc_value.y; + tile.x = tile.x + (tile.y >= upper_bound.y) * inc_value.x; + tile.y = (tile.y < upper_bound.y) * tile.y; +} + +template <> +CUTLASS_DEVICE void increment_tile( + NA3dDim& tile, + NA3dDim inc_value, + NA3dDim upper_bound) { + tile.z = tile.z + inc_value.z; + if (tile.z >= upper_bound.z) { + tile.z = 0; + tile.y += inc_value.y; + } + if (tile.y >= upper_bound.y) { + tile.y = 0; + tile.x += inc_value.x; + } +} + +template +CUTLASS_DEVICE bool is_tile_last(Dim& tile, Dim inc_value, Dim upper_bound); + +template <> +CUTLASS_DEVICE bool is_tile_last( + NA1dDim& tile, + NA1dDim inc_value, + NA1dDim upper_bound) { + return tile.x + inc_value.x >= upper_bound.x; +} + +template <> +CUTLASS_DEVICE bool is_tile_last( + NA2dDim& tile, + NA2dDim inc_value, + NA2dDim upper_bound) { + return tile.y + inc_value.y >= upper_bound.y && + tile.x + inc_value.x >= upper_bound.x; +} + +template <> +CUTLASS_DEVICE bool is_tile_last( + NA3dDim& tile, + NA3dDim inc_value, + NA3dDim upper_bound) { + return tile.z + inc_value.z >= upper_bound.z && + tile.y + inc_value.y >= upper_bound.y && + tile.x + inc_value.x >= upper_bound.x; +} + +} // namespace fna +} // namespace cuda +} // namespace natten diff --git a/csrc/include/natten/cuda/fna/transform/tile_smem_loader.h b/csrc/include/natten/cuda/fna/transform/tile_smem_loader.h new file mode 100644 index 0000000..c4c5c40 --- /dev/null +++ b/csrc/include/natten/cuda/fna/transform/tile_smem_loader.h @@ -0,0 +1,95 @@ +/* + * Copied from xFormers (https://github.com/facebookresearch/xformers/) + * + * Copyright (c) Meta Platforms, Inc. and affiliates. + * All rights reserved. + * + * BSD 3-Clause License + * + * Redistribution and use in source and binary forms, with or without + * modification, are permitted provided that the following conditions are met: + * + * 1. Redistributions of source code must retain the above copyright + * notice, this list of conditions and the following disclaimer. + * + * 2. Redistributions in binary form must reproduce the above copyright + * notice, this list of conditions and the following disclaimer in the + * documentation and/or other materials provided with the distribution. + * + * 3. Neither the names of Facebook, Deepmind Technologies, NYU, NEC + * Laboratories America and IDIAP Research Institute nor the names of its + * contributors may be used to endorse or promote products derived from this + * software without specific prior written permission. + * + * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" + * AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE + * IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE + * ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE + * LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR + * CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF + * SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS + * INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN + * CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) + * ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE + * POSSIBILITY OF SUCH DAMAGE. + */ + +#pragma once + +#include +#include +#include +#include +#include +#include +#include +#include +#include + +template < + typename scalar_t, // scalar type + typename ThreadblockTileShape, // size of tile to load + int Threads, // number of participating threads + int ElementsPerAccess> // thread access width in elements +class TileSmemLoader { + public: + using SmemTile = + cutlass::AlignedBuffer; + + using ThreadMap = cutlass::transform::PitchLinearStripminedThreadMap< + cutlass::layout::PitchLinearShape< + ThreadblockTileShape::kColumn, // contiguous + ThreadblockTileShape::kRow>, // strided + Threads, // Threads + ElementsPerAccess>; // ElementsPerAccess + + using GmemTileIterator = + cutlass::transform::threadblock::PredicatedTileIterator< + ThreadblockTileShape, // Shape + scalar_t, // Element + cutlass::layout::RowMajor, // Layout + 0, // AdvanceRank + ThreadMap>; // ThreadMap + + using SmemTileIterator = cutlass::transform::threadblock::RegularTileIterator< + ThreadblockTileShape, // Shape + scalar_t, // Element + cutlass::layout::RowMajor, // Layout + 0, // AdvanceRank + ThreadMap>; // ThreadMap + + using Fragment = typename GmemTileIterator::Fragment; + + /// load a tile from global memory into shared memory + CUTLASS_DEVICE + static void load( + GmemTileIterator tile_load_iter, + SmemTileIterator tile_store_iter) { + Fragment tb_frag; + tb_frag.clear(); + tile_load_iter.load(tb_frag); + tile_store_iter.store(tb_frag); + + __syncthreads(); + } +}; diff --git a/csrc/include/natten/cuda/gemm/kernel/default_na.cuh b/csrc/include/natten/cuda/gemm/kernel/default_na.cuh index 13d91e1..785bedb 100644 --- a/csrc/include/natten/cuda/gemm/kernel/default_na.cuh +++ b/csrc/include/natten/cuda/gemm/kernel/default_na.cuh @@ -50,7 +50,26 @@ namespace natten { namespace cuda { namespace gemm { -namespace kernel {} // namespace kernel +namespace kernel { + +namespace { + +template +struct GetFragmentsPerIter { + static_assert( + ArchTag::kMinComputeCapability == 80 || + ArchTag::kMinComputeCapability == 75); + static constexpr int value = EpilogueBase::kFragmentsPerIteration; +}; + +template +struct GetFragmentsPerIter { + static constexpr int value = 1; +}; + +} // namespace + +} // namespace kernel } // namespace gemm } // namespace cuda } // namespace natten diff --git a/csrc/include/natten/cuda/gemm/kernel/default_na1d_in.cuh b/csrc/include/natten/cuda/gemm/kernel/default_na1d_in.cuh index f08cd31..a780f21 100644 --- a/csrc/include/natten/cuda/gemm/kernel/default_na1d_in.cuh +++ b/csrc/include/natten/cuda/gemm/kernel/default_na1d_in.cuh @@ -42,13 +42,11 @@ #pragma once #include -#include -#include +#include +#include #include "natten/cuda/gemm/kernel/default_na.cuh" -#include "natten/cuda/gemm/threadblock/default_epilogue_simt.cuh" -#include "natten/cuda/gemm/threadblock/default_epilogue_tensor_op.cuh" #include "natten/cuda/gemm/threadblock/na1d_in_input_tile_iterator.cuh" #include "natten/cuda/gemm/threadblock/na1d_in_output_tile_iterator.cuh" #include "natten/cuda/gemm/threadblock/na1d_in_value_tile_iterator.cuh" @@ -127,6 +125,9 @@ struct DefaultNA1dIN< AlignmentA, AlignmentB> { static_assert(AlignmentA == 1); // NA requirement for IN + static_assert( + ArchTag::kMinComputeCapability >= 80, + "This specialization is intended for SM80 and above."); // Define the core components from GEMM using MmaCore = typename cutlass::gemm::threadblock::DefaultMmaCore< @@ -190,36 +191,31 @@ struct DefaultNA1dIN< static const int kPartitionsK = ThreadblockShape::kK / WarpShape::kK; - using EpilogueElementOutput = typename EpilogueOutputOp::ElementOutput; - - // Threadmap and output iterator were moved out of the default epilogue, - // which may seem counter-intuitive, but it was all done pretty lazily to - // allow different kernels to have their correct output iterator instantiated - // at the kernel level. - // May move this back and customize the epilogue in the future. - using OutputTileThreadMap = - typename cutlass::epilogue::threadblock::DefaultThreadMapTensorOp< - ThreadblockShape, - typename WarpMmaTensorOp::Shape, - kPartitionsK, - EpilogueElementOutput, - EpilogueOutputOp::kCount>::Type; - - static bool const UseCUDAStore = - cutlass::platform::is_same::value; - - using OutputTileIterator = natten::cuda::gemm::threadblock:: - NA1dINOutputTileIterator; - // Define the epilogue - using Epilogue = - typename natten::cuda::gemm::threadblock::DefaultEpilogueTensorOp< + using EpilogueBase = + typename cutlass::epilogue::threadblock::DefaultEpilogueTensorOp< ThreadblockShape, WarpMmaTensorOp, kPartitionsK, EpilogueOutputOp, - EpilogueOutputOp::kCount, - OutputTileIterator>::Epilogue; + EpilogueOutputOp::kCount>; + + using OutputTileIterator = + natten::cuda::gemm::threadblock::NA1dINOutputTileIterator< + typename EpilogueBase::OutputTileThreadMap, + typename EpilogueOutputOp::ElementOutput>; + + using Epilogue = cutlass::epilogue::threadblock::Epilogue< + typename EpilogueBase::Shape, + typename EpilogueBase::WarpMmaTensorOp, + EpilogueBase::kPartitionsK, + OutputTileIterator, + typename EpilogueBase::AccumulatorFragmentIterator, + typename EpilogueBase::WarpTileIterator, + typename EpilogueBase::SharedLoadIterator, + typename EpilogueBase::OutputOp, + typename EpilogueBase::Padding, + GetFragmentsPerIter::value>; // Define the kernel using Kernel = @@ -267,6 +263,10 @@ struct DefaultNA1dIN< AlignmentA, AlignmentB> { static_assert(AlignmentA == 1); // NA requirement for IN + static_assert( + ArchTag::kMinComputeCapability == 70 || + ArchTag::kMinComputeCapability == 75, + "This specialization is intended for SM70 and SM75."); // Define the core components from GEMM using MmaCore = typename cutlass::gemm::threadblock::DefaultMmaCore< @@ -328,307 +328,38 @@ struct DefaultNA1dIN< static const int kPartitionsK = ThreadblockShape::kK / WarpShape::kK; - using EpilogueElementOutput = typename EpilogueOutputOp::ElementOutput; - - // Threadmap and output iterator were moved out of the default epilogue, - // which may seem counter-intuitive, but it was all done pretty lazily to - // allow different kernels to have their correct output iterator instantiated - // at the kernel level. - // May move this back and customize the epilogue in the future. - using OutputTileThreadMap = - typename cutlass::epilogue::threadblock::DefaultThreadMapTensorOp< - ThreadblockShape, - typename WarpMmaTensorOp::Shape, - kPartitionsK, - EpilogueElementOutput, - EpilogueOutputOp::kCount>::Type; - - static bool const UseCUDAStore = - cutlass::platform::is_same::value; - - using OutputTileIterator = natten::cuda::gemm::threadblock:: - NA1dINOutputTileIterator; - // Define the epilogue - using Epilogue = - typename natten::cuda::gemm::threadblock::DefaultEpilogueTensorOp< + using EpilogueBase = typename std::conditional< + ArchTag::kMinComputeCapability == 75, + typename cutlass::epilogue::threadblock::DefaultEpilogueTensorOp< ThreadblockShape, WarpMmaTensorOp, kPartitionsK, EpilogueOutputOp, - EpilogueOutputOp::kCount, - OutputTileIterator>::Epilogue; - - // Define the kernel - using Kernel = - ImplicitGemmNA1d; -}; - -///////////////////////////////////////////////////////////////////////////////////////////////// -// SIMT implicit gemm -///////////////////////////////////////////////////////////////////////////////////////////////// - -template < - typename ElementA, - typename LayoutA, - typename ElementB, - typename LayoutB, - typename ElementC, - typename LayoutC, - typename ElementAccumulator, - typename ArchTag, - typename ThreadblockShape, - typename WarpShape, - typename InstructionShape, - typename EpilogueOutputOp, - typename ThreadblockSwizzle, - int Stages, - typename MathOperatorTag, - int AlignmentA, - int AlignmentB> -struct DefaultNA1dIN< - ElementA, - LayoutA, - ElementB, - LayoutB, - ElementC, - LayoutC, - ElementAccumulator, - cutlass::arch::OpClassSimt, - ArchTag, - ThreadblockShape, - WarpShape, - InstructionShape, - EpilogueOutputOp, - ThreadblockSwizzle, - Stages, - MathOperatorTag, - AlignmentA, - AlignmentB> { - static_assert(AlignmentA == 1 && AlignmentB == 1); - - // Define the core components from GEMM - using MmaCore = typename cutlass::gemm::threadblock::DefaultMmaCore< - ThreadblockShape, - WarpShape, - InstructionShape, - ElementA, - cutlass::layout::ColumnMajor, - ElementB, - cutlass::layout::RowMajor, - ElementAccumulator, - cutlass::layout::RowMajor, - cutlass::arch::OpClassSimt, - Stages, - MathOperatorTag>; - - // Define iterators over tiles from the A operand - using ThreadMapA = typename MmaCore::IteratorThreadMapA; - using IteratorA = natten::cuda::gemm::threadblock::NA1dINInputTileIterator< - cutlass::MatrixShape, - ElementA, - LayoutA, - ThreadMapA>; - - using SmemIteratorA = typename MmaCore::SmemIteratorA; - - // Define iterators over tiles from the B operand - using ThreadMapB = typename MmaCore::IteratorThreadMapB; - using IteratorB = natten::cuda::gemm::threadblock::NA1dINValueTileIterator< - cutlass::MatrixShape, - ElementB, - LayoutB, - ThreadMapB>; - - using SmemIteratorB = typename MmaCore::SmemIteratorB; - - // Warp-level GEMM components - using WarpMmaSimt = typename MmaCore::MmaWarpSimt; - using MmaPolicy = typename MmaCore::MmaPolicy; - - static cutlass::arch::CacheOperation::Kind const CacheOpB = - ((cutlass::sizeof_bits::value * AlignmentB) == 128) - ? cutlass::arch::CacheOperation::Global - : cutlass::arch::CacheOperation::Always; - - // Define the Mma - using Mma = natten::cuda::gemm::threadblock::ImplicitGemmMultistage< - ThreadblockShape, - IteratorA, - SmemIteratorA, - cutlass::arch::CacheOperation::Always, - IteratorB, - SmemIteratorB, - CacheOpB, - MmaPolicy, - Stages>; - - static const int kPartitionsK = ThreadblockShape::kK / WarpShape::kK; - - using EpilogueElementOutput = typename EpilogueOutputOp::ElementOutput; - - // Threadmap and output iterator were moved out of the default epilogue, - // which may seem counter-intuitive, but it was all done pretty lazily to - // allow different kernels to have their correct output iterator instantiated - // at the kernel level. - // May move this back and customize the epilogue in the future. - using OutputTileThreadMap = - typename cutlass::epilogue::threadblock::DefaultThreadMapSimt< + EpilogueOutputOp::kCount>, + typename cutlass::epilogue::threadblock::DefaultEpilogueVoltaTensorOp< ThreadblockShape, - typename WarpMmaSimt::Shape, - typename WarpMmaSimt::Policy, - kPartitionsK, - EpilogueElementOutput, - EpilogueOutputOp::kCount>::Type; - - static bool const UseCUDAStore = - cutlass::platform::is_same::value; - - using OutputTileIterator = natten::cuda::gemm::threadblock:: - NA1dINOutputTileIterator; - - // Define the epilogue - using Epilogue = - typename natten::cuda::gemm::threadblock::DefaultEpilogueSimt< - ThreadblockShape, - WarpMmaSimt, - EpilogueOutputOp, - EpilogueOutputOp::kCount, - OutputTileIterator>::Epilogue; - - // Define the kernel - using Kernel = - ImplicitGemmNA1d; -}; - -///////////////////////////////////////////////////////////////////////////////////////////////// -// Pipelined SIMT implicit gemm -///////////////////////////////////////////////////////////////////////////////////////////////// - -template < - typename ElementA, - typename LayoutA, - typename ElementB, - typename LayoutB, - typename ElementC, - typename LayoutC, - typename ElementAccumulator, - typename ArchTag, - typename ThreadblockShape, - typename WarpShape, - typename InstructionShape, - typename EpilogueOutputOp, - typename ThreadblockSwizzle, - typename MathOperatorTag, - int AlignmentA, - int AlignmentB> -struct DefaultNA1dIN< - ElementA, - LayoutA, - ElementB, - LayoutB, - ElementC, - LayoutC, - ElementAccumulator, - cutlass::arch::OpClassSimt, - ArchTag, - ThreadblockShape, - WarpShape, - InstructionShape, - EpilogueOutputOp, - ThreadblockSwizzle, - 2, - MathOperatorTag, - AlignmentA, - AlignmentB> { - static_assert(AlignmentA == 1 && AlignmentB == 1); - - // Define the core components from GEMM - using MmaCore = typename cutlass::gemm::threadblock::DefaultMmaCore< - ThreadblockShape, - WarpShape, - InstructionShape, - ElementA, - cutlass::layout::ColumnMajor, - ElementB, - cutlass::layout::RowMajor, - ElementAccumulator, - cutlass::layout::RowMajor, - cutlass::arch::OpClassSimt, - 2, - MathOperatorTag>; - - // Define iterators over tiles from the A operand - using ThreadMapA = typename MmaCore::IteratorThreadMapA; - using IteratorA = natten::cuda::gemm::threadblock::TileIterator< - natten::cuda::gemm::threadblock::NA1dINInputTileIterator< - cutlass::MatrixShape, - ElementA, - LayoutA, - ThreadMapA>, - 1>; - - using SmemIteratorA = typename MmaCore::SmemIteratorA; - - // Define iterators over tiles from the B operand - using ThreadMapB = typename MmaCore::IteratorThreadMapB; - using IteratorB = natten::cuda::gemm::threadblock::TileIterator< - natten::cuda::gemm::threadblock::NA1dINValueTileIterator< - cutlass::MatrixShape, - ElementB, - LayoutB, - ThreadMapB>, - 1>; - - using SmemIteratorB = typename MmaCore::SmemIteratorB; - - // Warp-level GEMM components - using WarpMmaSimt = typename MmaCore::MmaWarpSimt; - using MmaPolicy = typename MmaCore::MmaPolicy; - - // Define the Mma - using Mma = cutlass::conv::threadblock::ImplicitGemmPipelined< - ThreadblockShape, - IteratorA, - SmemIteratorA, - IteratorB, - SmemIteratorB, - ElementC, - LayoutC, - MmaPolicy>; - - static const int kPartitionsK = ThreadblockShape::kK / WarpShape::kK; - - using EpilogueElementOutput = typename EpilogueOutputOp::ElementOutput; - - // Threadmap and output iterator were moved out of the default epilogue, - // which may seem counter-intuitive, but it was all done pretty lazily to - // allow different kernels to have their correct output iterator instantiated - // at the kernel level. - // May move this back and customize the epilogue in the future. - using OutputTileThreadMap = - typename cutlass::epilogue::threadblock::DefaultThreadMapSimt< - ThreadblockShape, - typename WarpMmaSimt::Shape, - typename WarpMmaSimt::Policy, + WarpMmaTensorOp, kPartitionsK, - EpilogueElementOutput, - EpilogueOutputOp::kCount>::Type; - - static bool const UseCUDAStore = - cutlass::platform::is_same::value; - - using OutputTileIterator = natten::cuda::gemm::threadblock:: - NA1dINOutputTileIterator; - - // Define the epilogue - using Epilogue = - typename natten::cuda::gemm::threadblock::DefaultEpilogueSimt< - ThreadblockShape, - WarpMmaSimt, EpilogueOutputOp, - EpilogueOutputOp::kCount, - OutputTileIterator>::Epilogue; + EpilogueOutputOp::kCount>>::type; + + using OutputTileIterator = + natten::cuda::gemm::threadblock::NA1dINOutputTileIterator< + typename EpilogueBase::OutputTileThreadMap, + typename EpilogueOutputOp::ElementOutput>; + + using Epilogue = cutlass::epilogue::threadblock::Epilogue< + typename EpilogueBase::Shape, + typename EpilogueBase::WarpMmaTensorOp, + EpilogueBase::kPartitionsK, + OutputTileIterator, + typename EpilogueBase::AccumulatorFragmentIterator, + typename EpilogueBase::WarpTileIterator, + typename EpilogueBase::SharedLoadIterator, + typename EpilogueBase::OutputOp, + typename EpilogueBase::Padding, + GetFragmentsPerIter::value>; // Define the kernel using Kernel = diff --git a/csrc/include/natten/cuda/gemm/kernel/default_na1d_nn.cuh b/csrc/include/natten/cuda/gemm/kernel/default_na1d_nn.cuh index 6c145b8..b5f8e33 100644 --- a/csrc/include/natten/cuda/gemm/kernel/default_na1d_nn.cuh +++ b/csrc/include/natten/cuda/gemm/kernel/default_na1d_nn.cuh @@ -42,13 +42,11 @@ #pragma once #include -#include -#include +#include +#include #include "natten/cuda/gemm/kernel/default_na.cuh" -#include "natten/cuda/gemm/threadblock/default_epilogue_simt.cuh" -#include "natten/cuda/gemm/threadblock/default_epilogue_tensor_op.cuh" #include "natten/cuda/gemm/threadblock/na1d_nn_input_tile_iterator.cuh" #include "natten/cuda/gemm/threadblock/na1d_nn_output_tile_iterator.cuh" #include "natten/cuda/gemm/threadblock/na1d_nn_value_tile_iterator.cuh" @@ -127,6 +125,9 @@ struct DefaultNA1dNN< AlignmentA, AlignmentB> { static_assert(AlignmentA == 1); // NA requirement for NN + static_assert( + ArchTag::kMinComputeCapability >= 80, + "This specialization is intended for SM80 and above."); // Define the core components from GEMM using MmaCore = typename cutlass::gemm::threadblock::DefaultMmaCore< @@ -190,36 +191,31 @@ struct DefaultNA1dNN< static const int kPartitionsK = ThreadblockShape::kK / WarpShape::kK; - using EpilogueElementOutput = typename EpilogueOutputOp::ElementOutput; - - // Threadmap and output iterator were moved out of the default epilogue, - // which may seem counter-intuitive, but it was all done pretty lazily to - // allow different kernels to have their correct output iterator instantiated - // at the kernel level. - // May move this back and customize the epilogue in the future. - using OutputTileThreadMap = - typename cutlass::epilogue::threadblock::DefaultThreadMapTensorOp< - ThreadblockShape, - typename WarpMmaTensorOp::Shape, - kPartitionsK, - EpilogueElementOutput, - EpilogueOutputOp::kCount>::Type; - - static bool const UseCUDAStore = - cutlass::platform::is_same::value; - - using OutputTileIterator = natten::cuda::gemm::threadblock:: - NA1dNNOutputTileIterator; - // Define the epilogue - using Epilogue = - typename natten::cuda::gemm::threadblock::DefaultEpilogueTensorOp< + using EpilogueBase = + typename cutlass::epilogue::threadblock::DefaultEpilogueTensorOp< ThreadblockShape, WarpMmaTensorOp, kPartitionsK, EpilogueOutputOp, - EpilogueOutputOp::kCount, - OutputTileIterator>::Epilogue; + EpilogueOutputOp::kCount>; + + using OutputTileIterator = + natten::cuda::gemm::threadblock::NA1dNNOutputTileIterator< + typename EpilogueBase::OutputTileThreadMap, + typename EpilogueOutputOp::ElementOutput>; + + using Epilogue = cutlass::epilogue::threadblock::Epilogue< + typename EpilogueBase::Shape, + typename EpilogueBase::WarpMmaTensorOp, + EpilogueBase::kPartitionsK, + OutputTileIterator, + typename EpilogueBase::AccumulatorFragmentIterator, + typename EpilogueBase::WarpTileIterator, + typename EpilogueBase::SharedLoadIterator, + typename EpilogueBase::OutputOp, + typename EpilogueBase::Padding, + GetFragmentsPerIter::value>; // Define the kernel using Kernel = @@ -267,6 +263,10 @@ struct DefaultNA1dNN< AlignmentA, AlignmentB> { static_assert(AlignmentA == 1); // NA requirement for NN + static_assert( + ArchTag::kMinComputeCapability == 70 || + ArchTag::kMinComputeCapability == 75, + "This specialization is intended for SM70 and SM75."); // Define the core components from GEMM using MmaCore = typename cutlass::gemm::threadblock::DefaultMmaCore< @@ -328,307 +328,38 @@ struct DefaultNA1dNN< static const int kPartitionsK = ThreadblockShape::kK / WarpShape::kK; - using EpilogueElementOutput = typename EpilogueOutputOp::ElementOutput; - - // Threadmap and output iterator were moved out of the default epilogue, - // which may seem counter-intuitive, but it was all done pretty lazily to - // allow different kernels to have their correct output iterator instantiated - // at the kernel level. - // May move this back and customize the epilogue in the future. - using OutputTileThreadMap = - typename cutlass::epilogue::threadblock::DefaultThreadMapTensorOp< - ThreadblockShape, - typename WarpMmaTensorOp::Shape, - kPartitionsK, - EpilogueElementOutput, - EpilogueOutputOp::kCount>::Type; - - static bool const UseCUDAStore = - cutlass::platform::is_same::value; - - using OutputTileIterator = natten::cuda::gemm::threadblock:: - NA1dNNOutputTileIterator; - // Define the epilogue - using Epilogue = - typename natten::cuda::gemm::threadblock::DefaultEpilogueTensorOp< + using EpilogueBase = typename std::conditional< + ArchTag::kMinComputeCapability == 75, + typename cutlass::epilogue::threadblock::DefaultEpilogueTensorOp< ThreadblockShape, WarpMmaTensorOp, kPartitionsK, EpilogueOutputOp, - EpilogueOutputOp::kCount, - OutputTileIterator>::Epilogue; - - // Define the kernel - using Kernel = - ImplicitGemmNA1d; -}; - -///////////////////////////////////////////////////////////////////////////////////////////////// -// SIMT implicit gemm -///////////////////////////////////////////////////////////////////////////////////////////////// - -template < - typename ElementA, - typename LayoutA, - typename ElementB, - typename LayoutB, - typename ElementC, - typename LayoutC, - typename ElementAccumulator, - typename ArchTag, - typename ThreadblockShape, - typename WarpShape, - typename InstructionShape, - typename EpilogueOutputOp, - typename ThreadblockSwizzle, - int Stages, - typename MathOperatorTag, - int AlignmentA, - int AlignmentB> -struct DefaultNA1dNN< - ElementA, - LayoutA, - ElementB, - LayoutB, - ElementC, - LayoutC, - ElementAccumulator, - cutlass::arch::OpClassSimt, - ArchTag, - ThreadblockShape, - WarpShape, - InstructionShape, - EpilogueOutputOp, - ThreadblockSwizzle, - Stages, - MathOperatorTag, - AlignmentA, - AlignmentB> { - static_assert(AlignmentA == 1 && AlignmentB == 1); - - // Define the core components from GEMM - using MmaCore = typename cutlass::gemm::threadblock::DefaultMmaCore< - ThreadblockShape, - WarpShape, - InstructionShape, - ElementA, - cutlass::layout::RowMajor, - ElementB, - cutlass::layout::RowMajor, - ElementAccumulator, - cutlass::layout::RowMajor, - cutlass::arch::OpClassSimt, - Stages, - MathOperatorTag>; - - // Define iterators over tiles from the A operand - using ThreadMapA = typename MmaCore::IteratorThreadMapA; - using IteratorA = natten::cuda::gemm::threadblock::NA1dNNInputTileIterator< - cutlass::MatrixShape, - ElementA, - LayoutA, - ThreadMapA>; - - using SmemIteratorA = typename MmaCore::SmemIteratorA; - - // Define iterators over tiles from the B operand - using ThreadMapB = typename MmaCore::IteratorThreadMapB; - using IteratorB = natten::cuda::gemm::threadblock::NA1dNNValueTileIterator< - cutlass::MatrixShape, - ElementB, - LayoutB, - ThreadMapB>; - - using SmemIteratorB = typename MmaCore::SmemIteratorB; - - // Warp-level GEMM components - using WarpMmaSimt = typename MmaCore::MmaWarpSimt; - using MmaPolicy = typename MmaCore::MmaPolicy; - - static cutlass::arch::CacheOperation::Kind const CacheOpB = - ((cutlass::sizeof_bits::value * AlignmentB) == 128) - ? cutlass::arch::CacheOperation::Global - : cutlass::arch::CacheOperation::Always; - - // Define the Mma - using Mma = natten::cuda::gemm::threadblock::ImplicitGemmMultistage< - ThreadblockShape, - IteratorA, - SmemIteratorA, - cutlass::arch::CacheOperation::Always, - IteratorB, - SmemIteratorB, - CacheOpB, - MmaPolicy, - Stages>; - - static const int kPartitionsK = ThreadblockShape::kK / WarpShape::kK; - - using EpilogueElementOutput = typename EpilogueOutputOp::ElementOutput; - - // Threadmap and output iterator were moved out of the default epilogue, - // which may seem counter-intuitive, but it was all done pretty lazily to - // allow different kernels to have their correct output iterator instantiated - // at the kernel level. - // May move this back and customize the epilogue in the future. - using OutputTileThreadMap = - typename cutlass::epilogue::threadblock::DefaultThreadMapSimt< + EpilogueOutputOp::kCount>, + typename cutlass::epilogue::threadblock::DefaultEpilogueVoltaTensorOp< ThreadblockShape, - typename WarpMmaSimt::Shape, - typename WarpMmaSimt::Policy, - kPartitionsK, - EpilogueElementOutput, - EpilogueOutputOp::kCount>::Type; - - static bool const UseCUDAStore = - cutlass::platform::is_same::value; - - using OutputTileIterator = natten::cuda::gemm::threadblock:: - NA1dNNOutputTileIterator; - - // Define the epilogue - using Epilogue = - typename natten::cuda::gemm::threadblock::DefaultEpilogueSimt< - ThreadblockShape, - WarpMmaSimt, - EpilogueOutputOp, - EpilogueOutputOp::kCount, - OutputTileIterator>::Epilogue; - - // Define the kernel - using Kernel = - ImplicitGemmNA1d; -}; - -///////////////////////////////////////////////////////////////////////////////////////////////// -// Pipelined SIMT implicit gemm -///////////////////////////////////////////////////////////////////////////////////////////////// - -template < - typename ElementA, - typename LayoutA, - typename ElementB, - typename LayoutB, - typename ElementC, - typename LayoutC, - typename ElementAccumulator, - typename ArchTag, - typename ThreadblockShape, - typename WarpShape, - typename InstructionShape, - typename EpilogueOutputOp, - typename ThreadblockSwizzle, - typename MathOperatorTag, - int AlignmentA, - int AlignmentB> -struct DefaultNA1dNN< - ElementA, - LayoutA, - ElementB, - LayoutB, - ElementC, - LayoutC, - ElementAccumulator, - cutlass::arch::OpClassSimt, - ArchTag, - ThreadblockShape, - WarpShape, - InstructionShape, - EpilogueOutputOp, - ThreadblockSwizzle, - 2, - MathOperatorTag, - AlignmentA, - AlignmentB> { - static_assert(AlignmentA == 1 && AlignmentB == 1); - - // Define the core components from GEMM - using MmaCore = typename cutlass::gemm::threadblock::DefaultMmaCore< - ThreadblockShape, - WarpShape, - InstructionShape, - ElementA, - cutlass::layout::RowMajor, - ElementB, - cutlass::layout::RowMajor, - ElementAccumulator, - cutlass::layout::RowMajor, - cutlass::arch::OpClassSimt, - 2, - MathOperatorTag>; - - // Define iterators over tiles from the A operand - using ThreadMapA = typename MmaCore::IteratorThreadMapA; - using IteratorA = natten::cuda::gemm::threadblock::TileIterator< - natten::cuda::gemm::threadblock::NA1dNNInputTileIterator< - cutlass::MatrixShape, - ElementA, - LayoutA, - ThreadMapA>, - 1>; - - using SmemIteratorA = typename MmaCore::SmemIteratorA; - - // Define iterators over tiles from the B operand - using ThreadMapB = typename MmaCore::IteratorThreadMapB; - using IteratorB = natten::cuda::gemm::threadblock::TileIterator< - natten::cuda::gemm::threadblock::NA1dNNValueTileIterator< - cutlass::MatrixShape, - ElementB, - LayoutB, - ThreadMapB>, - 1>; - - using SmemIteratorB = typename MmaCore::SmemIteratorB; - - // Warp-level GEMM components - using WarpMmaSimt = typename MmaCore::MmaWarpSimt; - using MmaPolicy = typename MmaCore::MmaPolicy; - - // Define the Mma - using Mma = cutlass::conv::threadblock::ImplicitGemmPipelined< - ThreadblockShape, - IteratorA, - SmemIteratorA, - IteratorB, - SmemIteratorB, - ElementC, - LayoutC, - MmaPolicy>; - - static const int kPartitionsK = ThreadblockShape::kK / WarpShape::kK; - - using EpilogueElementOutput = typename EpilogueOutputOp::ElementOutput; - - // Threadmap and output iterator were moved out of the default epilogue, - // which may seem counter-intuitive, but it was all done pretty lazily to - // allow different kernels to have their correct output iterator instantiated - // at the kernel level. - // May move this back and customize the epilogue in the future. - using OutputTileThreadMap = - typename cutlass::epilogue::threadblock::DefaultThreadMapSimt< - ThreadblockShape, - typename WarpMmaSimt::Shape, - typename WarpMmaSimt::Policy, + WarpMmaTensorOp, kPartitionsK, - EpilogueElementOutput, - EpilogueOutputOp::kCount>::Type; - - static bool const UseCUDAStore = - cutlass::platform::is_same::value; - - using OutputTileIterator = natten::cuda::gemm::threadblock:: - NA1dNNOutputTileIterator; - - // Define the epilogue - using Epilogue = - typename natten::cuda::gemm::threadblock::DefaultEpilogueSimt< - ThreadblockShape, - WarpMmaSimt, EpilogueOutputOp, - EpilogueOutputOp::kCount, - OutputTileIterator>::Epilogue; + EpilogueOutputOp::kCount>>::type; + + using OutputTileIterator = + natten::cuda::gemm::threadblock::NA1dNNOutputTileIterator< + typename EpilogueBase::OutputTileThreadMap, + typename EpilogueOutputOp::ElementOutput>; + + using Epilogue = cutlass::epilogue::threadblock::Epilogue< + typename EpilogueBase::Shape, + typename EpilogueBase::WarpMmaTensorOp, + EpilogueBase::kPartitionsK, + OutputTileIterator, + typename EpilogueBase::AccumulatorFragmentIterator, + typename EpilogueBase::WarpTileIterator, + typename EpilogueBase::SharedLoadIterator, + typename EpilogueBase::OutputOp, + typename EpilogueBase::Padding, + GetFragmentsPerIter::value>; // Define the kernel using Kernel = diff --git a/csrc/include/natten/cuda/gemm/kernel/default_na1d_pn.cuh b/csrc/include/natten/cuda/gemm/kernel/default_na1d_pn.cuh index da9416e..9f9a168 100644 --- a/csrc/include/natten/cuda/gemm/kernel/default_na1d_pn.cuh +++ b/csrc/include/natten/cuda/gemm/kernel/default_na1d_pn.cuh @@ -42,13 +42,11 @@ #pragma once #include -#include -#include +#include +#include #include "natten/cuda/gemm/kernel/default_na.cuh" -#include "natten/cuda/gemm/threadblock/default_epilogue_simt.cuh" -#include "natten/cuda/gemm/threadblock/default_epilogue_tensor_op.cuh" #include "natten/cuda/gemm/threadblock/na1d_pn_input_tile_iterator.cuh" #include "natten/cuda/gemm/threadblock/na1d_pn_output_tile_iterator.cuh" @@ -125,6 +123,10 @@ struct DefaultNA1dPN< MathOperatorTag, AlignmentA, AlignmentB> { + static_assert( + ArchTag::kMinComputeCapability >= 80, + "This specialization is intended for SM80 and above."); + // Define the core components from GEMM using MmaCore = typename cutlass::gemm::threadblock::DefaultMmaCore< ThreadblockShape, @@ -192,36 +194,31 @@ struct DefaultNA1dPN< static const int kPartitionsK = ThreadblockShape::kK / WarpShape::kK; - using EpilogueElementOutput = typename EpilogueOutputOp::ElementOutput; - - // Threadmap and output iterator were moved out of the default epilogue, - // which may seem counter-intuitive, but it was all done pretty lazily to - // allow different kernels to have their correct output iterator instantiated - // at the kernel level. - // May move this back and customize the epilogue in the future. - using OutputTileThreadMap = - typename cutlass::epilogue::threadblock::DefaultThreadMapTensorOp< - ThreadblockShape, - typename WarpMmaTensorOp::Shape, - kPartitionsK, - EpilogueElementOutput, - EpilogueOutputOp::kCount>::Type; - - static bool const UseCUDAStore = - cutlass::platform::is_same::value; - - using OutputTileIterator = natten::cuda::gemm::threadblock:: - NA1dPNOutputTileIterator; - // Define the epilogue - using Epilogue = - typename natten::cuda::gemm::threadblock::DefaultEpilogueTensorOp< + using EpilogueBase = + typename cutlass::epilogue::threadblock::DefaultEpilogueTensorOp< ThreadblockShape, WarpMmaTensorOp, kPartitionsK, EpilogueOutputOp, - EpilogueOutputOp::kCount, - OutputTileIterator>::Epilogue; + EpilogueOutputOp::kCount>; + + using OutputTileIterator = + natten::cuda::gemm::threadblock::NA1dPNOutputTileIterator< + typename EpilogueBase::OutputTileThreadMap, + typename EpilogueOutputOp::ElementOutput>; + + using Epilogue = cutlass::epilogue::threadblock::Epilogue< + typename EpilogueBase::Shape, + typename EpilogueBase::WarpMmaTensorOp, + EpilogueBase::kPartitionsK, + OutputTileIterator, + typename EpilogueBase::AccumulatorFragmentIterator, + typename EpilogueBase::WarpTileIterator, + typename EpilogueBase::SharedLoadIterator, + typename EpilogueBase::OutputOp, + typename EpilogueBase::Padding, + GetFragmentsPerIter::value>; // Define the kernel using Kernel = @@ -268,6 +265,11 @@ struct DefaultNA1dPN< MathOperatorTag, AlignmentA, AlignmentB> { + static_assert( + ArchTag::kMinComputeCapability == 70 || + ArchTag::kMinComputeCapability == 75, + "This specialization is intended for SM70 and SM75."); + // Define the core components from GEMM using MmaCore = typename cutlass::gemm::threadblock::DefaultMmaCore< ThreadblockShape, @@ -328,312 +330,38 @@ struct DefaultNA1dPN< static const int kPartitionsK = ThreadblockShape::kK / WarpShape::kK; - using EpilogueElementOutput = typename EpilogueOutputOp::ElementOutput; - - // Threadmap and output iterator were moved out of the default epilogue, - // which may seem counter-intuitive, but it was all done pretty lazily to - // allow different kernels to have their correct output iterator instantiated - // at the kernel level. - // May move this back and customize the epilogue in the future. - using OutputTileThreadMap = - typename cutlass::epilogue::threadblock::DefaultThreadMapTensorOp< - ThreadblockShape, - typename WarpMmaTensorOp::Shape, - kPartitionsK, - EpilogueElementOutput, - EpilogueOutputOp::kCount>::Type; - - static bool const UseCUDAStore = - cutlass::platform::is_same::value; - - using OutputTileIterator = natten::cuda::gemm::threadblock:: - NA1dPNOutputTileIterator; - // Define the epilogue - using Epilogue = - typename natten::cuda::gemm::threadblock::DefaultEpilogueTensorOp< + using EpilogueBase = typename std::conditional< + ArchTag::kMinComputeCapability == 75, + typename cutlass::epilogue::threadblock::DefaultEpilogueTensorOp< ThreadblockShape, WarpMmaTensorOp, kPartitionsK, EpilogueOutputOp, - EpilogueOutputOp::kCount, - OutputTileIterator>::Epilogue; - - // Define the kernel - using Kernel = - ImplicitGemmNA1d; -}; - -///////////////////////////////////////////////////////////////////////////////////////////////// -// SIMT implicit gemm -///////////////////////////////////////////////////////////////////////////////////////////////// - -template < - typename ElementA, - typename LayoutA, - typename ElementB, - typename LayoutB, - typename ElementC, - typename LayoutC, - typename ElementAccumulator, - typename ArchTag, - typename ThreadblockShape, - typename WarpShape, - typename InstructionShape, - typename EpilogueOutputOp, - typename ThreadblockSwizzle, - int Stages, - typename MathOperatorTag, - int AlignmentA, - int AlignmentB> -struct DefaultNA1dPN< - ElementA, - LayoutA, - ElementB, - LayoutB, - ElementC, - LayoutC, - ElementAccumulator, - cutlass::arch::OpClassSimt, - ArchTag, - ThreadblockShape, - WarpShape, - InstructionShape, - EpilogueOutputOp, - ThreadblockSwizzle, - Stages, - MathOperatorTag, - AlignmentA, - AlignmentB> { - static_assert(AlignmentA == 1 && AlignmentB == 1); - - // Define the core components from GEMM - using MmaCore = typename cutlass::gemm::threadblock::DefaultMmaCore< - ThreadblockShape, - WarpShape, - InstructionShape, - ElementA, - cutlass::layout::RowMajor, - ElementB, - cutlass::layout::ColumnMajor, - ElementAccumulator, - cutlass::layout::RowMajor, - cutlass::arch::OpClassSimt, - Stages, - MathOperatorTag>; - - // Define iterators over tiles from the A operand - using ThreadMapA = typename MmaCore::IteratorThreadMapA; - using IteratorA = natten::cuda::gemm::threadblock::NA1dPNInputTileIterator< - cutlass::MatrixShape, - ElementA, - LayoutA, - ThreadMapA>; - - using SmemIteratorA = typename MmaCore::SmemIteratorA; - - // Define iterators over tiles from the B operand - using ThreadMapB = typename MmaCore::IteratorThreadMapB; - using IteratorB = natten::cuda::gemm::threadblock::NA1dPNInputTileIterator< - cutlass::MatrixShape, - ElementB, - LayoutB, - ThreadMapB>; - - using SmemIteratorB = typename MmaCore::SmemIteratorB; - - // Warp-level GEMM components - using WarpMmaSimt = typename MmaCore::MmaWarpSimt; - using MmaPolicy = typename MmaCore::MmaPolicy; - - static cutlass::arch::CacheOperation::Kind const CacheOpA = - ((cutlass::sizeof_bits::value * AlignmentA) == 128) - ? cutlass::arch::CacheOperation::Global - : cutlass::arch::CacheOperation::Always; - - static cutlass::arch::CacheOperation::Kind const CacheOpB = - ((cutlass::sizeof_bits::value * AlignmentB) == 128) - ? cutlass::arch::CacheOperation::Global - : cutlass::arch::CacheOperation::Always; - - // Define the Mma - using Mma = natten::cuda::gemm::threadblock::ImplicitGemmMultistage< - ThreadblockShape, - IteratorA, - SmemIteratorA, - CacheOpA, - IteratorB, - SmemIteratorB, - CacheOpB, - MmaPolicy, - Stages>; - - static const int kPartitionsK = ThreadblockShape::kK / WarpShape::kK; - - using EpilogueElementOutput = typename EpilogueOutputOp::ElementOutput; - - // Threadmap and output iterator were moved out of the default epilogue, - // which may seem counter-intuitive, but it was all done pretty lazily to - // allow different kernels to have their correct output iterator instantiated - // at the kernel level. - // May move this back and customize the epilogue in the future. - using OutputTileThreadMap = - typename cutlass::epilogue::threadblock::DefaultThreadMapSimt< - ThreadblockShape, - typename WarpMmaSimt::Shape, - typename WarpMmaSimt::Policy, - kPartitionsK, - EpilogueElementOutput, - EpilogueOutputOp::kCount>::Type; - - static bool const UseCUDAStore = - cutlass::platform::is_same::value; - - using OutputTileIterator = natten::cuda::gemm::threadblock:: - NA1dPNOutputTileIterator; - - // Define the epilogue - using Epilogue = - typename natten::cuda::gemm::threadblock::DefaultEpilogueSimt< - ThreadblockShape, - WarpMmaSimt, - EpilogueOutputOp, - EpilogueOutputOp::kCount, - OutputTileIterator>::Epilogue; - - // Define the kernel - using Kernel = - ImplicitGemmNA1d; -}; - -///////////////////////////////////////////////////////////////////////////////////////////////// -// Pipelined SIMT implicit gemm -///////////////////////////////////////////////////////////////////////////////////////////////// - -template < - typename ElementA, - typename LayoutA, - typename ElementB, - typename LayoutB, - typename ElementC, - typename LayoutC, - typename ElementAccumulator, - typename ArchTag, - typename ThreadblockShape, - typename WarpShape, - typename InstructionShape, - typename EpilogueOutputOp, - typename ThreadblockSwizzle, - typename MathOperatorTag, - int AlignmentA, - int AlignmentB> -struct DefaultNA1dPN< - ElementA, - LayoutA, - ElementB, - LayoutB, - ElementC, - LayoutC, - ElementAccumulator, - cutlass::arch::OpClassSimt, - ArchTag, - ThreadblockShape, - WarpShape, - InstructionShape, - EpilogueOutputOp, - ThreadblockSwizzle, - 2, - MathOperatorTag, - AlignmentA, - AlignmentB> { - static_assert(AlignmentA == 1 && AlignmentB == 1); - - // Define the core components from GEMM - using MmaCore = typename cutlass::gemm::threadblock::DefaultMmaCore< - ThreadblockShape, - WarpShape, - InstructionShape, - ElementA, - cutlass::layout::RowMajor, - ElementB, - cutlass::layout::ColumnMajor, - ElementAccumulator, - cutlass::layout::RowMajor, - cutlass::arch::OpClassSimt, - 2, - MathOperatorTag>; - - // Define iterators over tiles from the A operand - using ThreadMapA = typename MmaCore::IteratorThreadMapA; - using IteratorA = natten::cuda::gemm::threadblock::TileIterator< - natten::cuda::gemm::threadblock::NA1dPNInputTileIterator< - cutlass::MatrixShape, - ElementA, - LayoutA, - ThreadMapA>, - 1>; - - using SmemIteratorA = typename MmaCore::SmemIteratorA; - - // Define iterators over tiles from the B operand - using ThreadMapB = typename MmaCore::IteratorThreadMapB; - using IteratorB = natten::cuda::gemm::threadblock::TileIterator< - natten::cuda::gemm::threadblock::NA1dPNInputTileIterator< - cutlass::MatrixShape, - ElementB, - LayoutB, - ThreadMapB>, - 1>; - - using SmemIteratorB = typename MmaCore::SmemIteratorB; - - // Warp-level GEMM components - using WarpMmaSimt = typename MmaCore::MmaWarpSimt; - using MmaPolicy = typename MmaCore::MmaPolicy; - - // Define the Mma - using Mma = cutlass::conv::threadblock::ImplicitGemmPipelined< - ThreadblockShape, - IteratorA, - SmemIteratorA, - IteratorB, - SmemIteratorB, - ElementC, - LayoutC, - MmaPolicy>; - - static const int kPartitionsK = ThreadblockShape::kK / WarpShape::kK; - - using EpilogueElementOutput = typename EpilogueOutputOp::ElementOutput; - - // Threadmap and output iterator were moved out of the default epilogue, - // which may seem counter-intuitive, but it was all done pretty lazily to - // allow different kernels to have their correct output iterator instantiated - // at the kernel level. - // May move this back and customize the epilogue in the future. - using OutputTileThreadMap = - typename cutlass::epilogue::threadblock::DefaultThreadMapSimt< + EpilogueOutputOp::kCount>, + typename cutlass::epilogue::threadblock::DefaultEpilogueVoltaTensorOp< ThreadblockShape, - typename WarpMmaSimt::Shape, - typename WarpMmaSimt::Policy, + WarpMmaTensorOp, kPartitionsK, - EpilogueElementOutput, - EpilogueOutputOp::kCount>::Type; - - static bool const UseCUDAStore = - cutlass::platform::is_same::value; - - using OutputTileIterator = natten::cuda::gemm::threadblock:: - NA1dPNOutputTileIterator; - - // Define the epilogue - using Epilogue = - typename natten::cuda::gemm::threadblock::DefaultEpilogueSimt< - ThreadblockShape, - WarpMmaSimt, EpilogueOutputOp, - EpilogueOutputOp::kCount, - OutputTileIterator>::Epilogue; + EpilogueOutputOp::kCount>>::type; + + using OutputTileIterator = + natten::cuda::gemm::threadblock::NA1dPNOutputTileIterator< + typename EpilogueBase::OutputTileThreadMap, + typename EpilogueOutputOp::ElementOutput>; + + using Epilogue = cutlass::epilogue::threadblock::Epilogue< + typename EpilogueBase::Shape, + typename EpilogueBase::WarpMmaTensorOp, + EpilogueBase::kPartitionsK, + OutputTileIterator, + typename EpilogueBase::AccumulatorFragmentIterator, + typename EpilogueBase::WarpTileIterator, + typename EpilogueBase::SharedLoadIterator, + typename EpilogueBase::OutputOp, + typename EpilogueBase::Padding, + GetFragmentsPerIter::value>; // Define the kernel using Kernel = diff --git a/csrc/include/natten/cuda/gemm/kernel/default_na2d_in.cuh b/csrc/include/natten/cuda/gemm/kernel/default_na2d_in.cuh index 342cf73..a821a24 100644 --- a/csrc/include/natten/cuda/gemm/kernel/default_na2d_in.cuh +++ b/csrc/include/natten/cuda/gemm/kernel/default_na2d_in.cuh @@ -42,13 +42,11 @@ #pragma once #include -#include -#include +#include +#include #include "natten/cuda/gemm/kernel/default_na.cuh" -#include "natten/cuda/gemm/threadblock/default_epilogue_simt.cuh" -#include "natten/cuda/gemm/threadblock/default_epilogue_tensor_op.cuh" #include "natten/cuda/gemm/threadblock/na2d_in_input_tile_iterator.cuh" #include "natten/cuda/gemm/threadblock/na2d_in_output_tile_iterator.cuh" #include "natten/cuda/gemm/threadblock/na2d_in_value_tile_iterator.cuh" @@ -130,6 +128,9 @@ struct DefaultNA2dIN< AlignmentA, AlignmentB> { static_assert(AlignmentA == 1); // NA requirement for IN + static_assert( + ArchTag::kMinComputeCapability >= 80, + "This specialization is intended for SM80 and above."); // Define the core components from GEMM using MmaCore = typename cutlass::gemm::threadblock::DefaultMmaCore< @@ -195,39 +196,32 @@ struct DefaultNA2dIN< static const int kPartitionsK = ThreadblockShape::kK / WarpShape::kK; - using EpilogueElementOutput = typename EpilogueOutputOp::ElementOutput; - - // Threadmap and output iterator were moved out of the default epilogue, - // which may seem counter-intuitive, but it was all done pretty lazily to - // allow different kernels to have their correct output iterator instantiated - // at the kernel level. - // May move this back and customize the epilogue in the future. - using OutputTileThreadMap = - typename cutlass::epilogue::threadblock::DefaultThreadMapTensorOp< + // Define the epilogue + using EpilogueBase = + typename cutlass::epilogue::threadblock::DefaultEpilogueTensorOp< ThreadblockShape, - typename WarpMmaTensorOp::Shape, + WarpMmaTensorOp, kPartitionsK, - EpilogueElementOutput, - EpilogueOutputOp::kCount>::Type; - - static bool const UseCUDAStore = - cutlass::platform::is_same::value; + EpilogueOutputOp, + EpilogueOutputOp::kCount>; using OutputTileIterator = natten::cuda::gemm::threadblock::NA2dINOutputTileIterator< NAShape, - OutputTileThreadMap, - EpilogueElementOutput>; - - // Define the epilogue - using Epilogue = - typename natten::cuda::gemm::threadblock::DefaultEpilogueTensorOp< - ThreadblockShape, - WarpMmaTensorOp, - kPartitionsK, - EpilogueOutputOp, - EpilogueOutputOp::kCount, - OutputTileIterator>::Epilogue; + typename EpilogueBase::OutputTileThreadMap, + typename EpilogueOutputOp::ElementOutput>; + + using Epilogue = cutlass::epilogue::threadblock::Epilogue< + typename EpilogueBase::Shape, + typename EpilogueBase::WarpMmaTensorOp, + EpilogueBase::kPartitionsK, + OutputTileIterator, + typename EpilogueBase::AccumulatorFragmentIterator, + typename EpilogueBase::WarpTileIterator, + typename EpilogueBase::SharedLoadIterator, + typename EpilogueBase::OutputOp, + typename EpilogueBase::Padding, + GetFragmentsPerIter::value>; // Define the kernel using Kernel = ImplicitGemmNA2d< @@ -281,6 +275,10 @@ struct DefaultNA2dIN< AlignmentA, AlignmentB> { static_assert(AlignmentA == 1); // NA requirement for IN + static_assert( + ArchTag::kMinComputeCapability == 70 || + ArchTag::kMinComputeCapability == 75, + "This specialization is intended for SM70 and SM75."); // Define the core components from GEMM using MmaCore = typename cutlass::gemm::threadblock::DefaultMmaCore< @@ -344,332 +342,39 @@ struct DefaultNA2dIN< static const int kPartitionsK = ThreadblockShape::kK / WarpShape::kK; - using EpilogueElementOutput = typename EpilogueOutputOp::ElementOutput; - - // Threadmap and output iterator were moved out of the default epilogue, - // which may seem counter-intuitive, but it was all done pretty lazily to - // allow different kernels to have their correct output iterator instantiated - // at the kernel level. - // May move this back and customize the epilogue in the future. - using OutputTileThreadMap = - typename cutlass::epilogue::threadblock::DefaultThreadMapTensorOp< - ThreadblockShape, - typename WarpMmaTensorOp::Shape, - kPartitionsK, - EpilogueElementOutput, - EpilogueOutputOp::kCount>::Type; - - static bool const UseCUDAStore = - cutlass::platform::is_same::value; - - using OutputTileIterator = - natten::cuda::gemm::threadblock::NA2dINOutputTileIterator< - NAShape, - OutputTileThreadMap, - EpilogueElementOutput>; - // Define the epilogue - using Epilogue = - typename natten::cuda::gemm::threadblock::DefaultEpilogueTensorOp< + using EpilogueBase = typename std::conditional< + ArchTag::kMinComputeCapability == 75, + typename cutlass::epilogue::threadblock::DefaultEpilogueTensorOp< ThreadblockShape, WarpMmaTensorOp, kPartitionsK, EpilogueOutputOp, - EpilogueOutputOp::kCount, - OutputTileIterator>::Epilogue; - - // Define the kernel - using Kernel = ImplicitGemmNA2d< - Operator::kIN, - NAShape, - Mma, - Epilogue, - ThreadblockSwizzle>; -}; - -///////////////////////////////////////////////////////////////////////////////////////////////// -// SIMT implicit gemm -///////////////////////////////////////////////////////////////////////////////////////////////// - -template < - typename ElementA, - typename LayoutA, - typename ElementB, - typename LayoutB, - typename ElementC, - typename LayoutC, - typename ElementAccumulator, - typename ArchTag, - typename NAShape, - typename ThreadblockShape, - typename WarpShape, - typename InstructionShape, - typename EpilogueOutputOp, - typename ThreadblockSwizzle, - int Stages, - typename MathOperatorTag, - int AlignmentA, - int AlignmentB> -struct DefaultNA2dIN< - ElementA, - LayoutA, - ElementB, - LayoutB, - ElementC, - LayoutC, - ElementAccumulator, - cutlass::arch::OpClassSimt, - ArchTag, - NAShape, - ThreadblockShape, - WarpShape, - InstructionShape, - EpilogueOutputOp, - ThreadblockSwizzle, - Stages, - MathOperatorTag, - AlignmentA, - AlignmentB> { - static_assert(AlignmentA == 1 && AlignmentB == 1); - - // Define the core components from GEMM - using MmaCore = typename cutlass::gemm::threadblock::DefaultMmaCore< - ThreadblockShape, - WarpShape, - InstructionShape, - ElementA, - cutlass::layout::ColumnMajor, - ElementB, - cutlass::layout::RowMajor, - ElementAccumulator, - cutlass::layout::RowMajor, - cutlass::arch::OpClassSimt, - Stages, - MathOperatorTag>; - - // Define iterators over tiles from the A operand - using ThreadMapA = typename MmaCore::IteratorThreadMapA; - using IteratorA = natten::cuda::gemm::threadblock::NA2dINInputTileIterator< - NAShape, - cutlass::MatrixShape, - ElementA, - LayoutA, - ThreadMapA>; - - using SmemIteratorA = typename MmaCore::SmemIteratorA; - - // Define iterators over tiles from the B operand - using ThreadMapB = typename MmaCore::IteratorThreadMapB; - using IteratorB = natten::cuda::gemm::threadblock::NA2dINValueTileIterator< - NAShape, - cutlass::MatrixShape, - ElementB, - LayoutB, - ThreadMapB>; - - using SmemIteratorB = typename MmaCore::SmemIteratorB; - - // Warp-level GEMM components - using WarpMmaSimt = typename MmaCore::MmaWarpSimt; - using MmaPolicy = typename MmaCore::MmaPolicy; - - static cutlass::arch::CacheOperation::Kind const CacheOpB = - ((cutlass::sizeof_bits::value * AlignmentB) == 128) - ? cutlass::arch::CacheOperation::Global - : cutlass::arch::CacheOperation::Always; - - // Define the Mma - using Mma = natten::cuda::gemm::threadblock::ImplicitGemmMultistage< - ThreadblockShape, - IteratorA, - SmemIteratorA, - cutlass::arch::CacheOperation::Always, - IteratorB, - SmemIteratorB, - CacheOpB, - MmaPolicy, - Stages>; - - static const int kPartitionsK = ThreadblockShape::kK / WarpShape::kK; - - using EpilogueElementOutput = typename EpilogueOutputOp::ElementOutput; - - // Threadmap and output iterator were moved out of the default epilogue, - // which may seem counter-intuitive, but it was all done pretty lazily to - // allow different kernels to have their correct output iterator instantiated - // at the kernel level. - // May move this back and customize the epilogue in the future. - using OutputTileThreadMap = - typename cutlass::epilogue::threadblock::DefaultThreadMapSimt< + EpilogueOutputOp::kCount>, + typename cutlass::epilogue::threadblock::DefaultEpilogueVoltaTensorOp< ThreadblockShape, - typename WarpMmaSimt::Shape, - typename WarpMmaSimt::Policy, + WarpMmaTensorOp, kPartitionsK, - EpilogueElementOutput, - EpilogueOutputOp::kCount>::Type; - - static bool const UseCUDAStore = - cutlass::platform::is_same::value; - - using OutputTileIterator = - natten::cuda::gemm::threadblock::NA2dINOutputTileIterator< - NAShape, - OutputTileThreadMap, - EpilogueElementOutput>; - - // Define the epilogue - using Epilogue = - typename natten::cuda::gemm::threadblock::DefaultEpilogueSimt< - ThreadblockShape, - WarpMmaSimt, EpilogueOutputOp, - EpilogueOutputOp::kCount, - OutputTileIterator>::Epilogue; - - // Define the kernel - using Kernel = ImplicitGemmNA2d< - Operator::kIN, - NAShape, - Mma, - Epilogue, - ThreadblockSwizzle>; -}; - -///////////////////////////////////////////////////////////////////////////////////////////////// -// Pipelined SIMT implicit gemm -///////////////////////////////////////////////////////////////////////////////////////////////// - -template < - typename ElementA, - typename LayoutA, - typename ElementB, - typename LayoutB, - typename ElementC, - typename LayoutC, - typename ElementAccumulator, - typename ArchTag, - typename NAShape, - typename ThreadblockShape, - typename WarpShape, - typename InstructionShape, - typename EpilogueOutputOp, - typename ThreadblockSwizzle, - typename MathOperatorTag, - int AlignmentA, - int AlignmentB> -struct DefaultNA2dIN< - ElementA, - LayoutA, - ElementB, - LayoutB, - ElementC, - LayoutC, - ElementAccumulator, - cutlass::arch::OpClassSimt, - ArchTag, - NAShape, - ThreadblockShape, - WarpShape, - InstructionShape, - EpilogueOutputOp, - ThreadblockSwizzle, - 2, - MathOperatorTag, - AlignmentA, - AlignmentB> { - static_assert(AlignmentA == 1 && AlignmentB == 1); - - // Define the core components from GEMM - using MmaCore = typename cutlass::gemm::threadblock::DefaultMmaCore< - ThreadblockShape, - WarpShape, - InstructionShape, - ElementA, - cutlass::layout::ColumnMajor, - ElementB, - cutlass::layout::RowMajor, - ElementAccumulator, - cutlass::layout::RowMajor, - cutlass::arch::OpClassSimt, - 2, - MathOperatorTag>; - - // Define iterators over tiles from the A operand - using ThreadMapA = typename MmaCore::IteratorThreadMapA; - using IteratorA = natten::cuda::gemm::threadblock::TileIterator< - natten::cuda::gemm::threadblock::NA2dINInputTileIterator< - NAShape, - cutlass::MatrixShape, - ElementA, - LayoutA, - ThreadMapA>, - 2>; - - using SmemIteratorA = typename MmaCore::SmemIteratorA; - - // Define iterators over tiles from the B operand - using ThreadMapB = typename MmaCore::IteratorThreadMapB; - using IteratorB = natten::cuda::gemm::threadblock::TileIterator< - natten::cuda::gemm::threadblock::NA2dINValueTileIterator< - NAShape, - cutlass::MatrixShape, - ElementB, - LayoutB, - ThreadMapB>, - 2>; - - using SmemIteratorB = typename MmaCore::SmemIteratorB; - - // Warp-level GEMM components - using WarpMmaSimt = typename MmaCore::MmaWarpSimt; - using MmaPolicy = typename MmaCore::MmaPolicy; - - // Define the Mma - using Mma = cutlass::conv::threadblock::ImplicitGemmPipelined< - ThreadblockShape, - IteratorA, - SmemIteratorA, - IteratorB, - SmemIteratorB, - ElementC, - LayoutC, - MmaPolicy>; - - static const int kPartitionsK = ThreadblockShape::kK / WarpShape::kK; - - using EpilogueElementOutput = typename EpilogueOutputOp::ElementOutput; - - // Threadmap and output iterator were moved out of the default epilogue, - // which may seem counter-intuitive, but it was all done pretty lazily to - // allow different kernels to have their correct output iterator instantiated - // at the kernel level. - // May move this back and customize the epilogue in the future. - using OutputTileThreadMap = - typename cutlass::epilogue::threadblock::DefaultThreadMapSimt< - ThreadblockShape, - typename WarpMmaSimt::Shape, - typename WarpMmaSimt::Policy, - kPartitionsK, - EpilogueElementOutput, - EpilogueOutputOp::kCount>::Type; - - static bool const UseCUDAStore = - cutlass::platform::is_same::value; + EpilogueOutputOp::kCount>>::type; using OutputTileIterator = natten::cuda::gemm::threadblock::NA2dINOutputTileIterator< NAShape, - OutputTileThreadMap, - EpilogueElementOutput>; - - // Define the epilogue - using Epilogue = - typename natten::cuda::gemm::threadblock::DefaultEpilogueSimt< - ThreadblockShape, - WarpMmaSimt, - EpilogueOutputOp, - EpilogueOutputOp::kCount, - OutputTileIterator>::Epilogue; + typename EpilogueBase::OutputTileThreadMap, + typename EpilogueOutputOp::ElementOutput>; + + using Epilogue = cutlass::epilogue::threadblock::Epilogue< + typename EpilogueBase::Shape, + typename EpilogueBase::WarpMmaTensorOp, + EpilogueBase::kPartitionsK, + OutputTileIterator, + typename EpilogueBase::AccumulatorFragmentIterator, + typename EpilogueBase::WarpTileIterator, + typename EpilogueBase::SharedLoadIterator, + typename EpilogueBase::OutputOp, + typename EpilogueBase::Padding, + GetFragmentsPerIter::value>; // Define the kernel using Kernel = ImplicitGemmNA2d< diff --git a/csrc/include/natten/cuda/gemm/kernel/default_na2d_nn.cuh b/csrc/include/natten/cuda/gemm/kernel/default_na2d_nn.cuh index 9747616..6dab4af 100644 --- a/csrc/include/natten/cuda/gemm/kernel/default_na2d_nn.cuh +++ b/csrc/include/natten/cuda/gemm/kernel/default_na2d_nn.cuh @@ -42,13 +42,11 @@ #pragma once #include -#include -#include +#include +#include #include "natten/cuda/gemm/kernel/default_na.cuh" -#include "natten/cuda/gemm/threadblock/default_epilogue_simt.cuh" -#include "natten/cuda/gemm/threadblock/default_epilogue_tensor_op.cuh" #include "natten/cuda/gemm/threadblock/na2d_nn_input_tile_iterator.cuh" #include "natten/cuda/gemm/threadblock/na2d_nn_output_tile_iterator.cuh" #include "natten/cuda/gemm/threadblock/na2d_nn_value_tile_iterator.cuh" @@ -130,6 +128,9 @@ struct DefaultNA2dNN< AlignmentA, AlignmentB> { static_assert(AlignmentA == 1); // NA requirement for IN + static_assert( + ArchTag::kMinComputeCapability >= 80, + "This specialization is intended for SM80 and above."); // Define the core components from GEMM using MmaCore = typename cutlass::gemm::threadblock::DefaultMmaCore< @@ -195,39 +196,32 @@ struct DefaultNA2dNN< static const int kPartitionsK = ThreadblockShape::kK / WarpShape::kK; - using EpilogueElementOutput = typename EpilogueOutputOp::ElementOutput; - - // Threadmap and output iterator were moved out of the default epilogue, - // which may seem counter-intuitive, but it was all done pretty lazily to - // allow different kernels to have their correct output iterator instantiated - // at the kernel level. - // May move this back and customize the epilogue in the future. - using OutputTileThreadMap = - typename cutlass::epilogue::threadblock::DefaultThreadMapTensorOp< + // Define the epilogue + using EpilogueBase = + typename cutlass::epilogue::threadblock::DefaultEpilogueTensorOp< ThreadblockShape, - typename WarpMmaTensorOp::Shape, + WarpMmaTensorOp, kPartitionsK, - EpilogueElementOutput, - EpilogueOutputOp::kCount>::Type; - - static bool const UseCUDAStore = - cutlass::platform::is_same::value; + EpilogueOutputOp, + EpilogueOutputOp::kCount>; using OutputTileIterator = natten::cuda::gemm::threadblock::NA2dNNOutputTileIterator< NAShape, - OutputTileThreadMap, - EpilogueElementOutput>; - - // Define the epilogue - using Epilogue = - typename natten::cuda::gemm::threadblock::DefaultEpilogueTensorOp< - ThreadblockShape, - WarpMmaTensorOp, - kPartitionsK, - EpilogueOutputOp, - EpilogueOutputOp::kCount, - OutputTileIterator>::Epilogue; + typename EpilogueBase::OutputTileThreadMap, + typename EpilogueOutputOp::ElementOutput>; + + using Epilogue = cutlass::epilogue::threadblock::Epilogue< + typename EpilogueBase::Shape, + typename EpilogueBase::WarpMmaTensorOp, + EpilogueBase::kPartitionsK, + OutputTileIterator, + typename EpilogueBase::AccumulatorFragmentIterator, + typename EpilogueBase::WarpTileIterator, + typename EpilogueBase::SharedLoadIterator, + typename EpilogueBase::OutputOp, + typename EpilogueBase::Padding, + GetFragmentsPerIter::value>; // Define the kernel using Kernel = ImplicitGemmNA2d< @@ -281,6 +275,10 @@ struct DefaultNA2dNN< AlignmentA, AlignmentB> { static_assert(AlignmentA == 1); // NA requirement for NN + static_assert( + ArchTag::kMinComputeCapability == 70 || + ArchTag::kMinComputeCapability == 75, + "This specialization is intended for SM70 and SM75."); // Define the core components from GEMM using MmaCore = typename cutlass::gemm::threadblock::DefaultMmaCore< @@ -344,332 +342,39 @@ struct DefaultNA2dNN< static const int kPartitionsK = ThreadblockShape::kK / WarpShape::kK; - using EpilogueElementOutput = typename EpilogueOutputOp::ElementOutput; - - // Threadmap and output iterator were moved out of the default epilogue, - // which may seem counter-intuitive, but it was all done pretty lazily to - // allow different kernels to have their correct output iterator instantiated - // at the kernel level. - // May move this back and customize the epilogue in the future. - using OutputTileThreadMap = - typename cutlass::epilogue::threadblock::DefaultThreadMapTensorOp< - ThreadblockShape, - typename WarpMmaTensorOp::Shape, - kPartitionsK, - EpilogueElementOutput, - EpilogueOutputOp::kCount>::Type; - - static bool const UseCUDAStore = - cutlass::platform::is_same::value; - - using OutputTileIterator = - natten::cuda::gemm::threadblock::NA2dNNOutputTileIterator< - NAShape, - OutputTileThreadMap, - EpilogueElementOutput>; - // Define the epilogue - using Epilogue = - typename natten::cuda::gemm::threadblock::DefaultEpilogueTensorOp< + using EpilogueBase = typename std::conditional< + ArchTag::kMinComputeCapability == 75, + typename cutlass::epilogue::threadblock::DefaultEpilogueTensorOp< ThreadblockShape, WarpMmaTensorOp, kPartitionsK, EpilogueOutputOp, - EpilogueOutputOp::kCount, - OutputTileIterator>::Epilogue; - - // Define the kernel - using Kernel = ImplicitGemmNA2d< - Operator::kNN, - NAShape, - Mma, - Epilogue, - ThreadblockSwizzle>; -}; - -///////////////////////////////////////////////////////////////////////////////////////////////// -// SIMT implicit gemm -///////////////////////////////////////////////////////////////////////////////////////////////// - -template < - typename ElementA, - typename LayoutA, - typename ElementB, - typename LayoutB, - typename ElementC, - typename LayoutC, - typename ElementAccumulator, - typename ArchTag, - typename NAShape, - typename ThreadblockShape, - typename WarpShape, - typename InstructionShape, - typename EpilogueOutputOp, - typename ThreadblockSwizzle, - int Stages, - typename MathOperatorTag, - int AlignmentA, - int AlignmentB> -struct DefaultNA2dNN< - ElementA, - LayoutA, - ElementB, - LayoutB, - ElementC, - LayoutC, - ElementAccumulator, - cutlass::arch::OpClassSimt, - ArchTag, - NAShape, - ThreadblockShape, - WarpShape, - InstructionShape, - EpilogueOutputOp, - ThreadblockSwizzle, - Stages, - MathOperatorTag, - AlignmentA, - AlignmentB> { - static_assert(AlignmentA == 1 && AlignmentB == 1); - - // Define the core components from GEMM - using MmaCore = typename cutlass::gemm::threadblock::DefaultMmaCore< - ThreadblockShape, - WarpShape, - InstructionShape, - ElementA, - cutlass::layout::RowMajor, - ElementB, - cutlass::layout::RowMajor, - ElementAccumulator, - cutlass::layout::RowMajor, - cutlass::arch::OpClassSimt, - Stages, - MathOperatorTag>; - - // Define iterators over tiles from the A operand - using ThreadMapA = typename MmaCore::IteratorThreadMapA; - using IteratorA = natten::cuda::gemm::threadblock::NA2dNNInputTileIterator< - NAShape, - cutlass::MatrixShape, - ElementA, - LayoutA, - ThreadMapA>; - - using SmemIteratorA = typename MmaCore::SmemIteratorA; - - // Define iterators over tiles from the B operand - using ThreadMapB = typename MmaCore::IteratorThreadMapB; - using IteratorB = natten::cuda::gemm::threadblock::NA2dNNValueTileIterator< - NAShape, - cutlass::MatrixShape, - ElementB, - LayoutB, - ThreadMapB>; - - using SmemIteratorB = typename MmaCore::SmemIteratorB; - - // Warp-level GEMM components - using WarpMmaSimt = typename MmaCore::MmaWarpSimt; - using MmaPolicy = typename MmaCore::MmaPolicy; - - static cutlass::arch::CacheOperation::Kind const CacheOpB = - ((cutlass::sizeof_bits::value * AlignmentB) == 128) - ? cutlass::arch::CacheOperation::Global - : cutlass::arch::CacheOperation::Always; - - // Define the Mma - using Mma = natten::cuda::gemm::threadblock::ImplicitGemmMultistage< - ThreadblockShape, - IteratorA, - SmemIteratorA, - cutlass::arch::CacheOperation::Always, - IteratorB, - SmemIteratorB, - CacheOpB, - MmaPolicy, - Stages>; - - static const int kPartitionsK = ThreadblockShape::kK / WarpShape::kK; - - using EpilogueElementOutput = typename EpilogueOutputOp::ElementOutput; - - // Threadmap and output iterator were moved out of the default epilogue, - // which may seem counter-intuitive, but it was all done pretty lazily to - // allow different kernels to have their correct output iterator instantiated - // at the kernel level. - // May move this back and customize the epilogue in the future. - using OutputTileThreadMap = - typename cutlass::epilogue::threadblock::DefaultThreadMapSimt< + EpilogueOutputOp::kCount>, + typename cutlass::epilogue::threadblock::DefaultEpilogueVoltaTensorOp< ThreadblockShape, - typename WarpMmaSimt::Shape, - typename WarpMmaSimt::Policy, + WarpMmaTensorOp, kPartitionsK, - EpilogueElementOutput, - EpilogueOutputOp::kCount>::Type; - - static bool const UseCUDAStore = - cutlass::platform::is_same::value; - - using OutputTileIterator = - natten::cuda::gemm::threadblock::NA2dNNOutputTileIterator< - NAShape, - OutputTileThreadMap, - EpilogueElementOutput>; - - // Define the epilogue - using Epilogue = - typename natten::cuda::gemm::threadblock::DefaultEpilogueSimt< - ThreadblockShape, - WarpMmaSimt, EpilogueOutputOp, - EpilogueOutputOp::kCount, - OutputTileIterator>::Epilogue; - - // Define the kernel - using Kernel = ImplicitGemmNA2d< - Operator::kNN, - NAShape, - Mma, - Epilogue, - ThreadblockSwizzle>; -}; - -///////////////////////////////////////////////////////////////////////////////////////////////// -// Pipelined SIMT implicit gemm -///////////////////////////////////////////////////////////////////////////////////////////////// - -template < - typename ElementA, - typename LayoutA, - typename ElementB, - typename LayoutB, - typename ElementC, - typename LayoutC, - typename ElementAccumulator, - typename ArchTag, - typename NAShape, - typename ThreadblockShape, - typename WarpShape, - typename InstructionShape, - typename EpilogueOutputOp, - typename ThreadblockSwizzle, - typename MathOperatorTag, - int AlignmentA, - int AlignmentB> -struct DefaultNA2dNN< - ElementA, - LayoutA, - ElementB, - LayoutB, - ElementC, - LayoutC, - ElementAccumulator, - cutlass::arch::OpClassSimt, - ArchTag, - NAShape, - ThreadblockShape, - WarpShape, - InstructionShape, - EpilogueOutputOp, - ThreadblockSwizzle, - 2, - MathOperatorTag, - AlignmentA, - AlignmentB> { - static_assert(AlignmentA == 1 && AlignmentB == 1); - - // Define the core components from GEMM - using MmaCore = typename cutlass::gemm::threadblock::DefaultMmaCore< - ThreadblockShape, - WarpShape, - InstructionShape, - ElementA, - cutlass::layout::RowMajor, - ElementB, - cutlass::layout::RowMajor, - ElementAccumulator, - cutlass::layout::RowMajor, - cutlass::arch::OpClassSimt, - 2, - MathOperatorTag>; - - // Define iterators over tiles from the A operand - using ThreadMapA = typename MmaCore::IteratorThreadMapA; - using IteratorA = natten::cuda::gemm::threadblock::TileIterator< - natten::cuda::gemm::threadblock::NA2dNNInputTileIterator< - NAShape, - cutlass::MatrixShape, - ElementA, - LayoutA, - ThreadMapA>, - 2>; - - using SmemIteratorA = typename MmaCore::SmemIteratorA; - - // Define iterators over tiles from the B operand - using ThreadMapB = typename MmaCore::IteratorThreadMapB; - using IteratorB = natten::cuda::gemm::threadblock::TileIterator< - natten::cuda::gemm::threadblock::NA2dNNValueTileIterator< - NAShape, - cutlass::MatrixShape, - ElementB, - LayoutB, - ThreadMapB>, - 2>; - - using SmemIteratorB = typename MmaCore::SmemIteratorB; - - // Warp-level GEMM components - using WarpMmaSimt = typename MmaCore::MmaWarpSimt; - using MmaPolicy = typename MmaCore::MmaPolicy; - - // Define the Mma - using Mma = cutlass::conv::threadblock::ImplicitGemmPipelined< - ThreadblockShape, - IteratorA, - SmemIteratorA, - IteratorB, - SmemIteratorB, - ElementC, - LayoutC, - MmaPolicy>; - - static const int kPartitionsK = ThreadblockShape::kK / WarpShape::kK; - - using EpilogueElementOutput = typename EpilogueOutputOp::ElementOutput; - - // Threadmap and output iterator were moved out of the default epilogue, - // which may seem counter-intuitive, but it was all done pretty lazily to - // allow different kernels to have their correct output iterator instantiated - // at the kernel level. - // May move this back and customize the epilogue in the future. - using OutputTileThreadMap = - typename cutlass::epilogue::threadblock::DefaultThreadMapSimt< - ThreadblockShape, - typename WarpMmaSimt::Shape, - typename WarpMmaSimt::Policy, - kPartitionsK, - EpilogueElementOutput, - EpilogueOutputOp::kCount>::Type; - - static bool const UseCUDAStore = - cutlass::platform::is_same::value; + EpilogueOutputOp::kCount>>::type; using OutputTileIterator = natten::cuda::gemm::threadblock::NA2dNNOutputTileIterator< NAShape, - OutputTileThreadMap, - EpilogueElementOutput>; - - // Define the epilogue - using Epilogue = - typename natten::cuda::gemm::threadblock::DefaultEpilogueSimt< - ThreadblockShape, - WarpMmaSimt, - EpilogueOutputOp, - EpilogueOutputOp::kCount, - OutputTileIterator>::Epilogue; + typename EpilogueBase::OutputTileThreadMap, + typename EpilogueOutputOp::ElementOutput>; + + using Epilogue = cutlass::epilogue::threadblock::Epilogue< + typename EpilogueBase::Shape, + typename EpilogueBase::WarpMmaTensorOp, + EpilogueBase::kPartitionsK, + OutputTileIterator, + typename EpilogueBase::AccumulatorFragmentIterator, + typename EpilogueBase::WarpTileIterator, + typename EpilogueBase::SharedLoadIterator, + typename EpilogueBase::OutputOp, + typename EpilogueBase::Padding, + GetFragmentsPerIter::value>; // Define the kernel using Kernel = ImplicitGemmNA2d< diff --git a/csrc/include/natten/cuda/gemm/kernel/default_na2d_pn.cuh b/csrc/include/natten/cuda/gemm/kernel/default_na2d_pn.cuh index 4972c36..384b2f5 100644 --- a/csrc/include/natten/cuda/gemm/kernel/default_na2d_pn.cuh +++ b/csrc/include/natten/cuda/gemm/kernel/default_na2d_pn.cuh @@ -42,13 +42,11 @@ #pragma once #include -#include -#include +#include +#include #include "natten/cuda/gemm/kernel/default_na.cuh" -#include "natten/cuda/gemm/threadblock/default_epilogue_simt.cuh" -#include "natten/cuda/gemm/threadblock/default_epilogue_tensor_op.cuh" #include "natten/cuda/gemm/threadblock/na2d_pn_input_tile_iterator.cuh" #include "natten/cuda/gemm/threadblock/na2d_pn_output_tile_iterator.cuh" @@ -128,6 +126,10 @@ struct DefaultNA2dPN< MathOperatorTag, AlignmentA, AlignmentB> { + static_assert( + ArchTag::kMinComputeCapability >= 80, + "This specialization is intended for SM80 and above."); + // Define the core components from GEMM using MmaCore = typename cutlass::gemm::threadblock::DefaultMmaCore< ThreadblockShape, @@ -197,39 +199,32 @@ struct DefaultNA2dPN< static const int kPartitionsK = ThreadblockShape::kK / WarpShape::kK; - using EpilogueElementOutput = typename EpilogueOutputOp::ElementOutput; - - // Threadmap and output iterator were moved out of the default epilogue, - // which may seem counter-intuitive, but it was all done pretty lazily to - // allow different kernels to have their correct output iterator instantiated - // at the kernel level. - // May move this back and customize the epilogue in the future. - using OutputTileThreadMap = - typename cutlass::epilogue::threadblock::DefaultThreadMapTensorOp< + // Define the epilogue + using EpilogueBase = + typename cutlass::epilogue::threadblock::DefaultEpilogueTensorOp< ThreadblockShape, - typename WarpMmaTensorOp::Shape, + WarpMmaTensorOp, kPartitionsK, - EpilogueElementOutput, - EpilogueOutputOp::kCount>::Type; - - static bool const UseCUDAStore = - cutlass::platform::is_same::value; + EpilogueOutputOp, + EpilogueOutputOp::kCount>; using OutputTileIterator = natten::cuda::gemm::threadblock::NA2dPNOutputTileIterator< NAShape, - OutputTileThreadMap, - EpilogueElementOutput>; - - // Define the epilogue - using Epilogue = - typename natten::cuda::gemm::threadblock::DefaultEpilogueTensorOp< - ThreadblockShape, - WarpMmaTensorOp, - kPartitionsK, - EpilogueOutputOp, - EpilogueOutputOp::kCount, - OutputTileIterator>::Epilogue; + typename EpilogueBase::OutputTileThreadMap, + typename EpilogueOutputOp::ElementOutput>; + + using Epilogue = cutlass::epilogue::threadblock::Epilogue< + typename EpilogueBase::Shape, + typename EpilogueBase::WarpMmaTensorOp, + EpilogueBase::kPartitionsK, + OutputTileIterator, + typename EpilogueBase::AccumulatorFragmentIterator, + typename EpilogueBase::WarpTileIterator, + typename EpilogueBase::SharedLoadIterator, + typename EpilogueBase::OutputOp, + typename EpilogueBase::Padding, + GetFragmentsPerIter::value>; // Define the kernel using Kernel = ImplicitGemmNA2d< @@ -282,6 +277,11 @@ struct DefaultNA2dPN< MathOperatorTag, AlignmentA, AlignmentB> { + static_assert( + ArchTag::kMinComputeCapability == 70 || + ArchTag::kMinComputeCapability == 75, + "This specialization is intended for SM70 and SM75."); + // Define the core components from GEMM using MmaCore = typename cutlass::gemm::threadblock::DefaultMmaCore< ThreadblockShape, @@ -344,337 +344,39 @@ struct DefaultNA2dPN< static const int kPartitionsK = ThreadblockShape::kK / WarpShape::kK; - using EpilogueElementOutput = typename EpilogueOutputOp::ElementOutput; - - // Threadmap and output iterator were moved out of the default epilogue, - // which may seem counter-intuitive, but it was all done pretty lazily to - // allow different kernels to have their correct output iterator instantiated - // at the kernel level. - // May move this back and customize the epilogue in the future. - using OutputTileThreadMap = - typename cutlass::epilogue::threadblock::DefaultThreadMapTensorOp< - ThreadblockShape, - typename WarpMmaTensorOp::Shape, - kPartitionsK, - EpilogueElementOutput, - EpilogueOutputOp::kCount>::Type; - - static bool const UseCUDAStore = - cutlass::platform::is_same::value; - - using OutputTileIterator = - natten::cuda::gemm::threadblock::NA2dPNOutputTileIterator< - NAShape, - OutputTileThreadMap, - EpilogueElementOutput>; - // Define the epilogue - using Epilogue = - typename natten::cuda::gemm::threadblock::DefaultEpilogueTensorOp< + using EpilogueBase = typename std::conditional< + ArchTag::kMinComputeCapability == 75, + typename cutlass::epilogue::threadblock::DefaultEpilogueTensorOp< ThreadblockShape, WarpMmaTensorOp, kPartitionsK, EpilogueOutputOp, - EpilogueOutputOp::kCount, - OutputTileIterator>::Epilogue; - - // Define the kernel - using Kernel = ImplicitGemmNA2d< - Operator::kPN, - NAShape, - Mma, - Epilogue, - ThreadblockSwizzle>; -}; - -///////////////////////////////////////////////////////////////////////////////////////////////// -// SIMT implicit gemm -///////////////////////////////////////////////////////////////////////////////////////////////// - -template < - typename ElementA, - typename LayoutA, - typename ElementB, - typename LayoutB, - typename ElementC, - typename LayoutC, - typename ElementAccumulator, - typename ArchTag, - typename NAShape, - typename ThreadblockShape, - typename WarpShape, - typename InstructionShape, - typename EpilogueOutputOp, - typename ThreadblockSwizzle, - int Stages, - typename MathOperatorTag, - int AlignmentA, - int AlignmentB> -struct DefaultNA2dPN< - ElementA, - LayoutA, - ElementB, - LayoutB, - ElementC, - LayoutC, - ElementAccumulator, - cutlass::arch::OpClassSimt, - ArchTag, - NAShape, - ThreadblockShape, - WarpShape, - InstructionShape, - EpilogueOutputOp, - ThreadblockSwizzle, - Stages, - MathOperatorTag, - AlignmentA, - AlignmentB> { - static_assert(AlignmentA == 1 && AlignmentB == 1); - - // Define the core components from GEMM - using MmaCore = typename cutlass::gemm::threadblock::DefaultMmaCore< - ThreadblockShape, - WarpShape, - InstructionShape, - ElementA, - cutlass::layout::RowMajor, - ElementB, - cutlass::layout::ColumnMajor, - ElementAccumulator, - cutlass::layout::RowMajor, - cutlass::arch::OpClassSimt, - Stages, - MathOperatorTag>; - - // Define iterators over tiles from the A operand - using ThreadMapA = typename MmaCore::IteratorThreadMapA; - using IteratorA = natten::cuda::gemm::threadblock::NA2dPNInputTileIterator< - NAShape, - cutlass::MatrixShape, - ElementA, - LayoutA, - ThreadMapA>; - - using SmemIteratorA = typename MmaCore::SmemIteratorA; - - // Define iterators over tiles from the B operand - using ThreadMapB = typename MmaCore::IteratorThreadMapB; - using IteratorB = natten::cuda::gemm::threadblock::NA2dPNInputTileIterator< - NAShape, - cutlass::MatrixShape, - ElementB, - LayoutB, - ThreadMapB>; - - using SmemIteratorB = typename MmaCore::SmemIteratorB; - - // Warp-level GEMM components - using WarpMmaSimt = typename MmaCore::MmaWarpSimt; - using MmaPolicy = typename MmaCore::MmaPolicy; - - static cutlass::arch::CacheOperation::Kind const CacheOpA = - ((cutlass::sizeof_bits::value * AlignmentA) == 128) - ? cutlass::arch::CacheOperation::Global - : cutlass::arch::CacheOperation::Always; - - static cutlass::arch::CacheOperation::Kind const CacheOpB = - ((cutlass::sizeof_bits::value * AlignmentB) == 128) - ? cutlass::arch::CacheOperation::Global - : cutlass::arch::CacheOperation::Always; - - // Define the Mma - using Mma = natten::cuda::gemm::threadblock::ImplicitGemmMultistage< - ThreadblockShape, - IteratorA, - SmemIteratorA, - CacheOpA, - IteratorB, - SmemIteratorB, - CacheOpB, - MmaPolicy, - Stages>; - - static const int kPartitionsK = ThreadblockShape::kK / WarpShape::kK; - - using EpilogueElementOutput = typename EpilogueOutputOp::ElementOutput; - - // Threadmap and output iterator were moved out of the default epilogue, - // which may seem counter-intuitive, but it was all done pretty lazily to - // allow different kernels to have their correct output iterator instantiated - // at the kernel level. - // May move this back and customize the epilogue in the future. - using OutputTileThreadMap = - typename cutlass::epilogue::threadblock::DefaultThreadMapSimt< + EpilogueOutputOp::kCount>, + typename cutlass::epilogue::threadblock::DefaultEpilogueVoltaTensorOp< ThreadblockShape, - typename WarpMmaSimt::Shape, - typename WarpMmaSimt::Policy, + WarpMmaTensorOp, kPartitionsK, - EpilogueElementOutput, - EpilogueOutputOp::kCount>::Type; - - static bool const UseCUDAStore = - cutlass::platform::is_same::value; - - using OutputTileIterator = - natten::cuda::gemm::threadblock::NA2dPNOutputTileIterator< - NAShape, - OutputTileThreadMap, - EpilogueElementOutput>; - - // Define the epilogue - using Epilogue = - typename natten::cuda::gemm::threadblock::DefaultEpilogueSimt< - ThreadblockShape, - WarpMmaSimt, EpilogueOutputOp, - EpilogueOutputOp::kCount, - OutputTileIterator>::Epilogue; - - // Define the kernel - using Kernel = ImplicitGemmNA2d< - Operator::kPN, - NAShape, - Mma, - Epilogue, - ThreadblockSwizzle>; -}; - -///////////////////////////////////////////////////////////////////////////////////////////////// -// Pipelined SIMT implicit gemm -///////////////////////////////////////////////////////////////////////////////////////////////// - -template < - typename ElementA, - typename LayoutA, - typename ElementB, - typename LayoutB, - typename ElementC, - typename LayoutC, - typename ElementAccumulator, - typename ArchTag, - typename NAShape, - typename ThreadblockShape, - typename WarpShape, - typename InstructionShape, - typename EpilogueOutputOp, - typename ThreadblockSwizzle, - typename MathOperatorTag, - int AlignmentA, - int AlignmentB> -struct DefaultNA2dPN< - ElementA, - LayoutA, - ElementB, - LayoutB, - ElementC, - LayoutC, - ElementAccumulator, - cutlass::arch::OpClassSimt, - ArchTag, - NAShape, - ThreadblockShape, - WarpShape, - InstructionShape, - EpilogueOutputOp, - ThreadblockSwizzle, - 2, - MathOperatorTag, - AlignmentA, - AlignmentB> { - static_assert(AlignmentA == 1 && AlignmentB == 1); - - // Define the core components from GEMM - using MmaCore = typename cutlass::gemm::threadblock::DefaultMmaCore< - ThreadblockShape, - WarpShape, - InstructionShape, - ElementA, - cutlass::layout::RowMajor, - ElementB, - cutlass::layout::ColumnMajor, - ElementAccumulator, - cutlass::layout::RowMajor, - cutlass::arch::OpClassSimt, - 2, - MathOperatorTag>; - - // Define iterators over tiles from the A operand - using ThreadMapA = typename MmaCore::IteratorThreadMapA; - using IteratorA = natten::cuda::gemm::threadblock::TileIterator< - natten::cuda::gemm::threadblock::NA2dPNInputTileIterator< - NAShape, - cutlass::MatrixShape, - ElementA, - LayoutA, - ThreadMapA>, - 2>; - - using SmemIteratorA = typename MmaCore::SmemIteratorA; - - // Define iterators over tiles from the B operand - using ThreadMapB = typename MmaCore::IteratorThreadMapB; - using IteratorB = natten::cuda::gemm::threadblock::TileIterator< - natten::cuda::gemm::threadblock::NA2dPNInputTileIterator< - NAShape, - cutlass::MatrixShape, - ElementB, - LayoutB, - ThreadMapB>, - 2>; - - using SmemIteratorB = typename MmaCore::SmemIteratorB; - - // Warp-level GEMM components - using WarpMmaSimt = typename MmaCore::MmaWarpSimt; - using MmaPolicy = typename MmaCore::MmaPolicy; - - // Define the Mma - using Mma = cutlass::conv::threadblock::ImplicitGemmPipelined< - ThreadblockShape, - IteratorA, - SmemIteratorA, - IteratorB, - SmemIteratorB, - ElementC, - LayoutC, - MmaPolicy>; - - static const int kPartitionsK = ThreadblockShape::kK / WarpShape::kK; - - using EpilogueElementOutput = typename EpilogueOutputOp::ElementOutput; - - // Threadmap and output iterator were moved out of the default epilogue, - // which may seem counter-intuitive, but it was all done pretty lazily to - // allow different kernels to have their correct output iterator instantiated - // at the kernel level. - // May move this back and customize the epilogue in the future. - using OutputTileThreadMap = - typename cutlass::epilogue::threadblock::DefaultThreadMapSimt< - ThreadblockShape, - typename WarpMmaSimt::Shape, - typename WarpMmaSimt::Policy, - kPartitionsK, - EpilogueElementOutput, - EpilogueOutputOp::kCount>::Type; - - static bool const UseCUDAStore = - cutlass::platform::is_same::value; + EpilogueOutputOp::kCount>>::type; using OutputTileIterator = natten::cuda::gemm::threadblock::NA2dPNOutputTileIterator< NAShape, - OutputTileThreadMap, - EpilogueElementOutput>; - - // Define the epilogue - using Epilogue = - typename natten::cuda::gemm::threadblock::DefaultEpilogueSimt< - ThreadblockShape, - WarpMmaSimt, - EpilogueOutputOp, - EpilogueOutputOp::kCount, - OutputTileIterator>::Epilogue; + typename EpilogueBase::OutputTileThreadMap, + typename EpilogueOutputOp::ElementOutput>; + + using Epilogue = cutlass::epilogue::threadblock::Epilogue< + typename EpilogueBase::Shape, + typename EpilogueBase::WarpMmaTensorOp, + EpilogueBase::kPartitionsK, + OutputTileIterator, + typename EpilogueBase::AccumulatorFragmentIterator, + typename EpilogueBase::WarpTileIterator, + typename EpilogueBase::SharedLoadIterator, + typename EpilogueBase::OutputOp, + typename EpilogueBase::Padding, + GetFragmentsPerIter::value>; // Define the kernel using Kernel = ImplicitGemmNA2d< diff --git a/csrc/include/natten/cuda/gemm/na1d.cuh b/csrc/include/natten/cuda/gemm/na1d.cuh index e7dde02..f8f5893 100644 --- a/csrc/include/natten/cuda/gemm/na1d.cuh +++ b/csrc/include/natten/cuda/gemm/na1d.cuh @@ -33,13 +33,13 @@ #include #include +#include #include #include #include #include #include #include -#include namespace natten { namespace cuda { @@ -93,13 +93,13 @@ struct NA1DLauncher { DeviceKernel gemm; cutlass::Status status = gemm.can_implement(arguments); - CUTLASS_CHECK(status); + NATTEN_CUTLASS_CHECK(status); status = gemm.initialize(arguments, nullptr, stream); - CUTLASS_CHECK(status); + NATTEN_CUTLASS_CHECK(status); status = gemm(stream); - CUTLASS_CHECK(status); + NATTEN_CUTLASS_CHECK(status); } public: diff --git a/csrc/include/natten/cuda/gemm/na2d.cuh b/csrc/include/natten/cuda/gemm/na2d.cuh index 082052e..d9839b1 100644 --- a/csrc/include/natten/cuda/gemm/na2d.cuh +++ b/csrc/include/natten/cuda/gemm/na2d.cuh @@ -33,13 +33,13 @@ #include #include +#include #include #include #include #include #include #include -#include namespace natten { namespace cuda { @@ -93,13 +93,13 @@ struct NA2DLauncher { DeviceKernel gemm; cutlass::Status status = gemm.can_implement(arguments); - CUTLASS_CHECK(status); + NATTEN_CUTLASS_CHECK(status); status = gemm.initialize(arguments, nullptr, stream); - CUTLASS_CHECK(status); + NATTEN_CUTLASS_CHECK(status); status = gemm(stream); - CUTLASS_CHECK(status); + NATTEN_CUTLASS_CHECK(status); } public: @@ -251,7 +251,8 @@ struct PointwiseNeighborhood2D { // PN refs auto layout_ab = LayoutOperand( dim, dim * width, dim * height * width, dim * height * width * heads); - auto layout_c = LayoutOperand(attn_stride_3, attn_stride_2, attn_stride_1, attn_stride_0); + auto layout_c = LayoutOperand( + attn_stride_3, attn_stride_2, attn_stride_1, attn_stride_0); auto ref_a = RefOperand(static_cast(ptr_query), layout_ab); auto ref_b = RefOperand(static_cast(ptr_key), layout_ab); auto ref_c = RefOutput(static_cast(ptr_attn), layout_c); @@ -374,7 +375,8 @@ struct NeighborhoodNeighborhood2D { const float scale, cudaStream_t stream) { // NN refs - auto layout_a = LayoutOperand(attn_stride_3, attn_stride_2, attn_stride_1, attn_stride_0); + auto layout_a = LayoutOperand( + attn_stride_3, attn_stride_2, attn_stride_1, attn_stride_0); auto layout_bc = LayoutOperand( dim, dim * width, dim * height * width, dim * height * width * heads); auto ref_a = RefOperand(static_cast(ptr_attn), layout_a); @@ -477,7 +479,8 @@ struct InverseNeighborhood2D { const float scale, cudaStream_t stream) { // IN refs - auto layout_a = LayoutOperand(attn_stride_3, attn_stride_2, attn_stride_1, attn_stride_0); + auto layout_a = LayoutOperand( + attn_stride_3, attn_stride_2, attn_stride_1, attn_stride_0); auto layout_bc = LayoutOperand( dim, dim * width, dim * height * width, dim * height * width * heads); auto ref_a = RefOperand(static_cast(ptr_attn), layout_a); diff --git a/csrc/include/natten/cuda/gemm/threadblock/default_epilogue_simt.cuh b/csrc/include/natten/cuda/gemm/threadblock/default_epilogue_simt.cuh deleted file mode 100644 index b5d932a..0000000 --- a/csrc/include/natten/cuda/gemm/threadblock/default_epilogue_simt.cuh +++ /dev/null @@ -1,167 +0,0 @@ -/*************************************************************************************************** - * Copyright (c) 2022-2024 Ali Hassani. - * NATTEN's CUTLASS kernels started off from CUTLASS 2.X's implicit GEMM kernels - *for convolution. - **************************************************************************************************/ -/*************************************************************************************************** - * Copyright (c) 2017 - 2024 NVIDIA CORPORATION & AFFILIATES. All rights - *reserved. SPDX-License-Identifier: BSD-3-Clause - * - * Redistribution and use in source and binary forms, with or without - * modification, are permitted provided that the following conditions are met: - * - * 1. Redistributions of source code must retain the above copyright notice, - *this list of conditions and the following disclaimer. - * - * 2. Redistributions in binary form must reproduce the above copyright notice, - * this list of conditions and the following disclaimer in the documentation - * and/or other materials provided with the distribution. - * - * 3. Neither the name of the copyright holder nor the names of its - * contributors may be used to endorse or promote products derived from - * this software without specific prior written permission. - * - * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" - * AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE - * IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE - *ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE - *LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR - *CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF - *SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS - *INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN - *CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) - *ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE - *POSSIBILITY OF SUCH DAMAGE. - * - **************************************************************************************************/ -/*! \file - \brief Epilogue for threadblock scoped GEMMs using SIMT. - - The epilogue rearranges the result of a matrix product through shared memory - to match canonical tensor layouts in global memory. Epilogues support - conversion and reduction operations. - -*/ - -#pragma once - -#include "cutlass/array.h" -#include "cutlass/cutlass.h" -#include "cutlass/numeric_types.h" - -#include "cutlass/arch/mma.h" - -#include "cutlass/gemm/gemm.h" -#include "cutlass/gemm/warp/mma.h" - -#include "cutlass/epilogue/thread/conversion_op.h" -#include "cutlass/epilogue/thread/linear_combination.h" -#include "cutlass/epilogue/thread/linear_combination_clamp.h" -#include "cutlass/epilogue/thread/linear_combination_gelu.h" -#include "cutlass/epilogue/thread/linear_combination_planar_complex.h" -#include "cutlass/epilogue/thread/linear_combination_relu.h" -#include "cutlass/epilogue/thread/linear_combination_sigmoid.h" -#include "cutlass/epilogue/thread/reduction_op.h" - -#include "cutlass/transform/threadblock/regular_tile_iterator_pitch_linear.h" - -#include "cutlass/epilogue/threadblock/default_thread_map_simt.h" -#include "cutlass/epilogue/warp/fragment_iterator_simt.h" -#include "cutlass/epilogue/warp/tile_iterator_simt.h" -#include "cutlass/transform/pitch_linear_thread_map.h" - -#include "cutlass/epilogue/threadblock/epilogue.h" -#include "cutlass/epilogue/threadblock/epilogue_depthwise.h" -#include "cutlass/epilogue/threadblock/predicated_tile_iterator.h" -#include "cutlass/epilogue/threadblock/predicated_tile_iterator_affine.h" -#include "cutlass/epilogue/threadblock/predicated_tile_iterator_direct_conv.h" -#include "cutlass/epilogue/threadblock/predicated_tile_iterator_strided_dgrad.h" -#include "cutlass/epilogue/threadblock/shared_load_iterator.h" -#include "cutlass/epilogue/threadblock/shared_load_iterator_pitch_liner.h" - -#include "cutlass/layout/permute.h" - -///////////////////////////////////////////////////////////////////////////////////////////////// - -namespace natten { -namespace cuda { -namespace gemm { -namespace threadblock { - -///////////////////////////////////////////////////////////////////////////////////////////////// - -/// Defines sensible defaults for epilogues for SimtOps. -template < - typename Shape_, - typename WarpMmaSimt_, - typename OutputOp_, - int ElementsPerAccess, - typename OutputTileIterator, - bool ScatterD = false, - typename PermuteDLayout = cutlass::layout::NoPermute> -struct DefaultEpilogueSimt { - using Shape = Shape_; - using WarpMmaSimt = WarpMmaSimt_; - using OutputOp = OutputOp_; - static int const kElementsPerAccess = ElementsPerAccess; - static const int kPartitionsK = Shape::kK / WarpMmaSimt::Shape::kK; - - using ElementOutput = typename OutputOp::ElementOutput; - using LayoutC = typename WarpMmaSimt::LayoutC; - using ElementAccumulator = typename WarpMmaSimt::ElementC; - - // - // Thread map - // - - using OutputTileThreadMap = - typename cutlass::epilogue::threadblock::DefaultThreadMapSimt< - Shape, - typename WarpMmaSimt::Shape, - typename WarpMmaSimt::Policy, - kPartitionsK, - ElementOutput, - kElementsPerAccess>::Type; - - using AccumulatorFragmentIterator = - cutlass::epilogue::warp::FragmentIteratorSimt< - typename WarpMmaSimt::Shape, - typename WarpMmaSimt::ThreadMma, - cutlass::layout::RowMajor, - typename WarpMmaSimt::Policy>; - - using WarpTileIterator = cutlass::epilogue::warp::TileIteratorSimt< - typename WarpMmaSimt::Shape, - typename WarpMmaSimt::ThreadMma, - ElementAccumulator, - cutlass::layout::RowMajor, - typename WarpMmaSimt::Policy>; - - using SharedLoadIterator = cutlass::epilogue::threadblock::SharedLoadIterator< - typename OutputTileThreadMap::CompactedThreadMap, - ElementAccumulator>; - - /// Hard-coded padding elements added - using Padding = typename WarpTileIterator::Padding; - - // - // Define the epilogue - // - using Epilogue = cutlass::epilogue::threadblock::Epilogue< - Shape, - WarpMmaSimt, - kPartitionsK, - OutputTileIterator, - AccumulatorFragmentIterator, - WarpTileIterator, - SharedLoadIterator, - OutputOp, - Padding>; -}; - -} // namespace threadblock -} // namespace gemm -} // namespace cuda -} // namespace natten - -//////////////////////////////////////////////////////////////////////////////// diff --git a/csrc/include/natten/cuda/gemm/threadblock/default_epilogue_tensor_op.cuh b/csrc/include/natten/cuda/gemm/threadblock/default_epilogue_tensor_op.cuh deleted file mode 100644 index 1626bcc..0000000 --- a/csrc/include/natten/cuda/gemm/threadblock/default_epilogue_tensor_op.cuh +++ /dev/null @@ -1,173 +0,0 @@ -/*************************************************************************************************** - * Copyright (c) 2022-2024 Ali Hassani. - * NATTEN's CUTLASS kernels started off from CUTLASS 2.X's implicit GEMM kernels - *for convolution. - **************************************************************************************************/ -/*************************************************************************************************** - * Copyright (c) 2017 - 2024 NVIDIA CORPORATION & AFFILIATES. All rights - *reserved. SPDX-License-Identifier: BSD-3-Clause - * - * Redistribution and use in source and binary forms, with or without - * modification, are permitted provided that the following conditions are met: - * - * 1. Redistributions of source code must retain the above copyright notice, - *this list of conditions and the following disclaimer. - * - * 2. Redistributions in binary form must reproduce the above copyright notice, - * this list of conditions and the following disclaimer in the documentation - * and/or other materials provided with the distribution. - * - * 3. Neither the name of the copyright holder nor the names of its - * contributors may be used to endorse or promote products derived from - * this software without specific prior written permission. - * - * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" - * AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE - * IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE - *ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE - *LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR - *CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF - *SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS - *INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN - *CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) - *ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE - *POSSIBILITY OF SUCH DAMAGE. - * - **************************************************************************************************/ -/*! \file - \brief Epilogue for threadblock scoped GEMMs using Tensor Ops. - - The epilogue rearranges the result of a matrix product through shared memory - to match canonical tensor layouts in global memory. Epilogues support - conversion and reduction operations. - -*/ - -#pragma once - -#include -#include -#include - -#include - -#include - -#include -#include -#include -#include -#include -#include -#include -#include - -#include -#include - -#include - -#include -#include -#include -#include -#include -#include -#include -#include - -#include -#include - -#include - -//////////////////////////////////////////////////////////////////////////////// - -namespace natten { -namespace cuda { -namespace gemm { -namespace threadblock { - -//////////////////////////////////////////////////////////////////////////////// - -/// Defines sensible defaults for epilogues for TensorOps. -template < - typename Shape_, - typename WarpMmaTensorOp_, - int PartitionsK, - typename OutputOp_, - int ElementsPerAccess, - typename OutputTileIterator> -struct DefaultEpilogueTensorOp { - using Shape = Shape_; - using WarpMmaTensorOp = WarpMmaTensorOp_; - static int const kPartitionsK = PartitionsK; - using OutputOp = OutputOp_; - static int const kElementsPerAccess = ElementsPerAccess; - - using ElementOutput = typename OutputOp::ElementOutput; - using LayoutC = typename WarpMmaTensorOp::LayoutC; - using ElementAccumulator = typename WarpMmaTensorOp::ElementC; - - // - // Thread map - // - - using AccumulatorFragmentIterator = typename cutlass::platform::conditional< - cutlass::is_complex::value, - cutlass::epilogue::warp::FragmentIteratorComplexTensorOp< - typename WarpMmaTensorOp::Shape, - typename WarpMmaTensorOp::Policy::Operator::Shape, - typename WarpMmaTensorOp::Policy::Operator::ElementC, - typename WarpMmaTensorOp::Policy::Operator::FragmentC, - LayoutC>, - cutlass::epilogue::warp::FragmentIteratorTensorOp< - typename WarpMmaTensorOp::Shape, - typename WarpMmaTensorOp::Policy::Operator::Shape, - typename WarpMmaTensorOp::Policy::Operator::ElementC, - typename WarpMmaTensorOp::Policy::Operator::FragmentC, - LayoutC>>::type; - - /// Support several implementations depending on structure of epilogue - using DefaultIterators = - cutlass::epilogue::threadblock::detail::DefaultIteratorsTensorOp< - ElementOutput, - ElementAccumulator, - kElementsPerAccess, - Shape, - typename WarpMmaTensorOp::Shape, - typename WarpMmaTensorOp::Policy::Operator::Shape, - typename OutputTileIterator::ThreadMap::CompactedThreadMap>; - - using WarpTileIterator = typename DefaultIterators::WarpTileIterator; - using SharedLoadIterator = typename DefaultIterators::SharedLoadIterator; - - /// Hard-coded padding elements added - using Padding = cutlass:: - MatrixShape<0, 64 / cutlass::sizeof_bits::value * 4>; - - static int const kFragmentsPerIteration = - (kPartitionsK == 1 ? DefaultIterators::kFragmentsPerIteration : 1); - - // - // Define the epilogue - // - using Epilogue = cutlass::epilogue::threadblock::Epilogue< - Shape, - WarpMmaTensorOp, - kPartitionsK, - OutputTileIterator, - AccumulatorFragmentIterator, - WarpTileIterator, - SharedLoadIterator, - OutputOp, - Padding, - kFragmentsPerIteration>; -}; - -} // namespace threadblock -} // namespace gemm -} // namespace cuda -} // namespace natten - -//////////////////////////////////////////////////////////////////////////////// diff --git a/csrc/include/natten/cuda/gemm/threadblock/na1d_in_output_tile_iterator.cuh b/csrc/include/natten/cuda/gemm/threadblock/na1d_in_output_tile_iterator.cuh index 5e761b9..25d54e1 100644 --- a/csrc/include/natten/cuda/gemm/threadblock/na1d_in_output_tile_iterator.cuh +++ b/csrc/include/natten/cuda/gemm/threadblock/na1d_in_output_tile_iterator.cuh @@ -341,12 +341,12 @@ class NA1dINOutputTileIterator { } } - /// Adds a pointer offset in units of Element - CUTLASS_HOST_DEVICE - void add_pointer_offset(LongIndex pointer_offset) { - store_byte_pointer_ += - pointer_offset * cutlass::sizeof_bits::value / 8; - } + ///// Adds a pointer offset in units of Element + // CUTLASS_HOST_DEVICE + // void add_pointer_offset(LongIndex pointer_offset) { + // store_byte_pointer_ += + // pointer_offset * cutlass::sizeof_bits::value / 8; + //} /// Loads a fragment from memory CUTLASS_DEVICE @@ -406,14 +406,14 @@ class NA1dINOutputTileIterator { } } - if (group + 1 < ThreadMap::Iterations::kGroup) { - byte_pointer += params_.increment_group; - } + // if (group + 1 < ThreadMap::Iterations::kGroup) { + // byte_pointer += params_.increment_group; + //} } - if (cluster + 1 < ThreadMap::Iterations::kCluster) { - byte_pointer += params_.increment_cluster; - } + // if (cluster + 1 < ThreadMap::Iterations::kCluster) { + // byte_pointer += params_.increment_cluster; + //} } } @@ -450,7 +450,7 @@ class NA1dINOutputTileIterator { if (state_[0] == ThreadMap::Count::kRow) { state_[0] = 0; ++state_[1]; - store_byte_pointer_ += params_.advance_group; + // store_byte_pointer_ += params_.advance_group; thread_start_row_ += (ThreadMap::Shape::kGroup - 1) * ThreadMap::Shape::kRow * ThreadMap::Count::kRow; @@ -458,7 +458,7 @@ class NA1dINOutputTileIterator { if (state_[1] == ThreadMap::Count::kGroup) { state_[1] = 0; ++state_[2]; - store_byte_pointer_ += params_.advance_cluster; + // store_byte_pointer_ += params_.advance_cluster; thread_start_row_ += ThreadMap::Count::kGroup * ThreadMap::Shape::kGroup * ThreadMap::Count::kRow * @@ -466,7 +466,7 @@ class NA1dINOutputTileIterator { if (state_[2] == ThreadMap::Count::kCluster) { state_[2] = 0; - store_byte_pointer_ += params_.advance_tile; + // store_byte_pointer_ += params_.advance_tile; thread_start_row_ += ThreadMap::Shape::kGroup * ThreadMap::Shape::kRow * ThreadMap::Shape::kCluster * @@ -489,7 +489,7 @@ class NA1dINOutputTileIterator { int increment_row = state_[0] / ThreadMap::Count::kRow; state_[0] = state_[0] % ThreadMap::Count::kRow; - store_byte_pointer_ += (params_.advance_row * increment); + // store_byte_pointer_ += (params_.advance_row * increment); thread_start_row_ += (ThreadMap::Shape::kRow * increment); // Group @@ -497,7 +497,7 @@ class NA1dINOutputTileIterator { int increment_group = state_[1] / ThreadMap::Count::kGroup; state_[1] = state_[1] % ThreadMap::Count::kGroup; - store_byte_pointer_ += (params_.advance_group * increment_row); + // store_byte_pointer_ += (params_.advance_group * increment_row); thread_start_row_ += (ThreadMap::Shape::kGroup - 1) * ThreadMap::Shape::kRow * ThreadMap::Count::kRow * increment_row; @@ -506,12 +506,12 @@ class NA1dINOutputTileIterator { int increment_cluster = state_[2] / ThreadMap::Count::kCluster; state_[2] = state_[2] % ThreadMap::Count::kCluster; - store_byte_pointer_ += (params_.advance_cluster * increment_group); + // store_byte_pointer_ += (params_.advance_cluster * increment_group); thread_start_row_ += ThreadMap::Count::kGroup * ThreadMap::Shape::kGroup * ThreadMap::Count::kRow * ThreadMap::Shape::kRow * increment_group; // Tile - store_byte_pointer_ += (params_.advance_tile * increment_cluster); + // store_byte_pointer_ += (params_.advance_tile * increment_cluster); thread_start_row_ += ThreadMap::Shape::kGroup * ThreadMap::Shape::kRow * ThreadMap::Shape::kCluster * ThreadMap::Shape::kTile * increment_cluster; diff --git a/csrc/include/natten/cuda/gemm/threadblock/na1d_nn_output_tile_iterator.cuh b/csrc/include/natten/cuda/gemm/threadblock/na1d_nn_output_tile_iterator.cuh index ed75f1d..209064a 100644 --- a/csrc/include/natten/cuda/gemm/threadblock/na1d_nn_output_tile_iterator.cuh +++ b/csrc/include/natten/cuda/gemm/threadblock/na1d_nn_output_tile_iterator.cuh @@ -341,12 +341,12 @@ class NA1dNNOutputTileIterator { } } - /// Adds a pointer offset in units of Element - CUTLASS_HOST_DEVICE - void add_pointer_offset(LongIndex pointer_offset) { - store_byte_pointer_ += - pointer_offset * cutlass::sizeof_bits::value / 8; - } + ///// Adds a pointer offset in units of Element + // CUTLASS_HOST_DEVICE + // void add_pointer_offset(LongIndex pointer_offset) { + // store_byte_pointer_ += + // pointer_offset * cutlass::sizeof_bits::value / 8; + //} /// Loads a fragment from memory CUTLASS_DEVICE @@ -406,14 +406,14 @@ class NA1dNNOutputTileIterator { } } - if (group + 1 < ThreadMap::Iterations::kGroup) { - byte_pointer += params_.increment_group; - } + // if (group + 1 < ThreadMap::Iterations::kGroup) { + // byte_pointer += params_.increment_group; + //} } - if (cluster + 1 < ThreadMap::Iterations::kCluster) { - byte_pointer += params_.increment_cluster; - } + // if (cluster + 1 < ThreadMap::Iterations::kCluster) { + // byte_pointer += params_.increment_cluster; + //} } } @@ -450,7 +450,7 @@ class NA1dNNOutputTileIterator { if (state_[0] == ThreadMap::Count::kRow) { state_[0] = 0; ++state_[1]; - store_byte_pointer_ += params_.advance_group; + // store_byte_pointer_ += params_.advance_group; thread_start_row_ += (ThreadMap::Shape::kGroup - 1) * ThreadMap::Shape::kRow * ThreadMap::Count::kRow; @@ -458,7 +458,7 @@ class NA1dNNOutputTileIterator { if (state_[1] == ThreadMap::Count::kGroup) { state_[1] = 0; ++state_[2]; - store_byte_pointer_ += params_.advance_cluster; + // store_byte_pointer_ += params_.advance_cluster; thread_start_row_ += ThreadMap::Count::kGroup * ThreadMap::Shape::kGroup * ThreadMap::Count::kRow * @@ -466,7 +466,7 @@ class NA1dNNOutputTileIterator { if (state_[2] == ThreadMap::Count::kCluster) { state_[2] = 0; - store_byte_pointer_ += params_.advance_tile; + // store_byte_pointer_ += params_.advance_tile; thread_start_row_ += ThreadMap::Shape::kGroup * ThreadMap::Shape::kRow * ThreadMap::Shape::kCluster * @@ -489,7 +489,7 @@ class NA1dNNOutputTileIterator { int increment_row = state_[0] / ThreadMap::Count::kRow; state_[0] = state_[0] % ThreadMap::Count::kRow; - store_byte_pointer_ += (params_.advance_row * increment); + // store_byte_pointer_ += (params_.advance_row * increment); thread_start_row_ += (ThreadMap::Shape::kRow * increment); // Group @@ -497,7 +497,7 @@ class NA1dNNOutputTileIterator { int increment_group = state_[1] / ThreadMap::Count::kGroup; state_[1] = state_[1] % ThreadMap::Count::kGroup; - store_byte_pointer_ += (params_.advance_group * increment_row); + // store_byte_pointer_ += (params_.advance_group * increment_row); thread_start_row_ += (ThreadMap::Shape::kGroup - 1) * ThreadMap::Shape::kRow * ThreadMap::Count::kRow * increment_row; @@ -506,12 +506,12 @@ class NA1dNNOutputTileIterator { int increment_cluster = state_[2] / ThreadMap::Count::kCluster; state_[2] = state_[2] % ThreadMap::Count::kCluster; - store_byte_pointer_ += (params_.advance_cluster * increment_group); + // store_byte_pointer_ += (params_.advance_cluster * increment_group); thread_start_row_ += ThreadMap::Count::kGroup * ThreadMap::Shape::kGroup * ThreadMap::Count::kRow * ThreadMap::Shape::kRow * increment_group; // Tile - store_byte_pointer_ += (params_.advance_tile * increment_cluster); + // store_byte_pointer_ += (params_.advance_tile * increment_cluster); thread_start_row_ += ThreadMap::Shape::kGroup * ThreadMap::Shape::kRow * ThreadMap::Shape::kCluster * ThreadMap::Shape::kTile * increment_cluster; diff --git a/csrc/include/natten/cuda/gemm/threadblock/na1d_pn_output_tile_iterator.cuh b/csrc/include/natten/cuda/gemm/threadblock/na1d_pn_output_tile_iterator.cuh index 27198f0..bddd058 100644 --- a/csrc/include/natten/cuda/gemm/threadblock/na1d_pn_output_tile_iterator.cuh +++ b/csrc/include/natten/cuda/gemm/threadblock/na1d_pn_output_tile_iterator.cuh @@ -224,12 +224,12 @@ class NA1dPNOutputTileIterator { state_[0] = state_[1] = state_[2] = 0; } - /// Adds a pointer offset in units of Element - CUTLASS_HOST_DEVICE - void add_pointer_offset(LongIndex pointer_offset) { - store_byte_pointer_ += - pointer_offset * cutlass::sizeof_bits::value / 8; - } + ///// Adds a pointer offset in units of Element + // CUTLASS_HOST_DEVICE + // void add_pointer_offset(LongIndex pointer_offset) { + // store_byte_pointer_ += + // pointer_offset * cutlass::sizeof_bits::value / 8; + //} /// Loads a fragment from memory CUTLASS_DEVICE @@ -291,14 +291,14 @@ class NA1dPNOutputTileIterator { } } - if (group + 1 < ThreadMap::Iterations::kGroup) { - byte_pointer += params_.increment_group; - } + // if (group + 1 < ThreadMap::Iterations::kGroup) { + // byte_pointer += params_.increment_group; + //} } - if (cluster + 1 < ThreadMap::Iterations::kCluster) { - byte_pointer += params_.increment_cluster; - } + // if (cluster + 1 < ThreadMap::Iterations::kCluster) { + // byte_pointer += params_.increment_cluster; + //} } } @@ -392,14 +392,14 @@ class NA1dPNOutputTileIterator { } } - if (group + 1 < ThreadMap::Iterations::kGroup) { - byte_pointer += params_.increment_group; - } + // if (group + 1 < ThreadMap::Iterations::kGroup) { + // byte_pointer += params_.increment_group; + //} } - if (cluster + 1 < ThreadMap::Iterations::kCluster) { - byte_pointer += params_.increment_cluster; - } + // if (cluster + 1 < ThreadMap::Iterations::kCluster) { + // byte_pointer += params_.increment_cluster; + //} } } @@ -436,7 +436,7 @@ class NA1dPNOutputTileIterator { if (state_[0] == ThreadMap::Count::kRow) { state_[0] = 0; ++state_[1]; - store_byte_pointer_ += params_.advance_group; + // store_byte_pointer_ += params_.advance_group; thread_start_row_ += (ThreadMap::Shape::kGroup - 1) * ThreadMap::Shape::kRow * ThreadMap::Count::kRow; @@ -444,7 +444,7 @@ class NA1dPNOutputTileIterator { if (state_[1] == ThreadMap::Count::kGroup) { state_[1] = 0; ++state_[2]; - store_byte_pointer_ += params_.advance_cluster; + // store_byte_pointer_ += params_.advance_cluster; thread_start_row_ += ThreadMap::Count::kGroup * ThreadMap::Shape::kGroup * ThreadMap::Count::kRow * @@ -452,7 +452,7 @@ class NA1dPNOutputTileIterator { if (state_[2] == ThreadMap::Count::kCluster) { state_[2] = 0; - store_byte_pointer_ += params_.advance_tile; + // store_byte_pointer_ += params_.advance_tile; thread_start_row_ += ThreadMap::Shape::kGroup * ThreadMap::Shape::kRow * ThreadMap::Shape::kCluster * @@ -472,7 +472,7 @@ class NA1dPNOutputTileIterator { int increment_row = state_[0] / ThreadMap::Count::kRow; state_[0] = state_[0] % ThreadMap::Count::kRow; - store_byte_pointer_ += (params_.advance_row * increment); + // store_byte_pointer_ += (params_.advance_row * increment); thread_start_row_ += (ThreadMap::Shape::kRow * increment); // Group @@ -480,7 +480,7 @@ class NA1dPNOutputTileIterator { int increment_group = state_[1] / ThreadMap::Count::kGroup; state_[1] = state_[1] % ThreadMap::Count::kGroup; - store_byte_pointer_ += (params_.advance_group * increment_row); + // store_byte_pointer_ += (params_.advance_group * increment_row); thread_start_row_ += (ThreadMap::Shape::kGroup - 1) * ThreadMap::Shape::kRow * ThreadMap::Count::kRow * increment_row; @@ -489,12 +489,12 @@ class NA1dPNOutputTileIterator { int increment_cluster = state_[2] / ThreadMap::Count::kCluster; state_[2] = state_[2] % ThreadMap::Count::kCluster; - store_byte_pointer_ += (params_.advance_cluster * increment_group); + // store_byte_pointer_ += (params_.advance_cluster * increment_group); thread_start_row_ += ThreadMap::Count::kGroup * ThreadMap::Shape::kGroup * ThreadMap::Count::kRow * ThreadMap::Shape::kRow * increment_group; // Tile - store_byte_pointer_ += (params_.advance_tile * increment_cluster); + // store_byte_pointer_ += (params_.advance_tile * increment_cluster); thread_start_row_ += ThreadMap::Shape::kGroup * ThreadMap::Shape::kRow * ThreadMap::Shape::kCluster * ThreadMap::Shape::kTile * increment_cluster; diff --git a/csrc/include/natten/cuda/gemm/threadblock/na2d_in_output_tile_iterator.cuh b/csrc/include/natten/cuda/gemm/threadblock/na2d_in_output_tile_iterator.cuh index e14fe42..99b3bcc 100644 --- a/csrc/include/natten/cuda/gemm/threadblock/na2d_in_output_tile_iterator.cuh +++ b/csrc/include/natten/cuda/gemm/threadblock/na2d_in_output_tile_iterator.cuh @@ -348,12 +348,12 @@ class NA2dINOutputTileIterator { } } - /// Adds a pointer offset in units of Element - CUTLASS_HOST_DEVICE - void add_pointer_offset(LongIndex pointer_offset) { - store_byte_pointer_ += - pointer_offset * cutlass::sizeof_bits::value / 8; - } + ///// Adds a pointer offset in units of Element + // CUTLASS_HOST_DEVICE + // void add_pointer_offset(LongIndex pointer_offset) { + // store_byte_pointer_ += + // pointer_offset * cutlass::sizeof_bits::value / 8; + //} /// Loads a fragment from memory CUTLASS_DEVICE @@ -413,14 +413,14 @@ class NA2dINOutputTileIterator { } } - if (group + 1 < ThreadMap::Iterations::kGroup) { - byte_pointer += params_.increment_group; - } + // if (group + 1 < ThreadMap::Iterations::kGroup) { + // byte_pointer += params_.increment_group; + //} } - if (cluster + 1 < ThreadMap::Iterations::kCluster) { - byte_pointer += params_.increment_cluster; - } + // if (cluster + 1 < ThreadMap::Iterations::kCluster) { + // byte_pointer += params_.increment_cluster; + //} } } @@ -457,7 +457,7 @@ class NA2dINOutputTileIterator { if (state_[0] == ThreadMap::Count::kRow) { state_[0] = 0; ++state_[1]; - store_byte_pointer_ += params_.advance_group; + // store_byte_pointer_ += params_.advance_group; thread_start_row_ += (ThreadMap::Shape::kGroup - 1) * ThreadMap::Shape::kRow * ThreadMap::Count::kRow; @@ -465,7 +465,7 @@ class NA2dINOutputTileIterator { if (state_[1] == ThreadMap::Count::kGroup) { state_[1] = 0; ++state_[2]; - store_byte_pointer_ += params_.advance_cluster; + // store_byte_pointer_ += params_.advance_cluster; thread_start_row_ += ThreadMap::Count::kGroup * ThreadMap::Shape::kGroup * ThreadMap::Count::kRow * @@ -473,7 +473,7 @@ class NA2dINOutputTileIterator { if (state_[2] == ThreadMap::Count::kCluster) { state_[2] = 0; - store_byte_pointer_ += params_.advance_tile; + // store_byte_pointer_ += params_.advance_tile; thread_start_row_ += ThreadMap::Shape::kGroup * ThreadMap::Shape::kRow * ThreadMap::Shape::kCluster * @@ -496,7 +496,7 @@ class NA2dINOutputTileIterator { int increment_row = state_[0] / ThreadMap::Count::kRow; state_[0] = state_[0] % ThreadMap::Count::kRow; - store_byte_pointer_ += (params_.advance_row * increment); + // store_byte_pointer_ += (params_.advance_row * increment); thread_start_row_ += (ThreadMap::Shape::kRow * increment); // Group @@ -504,7 +504,7 @@ class NA2dINOutputTileIterator { int increment_group = state_[1] / ThreadMap::Count::kGroup; state_[1] = state_[1] % ThreadMap::Count::kGroup; - store_byte_pointer_ += (params_.advance_group * increment_row); + // store_byte_pointer_ += (params_.advance_group * increment_row); thread_start_row_ += (ThreadMap::Shape::kGroup - 1) * ThreadMap::Shape::kRow * ThreadMap::Count::kRow * increment_row; @@ -513,12 +513,12 @@ class NA2dINOutputTileIterator { int increment_cluster = state_[2] / ThreadMap::Count::kCluster; state_[2] = state_[2] % ThreadMap::Count::kCluster; - store_byte_pointer_ += (params_.advance_cluster * increment_group); + // store_byte_pointer_ += (params_.advance_cluster * increment_group); thread_start_row_ += ThreadMap::Count::kGroup * ThreadMap::Shape::kGroup * ThreadMap::Count::kRow * ThreadMap::Shape::kRow * increment_group; // Tile - store_byte_pointer_ += (params_.advance_tile * increment_cluster); + // store_byte_pointer_ += (params_.advance_tile * increment_cluster); thread_start_row_ += ThreadMap::Shape::kGroup * ThreadMap::Shape::kRow * ThreadMap::Shape::kCluster * ThreadMap::Shape::kTile * increment_cluster; diff --git a/csrc/include/natten/cuda/gemm/threadblock/na2d_nn_output_tile_iterator.cuh b/csrc/include/natten/cuda/gemm/threadblock/na2d_nn_output_tile_iterator.cuh index fb9c7a3..66d4b30 100644 --- a/csrc/include/natten/cuda/gemm/threadblock/na2d_nn_output_tile_iterator.cuh +++ b/csrc/include/natten/cuda/gemm/threadblock/na2d_nn_output_tile_iterator.cuh @@ -348,12 +348,12 @@ class NA2dNNOutputTileIterator { } } - /// Adds a pointer offset in units of Element - CUTLASS_HOST_DEVICE - void add_pointer_offset(LongIndex pointer_offset) { - store_byte_pointer_ += - pointer_offset * cutlass::sizeof_bits::value / 8; - } + ///// Adds a pointer offset in units of Element + // CUTLASS_HOST_DEVICE + // void add_pointer_offset(LongIndex pointer_offset) { + // store_byte_pointer_ += + // pointer_offset * cutlass::sizeof_bits::value / 8; + //} /// Loads a fragment from memory CUTLASS_DEVICE @@ -413,14 +413,14 @@ class NA2dNNOutputTileIterator { } } - if (group + 1 < ThreadMap::Iterations::kGroup) { - byte_pointer += params_.increment_group; - } + // if (group + 1 < ThreadMap::Iterations::kGroup) { + // byte_pointer += params_.increment_group; + //} } - if (cluster + 1 < ThreadMap::Iterations::kCluster) { - byte_pointer += params_.increment_cluster; - } + // if (cluster + 1 < ThreadMap::Iterations::kCluster) { + // byte_pointer += params_.increment_cluster; + //} } } @@ -457,7 +457,7 @@ class NA2dNNOutputTileIterator { if (state_[0] == ThreadMap::Count::kRow) { state_[0] = 0; ++state_[1]; - store_byte_pointer_ += params_.advance_group; + // store_byte_pointer_ += params_.advance_group; thread_start_row_ += (ThreadMap::Shape::kGroup - 1) * ThreadMap::Shape::kRow * ThreadMap::Count::kRow; @@ -465,7 +465,7 @@ class NA2dNNOutputTileIterator { if (state_[1] == ThreadMap::Count::kGroup) { state_[1] = 0; ++state_[2]; - store_byte_pointer_ += params_.advance_cluster; + // store_byte_pointer_ += params_.advance_cluster; thread_start_row_ += ThreadMap::Count::kGroup * ThreadMap::Shape::kGroup * ThreadMap::Count::kRow * @@ -473,7 +473,7 @@ class NA2dNNOutputTileIterator { if (state_[2] == ThreadMap::Count::kCluster) { state_[2] = 0; - store_byte_pointer_ += params_.advance_tile; + // store_byte_pointer_ += params_.advance_tile; thread_start_row_ += ThreadMap::Shape::kGroup * ThreadMap::Shape::kRow * ThreadMap::Shape::kCluster * @@ -496,7 +496,7 @@ class NA2dNNOutputTileIterator { int increment_row = state_[0] / ThreadMap::Count::kRow; state_[0] = state_[0] % ThreadMap::Count::kRow; - store_byte_pointer_ += (params_.advance_row * increment); + // store_byte_pointer_ += (params_.advance_row * increment); thread_start_row_ += (ThreadMap::Shape::kRow * increment); // Group @@ -504,7 +504,7 @@ class NA2dNNOutputTileIterator { int increment_group = state_[1] / ThreadMap::Count::kGroup; state_[1] = state_[1] % ThreadMap::Count::kGroup; - store_byte_pointer_ += (params_.advance_group * increment_row); + // store_byte_pointer_ += (params_.advance_group * increment_row); thread_start_row_ += (ThreadMap::Shape::kGroup - 1) * ThreadMap::Shape::kRow * ThreadMap::Count::kRow * increment_row; @@ -513,12 +513,12 @@ class NA2dNNOutputTileIterator { int increment_cluster = state_[2] / ThreadMap::Count::kCluster; state_[2] = state_[2] % ThreadMap::Count::kCluster; - store_byte_pointer_ += (params_.advance_cluster * increment_group); + // store_byte_pointer_ += (params_.advance_cluster * increment_group); thread_start_row_ += ThreadMap::Count::kGroup * ThreadMap::Shape::kGroup * ThreadMap::Count::kRow * ThreadMap::Shape::kRow * increment_group; // Tile - store_byte_pointer_ += (params_.advance_tile * increment_cluster); + // store_byte_pointer_ += (params_.advance_tile * increment_cluster); thread_start_row_ += ThreadMap::Shape::kGroup * ThreadMap::Shape::kRow * ThreadMap::Shape::kCluster * ThreadMap::Shape::kTile * increment_cluster; diff --git a/csrc/include/natten/cuda/gemm/threadblock/na2d_pn_output_tile_iterator.cuh b/csrc/include/natten/cuda/gemm/threadblock/na2d_pn_output_tile_iterator.cuh index cfc344b..76e9c52 100644 --- a/csrc/include/natten/cuda/gemm/threadblock/na2d_pn_output_tile_iterator.cuh +++ b/csrc/include/natten/cuda/gemm/threadblock/na2d_pn_output_tile_iterator.cuh @@ -224,12 +224,12 @@ class NA2dPNOutputTileIterator { state_[0] = state_[1] = state_[2] = 0; } - /// Adds a pointer offset in units of Element - CUTLASS_HOST_DEVICE - void add_pointer_offset(LongIndex pointer_offset) { - store_byte_pointer_ += - pointer_offset * cutlass::sizeof_bits::value / 8; - } + ///// Adds a pointer offset in units of Element + // CUTLASS_HOST_DEVICE + // void add_pointer_offset(LongIndex pointer_offset) { + // store_byte_pointer_ += + // pointer_offset * cutlass::sizeof_bits::value / 8; + //} /// Loads a fragment from memory CUTLASS_DEVICE @@ -308,14 +308,14 @@ class NA2dPNOutputTileIterator { } } - if (group + 1 < ThreadMap::Iterations::kGroup) { - byte_pointer += params_.increment_group; - } + // if (group + 1 < ThreadMap::Iterations::kGroup) { + // byte_pointer += params_.increment_group; + //} } - if (cluster + 1 < ThreadMap::Iterations::kCluster) { - byte_pointer += params_.increment_cluster; - } + // if (cluster + 1 < ThreadMap::Iterations::kCluster) { + // byte_pointer += params_.increment_cluster; + //} } } @@ -425,14 +425,14 @@ class NA2dPNOutputTileIterator { } } - if (group + 1 < ThreadMap::Iterations::kGroup) { - byte_pointer += params_.increment_group; - } + // if (group + 1 < ThreadMap::Iterations::kGroup) { + // byte_pointer += params_.increment_group; + //} } - if (cluster + 1 < ThreadMap::Iterations::kCluster) { - byte_pointer += params_.increment_cluster; - } + // if (cluster + 1 < ThreadMap::Iterations::kCluster) { + // byte_pointer += params_.increment_cluster; + //} } } @@ -469,7 +469,7 @@ class NA2dPNOutputTileIterator { if (state_[0] == ThreadMap::Count::kRow) { state_[0] = 0; ++state_[1]; - store_byte_pointer_ += params_.advance_group; + // store_byte_pointer_ += params_.advance_group; thread_start_row_ += (ThreadMap::Shape::kGroup - 1) * ThreadMap::Shape::kRow * ThreadMap::Count::kRow; @@ -477,7 +477,7 @@ class NA2dPNOutputTileIterator { if (state_[1] == ThreadMap::Count::kGroup) { state_[1] = 0; ++state_[2]; - store_byte_pointer_ += params_.advance_cluster; + // store_byte_pointer_ += params_.advance_cluster; thread_start_row_ += ThreadMap::Count::kGroup * ThreadMap::Shape::kGroup * ThreadMap::Count::kRow * @@ -485,7 +485,7 @@ class NA2dPNOutputTileIterator { if (state_[2] == ThreadMap::Count::kCluster) { state_[2] = 0; - store_byte_pointer_ += params_.advance_tile; + // store_byte_pointer_ += params_.advance_tile; thread_start_row_ += ThreadMap::Shape::kGroup * ThreadMap::Shape::kRow * ThreadMap::Shape::kCluster * @@ -505,7 +505,7 @@ class NA2dPNOutputTileIterator { int increment_row = state_[0] / ThreadMap::Count::kRow; state_[0] = state_[0] % ThreadMap::Count::kRow; - store_byte_pointer_ += (params_.advance_row * increment); + // store_byte_pointer_ += (params_.advance_row * increment); thread_start_row_ += (ThreadMap::Shape::kRow * increment); // Group @@ -513,7 +513,7 @@ class NA2dPNOutputTileIterator { int increment_group = state_[1] / ThreadMap::Count::kGroup; state_[1] = state_[1] % ThreadMap::Count::kGroup; - store_byte_pointer_ += (params_.advance_group * increment_row); + // store_byte_pointer_ += (params_.advance_group * increment_row); thread_start_row_ += (ThreadMap::Shape::kGroup - 1) * ThreadMap::Shape::kRow * ThreadMap::Count::kRow * increment_row; @@ -522,12 +522,12 @@ class NA2dPNOutputTileIterator { int increment_cluster = state_[2] / ThreadMap::Count::kCluster; state_[2] = state_[2] % ThreadMap::Count::kCluster; - store_byte_pointer_ += (params_.advance_cluster * increment_group); + // store_byte_pointer_ += (params_.advance_cluster * increment_group); thread_start_row_ += ThreadMap::Count::kGroup * ThreadMap::Shape::kGroup * ThreadMap::Count::kRow * ThreadMap::Shape::kRow * increment_group; // Tile - store_byte_pointer_ += (params_.advance_tile * increment_cluster); + // store_byte_pointer_ += (params_.advance_tile * increment_cluster); thread_start_row_ += ThreadMap::Shape::kGroup * ThreadMap::Shape::kRow * ThreadMap::Shape::kCluster * ThreadMap::Shape::kTile * increment_cluster; diff --git a/csrc/include/natten/cuda/na1d.cuh b/csrc/include/natten/cuda/na1d.cuh index 8c054b8..3665728 100644 --- a/csrc/include/natten/cuda/na1d.cuh +++ b/csrc/include/natten/cuda/na1d.cuh @@ -27,33 +27,92 @@ #pragma once #include +#include #include #ifdef NATTEN_WITH_CUTLASS #include +#include #endif namespace natten { namespace cuda { +template +void na1d_forward( + int32_t cc, + size_t max_smem, + cudaStream_t stream, + void* query_ptr, + void* key_ptr, + void* value_ptr, + void* out_ptr, + void* rpb_ptr, + int32_t batch_size, + int32_t length, + int32_t heads, + int32_t dim, + const std::tuple& kernel_size, + const std::tuple& dilation, + const std::tuple& is_causal, + float attn_scale, + const std::tuple& query_tile_size, + const std::tuple& key_tile_size) { +#ifdef NATTEN_WITH_CUTLASS + if (cc >= 80 || (cc >= 50 && !std::is_same::value)) { + natten::cuda::fna::fna_forward_generic( + cc, + max_smem, + stream, + query_ptr, + key_ptr, + value_ptr, + out_ptr, + rpb_ptr, + batch_size, + {length}, + heads, + dim, + dim, // dim_value + kernel_size, + dilation, + is_causal, + attn_scale, + nullptr, // TODO: pass logsumexp_ptr when backward kernel is implemented + query_tile_size, + key_tile_size); + } else { +#endif + NATTEN_FAILURE( + "Fused kernels are only available on devices with " + "compute capability >= 50 for FP16/FP32 inputs, and devices with " + "compute capability >= 80 for FP32, BF16, and FP16 inputs."); +#ifdef NATTEN_WITH_CUTLASS + } +#endif +} + template void na1d_qk_forward( - const int cc, + int32_t cc, + size_t max_smem, cudaStream_t stream, void* query_ptr, void* key_ptr, void* bias_ptr, void* attn_ptr, - int batch_size, - int heads, - int length, - int dim, + int32_t batch_size, + int32_t heads, + int32_t length, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, - int kernel_size, - int dilation) { + const std::tuple& kernel_size, + const std::tuple& dilation, + const std::tuple& is_causal) { #ifdef NATTEN_WITH_CUTLASS if (natten::kEnableGemmNA && + !any_true(is_causal) && // TODO: remove when GEMM supports causal masking (cc >= 80 || (cc >= 70 && std::is_same::value))) { LAUNCH_na1d_pn_cuda_gemm( cc, @@ -70,8 +129,8 @@ void na1d_qk_forward( attn_stride_0, attn_stride_1, attn_stride_2, - kernel_size, - dilation, + std::get<0>(kernel_size), + std::get<0>(dilation), 1.0, stream); } else { @@ -79,10 +138,12 @@ void na1d_qk_forward( if (bias_ptr == nullptr) { DISPATCH_DTYPE_na1d_pn_cuda_naive( T, - kernel_size, - dilation, + is_causal, + // kernel_size, + // dilation, cc, stream, + /* is_grad = */ false, query_ptr, key_ptr, attn_ptr, @@ -96,10 +157,14 @@ void na1d_qk_forward( kernel_size, dilation); } else { + NATTEN_CHECK( + !any_true(is_causal), + "Neighborhood attention with causal masking does not support positional biases yet."); DISPATCH_DTYPE_na1d_pn_bias_cuda_naive( T, - kernel_size, - dilation, + is_causal, + // kernel_size, + // dilation, cc, stream, query_ptr, @@ -123,7 +188,8 @@ void na1d_qk_forward( template void na1d_qk_backward( - const int cc, + int32_t cc, + size_t max_smem, cudaStream_t stream, void* query_ptr, void* key_ptr, @@ -131,17 +197,19 @@ void na1d_qk_backward( void* d_query_ptr, void* d_key_ptr, void* d_bias_ptr, - int batch_size, - int heads, - int length, - int dim, + int32_t batch_size, + int32_t heads, + int32_t length, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, - int kernel_size, - int dilation) { + const std::tuple& kernel_size, + const std::tuple& dilation, + const std::tuple& is_causal) { #ifdef NATTEN_WITH_CUTLASS if (natten::kEnableGemmNA && + !any_true(is_causal) && // TODO: remove when GEMM supports causal masking (cc >= 80 || (cc >= 70 && std::is_same::value))) { LAUNCH_na1d_nn_cuda_gemm( cc, @@ -157,8 +225,8 @@ void na1d_qk_backward( attn_stride_0, attn_stride_1, attn_stride_2, - kernel_size, - dilation, + std::get<0>(kernel_size), + std::get<0>(dilation), 1.0, stream); LAUNCH_na1d_in_cuda_gemm( @@ -175,16 +243,17 @@ void na1d_qk_backward( attn_stride_0, attn_stride_1, attn_stride_2, - kernel_size, - dilation, + std::get<0>(kernel_size), + std::get<0>(dilation), 1.0, stream); } else { #endif DISPATCH_DTYPE_na1d_nn_cuda_naive( T, - kernel_size, - dilation, + is_causal, + // kernel_size, + // dilation, cc, stream, d_attn_ptr, @@ -201,8 +270,9 @@ void na1d_qk_backward( dilation); DISPATCH_DTYPE_na1d_in_cuda_naive( T, - kernel_size, - dilation, + is_causal, + // kernel_size, + // dilation, cc, stream, d_attn_ptr, @@ -221,10 +291,14 @@ void na1d_qk_backward( } #endif if (d_bias_ptr != nullptr) { + NATTEN_CHECK( + !any_true(is_causal), + "Neighborhood attention with causal masking does not support positional biases yet."); DISPATCH_DTYPE_na1d_rpbgrad_cuda_naive( T, - kernel_size, - dilation, + is_causal, + // kernel_size, + // dilation, cc, stream, d_bias_ptr, @@ -243,22 +317,25 @@ void na1d_qk_backward( template void na1d_av_forward( - const int cc, + int32_t cc, + size_t max_smem, cudaStream_t stream, void* attn_ptr, void* value_ptr, void* output_ptr, - int batch_size, - int heads, - int length, - int dim, + int32_t batch_size, + int32_t heads, + int32_t length, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, - int kernel_size, - int dilation) { + const std::tuple& kernel_size, + const std::tuple& dilation, + const std::tuple& is_causal) { #ifdef NATTEN_WITH_CUTLASS if (natten::kEnableGemmNA && + !any_true(is_causal) && // TODO: remove when GEMM supports causal masking (cc >= 80 || (cc >= 70 && std::is_same::value))) { LAUNCH_na1d_nn_cuda_gemm( cc, @@ -274,16 +351,17 @@ void na1d_av_forward( attn_stride_0, attn_stride_1, attn_stride_2, - kernel_size, - dilation, + std::get<0>(kernel_size), + std::get<0>(dilation), 1.0, stream); } else { #endif DISPATCH_DTYPE_na1d_nn_cuda_naive( T, - kernel_size, - dilation, + is_causal, + // kernel_size, + // dilation, cc, stream, attn_ptr, @@ -305,24 +383,27 @@ void na1d_av_forward( template void na1d_av_backward( - const int cc, + int32_t cc, + size_t max_smem, cudaStream_t stream, void* attn_ptr, void* value_ptr, void* d_output_ptr, void* d_attn_ptr, void* d_value_ptr, - int batch_size, - int heads, - int length, - int dim, + int32_t batch_size, + int32_t heads, + int32_t length, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, - int kernel_size, - int dilation) { + const std::tuple& kernel_size, + const std::tuple& dilation, + const std::tuple& is_causal) { #ifdef NATTEN_WITH_CUTLASS if (natten::kEnableGemmNA && + !any_true(is_causal) && // TODO: remove when GEMM supports causal masking (cc >= 80 || (cc >= 70 && std::is_same::value))) { LAUNCH_na1d_pn_cuda_gemm( cc, @@ -339,8 +420,8 @@ void na1d_av_backward( attn_stride_0, attn_stride_1, attn_stride_2, - kernel_size, - dilation, + std::get<0>(kernel_size), + std::get<0>(dilation), 1.0, stream); LAUNCH_na1d_in_cuda_gemm( @@ -357,18 +438,20 @@ void na1d_av_backward( attn_stride_0, attn_stride_1, attn_stride_2, - kernel_size, - dilation, + std::get<0>(kernel_size), + std::get<0>(dilation), 1.0, stream); } else { #endif DISPATCH_DTYPE_na1d_pn_cuda_naive( T, - kernel_size, - dilation, + is_causal, + // kernel_size, + // dilation, cc, stream, + /* is_grad = */ true, d_output_ptr, value_ptr, d_attn_ptr, @@ -383,8 +466,9 @@ void na1d_av_backward( dilation); DISPATCH_DTYPE_na1d_in_cuda_naive( T, - kernel_size, - dilation, + is_causal, + // kernel_size, + // dilation, cc, stream, attn_ptr, diff --git a/csrc/include/natten/cuda/na2d.cuh b/csrc/include/natten/cuda/na2d.cuh index 37f9e93..f4eea85 100644 --- a/csrc/include/natten/cuda/na2d.cuh +++ b/csrc/include/natten/cuda/na2d.cuh @@ -26,40 +26,105 @@ #pragma once +#include #include #ifdef NATTEN_WITH_CUTLASS #include +#include #endif namespace natten { namespace cuda { +template +void na2d_forward( + int32_t cc, + size_t max_smem, + cudaStream_t stream, + void* query_ptr, + void* key_ptr, + void* value_ptr, + void* out_ptr, + void* rpb_ptr, + int32_t batch_size, + int32_t height, + int32_t width, + int32_t heads, + int32_t dim, + const std::tuple& kernel_size, + const std::tuple& dilation, + const std::tuple& is_causal, + float attn_scale, + const std::tuple& query_tile_size, + const std::tuple& key_tile_size) { +#ifdef NATTEN_WITH_CUTLASS + if (cc >= 80 || (cc >= 50 && !std::is_same::value)) { + natten::cuda::fna::fna_forward_generic( + cc, + max_smem, + stream, + query_ptr, + key_ptr, + value_ptr, + out_ptr, + rpb_ptr, + batch_size, + {height, width}, + heads, + dim, + dim, // dim_value + kernel_size, + dilation, + is_causal, + attn_scale, + nullptr, // TODO: pass logsumexp_ptr when backward kernel is implemented + query_tile_size, + key_tile_size); + } else { +#endif + NATTEN_FAILURE( + "Fused kernels are only available on devices with " + "compute capability >= 50 for FP16/FP32 inputs, and devices with " + "compute capability >= 80 for FP32, BF16, and FP16 inputs."); +#ifdef NATTEN_WITH_CUTLASS + } +#endif +} + template void na2d_qk_forward( - const int cc, + int32_t cc, + size_t max_smem, cudaStream_t stream, void* query_ptr, void* key_ptr, void* bias_ptr, void* attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, + int32_t batch_size, + int32_t heads, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, - int kernel_size, - int dilation) { + const std::tuple& kernel_size, + const std::tuple& dilation, + const std::tuple& is_causal) { #ifdef NATTEN_WITH_CUTLASS if (natten::kEnableGemmNA && + !any_true(is_causal) && // TODO: remove when GEMM supports causal masking + all_dims_match(kernel_size) && // TODO: remove when GEMM supports varying + // kernel sizes + all_dims_match( + dilation) && // TODO: remove when GEMM supports varying dilations (cc >= 80 || (cc >= 70 && std::is_same::value))) { LAUNCH_na2d_pn_cuda_gemm( cc, T, - kernel_size, + std::get<0>(kernel_size), // TODO: remove when GEMM supports varying + // kernel sizes dim, query_ptr, key_ptr, @@ -74,8 +139,10 @@ void na2d_qk_forward( attn_stride_1, attn_stride_2, attn_stride_3, - kernel_size, - dilation, + std::get<0>(kernel_size), // TODO: remove when GEMM supports varying + // kernel sizes + std::get<0>( + dilation), // TODO: remove when GEMM supports varying dilations 1.0, stream); } else { @@ -83,10 +150,12 @@ void na2d_qk_forward( if (bias_ptr == nullptr) { DISPATCH_DTYPE_na2d_pn_cuda_naive( T, - kernel_size, - dilation, + is_causal, + // kernel_size, + // dilation, cc, stream, + /* is_grad = */ false, query_ptr, key_ptr, attn_ptr, @@ -102,10 +171,14 @@ void na2d_qk_forward( kernel_size, dilation); } else { + NATTEN_CHECK( + !any_true(is_causal), + "Neighborhood attention with causal masking does not support positional biases yet."); DISPATCH_DTYPE_na2d_pn_bias_cuda_naive( T, - kernel_size, - dilation, + is_causal, + // kernel_size, + // dilation, cc, stream, query_ptr, @@ -131,7 +204,8 @@ void na2d_qk_forward( template void na2d_qk_backward( - const int cc, + int32_t cc, + size_t max_smem, cudaStream_t stream, void* query_ptr, void* key_ptr, @@ -139,24 +213,31 @@ void na2d_qk_backward( void* d_query_ptr, void* d_key_ptr, void* d_bias_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, + int32_t batch_size, + int32_t heads, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, - int kernel_size, - int dilation) { + const std::tuple& kernel_size, + const std::tuple& dilation, + const std::tuple& is_causal) { #ifdef NATTEN_WITH_CUTLASS if (natten::kEnableGemmNA && + !any_true(is_causal) && // TODO: remove when GEMM supports causal masking + all_dims_match(kernel_size) && // TODO: remove when GEMM supports varying + // kernel sizes + all_dims_match( + dilation) && // TODO: remove when GEMM supports varying dilations (cc >= 80 || (cc >= 70 && std::is_same::value))) { LAUNCH_na2d_nn_cuda_gemm( cc, T, - kernel_size, + std::get<0>(kernel_size), // TODO: remove when GEMM supports varying + // kernel sizes dim, d_attn_ptr, key_ptr, @@ -170,14 +251,17 @@ void na2d_qk_backward( attn_stride_1, attn_stride_2, attn_stride_3, - kernel_size, - dilation, + std::get<0>(kernel_size), // TODO: remove when GEMM supports varying + // kernel sizes + std::get<0>( + dilation), // TODO: remove when GEMM supports varying dilations 1.0, stream); LAUNCH_na2d_in_cuda_gemm( cc, T, - kernel_size, + std::get<0>(kernel_size), // TODO: remove when GEMM supports varying + // kernel sizes dim, d_attn_ptr, query_ptr, @@ -191,16 +275,19 @@ void na2d_qk_backward( attn_stride_1, attn_stride_2, attn_stride_3, - kernel_size, - dilation, + std::get<0>(kernel_size), // TODO: remove when GEMM supports varying + // kernel sizes + std::get<0>( + dilation), // TODO: remove when GEMM supports varying dilations 1.0, stream); } else { #endif DISPATCH_DTYPE_na2d_nn_cuda_naive( T, - kernel_size, - dilation, + is_causal, + // kernel_size, + // dilation, cc, stream, d_attn_ptr, @@ -219,8 +306,9 @@ void na2d_qk_backward( dilation); DISPATCH_DTYPE_na2d_in_cuda_naive( T, - kernel_size, - dilation, + is_causal, + // kernel_size, + // dilation, cc, stream, d_attn_ptr, @@ -241,10 +329,14 @@ void na2d_qk_backward( } #endif if (d_bias_ptr != nullptr) { + NATTEN_CHECK( + !any_true(is_causal), + "Neighborhood attention with causal masking does not support positional biases yet."); DISPATCH_DTYPE_na2d_rpbgrad_cuda_naive( T, - kernel_size, - dilation, + is_causal, + // kernel_size, + // dilation, cc, stream, d_bias_ptr, @@ -265,29 +357,37 @@ void na2d_qk_backward( template void na2d_av_forward( - const int cc, + int32_t cc, + size_t max_smem, cudaStream_t stream, void* attn_ptr, void* value_ptr, void* output_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, + int32_t batch_size, + int32_t heads, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, - int kernel_size, - int dilation) { + const std::tuple& kernel_size, + const std::tuple& dilation, + const std::tuple& is_causal) { #ifdef NATTEN_WITH_CUTLASS if (natten::kEnableGemmNA && + !any_true(is_causal) && // TODO: remove when GEMM supports causal masking + all_dims_match(kernel_size) && // TODO: remove when GEMM supports varying + // kernel sizes + all_dims_match( + dilation) && // TODO: remove when GEMM supports varying dilations (cc >= 80 || (cc >= 70 && std::is_same::value))) { LAUNCH_na2d_nn_cuda_gemm( cc, T, - kernel_size, + std::get<0>(kernel_size), // TODO: remove when GEMM supports varying + // kernel sizes dim, attn_ptr, value_ptr, @@ -301,16 +401,19 @@ void na2d_av_forward( attn_stride_1, attn_stride_2, attn_stride_3, - kernel_size, - dilation, + std::get<0>(kernel_size), // TODO: remove when GEMM supports varying + // kernel sizes + std::get<0>( + dilation), // TODO: remove when GEMM supports varying dilations 1.0, stream); } else { #endif DISPATCH_DTYPE_na2d_nn_cuda_naive( T, - kernel_size, - dilation, + is_causal, + // kernel_size, + // dilation, cc, stream, attn_ptr, @@ -334,31 +437,39 @@ void na2d_av_forward( template void na2d_av_backward( - const int cc, + int32_t cc, + size_t max_smem, cudaStream_t stream, void* attn_ptr, void* value_ptr, void* d_output_ptr, void* d_attn_ptr, void* d_value_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, + int32_t batch_size, + int32_t heads, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, - int kernel_size, - int dilation) { + const std::tuple& kernel_size, + const std::tuple& dilation, + const std::tuple& is_causal) { #ifdef NATTEN_WITH_CUTLASS if (natten::kEnableGemmNA && + !any_true(is_causal) && // TODO: remove when GEMM supports causal masking + all_dims_match(kernel_size) && // TODO: remove when GEMM supports varying + // kernel sizes + all_dims_match( + dilation) && // TODO: remove when GEMM supports varying dilations (cc >= 80 || (cc >= 70 && std::is_same::value))) { LAUNCH_na2d_pn_cuda_gemm( cc, T, - kernel_size, + std::get<0>(kernel_size), // TODO: remove when GEMM supports varying + // kernel sizes dim, d_output_ptr, value_ptr, @@ -373,14 +484,17 @@ void na2d_av_backward( attn_stride_1, attn_stride_2, attn_stride_3, - kernel_size, - dilation, + std::get<0>(kernel_size), // TODO: remove when GEMM supports varying + // kernel sizes + std::get<0>( + dilation), // TODO: remove when GEMM supports varying dilations 1.0, stream); LAUNCH_na2d_in_cuda_gemm( cc, T, - kernel_size, + std::get<0>(kernel_size), // TODO: remove when GEMM supports varying + // kernel sizes dim, attn_ptr, d_output_ptr, @@ -394,18 +508,22 @@ void na2d_av_backward( attn_stride_1, attn_stride_2, attn_stride_3, - kernel_size, - dilation, + std::get<0>(kernel_size), // TODO: remove when GEMM supports varying + // kernel sizes + std::get<0>( + dilation), // TODO: remove when GEMM supports varying dilations 1.0, stream); } else { #endif DISPATCH_DTYPE_na2d_pn_cuda_naive( T, - kernel_size, - dilation, + is_causal, + // kernel_size, + // dilation, cc, stream, + /* is_grad = */ true, d_output_ptr, value_ptr, d_attn_ptr, @@ -422,8 +540,9 @@ void na2d_av_backward( dilation); DISPATCH_DTYPE_na2d_in_cuda_naive( T, - kernel_size, - dilation, + is_causal, + // kernel_size, + // dilation, cc, stream, attn_ptr, diff --git a/csrc/include/natten/cuda/na3d.cuh b/csrc/include/natten/cuda/na3d.cuh index 063df23..ca62659 100644 --- a/csrc/include/natten/cuda/na3d.cuh +++ b/csrc/include/natten/cuda/na3d.cuh @@ -26,41 +26,103 @@ #pragma once +#include #include +#ifdef NATTEN_WITH_CUTLASS +#include +#endif namespace natten { namespace cuda { +template +void na3d_forward( + int32_t cc, + size_t max_smem, + cudaStream_t stream, + void* query_ptr, + void* key_ptr, + void* value_ptr, + void* out_ptr, + void* rpb_ptr, + int32_t batch_size, + int32_t depth, + int32_t height, + int32_t width, + int32_t heads, + int32_t dim, + const std::tuple& kernel_size, + const std::tuple& dilation, + const std::tuple& is_causal, + float attn_scale, + const std::tuple& query_tile_size, + const std::tuple& key_tile_size) { +#ifdef NATTEN_WITH_CUTLASS + if (cc >= 80 || (cc >= 50 && !std::is_same::value)) { + natten::cuda::fna::fna_forward_generic( + cc, + max_smem, + stream, + query_ptr, + key_ptr, + value_ptr, + out_ptr, + rpb_ptr, + batch_size, + {depth, height, width}, + heads, + dim, + dim, // dim_value + kernel_size, + dilation, + is_causal, + attn_scale, + nullptr, // TODO: pass logsumexp_ptr when backward kernel is implemented + query_tile_size, + key_tile_size); + } else { +#endif + NATTEN_FAILURE( + "Fused kernels are only available on devices with " + "compute capability >= 50 for FP16/FP32 inputs, and devices with " + "compute capability >= 80 for FP32, BF16, and FP16 inputs."); +#ifdef NATTEN_WITH_CUTLASS + } +#endif +} + template void na3d_qk_forward( - const int cc, + int32_t cc, + size_t max_smem, cudaStream_t stream, void* query_ptr, void* key_ptr, void* bias_ptr, void* attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, + int32_t batch_size, + int32_t heads, + int32_t depth, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, int64_t attn_stride_4, - int kernel_size, - int dilation, - int depth_kernel_size, - int depth_dilation) { + const std::tuple& kernel_size, + const std::tuple& dilation, + const std::tuple& is_causal) { if (bias_ptr == nullptr) { DISPATCH_DTYPE_na3d_pn_cuda_naive( T, - kernel_size, - dilation, + is_causal, + // kernel_size, + // dilation, cc, stream, + /* is_grad = */ false, query_ptr, key_ptr, attn_ptr, @@ -76,14 +138,16 @@ void na3d_qk_forward( attn_stride_3, attn_stride_4, kernel_size, - depth_kernel_size, - dilation, - depth_dilation); + dilation); } else { + NATTEN_CHECK( + !any_true(is_causal), + "Neighborhood attention with causal masking does not support positional biases yet."); DISPATCH_DTYPE_na3d_pn_bias_cuda_naive( T, - kernel_size, - dilation, + is_causal, + // kernel_size, + // dilation, cc, stream, query_ptr, @@ -102,15 +166,14 @@ void na3d_qk_forward( attn_stride_3, attn_stride_4, kernel_size, - depth_kernel_size, - dilation, - depth_dilation); + dilation); } } template void na3d_qk_backward( - const int cc, + int32_t cc, + size_t max_smem, cudaStream_t stream, void* query_ptr, void* key_ptr, @@ -118,25 +181,25 @@ void na3d_qk_backward( void* d_query_ptr, void* d_key_ptr, void* d_bias_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, + int32_t batch_size, + int32_t heads, + int32_t depth, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, int64_t attn_stride_4, - int kernel_size, - int dilation, - int depth_kernel_size, - int depth_dilation) { + const std::tuple& kernel_size, + const std::tuple& dilation, + const std::tuple& is_causal) { DISPATCH_DTYPE_na3d_nn_cuda_naive( T, - kernel_size, - dilation, + is_causal, + // kernel_size, + // dilation, cc, stream, d_attn_ptr, @@ -154,13 +217,12 @@ void na3d_qk_backward( attn_stride_3, attn_stride_4, kernel_size, - depth_kernel_size, - dilation, - depth_dilation); + dilation); DISPATCH_DTYPE_na3d_in_cuda_naive( T, - kernel_size, - dilation, + is_causal, + // kernel_size, + // dilation, cc, stream, d_attn_ptr, @@ -178,14 +240,16 @@ void na3d_qk_backward( attn_stride_3, attn_stride_4, kernel_size, - depth_kernel_size, - dilation, - depth_dilation); + dilation); if (d_bias_ptr != nullptr) { + NATTEN_CHECK( + !any_true(is_causal), + "Neighborhood attention with causal masking does not support positional biases yet."); DISPATCH_DTYPE_na3d_rpbgrad_cuda_naive( T, - kernel_size, - dilation, + is_causal, + // kernel_size, + // dilation, cc, stream, d_bias_ptr, @@ -202,38 +266,37 @@ void na3d_qk_backward( attn_stride_3, attn_stride_4, kernel_size, - depth_kernel_size, - dilation, - depth_dilation); + dilation); } } template void na3d_av_forward( - const int cc, + int32_t cc, + size_t max_smem, cudaStream_t stream, void* attn_ptr, void* value_ptr, void* output_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, + int32_t batch_size, + int32_t heads, + int32_t depth, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, int64_t attn_stride_4, - int kernel_size, - int dilation, - int depth_kernel_size, - int depth_dilation) { + const std::tuple& kernel_size, + const std::tuple& dilation, + const std::tuple& is_causal) { DISPATCH_DTYPE_na3d_nn_cuda_naive( T, - kernel_size, - dilation, + is_causal, + // kernel_size, + // dilation, cc, stream, attn_ptr, @@ -251,41 +314,41 @@ void na3d_av_forward( attn_stride_3, attn_stride_4, kernel_size, - depth_kernel_size, - dilation, - depth_dilation); + dilation); } template void na3d_av_backward( - const int cc, + int32_t cc, + size_t max_smem, cudaStream_t stream, void* attn_ptr, void* value_ptr, void* d_output_ptr, void* d_attn_ptr, void* d_value_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, + int32_t batch_size, + int32_t heads, + int32_t depth, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, int64_t attn_stride_4, - int kernel_size, - int dilation, - int depth_kernel_size, - int depth_dilation) { + const std::tuple& kernel_size, + const std::tuple& dilation, + const std::tuple& is_causal) { DISPATCH_DTYPE_na3d_pn_cuda_naive( T, - kernel_size, - dilation, + is_causal, + // kernel_size, + // dilation, cc, stream, + /* is_grad = */ true, d_output_ptr, value_ptr, d_attn_ptr, @@ -301,13 +364,12 @@ void na3d_av_backward( attn_stride_3, attn_stride_4, kernel_size, - depth_kernel_size, - dilation, - depth_dilation); + dilation); DISPATCH_DTYPE_na3d_in_cuda_naive( T, - kernel_size, - dilation, + is_causal, + // kernel_size, + // dilation, cc, stream, attn_ptr, @@ -325,9 +387,7 @@ void na3d_av_backward( attn_stride_3, attn_stride_4, kernel_size, - depth_kernel_size, - dilation, - depth_dilation); + dilation); } } // namespace cuda diff --git a/csrc/include/natten/cuda/naive/inverse_neighborhood_1d.cuh b/csrc/include/natten/cuda/naive/inverse_neighborhood_1d.cuh index 6325d78..8fd2e1c 100644 --- a/csrc/include/natten/cuda/naive/inverse_neighborhood_1d.cuh +++ b/csrc/include/natten/cuda/naive/inverse_neighborhood_1d.cuh @@ -31,6 +31,8 @@ #include #include +#include +#include #include namespace natten { @@ -43,14 +45,14 @@ struct InverseNeighborhood1DBase { scalar_t* weights; scalar_t* values; scalar_t* output; - const int length; - const int heads; - const int kernel_size_in; - const int dilation_in; - const int dim; - const int64_t problem_size; - const int64_t weights_stride_0, weights_stride_1, weights_stride_2; - const int64_t values_stride_0, values_stride_1, values_stride_2; + int32_t length; + int32_t heads; + int32_t kernel_size; + int32_t dilation; + int32_t dim; + int64_t problem_size; + int64_t weights_stride_0, weights_stride_1, weights_stride_2; + int64_t values_stride_0, values_stride_1, values_stride_2; __device__ __host__ Params() {} @@ -58,22 +60,22 @@ struct InverseNeighborhood1DBase { scalar_t* weights, // d_attn / attn scalar_t* values, // query / d_out scalar_t* output, // d_key / d_value - const int length, - const int heads, - const int kernel_size_in, - const int dilation_in, - const int dim, - const int64_t weights_stride_0, - const int64_t weights_stride_1, - const int64_t weights_stride_2, - const int64_t output_numel) + int32_t length, + int32_t heads, + int32_t kernel_size, + int32_t dilation, + int32_t dim, + int64_t weights_stride_0, + int64_t weights_stride_1, + int64_t weights_stride_2, + int64_t output_numel) : weights(weights), values(values), output(output), length(length), heads(heads), - kernel_size_in(kernel_size_in), - dilation_in(dilation_in), + kernel_size(kernel_size), + dilation(dilation), dim(dim), problem_size(output_numel), weights_stride_2(weights_stride_2), @@ -97,49 +99,46 @@ struct InverseNeighborhood1DBase { } }; -template +template struct InverseNeighborhood1DFull : InverseNeighborhood1DBase { using Base = InverseNeighborhood1DBase; using Params = typename Base::Params; + using CausalMask = CausalMask_; static constexpr bool IsBF16Kernel = false; static constexpr bool IsHalfKernel = false; static constexpr bool UsesSmem = false; __device__ __host__ InverseNeighborhood1DFull() : Base() {} - static __host__ int get_dim(int dim) { + static __host__ int32_t get_dim(int32_t dim) { return dim; } __device__ void launch(Params p) { - const int KERNEL_SIZE = (KS > 1) ? KS : p.kernel_size_in; - const int NEIGHBORHOOD_SIZE = (NS > 0) ? NS : KERNEL_SIZE / 2; - const int dilation = (DILATION > 0) ? DILATION : p.dilation_in; - const int64_t linearIndex = blockIdx.x * blockDim.x + threadIdx.x; + auto mask_0 = + NeighborhoodMask(p.length, p.kernel_size, p.dilation); + int64_t linearIndex = blockIdx.x * blockDim.x + threadIdx.x; if (linearIndex < p.problem_size) { - int indtmp1 = linearIndex / p.dim; - const int d = linearIndex - indtmp1 * p.dim; - int indtmp2 = indtmp1 / p.length; - const int i = indtmp1 - indtmp2 * p.length; + int32_t indtmp1 = linearIndex / p.dim; + auto d = linearIndex - indtmp1 * p.dim; + int32_t indtmp2 = indtmp1 / p.length; + auto i = indtmp1 - indtmp2 * p.length; indtmp1 = indtmp2; indtmp2 = indtmp1 / p.heads; - const int h = indtmp1 - indtmp2 * p.heads; - const int b = indtmp2; - const int ni = get_backward_window_start( - i, KERNEL_SIZE, NEIGHBORHOOD_SIZE, dilation); - const int ei = get_backward_window_end( - i, p.length, KERNEL_SIZE, NEIGHBORHOOD_SIZE, dilation); - const int64_t weightsOffset = b * p.weights_stride_0 + h * p.weights_stride_1; - const int64_t valuesOffset = - b * p.values_stride_0 + h * p.values_stride_1 + d; + auto h = indtmp1 - indtmp2 * p.heads; + auto& b = indtmp2; + + auto ni = mask_0.get_backward_window_start(i); + auto ei = mask_0.get_backward_window_end(i); + + int64_t weightsOffset = b * p.weights_stride_0 + h * p.weights_stride_1; + int64_t valuesOffset = b * p.values_stride_0 + h * p.values_stride_1 + d; scalar_t output_update = scalar_t(0); -#pragma unroll - for (int xi = ni; xi < ei; xi += dilation) { - const int oni = get_window_start( - xi, p.length, KERNEL_SIZE, NEIGHBORHOOD_SIZE, dilation); - const int64_t valuesIndex = valuesOffset + xi * p.values_stride_2; - const int64_t weightsIndex = - weightsOffset + xi * p.weights_stride_2 + int((i - oni) / dilation); + for (int32_t xi = ni; xi < ei; xi += p.dilation) { + auto oni = mask_0.get_window_start(xi); + int64_t valuesIndex = valuesOffset + xi * p.values_stride_2; + int64_t weightsIndex = weightsOffset + xi * p.weights_stride_2 + + int32_t((i - oni) / p.dilation); output_update += p.weights[weightsIndex] * p.values[valuesIndex]; } p.output[linearIndex] = output_update; @@ -147,10 +146,11 @@ struct InverseNeighborhood1DFull : InverseNeighborhood1DBase { } }; -template +template struct InverseNeighborhood1DHalf : InverseNeighborhood1DBase { using Base = InverseNeighborhood1DBase; using Params = typename Base::Params; + using CausalMask = CausalMask_; static constexpr bool IsBF16Kernel = IsBF16::value; static constexpr bool IsHalfKernel = true; static constexpr bool UsesSmem = false; @@ -158,7 +158,7 @@ struct InverseNeighborhood1DHalf : InverseNeighborhood1DBase { __device__ __host__ InverseNeighborhood1DHalf() : Base() {} using HalfHelper = typename HalfArray::Base; - static __host__ int get_dim(int dim) { + static __host__ int32_t get_dim(int32_t dim) { if (dim % 2 != 0) { std::cerr << "Naive NATTEN half-precision kernels only support 32-bit alignment. " @@ -170,36 +170,33 @@ struct InverseNeighborhood1DHalf : InverseNeighborhood1DBase { } __device__ void launch(Params p) { - const int KERNEL_SIZE = (KS > 1) ? KS : p.kernel_size_in; - const int NEIGHBORHOOD_SIZE = (NS > 0) ? NS : KERNEL_SIZE / 2; - const int dilation = (DILATION > 0) ? DILATION : p.dilation_in; - const int64_t linearIndex = blockIdx.x * blockDim.x + threadIdx.x; + auto mask_0 = + NeighborhoodMask(p.length, p.kernel_size, p.dilation); + int64_t linearIndex = blockIdx.x * blockDim.x + threadIdx.x; if (linearIndex < p.problem_size) { auto values2 = HalfHelper::typecast(p.values); auto output2 = HalfHelper::typecast(p.output); - int indtmp1 = linearIndex / p.dim; - const int d = linearIndex - indtmp1 * p.dim; - int indtmp2 = indtmp1 / p.length; - const int i = indtmp1 - indtmp2 * p.length; + int32_t indtmp1 = linearIndex / p.dim; + auto d = linearIndex - indtmp1 * p.dim; + int32_t indtmp2 = indtmp1 / p.length; + auto i = indtmp1 - indtmp2 * p.length; indtmp1 = indtmp2; indtmp2 = indtmp1 / p.heads; - const int h = indtmp1 - indtmp2 * p.heads; - const int b = indtmp2; - const int ni = get_backward_window_start( - i, KERNEL_SIZE, NEIGHBORHOOD_SIZE, dilation); - const int ei = get_backward_window_end( - i, p.length, KERNEL_SIZE, NEIGHBORHOOD_SIZE, dilation); - const int weightsOffset = b * p.weights_stride_0 + h * p.weights_stride_1; - const int valuesOffset = + auto h = indtmp1 - indtmp2 * p.heads; + auto& b = indtmp2; + + auto ni = mask_0.get_backward_window_start(i); + auto ei = mask_0.get_backward_window_end(i); + + int32_t weightsOffset = b * p.weights_stride_0 + h * p.weights_stride_1; + int32_t valuesOffset = b * (p.dim * p.length * p.heads) + h * (p.dim * p.length) + d; - auto output_update = HalfHelper::zero(); -#pragma unroll - for (int xi = ni; xi < ei; xi += dilation) { - const int oni = get_window_start( - xi, p.length, KERNEL_SIZE, NEIGHBORHOOD_SIZE, dilation); - const int64_t valuesIndex = valuesOffset + xi * p.dim; - const int64_t weightsIndex = - weightsOffset + xi * p.weights_stride_2 + int((i - oni) / dilation); + auto output_update = HalfHelper::zeros(); + for (int32_t xi = ni; xi < ei; xi += p.dilation) { + auto oni = mask_0.get_window_start(xi); + int64_t valuesIndex = valuesOffset + xi * p.dim; + int64_t weightsIndex = weightsOffset + xi * p.weights_stride_2 + + int32_t((i - oni) / p.dilation); scalar_t a = p.weights[weightsIndex]; output_update = HalfHelper::fma(values2[valuesIndex], a, output_update); } @@ -211,31 +208,29 @@ struct InverseNeighborhood1DHalf : InverseNeighborhood1DBase { template struct InverseNeighborhood1D { using Args = Args_; - static constexpr int KS = Args::KernelSize; - static constexpr int NS = Args::NeighborhoodSize; - static constexpr int DILATION = Args::Dilation; using scalar_t = typename Args::Dtype; + using CausalMask = typename Args::CausalMask; using Kernel = typename std::conditional< sizeof(scalar_t) >= 4, - InverseNeighborhood1DFull, - InverseNeighborhood1DHalf>::type; + InverseNeighborhood1DFull, + InverseNeighborhood1DHalf>::type; using Params = typename Kernel::Params; void operator()( - const int cc, + int32_t cc, cudaStream_t stream, void* attn_ptr, void* d_output_ptr, void* d_value_ptr, - int batch_size, - int heads, - int length, - int dim, + int32_t batch_size, + int32_t heads, + int32_t length, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, - int kernel_size, - int dilation) { + const std::tuple& kernel_size, + const std::tuple& dilation) { dim = Kernel::get_dim(dim); int64_t problem_size = batch_size * heads * length * dim; auto grid = Kernel::Base::get_grid(problem_size); @@ -246,8 +241,8 @@ struct InverseNeighborhood1D { reinterpret_cast(d_value_ptr), length, heads, - kernel_size, - dilation, + std::get<0>(kernel_size), + std::get<0>(dilation), dim, attn_stride_0, attn_stride_1, diff --git a/csrc/include/natten/cuda/naive/inverse_neighborhood_2d.cuh b/csrc/include/natten/cuda/naive/inverse_neighborhood_2d.cuh index cf75634..47dae08 100644 --- a/csrc/include/natten/cuda/naive/inverse_neighborhood_2d.cuh +++ b/csrc/include/natten/cuda/naive/inverse_neighborhood_2d.cuh @@ -31,6 +31,8 @@ #include #include +#include +#include #include namespace natten { @@ -43,17 +45,16 @@ struct InverseNeighborhood2DBase { scalar_t* weights; scalar_t* values; scalar_t* output; - const int height; - const int width; - const int heads; - const int kernel_size_in; - const int dilation_in; - const int dim; - const int64_t problem_size; - const int64_t weights_stride_0, weights_stride_1, weights_stride_2, + int32_t height; + int32_t width; + int32_t heads; + int32_t kernel_size_0, kernel_size_1; + int32_t dilation_0, dilation_1; + int32_t dim; + int64_t problem_size; + int64_t weights_stride_0, weights_stride_1, weights_stride_2, weights_stride_3; - const int64_t values_stride_0, values_stride_1, values_stride_2, - values_stride_3; + int64_t values_stride_0, values_stride_1, values_stride_2, values_stride_3; __device__ __host__ Params() {} @@ -61,25 +62,29 @@ struct InverseNeighborhood2DBase { scalar_t* weights, // attn / d_attn scalar_t* values, // value / key scalar_t* output, // output / d_query - const int height, - const int width, - const int heads, - const int kernel_size_in, - const int dilation_in, - const int dim, - const int64_t weights_stride_0, - const int64_t weights_stride_1, - const int64_t weights_stride_2, - const int64_t weights_stride_3, - const int problem_size) + int32_t height, + int32_t width, + int32_t heads, + int32_t kernel_size_0, + int32_t kernel_size_1, + int32_t dilation_0, + int32_t dilation_1, + int32_t dim, + int64_t weights_stride_0, + int64_t weights_stride_1, + int64_t weights_stride_2, + int64_t weights_stride_3, + int32_t problem_size) : weights(weights), values(values), output(output), height(height), width(width), heads(heads), - kernel_size_in(kernel_size_in), - dilation_in(dilation_in), + kernel_size_0(kernel_size_0), + kernel_size_1(kernel_size_1), + dilation_0(dilation_0), + dilation_1(dilation_1), dim(dim), problem_size(problem_size), weights_stride_3(weights_stride_3), @@ -105,63 +110,58 @@ struct InverseNeighborhood2DBase { } }; -template +template struct InverseNeighborhood2DFull : InverseNeighborhood2DBase { using Base = InverseNeighborhood2DBase; using Params = typename Base::Params; + using CausalMask = CausalMask_; static constexpr bool IsBF16Kernel = false; static constexpr bool IsHalfKernel = false; static constexpr bool UsesSmem = false; __device__ __host__ InverseNeighborhood2DFull() : Base() {} - static __host__ int get_dim(int dim) { + static __host__ int32_t get_dim(int32_t dim) { return dim; } __device__ void launch(Params p) { - const int KERNEL_SIZE = (KS > 1) ? KS : p.kernel_size_in; - const int NEIGHBORHOOD_SIZE = (NS > 0) ? NS : KERNEL_SIZE / 2; - const int dilation = (DILATION > 0) ? DILATION : p.dilation_in; - const int64_t linearIndex = blockIdx.x * blockDim.x + threadIdx.x; + auto mask_0 = NeighborhoodMask( + p.height, p.kernel_size_0, p.dilation_0); + auto mask_1 = NeighborhoodMask( + p.width, p.kernel_size_1, p.dilation_1); + int64_t linearIndex = blockIdx.x * blockDim.x + threadIdx.x; if (linearIndex < p.problem_size) { - int indtmp1 = linearIndex / p.dim; - const int d = linearIndex - indtmp1 * p.dim; - int indtmp2 = indtmp1 / p.width; - const int j = indtmp1 - indtmp2 * p.width; + int32_t indtmp1 = linearIndex / p.dim; + auto d = linearIndex - indtmp1 * p.dim; + int32_t indtmp2 = indtmp1 / p.width; + auto j = indtmp1 - indtmp2 * p.width; indtmp1 = indtmp2; indtmp2 = indtmp1 / p.height; - const int i = indtmp1 - indtmp2 * p.height; + auto i = indtmp1 - indtmp2 * p.height; indtmp1 = indtmp2; indtmp2 = indtmp1 / p.heads; - const int h = indtmp1 - indtmp2 * p.heads; - const int b = indtmp2; - const int ni = get_backward_window_start( - i, KERNEL_SIZE, NEIGHBORHOOD_SIZE, dilation); - const int nj = get_backward_window_start( - j, KERNEL_SIZE, NEIGHBORHOOD_SIZE, dilation); - const int ei = get_backward_window_end( - i, p.height, KERNEL_SIZE, NEIGHBORHOOD_SIZE, dilation); - const int ej = get_backward_window_end( - j, p.width, KERNEL_SIZE, NEIGHBORHOOD_SIZE, dilation); - const int64_t weightsOffset = b * p.weights_stride_0 + h * p.weights_stride_1; - const int64_t valuesOffset = - b * p.values_stride_0 + h * p.values_stride_1 + d; + auto h = indtmp1 - indtmp2 * p.heads; + auto& b = indtmp2; + + auto ni = mask_0.get_backward_window_start(i); + auto nj = mask_1.get_backward_window_start(j); + auto ei = mask_0.get_backward_window_end(i); + auto ej = mask_1.get_backward_window_end(j); + + int64_t weightsOffset = b * p.weights_stride_0 + h * p.weights_stride_1; + int64_t valuesOffset = b * p.values_stride_0 + h * p.values_stride_1 + d; scalar_t output_update = scalar_t(0); -#pragma unroll - for (int xi = ni; xi < ei; xi += dilation) { - const int oni = get_window_start( - xi, p.height, KERNEL_SIZE, NEIGHBORHOOD_SIZE, dilation); -#pragma unroll - for (int xj = nj; xj < ej; xj += dilation) { - const int onj = get_window_start( - xj, p.width, KERNEL_SIZE, NEIGHBORHOOD_SIZE, dilation); - const int64_t valuesIndex = + for (int32_t xi = ni; xi < ei; xi += p.dilation_0) { + auto oni = mask_0.get_window_start(xi); + for (int32_t xj = nj; xj < ej; xj += p.dilation_1) { + auto onj = mask_1.get_window_start(xj); + int64_t valuesIndex = valuesOffset + xi * p.values_stride_2 + xj * p.values_stride_3; - const int64_t weightsIndex = weightsOffset + xi * p.weights_stride_2 + + int64_t weightsIndex = weightsOffset + xi * p.weights_stride_2 + xj * p.weights_stride_3 + - int((i - oni) / dilation) * KERNEL_SIZE + - int((j - onj) / dilation); + int32_t((i - oni) / p.dilation_0) * p.kernel_size_1 + + int32_t((j - onj) / p.dilation_1); output_update += p.values[valuesIndex] * p.weights[weightsIndex]; } } @@ -170,10 +170,11 @@ struct InverseNeighborhood2DFull : InverseNeighborhood2DBase { } }; -template +template struct InverseNeighborhood2DHalf : InverseNeighborhood2DBase { using Base = InverseNeighborhood2DBase; using Params = typename Base::Params; + using CausalMask = CausalMask_; static constexpr bool IsBF16Kernel = IsBF16::value; static constexpr bool IsHalfKernel = true; static constexpr bool UsesSmem = false; @@ -182,7 +183,7 @@ struct InverseNeighborhood2DHalf : InverseNeighborhood2DBase { using HalfHelper = typename HalfArray::Base; - static __host__ int get_dim(int dim) { + static __host__ int32_t get_dim(int32_t dim) { if (dim % 2 != 0) { std::cerr << "Naive NATTEN half-precision kernels only support 32-bit alignment. " @@ -194,50 +195,44 @@ struct InverseNeighborhood2DHalf : InverseNeighborhood2DBase { } __device__ void launch(Params p) { - const int KERNEL_SIZE = (KS > 1) ? KS : p.kernel_size_in; - const int NEIGHBORHOOD_SIZE = (NS > 0) ? NS : KERNEL_SIZE / 2; - const int dilation = (DILATION > 0) ? DILATION : p.dilation_in; - const int64_t linearIndex = blockIdx.x * blockDim.x + threadIdx.x; + auto mask_0 = NeighborhoodMask( + p.height, p.kernel_size_0, p.dilation_0); + auto mask_1 = NeighborhoodMask( + p.width, p.kernel_size_1, p.dilation_1); + int64_t linearIndex = blockIdx.x * blockDim.x + threadIdx.x; if (linearIndex < p.problem_size) { auto values2 = HalfHelper::typecast(p.values); auto output2 = HalfHelper::typecast(p.output); - int indtmp1 = linearIndex / p.dim; - const int d = linearIndex - indtmp1 * p.dim; - int indtmp2 = indtmp1 / p.width; - const int j = indtmp1 - indtmp2 * p.width; + int32_t indtmp1 = linearIndex / p.dim; + auto d = linearIndex - indtmp1 * p.dim; + int32_t indtmp2 = indtmp1 / p.width; + auto j = indtmp1 - indtmp2 * p.width; indtmp1 = indtmp2; indtmp2 = indtmp1 / p.height; - const int i = indtmp1 - indtmp2 * p.height; + auto i = indtmp1 - indtmp2 * p.height; indtmp1 = indtmp2; indtmp2 = indtmp1 / p.heads; - const int h = indtmp1 - indtmp2 * p.heads; - const int b = indtmp2; - const int ni = get_backward_window_start( - i, KERNEL_SIZE, NEIGHBORHOOD_SIZE, dilation); - const int nj = get_backward_window_start( - j, KERNEL_SIZE, NEIGHBORHOOD_SIZE, dilation); - const int ei = get_backward_window_end( - i, p.height, KERNEL_SIZE, NEIGHBORHOOD_SIZE, dilation); - const int ej = get_backward_window_end( - j, p.width, KERNEL_SIZE, NEIGHBORHOOD_SIZE, dilation); - const int64_t weightsOffset = b * p.weights_stride_0 + h * p.weights_stride_1; - const int64_t valuesOffset = - b * p.values_stride_0 + h * p.values_stride_1 + d; - auto output_update = HalfHelper::zero(); -#pragma unroll - for (int xi = ni; xi < ei; xi += dilation) { - const int oni = get_window_start( - xi, p.height, KERNEL_SIZE, NEIGHBORHOOD_SIZE, dilation); -#pragma unroll - for (int xj = nj; xj < ej; xj += dilation) { - const int onj = get_window_start( - xj, p.width, KERNEL_SIZE, NEIGHBORHOOD_SIZE, dilation); - const int64_t valuesIndex = + auto h = indtmp1 - indtmp2 * p.heads; + auto& b = indtmp2; + + auto ni = mask_0.get_backward_window_start(i); + auto nj = mask_1.get_backward_window_start(j); + auto ei = mask_0.get_backward_window_end(i); + auto ej = mask_1.get_backward_window_end(j); + + int64_t weightsOffset = b * p.weights_stride_0 + h * p.weights_stride_1; + int64_t valuesOffset = b * p.values_stride_0 + h * p.values_stride_1 + d; + auto output_update = HalfHelper::zeros(); + for (int32_t xi = ni; xi < ei; xi += p.dilation_0) { + auto oni = mask_0.get_window_start(xi); + for (int32_t xj = nj; xj < ej; xj += p.dilation_1) { + auto onj = mask_1.get_window_start(xj); + int64_t valuesIndex = valuesOffset + xi * p.values_stride_2 + xj * p.values_stride_3; - const int64_t weightsIndex = weightsOffset + xi * p.weights_stride_2 + + int64_t weightsIndex = weightsOffset + xi * p.weights_stride_2 + xj * p.weights_stride_3 + - int((i - oni) / dilation) * KERNEL_SIZE + - int((j - onj) / dilation); + int32_t((i - oni) / p.dilation_0) * p.kernel_size_1 + + int32_t((j - onj) / p.dilation_1); output_update = HalfHelper::fma( values2[valuesIndex], p.weights[weightsOffset], output_update); } @@ -250,33 +245,31 @@ struct InverseNeighborhood2DHalf : InverseNeighborhood2DBase { template struct InverseNeighborhood2D { using Args = Args_; - static constexpr int KS = Args::KernelSize; - static constexpr int NS = Args::NeighborhoodSize; - static constexpr int DILATION = Args::Dilation; using scalar_t = typename Args::Dtype; + using CausalMask = typename Args::CausalMask; using Kernel = typename std::conditional< sizeof(scalar_t) >= 4, - InverseNeighborhood2DFull, - InverseNeighborhood2DHalf>::type; + InverseNeighborhood2DFull, + InverseNeighborhood2DHalf>::type; using Params = typename Kernel::Params; void operator()( - const int cc, + int32_t cc, cudaStream_t stream, void* attn_ptr, void* d_output_ptr, void* d_value_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, + int32_t batch_size, + int32_t heads, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, - int kernel_size, - int dilation) { + const std::tuple& kernel_size, + const std::tuple& dilation) { dim = Kernel::get_dim(dim); int64_t problem_size = batch_size * heads * height * width * dim; auto grid = Kernel::Base::get_grid(problem_size); @@ -288,8 +281,10 @@ struct InverseNeighborhood2D { height, width, heads, - kernel_size, - dilation, + std::get<0>(kernel_size), + std::get<1>(kernel_size), + std::get<0>(dilation), + std::get<1>(dilation), dim, attn_stride_0, attn_stride_1, diff --git a/csrc/include/natten/cuda/naive/inverse_neighborhood_3d.cuh b/csrc/include/natten/cuda/naive/inverse_neighborhood_3d.cuh index fddb13d..0ba8e73 100644 --- a/csrc/include/natten/cuda/naive/inverse_neighborhood_3d.cuh +++ b/csrc/include/natten/cuda/naive/inverse_neighborhood_3d.cuh @@ -31,7 +31,9 @@ #include #include -#include +#include +#include +#include namespace natten { namespace cuda { @@ -43,20 +45,18 @@ struct InverseNeighborhood3DBase { scalar_t* weights; scalar_t* values; scalar_t* output; - const int depth; - const int height; - const int width; - const int heads; - const int kernel_size_in; - const int dilation_in; - const int depth_kernel_size_in; - const int depth_dilation_in; - const int dim; - const int64_t problem_size; - const int64_t weights_stride_0, weights_stride_1, weights_stride_2, + int32_t depth; + int32_t height; + int32_t width; + int32_t heads; + int32_t kernel_size_0, kernel_size_1, kernel_size_2; + int32_t dilation_0, dilation_1, dilation_2; + int32_t dim; + int64_t problem_size; + int64_t weights_stride_0, weights_stride_1, weights_stride_2, weights_stride_3, weights_stride_4; - const int64_t values_stride_0, values_stride_1, values_stride_2, - values_stride_3, values_stride_4; + int64_t values_stride_0, values_stride_1, values_stride_2, values_stride_3, + values_stride_4; __device__ __host__ Params() {} @@ -64,21 +64,23 @@ struct InverseNeighborhood3DBase { scalar_t* weights, // attn / d_attn scalar_t* values, // value / key scalar_t* output, // output / d_query - const int depth, - const int height, - const int width, - const int heads, - const int kernel_size_in, - const int dilation_in, - const int depth_kernel_size_in, - const int depth_dilation_in, - const int dim, - const int64_t weights_stride_0, - const int64_t weights_stride_1, - const int64_t weights_stride_2, - const int64_t weights_stride_3, - const int64_t weights_stride_4, - const int problem_size) + int32_t depth, + int32_t height, + int32_t width, + int32_t heads, + int32_t kernel_size_0, + int32_t kernel_size_1, + int32_t kernel_size_2, + int32_t dilation_0, + int32_t dilation_1, + int32_t dilation_2, + int32_t dim, + int64_t weights_stride_0, + int64_t weights_stride_1, + int64_t weights_stride_2, + int64_t weights_stride_3, + int64_t weights_stride_4, + int32_t problem_size) : weights(weights), values(values), output(output), @@ -86,10 +88,12 @@ struct InverseNeighborhood3DBase { height(height), width(width), heads(heads), - kernel_size_in(kernel_size_in), - dilation_in(dilation_in), - depth_kernel_size_in(depth_kernel_size_in), - depth_dilation_in(depth_dilation_in), + kernel_size_0(kernel_size_0), + kernel_size_1(kernel_size_1), + kernel_size_2(kernel_size_2), + dilation_0(dilation_0), + dilation_1(dilation_1), + dilation_2(dilation_2), dim(dim), problem_size(problem_size), weights_stride_4(weights_stride_4), @@ -117,85 +121,69 @@ struct InverseNeighborhood3DBase { } }; -template < - typename scalar_t, - int KS, - int NS, - int DILATION, - int DKS, - int DNS, - int DDILATION> +template struct InverseNeighborhood3DFull : InverseNeighborhood3DBase { using Base = InverseNeighborhood3DBase; using Params = typename Base::Params; + using CausalMask = CausalMask_; static constexpr bool IsBF16Kernel = false; static constexpr bool IsHalfKernel = false; static constexpr bool UsesSmem = false; __device__ __host__ InverseNeighborhood3DFull() : Base() {} - static __host__ int get_dim(int dim) { + static __host__ int32_t get_dim(int32_t dim) { return dim; } __device__ void launch(Params p) { - const int KERNEL_SIZE = (KS > 1) ? KS : p.kernel_size_in; - const int KERNEL_SIZE_D = (DKS > 1) ? DKS : p.depth_kernel_size_in; - const int NEIGHBORHOOD_SIZE = (NS > 0) ? NS : KERNEL_SIZE / 2; - const int NEIGHBORHOOD_SIZE_D = (DNS > 0) ? DNS : KERNEL_SIZE_D / 2; - const int dilation = (DILATION > 0) ? DILATION : p.dilation_in; - const int dilation_d = (DDILATION > 0) ? DDILATION : p.depth_dilation_in; - const int64_t linearIndex = blockIdx.x * blockDim.x + threadIdx.x; + auto mask_0 = NeighborhoodMask( + p.depth, p.kernel_size_0, p.dilation_0); + auto mask_1 = NeighborhoodMask( + p.height, p.kernel_size_1, p.dilation_1); + auto mask_2 = NeighborhoodMask( + p.width, p.kernel_size_2, p.dilation_2); + int64_t linearIndex = blockIdx.x * blockDim.x + threadIdx.x; if (linearIndex < p.problem_size) { - int indtmp1 = linearIndex / p.dim; - const int d = linearIndex - indtmp1 * p.dim; - int indtmp2 = indtmp1 / p.width; - const int j = indtmp1 - indtmp2 * p.width; + int32_t indtmp1 = linearIndex / p.dim; + auto d = linearIndex - indtmp1 * p.dim; + int32_t indtmp2 = indtmp1 / p.width; + auto j = indtmp1 - indtmp2 * p.width; indtmp1 = indtmp2; indtmp2 = indtmp1 / p.height; - const int i = indtmp1 - indtmp2 * p.height; + auto i = indtmp1 - indtmp2 * p.height; indtmp1 = indtmp2; indtmp2 = indtmp1 / p.depth; - const int k = indtmp1 - indtmp2 * p.depth; + auto k = indtmp1 - indtmp2 * p.depth; indtmp1 = indtmp2; indtmp2 = indtmp1 / p.heads; - const int h = indtmp1 - indtmp2 * p.heads; - const int b = indtmp2; - const int ni = get_backward_window_start( - i, KERNEL_SIZE, NEIGHBORHOOD_SIZE, dilation); - const int nj = get_backward_window_start( - j, KERNEL_SIZE, NEIGHBORHOOD_SIZE, dilation); - const int nk = get_backward_window_start( - k, KERNEL_SIZE_D, NEIGHBORHOOD_SIZE_D, dilation_d); - const int ei = get_backward_window_end( - i, p.height, KERNEL_SIZE, NEIGHBORHOOD_SIZE, dilation); - const int ej = get_backward_window_end( - j, p.width, KERNEL_SIZE, NEIGHBORHOOD_SIZE, dilation); - const int ek = get_backward_window_end( - k, p.depth, KERNEL_SIZE_D, NEIGHBORHOOD_SIZE_D, dilation_d); - const int64_t weightsOffset = b * p.weights_stride_0 + h * p.weights_stride_1; - const int64_t valuesOffset = - b * p.values_stride_0 + h * p.values_stride_1 + d; + auto h = indtmp1 - indtmp2 * p.heads; + auto& b = indtmp2; + + auto nk = mask_0.get_backward_window_start(k); + auto ni = mask_1.get_backward_window_start(i); + auto nj = mask_2.get_backward_window_start(j); + auto ek = mask_0.get_backward_window_end(k); + auto ei = mask_1.get_backward_window_end(i); + auto ej = mask_2.get_backward_window_end(j); + + int64_t weightsOffset = b * p.weights_stride_0 + h * p.weights_stride_1; + int64_t valuesOffset = b * p.values_stride_0 + h * p.values_stride_1 + d; scalar_t output_update = scalar_t(0); -#pragma unroll - for (int xk = nk; xk < ek; xk += dilation_d) { - const int onk = get_window_start( - xk, p.depth, KERNEL_SIZE_D, NEIGHBORHOOD_SIZE_D, dilation_d); -#pragma unroll - for (int xi = ni; xi < ei; xi += dilation) { - const int oni = get_window_start( - xi, p.height, KERNEL_SIZE, NEIGHBORHOOD_SIZE, dilation); -#pragma unroll - for (int xj = nj; xj < ej; xj += dilation) { - const int onj = get_window_start( - xj, p.width, KERNEL_SIZE, NEIGHBORHOOD_SIZE, dilation); - const int64_t valuesIndex = valuesOffset + xk * p.values_stride_2 + + for (int32_t xk = nk; xk < ek; xk += p.dilation_0) { + auto onk = mask_0.get_window_start(xk); + for (int32_t xi = ni; xi < ei; xi += p.dilation_1) { + auto oni = mask_1.get_window_start(xi); + for (int32_t xj = nj; xj < ej; xj += p.dilation_2) { + auto onj = mask_2.get_window_start(xj); + int64_t valuesIndex = valuesOffset + xk * p.values_stride_2 + xi * p.values_stride_3 + xj * p.values_stride_4; - const int64_t weightsIndex = weightsOffset + xk * p.weights_stride_2 + + int64_t weightsIndex = weightsOffset + xk * p.weights_stride_2 + xi * p.weights_stride_3 + xj * p.weights_stride_4 + - (int((k - onk) / dilation_d) * KERNEL_SIZE * KERNEL_SIZE) + - int((i - oni) / dilation) * KERNEL_SIZE + - int((j - onj) / dilation); + (int32_t((k - onk) / p.dilation_0) * p.kernel_size_1 * + p.kernel_size_2) + + int32_t((i - oni) / p.dilation_1) * p.kernel_size_2 + + int32_t((j - onj) / p.dilation_2); output_update += p.values[valuesIndex] * p.weights[weightsIndex]; } } @@ -205,17 +193,11 @@ struct InverseNeighborhood3DFull : InverseNeighborhood3DBase { } }; -template < - typename scalar_t, - int KS, - int NS, - int DILATION, - int DKS, - int DNS, - int DDILATION> +template struct InverseNeighborhood3DHalf : InverseNeighborhood3DBase { using Base = InverseNeighborhood3DBase; using Params = typename Base::Params; + using CausalMask = CausalMask_; static constexpr bool IsBF16Kernel = IsBF16::value; static constexpr bool IsHalfKernel = true; static constexpr bool UsesSmem = false; @@ -224,7 +206,7 @@ struct InverseNeighborhood3DHalf : InverseNeighborhood3DBase { using HalfHelper = typename HalfArray::Base; - static __host__ int get_dim(int dim) { + static __host__ int32_t get_dim(int32_t dim) { if (dim % 2 != 0) { std::cerr << "Naive NATTEN half-precision kernels only support 32-bit alignment. " @@ -236,65 +218,55 @@ struct InverseNeighborhood3DHalf : InverseNeighborhood3DBase { } __device__ void launch(Params p) { - const int KERNEL_SIZE = (KS > 1) ? KS : p.kernel_size_in; - const int KERNEL_SIZE_D = (DKS > 1) ? DKS : p.depth_kernel_size_in; - const int NEIGHBORHOOD_SIZE = (NS > 0) ? NS : KERNEL_SIZE / 2; - const int NEIGHBORHOOD_SIZE_D = (DNS > 0) ? DNS : KERNEL_SIZE_D / 2; - const int dilation = (DILATION > 0) ? DILATION : p.dilation_in; - const int dilation_d = (DDILATION > 0) ? DDILATION : p.depth_dilation_in; - const int64_t linearIndex = blockIdx.x * blockDim.x + threadIdx.x; + auto mask_0 = NeighborhoodMask( + p.depth, p.kernel_size_0, p.dilation_0); + auto mask_1 = NeighborhoodMask( + p.height, p.kernel_size_1, p.dilation_1); + auto mask_2 = NeighborhoodMask( + p.width, p.kernel_size_2, p.dilation_2); + int64_t linearIndex = blockIdx.x * blockDim.x + threadIdx.x; if (linearIndex < p.problem_size) { auto values2 = HalfHelper::typecast(p.values); auto output2 = HalfHelper::typecast(p.output); - int indtmp1 = linearIndex / p.dim; - const int d = linearIndex - indtmp1 * p.dim; - int indtmp2 = indtmp1 / p.width; - const int j = indtmp1 - indtmp2 * p.width; + int32_t indtmp1 = linearIndex / p.dim; + auto d = linearIndex - indtmp1 * p.dim; + int32_t indtmp2 = indtmp1 / p.width; + auto j = indtmp1 - indtmp2 * p.width; indtmp1 = indtmp2; indtmp2 = indtmp1 / p.height; - const int i = indtmp1 - indtmp2 * p.height; + auto i = indtmp1 - indtmp2 * p.height; indtmp1 = indtmp2; indtmp2 = indtmp1 / p.depth; - const int k = indtmp1 - indtmp2 * p.depth; + auto k = indtmp1 - indtmp2 * p.depth; indtmp1 = indtmp2; indtmp2 = indtmp1 / p.heads; - const int h = indtmp1 - indtmp2 * p.heads; - const int b = indtmp2; - const int ni = get_backward_window_start( - i, KERNEL_SIZE, NEIGHBORHOOD_SIZE, dilation); - const int nj = get_backward_window_start( - j, KERNEL_SIZE, NEIGHBORHOOD_SIZE, dilation); - const int nk = get_backward_window_start( - k, KERNEL_SIZE_D, NEIGHBORHOOD_SIZE_D, dilation_d); - const int ei = get_backward_window_end( - i, p.height, KERNEL_SIZE, NEIGHBORHOOD_SIZE, dilation); - const int ej = get_backward_window_end( - j, p.width, KERNEL_SIZE, NEIGHBORHOOD_SIZE, dilation); - const int ek = get_backward_window_end( - k, p.depth, KERNEL_SIZE_D, NEIGHBORHOOD_SIZE_D, dilation_d); - const int64_t weightsOffset = b * p.weights_stride_0 + h * p.weights_stride_1; - const int64_t valuesOffset = - b * p.values_stride_0 + h * p.values_stride_1 + d; - auto output_update = HalfHelper::zero(); -#pragma unroll - for (int xk = nk; xk < ek; xk += dilation_d) { - const int onk = get_window_start( - xk, p.depth, KERNEL_SIZE_D, NEIGHBORHOOD_SIZE_D, dilation_d); -#pragma unroll - for (int xi = ni; xi < ei; xi += dilation) { - const int oni = get_window_start( - xi, p.height, KERNEL_SIZE, NEIGHBORHOOD_SIZE, dilation); -#pragma unroll - for (int xj = nj; xj < ej; xj += dilation) { - const int onj = get_window_start( - xj, p.width, KERNEL_SIZE, NEIGHBORHOOD_SIZE, dilation); - const int64_t valuesIndex = valuesOffset + xk * p.values_stride_2 + + auto h = indtmp1 - indtmp2 * p.heads; + auto& b = indtmp2; + + auto nk = mask_0.get_backward_window_start(k); + auto ni = mask_1.get_backward_window_start(i); + auto nj = mask_2.get_backward_window_start(j); + auto ek = mask_0.get_backward_window_end(k); + auto ei = mask_1.get_backward_window_end(i); + auto ej = mask_2.get_backward_window_end(j); + + int64_t weightsOffset = b * p.weights_stride_0 + h * p.weights_stride_1; + int64_t valuesOffset = b * p.values_stride_0 + h * p.values_stride_1 + d; + auto output_update = HalfHelper::zeros(); + for (int32_t xk = nk; xk < ek; xk += p.dilation_0) { + auto onk = mask_0.get_window_start(xk); + for (int32_t xi = ni; xi < ei; xi += p.dilation_1) { + auto oni = mask_1.get_window_start(xi); + for (int32_t xj = nj; xj < ej; xj += p.dilation_2) { + auto onj = mask_2.get_window_start(xj); + int64_t valuesIndex = valuesOffset + xk * p.values_stride_2 + xi * p.values_stride_3 + xj * p.values_stride_4; - const int64_t weightsIndex = weightsOffset + xk * p.weights_stride_2 + + int64_t weightsIndex = weightsOffset + xk * p.weights_stride_2 + xi * p.weights_stride_3 + xj * p.weights_stride_4 + - (int((k - onk) / dilation_d) * KERNEL_SIZE * KERNEL_SIZE) + - int((i - oni) / dilation) * KERNEL_SIZE + - int((j - onj) / dilation); + (int32_t((k - onk) / p.dilation_0) * p.kernel_size_1 * + p.kernel_size_2) + + int32_t((i - oni) / p.dilation_1) * p.kernel_size_2 + + int32_t((j - onj) / p.dilation_2); output_update = HalfHelper::fma( values2[valuesIndex], p.weights[weightsOffset], output_update); } @@ -308,54 +280,33 @@ struct InverseNeighborhood3DHalf : InverseNeighborhood3DBase { template struct InverseNeighborhood3D { using Args = Args_; - static constexpr int KS = Args::KernelSize; - static constexpr int NS = Args::NeighborhoodSize; - static constexpr int DKS = Args::DepthKernelSize; - static constexpr int DNS = Args::DepthNeighborhoodSize; - static constexpr int DILATION = Args::Dilation; - static constexpr int DDILATION = Args::DepthDilation; using scalar_t = typename Args::Dtype; + using CausalMask = typename Args::CausalMask; using Kernel = typename std::conditional< sizeof(scalar_t) >= 4, - InverseNeighborhood3DFull< - scalar_t, - KS, - NS, - DILATION, - DKS, - DNS, - DDILATION>, - InverseNeighborhood3DHalf< - scalar_t, - KS, - NS, - DILATION, - DKS, - DNS, - DDILATION>>::type; + InverseNeighborhood3DFull, + InverseNeighborhood3DHalf>::type; using Params = typename Kernel::Params; void operator()( - const int cc, + int32_t cc, cudaStream_t stream, void* attn_ptr, void* d_output_ptr, void* d_value_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, + int32_t batch_size, + int32_t heads, + int32_t depth, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, int64_t attn_stride_4, - int kernel_size, - int kernel_size_depth, - int dilation, - int dilation_depth) { + const std::tuple& kernel_size, + const std::tuple& dilation) { dim = Kernel::get_dim(dim); int64_t problem_size = batch_size * heads * depth * height * width * dim; auto grid = Kernel::Base::get_grid(problem_size); @@ -368,10 +319,12 @@ struct InverseNeighborhood3D { height, width, heads, - kernel_size, - dilation, - kernel_size_depth, - dilation_depth, + std::get<0>(kernel_size), + std::get<1>(kernel_size), + std::get<2>(kernel_size), + std::get<0>(dilation), + std::get<1>(dilation), + std::get<2>(dilation), dim, attn_stride_0, attn_stride_1, diff --git a/csrc/include/natten/cuda/naive/natten_commons.cuh b/csrc/include/natten/cuda/naive/natten_commons.cuh index 21cf925..2d08a21 100644 --- a/csrc/include/natten/cuda/naive/natten_commons.cuh +++ b/csrc/include/natten/cuda/naive/natten_commons.cuh @@ -26,7 +26,7 @@ #pragma once -#ifndef __CUDA_ARCH__ +#ifndef __CUDA_ARCH__ #include #endif @@ -38,11 +38,11 @@ #define CUDA_NUM_THREADS 1024 // CUDA: number of blocks for threads. -inline int GET_BLOCKS( - const int64_t N, - const int64_t max_threads_per_block = CUDA_NUM_THREADS) { +inline int32_t GET_BLOCKS( + int64_t N, + int64_t max_threads_per_block = CUDA_NUM_THREADS) { auto block_num = (N - 1) / max_threads_per_block + 1; - return static_cast(block_num); + return static_cast(block_num); } namespace natten { @@ -125,9 +125,14 @@ struct HalfArrayBase { return __half2float(s); } - __device__ __inline__ static ElementVector zero() { + __device__ __inline__ static ElementScalar zero() { + return __float2half(0.f); + } + + __device__ __inline__ static ElementVector zeros() { return __float2half2_rn(0.f); } + __device__ __inline__ static ElementVector fma( ElementVector a, ElementVector b, @@ -159,40 +164,6 @@ struct HalfArrayBase { using ElementNatten = natten::float16; using ElementScalar = natten::float16; using ElementVector = float162; - - __device__ __inline__ static ElementVector* typecast( - ElementScalar* ptr_scalar) { - asm volatile("brkpt;\n"); - } - - __device__ __inline__ static ElementNatten cast_back(ElementScalar s) { - asm volatile("brkpt;\n"); - } - - __device__ __inline__ static float to_float(ElementScalar s) { - asm volatile("brkpt;\n"); - } - - __device__ __inline__ static ElementVector zero() { - asm volatile("brkpt;\n"); - } - __device__ __inline__ static ElementVector fma( - ElementVector a, - ElementVector b, - ElementVector c) { - asm volatile("brkpt;\n"); - } - __device__ __inline__ static ElementVector fma( - ElementVector a, - ElementScalar b, - ElementVector c) { - asm volatile("brkpt;\n"); - } - __device__ __inline__ static ElementScalar add( - ElementScalar a, - ElementScalar b) { - asm volatile("brkpt;\n"); - } }; template <> @@ -222,9 +193,14 @@ struct HalfArrayBase { return __bfloat162float(s); } - __device__ __inline__ static ElementVector zero() { + __device__ __inline__ static ElementScalar zero() { + return __float2bfloat16(0.f); + } + + __device__ __inline__ static ElementVector zeros() { return __float2bfloat162_rn(0.f); } + __device__ __inline__ static ElementVector fma( ElementVector a, ElementVector b, @@ -256,86 +232,122 @@ struct HalfArrayBase { using ElementNatten = natten::bfloat16; using ElementScalar = natten::bfloat16; using ElementVector = bfloat162; +}; - __device__ __inline__ static ElementVector* typecast( - ElementScalar* ptr_scalar) { - asm volatile("brkpt;\n"); - } +template <> +struct HalfArray { + using Base = HalfArrayBase; +}; +#endif - __device__ __inline__ static ElementNatten cast_back(ElementScalar s) { - asm volatile("brkpt;\n"); +template +struct NeighborhoodMask { + static constexpr bool IsCausal = IsCausal_; + + int32_t length; + int32_t kernel_size; + int32_t neighborhood_size; + int32_t dilation; + + inline __device__ NeighborhoodMask( + int32_t length, + int32_t kernel_size, + int32_t dilation) + : length(length), + kernel_size(kernel_size), + neighborhood_size(kernel_size / 2), + dilation(dilation) {} + + inline __device__ int32_t get_backward_window_start(int32_t index) { + if constexpr (!IsCausal) { + return (index < kernel_size * dilation) + ? (index % dilation) + : index - neighborhood_size * dilation; + } else { + return index; + } } - __device__ __inline__ static float to_float(ElementScalar s) { - asm volatile("brkpt;\n"); + inline __device__ int32_t get_backward_window_end(int32_t index) { + if constexpr (!IsCausal) { + return (index >= length - kernel_size * dilation) + ? (length) + : (index + (neighborhood_size + 1) * dilation); + } else { + return min(index + kernel_size * dilation, length); + } } - __device__ __inline__ static ElementVector zero() { - asm volatile("brkpt;\n"); - } - __device__ __inline__ static ElementVector fma( - ElementVector a, - ElementVector b, - ElementVector c) { - asm volatile("brkpt;\n"); - } - __device__ __inline__ static ElementVector fma( - ElementVector a, - ElementScalar b, - ElementVector c) { - asm volatile("brkpt;\n"); + inline __device__ int32_t get_window_start(int32_t index) { + if constexpr (!IsCausal) { + if (dilation <= 1) + return max(index - neighborhood_size, 0) + + (index + neighborhood_size >= length) * + (length - index - neighborhood_size - 1); + int32_t ni = index - neighborhood_size * dilation; + if (ni < 0) + return index % dilation; + if (index + neighborhood_size * dilation >= length) { + int32_t imodd = index % dilation; + int32_t a = int32_t(length / dilation) * dilation; + int32_t b = length - a; + if (imodd < b) + return length - b + imodd - 2 * neighborhood_size * dilation; + return a + imodd - kernel_size * dilation; + } + return ni; + } else { + auto dilation_idx = index % dilation; + auto index_pdp = index / dilation; + return max(index_pdp - kernel_size + 1, 0) * dilation + dilation_idx; + } } - __device__ __inline__ static ElementScalar add( - ElementScalar a, - ElementScalar b) { - asm volatile("brkpt;\n"); + + inline __device__ int32_t get_window_end(int32_t index, int32_t start_index) { + if constexpr (!IsCausal) { + return min(length, start_index + kernel_size * dilation); + } else { + return min(length, index + dilation); + } } -}; -template <> -struct HalfArray { - using Base = HalfArrayBase; + inline __device__ int32_t get_pb_start(int32_t index) { + if constexpr (!IsCausal) { + if (dilation <= 1) + return neighborhood_size + + (index < neighborhood_size) * (neighborhood_size - index) + + (index + neighborhood_size >= length) * + (length - index - 1 - neighborhood_size); + if (index - neighborhood_size * dilation < 0) + return kernel_size - 1 - (index / dilation); + if (index + neighborhood_size * dilation >= length) + return (length - index - 1) / dilation; + return neighborhood_size; + } else { + printf( + "FATAL: RPB kernels do not support causal masking. Please open an issue."); + asm volatile("brkpt;\n"); + } + } }; -#endif -inline __host__ __device__ int get_backward_window_start( - const int index, - const int KERNEL_SIZE, - const int NEIGHBORHOOD_SIZE, - const int dilation) { - return (index < KERNEL_SIZE * dilation) - ? (index % dilation) - : index - NEIGHBORHOOD_SIZE * dilation; -} - -inline __host__ __device__ int get_backward_window_end( - const int index, - const int length, - const int KERNEL_SIZE, - const int NEIGHBORHOOD_SIZE, - const int dilation) { - return (index >= length - KERNEL_SIZE * dilation) - ? (length) - : (index + (NEIGHBORHOOD_SIZE + 1) * dilation); -} - -inline __host__ __device__ int get_window_start( - const int index, - const int length, - const int KERNEL_SIZE, - const int NEIGHBORHOOD_SIZE, - const int dilation) { +inline __device__ int32_t get_window_start( + int32_t index, + int32_t length, + int32_t KERNEL_SIZE, + int32_t NEIGHBORHOOD_SIZE, + int32_t dilation) { if (dilation <= 1) return max(index - NEIGHBORHOOD_SIZE, 0) + (index + NEIGHBORHOOD_SIZE >= length) * (length - index - NEIGHBORHOOD_SIZE - 1); - int ni = index - NEIGHBORHOOD_SIZE * dilation; + int32_t ni = index - NEIGHBORHOOD_SIZE * dilation; if (ni < 0) return index % dilation; if (index + NEIGHBORHOOD_SIZE * dilation >= length) { - const int imodd = index % dilation; - const int a = int(length / dilation) * dilation; - const int b = length - a; + int32_t imodd = index % dilation; + int32_t a = int32_t(length / dilation) * dilation; + int32_t b = length - a; if (imodd < b) return length - b + imodd - 2 * NEIGHBORHOOD_SIZE * dilation; return a + imodd - KERNEL_SIZE * dilation; @@ -343,12 +355,12 @@ inline __host__ __device__ int get_window_start( return ni; } -inline __host__ __device__ int get_pb_start( - const int index, - const int length, - const int KERNEL_SIZE, - const int NEIGHBORHOOD_SIZE, - const int dilation) { +inline __device__ int32_t get_pb_start( + int32_t index, + int32_t length, + int32_t KERNEL_SIZE, + int32_t NEIGHBORHOOD_SIZE, + int32_t dilation) { if (dilation <= 1) return NEIGHBORHOOD_SIZE + (index < NEIGHBORHOOD_SIZE) * (NEIGHBORHOOD_SIZE - index) + @@ -361,6 +373,49 @@ inline __host__ __device__ int get_pb_start( return NEIGHBORHOOD_SIZE; } +template +struct AttnMask; + +#ifdef __CUDA_ARCH__ + +// TODO: bitcast into a constexpr instead of evaluating at runtime + +template <> +struct AttnMask { + static __device__ auto value(bool is_grad) { + return is_grad ? 0.0 : -__longlong_as_double(0x7ff0000000000000ULL); + } +}; + +template <> +struct AttnMask { + static __device__ auto value(bool is_grad) { + return is_grad ? 0.f : -__int_as_float(0x7f800000U); + } +}; + +#if (__CUDACC_VER_MAJOR__ >= 11) && (__CUDA_ARCH__ >= 600) +template <> +struct AttnMask { + static __device__ auto value(bool is_grad) { + return is_grad ? __float2half(0.f) + : __hneg(__ushort_as_half((unsigned short)0x7C00U)); + } +}; +#endif + +#if (__CUDACC_VER_MAJOR__ >= 11) && (__CUDA_ARCH__ >= 800) +template <> +struct AttnMask { + static __device__ auto value(bool is_grad) { + return is_grad ? __float2bfloat16(0.f) + : __hneg(__ushort_as_bfloat16((unsigned short)0x7F80U)); + } +}; +#endif + +#endif + } // namespace naive } // namespace cuda } // namespace natten diff --git a/csrc/include/natten/cuda/naive/neighborhood_neighborhood_1d.cuh b/csrc/include/natten/cuda/naive/neighborhood_neighborhood_1d.cuh index 1916948..5be46fa 100644 --- a/csrc/include/natten/cuda/naive/neighborhood_neighborhood_1d.cuh +++ b/csrc/include/natten/cuda/naive/neighborhood_neighborhood_1d.cuh @@ -30,6 +30,8 @@ #include #include +#include +#include #include namespace natten { @@ -42,14 +44,14 @@ struct NeighborhoodNeighborhood1DBase { scalar_t* weights; scalar_t* values; scalar_t* output; - const int length; - const int heads; - const int kernel_size_in; - const int dilation_in; - const int dim; - const int64_t problem_size; - const int64_t weights_stride_0, weights_stride_1, weights_stride_2; - const int64_t values_stride_0, values_stride_1, values_stride_2; + int32_t length; + int32_t heads; + int32_t kernel_size; + int32_t dilation; + int32_t dim; + int64_t problem_size; + int64_t weights_stride_0, weights_stride_1, weights_stride_2; + int64_t values_stride_0, values_stride_1, values_stride_2; __device__ __host__ Params() {} @@ -57,22 +59,22 @@ struct NeighborhoodNeighborhood1DBase { scalar_t* weights, // attn / d_attn scalar_t* values, // value / key scalar_t* output, // output / d_query - const int length, - const int heads, - const int kernel_size_in, - const int dilation_in, - const int dim, - const int64_t weights_stride_0, - const int64_t weights_stride_1, - const int64_t weights_stride_2, - const int64_t problem_size) + int32_t length, + int32_t heads, + int32_t kernel_size, + int32_t dilation, + int32_t dim, + int64_t weights_stride_0, + int64_t weights_stride_1, + int64_t weights_stride_2, + int64_t problem_size) : weights(weights), values(values), output(output), length(length), heads(heads), - kernel_size_in(kernel_size_in), - dilation_in(dilation_in), + kernel_size(kernel_size), + dilation(dilation), dim(dim), problem_size(problem_size), weights_stride_2(weights_stride_2), @@ -96,46 +98,45 @@ struct NeighborhoodNeighborhood1DBase { } }; -template +template struct NeighborhoodNeighborhood1DFull : NeighborhoodNeighborhood1DBase { using Base = NeighborhoodNeighborhood1DBase; using Params = typename Base::Params; + using CausalMask = CausalMask_; static constexpr bool IsBF16Kernel = false; static constexpr bool IsHalfKernel = false; static constexpr bool UsesSmem = false; __device__ __host__ NeighborhoodNeighborhood1DFull() : Base() {} - static __host__ int get_dim(int dim) { + static __host__ int32_t get_dim(int32_t dim) { return dim; } __device__ void launch(Params p) { - const int KERNEL_SIZE = (KS > 1) ? KS : p.kernel_size_in; - const int NEIGHBORHOOD_SIZE = (NS > 0) ? NS : KERNEL_SIZE / 2; - const int dilation = (DILATION > 0) ? DILATION : p.dilation_in; - const int64_t linearIndex = blockIdx.x * blockDim.x + threadIdx.x; + auto mask_0 = + NeighborhoodMask(p.length, p.kernel_size, p.dilation); + int64_t linearIndex = blockIdx.x * blockDim.x + threadIdx.x; if (linearIndex < p.problem_size) { - int indtmp1 = linearIndex / p.dim; - const int d = linearIndex - indtmp1 * p.dim; - int indtmp2 = indtmp1 / p.length; - const int i = indtmp1 - indtmp2 * p.length; + int32_t indtmp1 = linearIndex / p.dim; + auto d = linearIndex - indtmp1 * p.dim; + int32_t indtmp2 = indtmp1 / p.length; + auto i = indtmp1 - indtmp2 * p.length; indtmp1 = indtmp2; indtmp2 = indtmp1 / p.heads; - const int h = indtmp1 - indtmp2 * p.heads; - const int b = indtmp2; + auto h = indtmp1 - indtmp2 * p.heads; + auto& b = indtmp2; + + auto ni = mask_0.get_window_start(i); + auto ei = mask_0.get_window_end(i, ni); - const int ni = get_window_start( - i, p.length, KERNEL_SIZE, NEIGHBORHOOD_SIZE, dilation); scalar_t updt = scalar_t(0); - int weightsOffset = b * p.weights_stride_0 + h * p.weights_stride_1 + + int32_t weightsOffset = b * p.weights_stride_0 + h * p.weights_stride_1 + i * p.weights_stride_2; - const int64_t valuesOffset = - b * p.values_stride_0 + h * p.values_stride_1 + d; -#pragma unroll - for (int xi = ni; xi < ni + KERNEL_SIZE * dilation; xi += dilation) { - const int64_t valuesIndex = valuesOffset + xi * p.values_stride_2; + int64_t valuesOffset = b * p.values_stride_0 + h * p.values_stride_1 + d; + for (int32_t xi = ni; xi < ei; xi += p.dilation) { + int64_t valuesIndex = valuesOffset + xi * p.values_stride_2; updt += p.weights[weightsOffset] * p.values[valuesIndex]; ++weightsOffset; } @@ -144,11 +145,12 @@ struct NeighborhoodNeighborhood1DFull } }; -template +template struct NeighborhoodNeighborhood1DHalf : NeighborhoodNeighborhood1DBase { using Base = NeighborhoodNeighborhood1DBase; using Params = typename Base::Params; + using CausalMask = CausalMask_; static constexpr bool IsBF16Kernel = IsBF16::value; static constexpr bool IsHalfKernel = true; static constexpr bool UsesSmem = false; @@ -156,7 +158,7 @@ struct NeighborhoodNeighborhood1DHalf __device__ __host__ NeighborhoodNeighborhood1DHalf() : Base() {} using HalfHelper = typename HalfArray::Base; - static __host__ int get_dim(int dim) { + static __host__ int32_t get_dim(int32_t dim) { if (dim % 2 != 0) { std::cerr << "Naive NATTEN half-precision kernels only support 32-bit alignment. " @@ -168,32 +170,30 @@ struct NeighborhoodNeighborhood1DHalf } __device__ void launch(Params p) { - const int KERNEL_SIZE = (KS > 1) ? KS : p.kernel_size_in; - const int NEIGHBORHOOD_SIZE = (NS > 0) ? NS : KERNEL_SIZE / 2; - const int dilation = (DILATION > 0) ? DILATION : p.dilation_in; - const int64_t linearIndex = blockIdx.x * blockDim.x + threadIdx.x; + auto mask_0 = + NeighborhoodMask(p.length, p.kernel_size, p.dilation); + int64_t linearIndex = blockIdx.x * blockDim.x + threadIdx.x; if (linearIndex < p.problem_size) { auto values2 = HalfHelper::typecast(p.values); auto output2 = HalfHelper::typecast(p.output); - int indtmp1 = linearIndex / p.dim; - const int d = linearIndex - indtmp1 * p.dim; - int indtmp2 = indtmp1 / p.length; - const int i = indtmp1 - indtmp2 * p.length; + int32_t indtmp1 = linearIndex / p.dim; + auto d = linearIndex - indtmp1 * p.dim; + int32_t indtmp2 = indtmp1 / p.length; + auto i = indtmp1 - indtmp2 * p.length; indtmp1 = indtmp2; indtmp2 = indtmp1 / p.heads; - const int h = indtmp1 - indtmp2 * p.heads; - const int b = indtmp2; + auto h = indtmp1 - indtmp2 * p.heads; + auto& b = indtmp2; + + auto ni = mask_0.get_window_start(i); + auto ei = mask_0.get_window_end(i, ni); - const int ni = get_window_start( - i, p.length, KERNEL_SIZE, NEIGHBORHOOD_SIZE, dilation); - auto output_update = HalfHelper::zero(); - int weightsOffset = b * p.weights_stride_0 + h * p.weights_stride_1 + + auto output_update = HalfHelper::zeros(); + int64_t weightsOffset = b * p.weights_stride_0 + h * p.weights_stride_1 + i * p.weights_stride_2; - const int64_t valuesOffset = - b * p.values_stride_0 + h * p.values_stride_1 + d; -#pragma unroll - for (int xi = ni; xi < ni + KERNEL_SIZE * dilation; xi += dilation) { - const int64_t valuesIndex = valuesOffset + xi * p.values_stride_2; + int64_t valuesOffset = b * p.values_stride_0 + h * p.values_stride_1 + d; + for (int32_t xi = ni; xi < ei; xi += p.dilation) { + int64_t valuesIndex = valuesOffset + xi * p.values_stride_2; scalar_t a = p.weights[weightsOffset]; output_update = HalfHelper::fma(values2[valuesIndex], a, output_update); ++weightsOffset; @@ -206,31 +206,29 @@ struct NeighborhoodNeighborhood1DHalf template struct NeighborhoodNeighborhood1D { using Args = Args_; - static constexpr int KS = Args::KernelSize; - static constexpr int NS = Args::NeighborhoodSize; - static constexpr int DILATION = Args::Dilation; using scalar_t = typename Args::Dtype; + using CausalMask = typename Args::CausalMask; using Kernel = typename std::conditional< sizeof(scalar_t) >= 4, - NeighborhoodNeighborhood1DFull, - NeighborhoodNeighborhood1DHalf>::type; + NeighborhoodNeighborhood1DFull, + NeighborhoodNeighborhood1DHalf>::type; using Params = typename Kernel::Params; void operator()( - const int cc, + int32_t cc, cudaStream_t stream, void* attn_ptr, void* value_ptr, void* output_ptr, - int batch_size, - int heads, - int length, - int dim, + int32_t batch_size, + int32_t heads, + int32_t length, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, - int kernel_size, - int dilation) { + const std::tuple& kernel_size, + const std::tuple& dilation) { dim = Kernel::get_dim(dim); int64_t problem_size = batch_size * heads * length * dim; auto grid = Kernel::Base::get_grid(problem_size); @@ -241,8 +239,8 @@ struct NeighborhoodNeighborhood1D { reinterpret_cast(output_ptr), length, heads, - kernel_size, - dilation, + std::get<0>(kernel_size), + std::get<0>(dilation), dim, attn_stride_0, attn_stride_1, diff --git a/csrc/include/natten/cuda/naive/neighborhood_neighborhood_2d.cuh b/csrc/include/natten/cuda/naive/neighborhood_neighborhood_2d.cuh index 09bfb2b..648ccff 100644 --- a/csrc/include/natten/cuda/naive/neighborhood_neighborhood_2d.cuh +++ b/csrc/include/natten/cuda/naive/neighborhood_neighborhood_2d.cuh @@ -30,6 +30,8 @@ #include #include +#include +#include #include namespace natten { @@ -42,17 +44,16 @@ struct NeighborhoodNeighborhood2DBase { scalar_t* weights; scalar_t* values; scalar_t* output; - const int height; - const int width; - const int heads; - const int kernel_size_in; - const int dilation_in; - const int dim; - const int64_t problem_size; - const int64_t weights_stride_0, weights_stride_1, weights_stride_2, + int32_t height; + int32_t width; + int32_t heads; + int32_t kernel_size_0, kernel_size_1; + int32_t dilation_0, dilation_1; + int32_t dim; + int64_t problem_size; + int64_t weights_stride_0, weights_stride_1, weights_stride_2, weights_stride_3; - const int64_t values_stride_0, values_stride_1, values_stride_2, - values_stride_3; + int64_t values_stride_0, values_stride_1, values_stride_2, values_stride_3; __device__ __host__ Params() {} @@ -60,25 +61,29 @@ struct NeighborhoodNeighborhood2DBase { scalar_t* weights, // attn / d_attn scalar_t* values, // value / key scalar_t* output, // output / d_query - const int height, - const int width, - const int heads, - const int kernel_size_in, - const int dilation_in, - const int dim, - const int64_t weights_stride_0, - const int64_t weights_stride_1, - const int64_t weights_stride_2, - const int64_t weights_stride_3, - const int problem_size) + int32_t height, + int32_t width, + int32_t heads, + int32_t kernel_size_0, + int32_t kernel_size_1, + int32_t dilation_0, + int32_t dilation_1, + int32_t dim, + int64_t weights_stride_0, + int64_t weights_stride_1, + int64_t weights_stride_2, + int64_t weights_stride_3, + int32_t problem_size) : weights(weights), values(values), output(output), height(height), width(width), heads(heads), - kernel_size_in(kernel_size_in), - dilation_in(dilation_in), + kernel_size_0(kernel_size_0), + kernel_size_1(kernel_size_1), + dilation_0(dilation_0), + dilation_1(dilation_1), dim(dim), problem_size(problem_size), weights_stride_3(weights_stride_3), @@ -104,56 +109,58 @@ struct NeighborhoodNeighborhood2DBase { } }; -template +template struct NeighborhoodNeighborhood2DFull : NeighborhoodNeighborhood2DBase { using Base = NeighborhoodNeighborhood2DBase; using Params = typename Base::Params; + using CausalMask = CausalMask_; static constexpr bool IsBF16Kernel = false; static constexpr bool IsHalfKernel = false; static constexpr bool UsesSmem = false; __device__ __host__ NeighborhoodNeighborhood2DFull() : Base() {} - static __host__ int get_dim(int dim) { + static __host__ int32_t get_dim(int32_t dim) { return dim; } __device__ void launch(Params p) { - const int KERNEL_SIZE = (KS > 1) ? KS : p.kernel_size_in; - const int NEIGHBORHOOD_SIZE = (NS > 0) ? NS : KERNEL_SIZE / 2; - const int dilation = (DILATION > 0) ? DILATION : p.dilation_in; - const int64_t linearIndex = blockIdx.x * blockDim.x + threadIdx.x; + auto mask_0 = NeighborhoodMask( + p.height, p.kernel_size_0, p.dilation_0); + auto mask_1 = NeighborhoodMask( + p.width, p.kernel_size_1, p.dilation_1); + int64_t linearIndex = blockIdx.x * blockDim.x + threadIdx.x; if (linearIndex < p.problem_size) { - int indtmp1 = linearIndex / p.dim; - const int d = linearIndex - indtmp1 * p.dim; - int indtmp2 = indtmp1 / p.width; - const int j = indtmp1 - indtmp2 * p.width; + int32_t indtmp1 = linearIndex / p.dim; + auto d = linearIndex - indtmp1 * p.dim; + int32_t indtmp2 = indtmp1 / p.width; + auto j = indtmp1 - indtmp2 * p.width; indtmp1 = indtmp2; indtmp2 = indtmp1 / p.height; - const int i = indtmp1 - indtmp2 * p.height; + auto i = indtmp1 - indtmp2 * p.height; indtmp1 = indtmp2; indtmp2 = indtmp1 / p.heads; - const int h = indtmp1 - indtmp2 * p.heads; - const int b = indtmp2; + auto h = indtmp1 - indtmp2 * p.heads; + auto& b = indtmp2; + + auto ni = mask_0.get_window_start(i); + auto nj = mask_1.get_window_start(j); + auto ei = mask_0.get_window_end(i, ni); + auto ej = mask_1.get_window_end(j, nj); - const int ni = get_window_start( - i, p.height, KERNEL_SIZE, NEIGHBORHOOD_SIZE, dilation); - const int nj = get_window_start( - j, p.width, KERNEL_SIZE, NEIGHBORHOOD_SIZE, dilation); scalar_t updt = scalar_t(0); - int weightsOffset = b * p.weights_stride_0 + h * p.weights_stride_1 + + int64_t weightsOffset = b * p.weights_stride_0 + h * p.weights_stride_1 + i * p.weights_stride_2 + j * p.weights_stride_3; - const int64_t valuesOffset = - b * p.values_stride_0 + h * p.values_stride_1 + d; -#pragma unroll - for (int xi = ni; xi < ni + KERNEL_SIZE * dilation; xi += dilation) { -#pragma unroll - for (int xj = nj; xj < nj + KERNEL_SIZE * dilation; xj += dilation) { - const int64_t valuesIndex = + int64_t valuesOffset = b * p.values_stride_0 + h * p.values_stride_1 + d; + for (int32_t xi = ni; xi < ei; xi += p.dilation_0) { + for (int32_t xj = nj; xj < ej; xj += p.dilation_1) { + int64_t valuesIndex = valuesOffset + xi * p.values_stride_2 + xj * p.values_stride_3; - updt += p.weights[weightsOffset] * p.values[valuesIndex]; - ++weightsOffset; + int64_t weightsIndex = weightsOffset + + int32_t((xi - ni) / p.dilation_0) * p.kernel_size_1 + + int32_t((xj - nj) / p.dilation_1); + updt += p.weights[weightsIndex] * p.values[valuesIndex]; } } p.output[linearIndex] = updt; @@ -161,11 +168,12 @@ struct NeighborhoodNeighborhood2DFull } }; -template +template struct NeighborhoodNeighborhood2DHalf : NeighborhoodNeighborhood2DBase { using Base = NeighborhoodNeighborhood2DBase; using Params = typename Base::Params; + using CausalMask = CausalMask_; static constexpr bool IsBF16Kernel = IsBF16::value; static constexpr bool IsHalfKernel = true; static constexpr bool UsesSmem = false; @@ -174,7 +182,7 @@ struct NeighborhoodNeighborhood2DHalf using HalfHelper = typename HalfArray::Base; - static __host__ int get_dim(int dim) { + static __host__ int32_t get_dim(int32_t dim) { if (dim % 2 != 0) { std::cerr << "Naive NATTEN half-precision kernels only support 32-bit alignment. " @@ -186,44 +194,46 @@ struct NeighborhoodNeighborhood2DHalf } __device__ void launch(Params p) { - const int KERNEL_SIZE = (KS > 1) ? KS : p.kernel_size_in; - const int NEIGHBORHOOD_SIZE = (NS > 0) ? NS : KERNEL_SIZE / 2; - const int dilation = (DILATION > 0) ? DILATION : p.dilation_in; - const int64_t linearIndex = blockIdx.x * blockDim.x + threadIdx.x; + auto mask_0 = NeighborhoodMask( + p.height, p.kernel_size_0, p.dilation_0); + auto mask_1 = NeighborhoodMask( + p.width, p.kernel_size_1, p.dilation_1); + int64_t linearIndex = blockIdx.x * blockDim.x + threadIdx.x; if (linearIndex < p.problem_size) { auto values2 = HalfHelper::typecast(p.values); auto output2 = HalfHelper::typecast(p.output); - int indtmp1 = linearIndex / p.dim; - const int d = linearIndex - indtmp1 * p.dim; - int indtmp2 = indtmp1 / p.width; - const int j = indtmp1 - indtmp2 * p.width; + int32_t indtmp1 = linearIndex / p.dim; + auto d = linearIndex - indtmp1 * p.dim; + int32_t indtmp2 = indtmp1 / p.width; + auto j = indtmp1 - indtmp2 * p.width; indtmp1 = indtmp2; indtmp2 = indtmp1 / p.height; - const int i = indtmp1 - indtmp2 * p.height; + auto i = indtmp1 - indtmp2 * p.height; indtmp1 = indtmp2; indtmp2 = indtmp1 / p.heads; - const int h = indtmp1 - indtmp2 * p.heads; - const int b = indtmp2; + auto h = indtmp1 - indtmp2 * p.heads; + auto& b = indtmp2; + + auto ni = mask_0.get_window_start(i); + auto nj = mask_1.get_window_start(j); + auto ei = mask_0.get_window_end(i, ni); + auto ej = mask_1.get_window_end(j, nj); - const int ni = get_window_start( - i, p.height, KERNEL_SIZE, NEIGHBORHOOD_SIZE, dilation); - const int nj = get_window_start( - j, p.width, KERNEL_SIZE, NEIGHBORHOOD_SIZE, dilation); - auto updt = HalfHelper::zero(); - int weightsOffset = b * p.weights_stride_0 + h * p.weights_stride_1 + + auto updt = HalfHelper::zeros(); + int64_t weightsOffset = b * p.weights_stride_0 + h * p.weights_stride_1 + i * p.weights_stride_2 + j * p.weights_stride_3; - const int64_t valuesOffset = - b * p.values_stride_0 + h * p.values_stride_1 + d; -#pragma unroll - for (int xi = ni; xi < ni + KERNEL_SIZE * dilation; xi += dilation) -#pragma unroll - for (int xj = nj; xj < nj + KERNEL_SIZE * dilation; xj += dilation) { - const int64_t valuesIndex = + int64_t valuesOffset = b * p.values_stride_0 + h * p.values_stride_1 + d; + for (int32_t xi = ni; xi < ei; xi += p.dilation_0) { + for (int32_t xj = nj; xj < ej; xj += p.dilation_1) { + int64_t valuesIndex = valuesOffset + xi * p.values_stride_2 + xj * p.values_stride_3; + int64_t weightsIndex = weightsOffset + + int32_t((xi - ni) / p.dilation_0) * p.kernel_size_1 + + int32_t((xj - nj) / p.dilation_1); updt = HalfHelper::fma( - values2[valuesIndex], p.weights[weightsOffset], updt); - ++weightsOffset; + values2[valuesIndex], p.weights[weightsIndex], updt); } + } output2[linearIndex] = updt; } } @@ -232,33 +242,31 @@ struct NeighborhoodNeighborhood2DHalf template struct NeighborhoodNeighborhood2D { using Args = Args_; - static constexpr int KS = Args::KernelSize; - static constexpr int NS = Args::NeighborhoodSize; - static constexpr int DILATION = Args::Dilation; using scalar_t = typename Args::Dtype; + using CausalMask = typename Args::CausalMask; using Kernel = typename std::conditional< sizeof(scalar_t) >= 4, - NeighborhoodNeighborhood2DFull, - NeighborhoodNeighborhood2DHalf>::type; + NeighborhoodNeighborhood2DFull, + NeighborhoodNeighborhood2DHalf>::type; using Params = typename Kernel::Params; void operator()( - const int cc, + int32_t cc, cudaStream_t stream, void* attn_ptr, void* value_ptr, void* output_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, + int32_t batch_size, + int32_t heads, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, - int kernel_size, - int dilation) { + const std::tuple& kernel_size, + const std::tuple& dilation) { dim = Kernel::get_dim(dim); int64_t problem_size = batch_size * heads * height * width * dim; auto grid = Kernel::Base::get_grid(problem_size); @@ -270,8 +278,10 @@ struct NeighborhoodNeighborhood2D { height, width, heads, - kernel_size, - dilation, + std::get<0>(kernel_size), + std::get<1>(kernel_size), + std::get<0>(dilation), + std::get<1>(dilation), dim, attn_stride_0, attn_stride_1, diff --git a/csrc/include/natten/cuda/naive/neighborhood_neighborhood_3d.cuh b/csrc/include/natten/cuda/naive/neighborhood_neighborhood_3d.cuh index fd71fc3..d391ac0 100644 --- a/csrc/include/natten/cuda/naive/neighborhood_neighborhood_3d.cuh +++ b/csrc/include/natten/cuda/naive/neighborhood_neighborhood_3d.cuh @@ -30,6 +30,8 @@ #include #include +#include +#include #include namespace natten { @@ -42,20 +44,18 @@ struct NeighborhoodNeighborhood3DBase { scalar_t* weights; scalar_t* values; scalar_t* output; - const int depth; - const int height; - const int width; - const int heads; - const int kernel_size_in; - const int dilation_in; - const int depth_kernel_size_in; - const int depth_dilation_in; - const int dim; - const int64_t problem_size; - const int64_t weights_stride_0, weights_stride_1, weights_stride_2, + int32_t depth; + int32_t height; + int32_t width; + int32_t heads; + int32_t kernel_size_0, kernel_size_1, kernel_size_2; + int32_t dilation_0, dilation_1, dilation_2; + int32_t dim; + int64_t problem_size; + int64_t weights_stride_0, weights_stride_1, weights_stride_2, weights_stride_3, weights_stride_4; - const int64_t values_stride_0, values_stride_1, values_stride_2, - values_stride_3, values_stride_4; + int64_t values_stride_0, values_stride_1, values_stride_2, values_stride_3, + values_stride_4; __device__ __host__ Params() {} @@ -63,21 +63,23 @@ struct NeighborhoodNeighborhood3DBase { scalar_t* weights, // attn / d_attn scalar_t* values, // value / key scalar_t* output, // output / d_query - const int depth, - const int height, - const int width, - const int heads, - const int kernel_size_in, - const int dilation_in, - const int depth_kernel_size_in, - const int depth_dilation_in, - const int dim, - const int64_t weights_stride_0, - const int64_t weights_stride_1, - const int64_t weights_stride_2, - const int64_t weights_stride_3, - const int64_t weights_stride_4, - const int problem_size) + int32_t depth, + int32_t height, + int32_t width, + int32_t heads, + int32_t kernel_size_0, + int32_t kernel_size_1, + int32_t kernel_size_2, + int32_t dilation_0, + int32_t dilation_1, + int32_t dilation_2, + int32_t dim, + int64_t weights_stride_0, + int64_t weights_stride_1, + int64_t weights_stride_2, + int64_t weights_stride_3, + int64_t weights_stride_4, + int32_t problem_size) : weights(weights), values(values), output(output), @@ -85,10 +87,12 @@ struct NeighborhoodNeighborhood3DBase { height(height), width(width), heads(heads), - kernel_size_in(kernel_size_in), - dilation_in(dilation_in), - depth_kernel_size_in(depth_kernel_size_in), - depth_dilation_in(depth_dilation_in), + kernel_size_0(kernel_size_0), + kernel_size_1(kernel_size_1), + kernel_size_2(kernel_size_2), + dilation_0(dilation_0), + dilation_1(dilation_1), + dilation_2(dilation_2), dim(dim), problem_size(problem_size), weights_stride_4(weights_stride_4), @@ -116,92 +120,81 @@ struct NeighborhoodNeighborhood3DBase { } }; -template < - typename scalar_t, - int KS, - int NS, - int DILATION, - int DKS, - int DNS, - int DDILATION> +template struct NeighborhoodNeighborhood3DFull : NeighborhoodNeighborhood3DBase { using Base = NeighborhoodNeighborhood3DBase; using Params = typename Base::Params; + using CausalMask = CausalMask_; static constexpr bool IsBF16Kernel = false; static constexpr bool IsHalfKernel = false; static constexpr bool UsesSmem = false; __device__ __host__ NeighborhoodNeighborhood3DFull() : Base() {} - static __host__ int get_dim(int dim) { + static __host__ int32_t get_dim(int32_t dim) { return dim; } __device__ void launch(Params p) { - const int KERNEL_SIZE = (KS > 1) ? KS : p.kernel_size_in; - const int KERNEL_SIZE_D = (DKS > 1) ? DKS : p.depth_kernel_size_in; - const int NEIGHBORHOOD_SIZE = (NS > 0) ? NS : KERNEL_SIZE / 2; - const int NEIGHBORHOOD_SIZE_D = (DNS > 0) ? DNS : KERNEL_SIZE_D / 2; - const int dilation = (DILATION > 0) ? DILATION : p.dilation_in; - const int dilation_d = (DDILATION > 0) ? DDILATION : p.depth_dilation_in; - const int64_t linearIndex = blockIdx.x * blockDim.x + threadIdx.x; + auto mask_0 = NeighborhoodMask( + p.depth, p.kernel_size_0, p.dilation_0); + auto mask_1 = NeighborhoodMask( + p.height, p.kernel_size_1, p.dilation_1); + auto mask_2 = NeighborhoodMask( + p.width, p.kernel_size_2, p.dilation_2); + int64_t linearIndex = blockIdx.x * blockDim.x + threadIdx.x; if (linearIndex < p.problem_size) { - int indtmp1 = linearIndex / p.dim; - const int d = linearIndex - indtmp1 * p.dim; - int indtmp2 = indtmp1 / p.width; - const int j = indtmp1 - indtmp2 * p.width; + int32_t indtmp1 = linearIndex / p.dim; + auto d = linearIndex - indtmp1 * p.dim; + int32_t indtmp2 = indtmp1 / p.width; + auto j = indtmp1 - indtmp2 * p.width; indtmp1 = indtmp2; indtmp2 = indtmp1 / p.height; - const int i = indtmp1 - indtmp2 * p.height; + auto i = indtmp1 - indtmp2 * p.height; indtmp1 = indtmp2; indtmp2 = indtmp1 / p.depth; - const int k = indtmp1 - indtmp2 * p.depth; + auto k = indtmp1 - indtmp2 * p.depth; indtmp1 = indtmp2; indtmp2 = indtmp1 / p.heads; - const int h = indtmp1 - indtmp2 * p.heads; - const int b = indtmp2; + auto h = indtmp1 - indtmp2 * p.heads; + auto& b = indtmp2; + + auto nk = mask_0.get_window_start(k); + auto ni = mask_1.get_window_start(i); + auto nj = mask_2.get_window_start(j); + auto ek = mask_0.get_window_end(k, nk); + auto ei = mask_1.get_window_end(i, ni); + auto ej = mask_2.get_window_end(j, nj); - const int ni = get_window_start( - i, p.height, KERNEL_SIZE, NEIGHBORHOOD_SIZE, dilation); - const int nj = get_window_start( - j, p.width, KERNEL_SIZE, NEIGHBORHOOD_SIZE, dilation); - const int nk = get_window_start( - k, p.depth, KERNEL_SIZE_D, NEIGHBORHOOD_SIZE_D, dilation_d); scalar_t updt = scalar_t(0); - int weightsOffset = b * p.weights_stride_0 + h * p.weights_stride_1 + + int64_t weightsOffset = b * p.weights_stride_0 + h * p.weights_stride_1 + k * p.weights_stride_2 + i * p.weights_stride_3 + j * p.weights_stride_4; - const int64_t valuesOffset = - b * p.values_stride_0 + h * p.values_stride_1 + d; -#pragma unroll - for (int xk = nk; xk < nk + KERNEL_SIZE_D * dilation_d; xk += dilation_d) -#pragma unroll - for (int xi = ni; xi < ni + KERNEL_SIZE * dilation; xi += dilation) -#pragma unroll - for (int xj = nj; xj < nj + KERNEL_SIZE * dilation; xj += dilation) { - const int64_t valuesIndex = valuesOffset + xk * p.values_stride_2 + + int64_t valuesOffset = b * p.values_stride_0 + h * p.values_stride_1 + d; + for (int32_t xk = nk; xk < ek; xk += p.dilation_0) + for (int32_t xi = ni; xi < ei; xi += p.dilation_1) + for (int32_t xj = nj; xj < ej; xj += p.dilation_2) { + int64_t valuesIndex = valuesOffset + xk * p.values_stride_2 + xi * p.values_stride_3 + xj * p.values_stride_4; - updt += p.weights[weightsOffset] * p.values[valuesIndex]; - ++weightsOffset; + int64_t weightsIndex = weightsOffset + + int32_t((xk - nk) / p.dilation_0) * + (p.kernel_size_1 * p.kernel_size_2) + + int32_t((xi - ni) / p.dilation_1) * p.kernel_size_2 + + int32_t((xj - nj) / p.dilation_2); + updt += p.weights[weightsIndex] * p.values[valuesIndex]; } p.output[linearIndex] = updt; } } }; -template < - typename scalar_t, - int KS, - int NS, - int DILATION, - int DKS, - int DNS, - int DDILATION> +template struct NeighborhoodNeighborhood3DHalf : NeighborhoodNeighborhood3DBase { using Base = NeighborhoodNeighborhood3DBase; using Params = typename Base::Params; + using CausalMask = CausalMask_; static constexpr bool IsBF16Kernel = IsBF16::value; static constexpr bool IsHalfKernel = true; static constexpr bool UsesSmem = false; @@ -210,7 +203,7 @@ struct NeighborhoodNeighborhood3DHalf using HalfHelper = typename HalfArray::Base; - static __host__ int get_dim(int dim) { + static __host__ int32_t get_dim(int32_t dim) { if (dim % 2 != 0) { std::cerr << "Naive NATTEN half-precision kernels only support 32-bit alignment. " @@ -222,54 +215,55 @@ struct NeighborhoodNeighborhood3DHalf } __device__ void launch(Params p) { - const int KERNEL_SIZE = (KS > 1) ? KS : p.kernel_size_in; - const int KERNEL_SIZE_D = (DKS > 1) ? DKS : p.depth_kernel_size_in; - const int NEIGHBORHOOD_SIZE = (NS > 0) ? NS : KERNEL_SIZE / 2; - const int NEIGHBORHOOD_SIZE_D = (DNS > 0) ? DNS : KERNEL_SIZE_D / 2; - const int dilation = (DILATION > 0) ? DILATION : p.dilation_in; - const int dilation_d = (DDILATION > 0) ? DDILATION : p.depth_dilation_in; - const int64_t linearIndex = blockIdx.x * blockDim.x + threadIdx.x; + auto mask_0 = NeighborhoodMask( + p.depth, p.kernel_size_0, p.dilation_0); + auto mask_1 = NeighborhoodMask( + p.height, p.kernel_size_1, p.dilation_1); + auto mask_2 = NeighborhoodMask( + p.width, p.kernel_size_2, p.dilation_2); + int64_t linearIndex = blockIdx.x * blockDim.x + threadIdx.x; if (linearIndex < p.problem_size) { auto values2 = HalfHelper::typecast(p.values); auto output2 = HalfHelper::typecast(p.output); - int indtmp1 = linearIndex / p.dim; - const int d = linearIndex - indtmp1 * p.dim; - int indtmp2 = indtmp1 / p.width; - const int j = indtmp1 - indtmp2 * p.width; + int32_t indtmp1 = linearIndex / p.dim; + auto d = linearIndex - indtmp1 * p.dim; + int32_t indtmp2 = indtmp1 / p.width; + auto j = indtmp1 - indtmp2 * p.width; indtmp1 = indtmp2; indtmp2 = indtmp1 / p.height; - const int i = indtmp1 - indtmp2 * p.height; + auto i = indtmp1 - indtmp2 * p.height; indtmp1 = indtmp2; indtmp2 = indtmp1 / p.depth; - const int k = indtmp1 - indtmp2 * p.depth; + auto k = indtmp1 - indtmp2 * p.depth; indtmp1 = indtmp2; indtmp2 = indtmp1 / p.heads; - const int h = indtmp1 - indtmp2 * p.heads; - const int b = indtmp2; + auto h = indtmp1 - indtmp2 * p.heads; + auto& b = indtmp2; + + auto nk = mask_0.get_window_start(k); + auto ni = mask_1.get_window_start(i); + auto nj = mask_2.get_window_start(j); + auto ek = mask_0.get_window_end(k, nk); + auto ei = mask_1.get_window_end(i, ni); + auto ej = mask_2.get_window_end(j, nj); - const int ni = get_window_start( - i, p.height, KERNEL_SIZE, NEIGHBORHOOD_SIZE, dilation); - const int nj = get_window_start( - j, p.width, KERNEL_SIZE, NEIGHBORHOOD_SIZE, dilation); - const int nk = get_window_start( - k, p.depth, KERNEL_SIZE_D, NEIGHBORHOOD_SIZE_D, dilation_d); - auto updt = HalfHelper::zero(); - int weightsOffset = b * p.weights_stride_0 + h * p.weights_stride_1 + + auto updt = HalfHelper::zeros(); + int64_t weightsOffset = b * p.weights_stride_0 + h * p.weights_stride_1 + k * p.weights_stride_2 + i * p.weights_stride_3 + j * p.weights_stride_4; - const int64_t valuesOffset = - b * p.values_stride_0 + h * p.values_stride_1 + d; -#pragma unroll - for (int xk = nk; xk < nk + KERNEL_SIZE_D * dilation_d; xk += dilation_d) -#pragma unroll - for (int xi = ni; xi < ni + KERNEL_SIZE * dilation; xi += dilation) -#pragma unroll - for (int xj = nj; xj < nj + KERNEL_SIZE * dilation; xj += dilation) { - const int64_t valuesIndex = valuesOffset + xk * p.values_stride_2 + + int64_t valuesOffset = b * p.values_stride_0 + h * p.values_stride_1 + d; + for (int32_t xk = nk; xk < ek; xk += p.dilation_0) + for (int32_t xi = ni; xi < ei; xi += p.dilation_1) + for (int32_t xj = nj; xj < ej; xj += p.dilation_2) { + int64_t valuesIndex = valuesOffset + xk * p.values_stride_2 + xi * p.values_stride_3 + xj * p.values_stride_4; + int64_t weightsIndex = weightsOffset + + int32_t((xk - nk) / p.dilation_0) * + (p.kernel_size_1 * p.kernel_size_2) + + int32_t((xi - ni) / p.dilation_1) * p.kernel_size_2 + + int32_t((xj - nj) / p.dilation_2); updt = HalfHelper::fma( - values2[valuesIndex], p.weights[weightsOffset], updt); - ++weightsOffset; + values2[valuesIndex], p.weights[weightsIndex], updt); } output2[linearIndex] = updt; } @@ -279,54 +273,33 @@ struct NeighborhoodNeighborhood3DHalf template struct NeighborhoodNeighborhood3D { using Args = Args_; - static constexpr int KS = Args::KernelSize; - static constexpr int NS = Args::NeighborhoodSize; - static constexpr int DKS = Args::DepthKernelSize; - static constexpr int DNS = Args::DepthNeighborhoodSize; - static constexpr int DILATION = Args::Dilation; - static constexpr int DDILATION = Args::DepthDilation; using scalar_t = typename Args::Dtype; + using CausalMask = typename Args::CausalMask; using Kernel = typename std::conditional< sizeof(scalar_t) >= 4, - NeighborhoodNeighborhood3DFull< - scalar_t, - KS, - NS, - DILATION, - DKS, - DNS, - DDILATION>, - NeighborhoodNeighborhood3DHalf< - scalar_t, - KS, - NS, - DILATION, - DKS, - DNS, - DDILATION>>::type; + NeighborhoodNeighborhood3DFull, + NeighborhoodNeighborhood3DHalf>::type; using Params = typename Kernel::Params; void operator()( - const int cc, + int32_t cc, cudaStream_t stream, void* attn_ptr, void* value_ptr, void* output_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, + int32_t batch_size, + int32_t heads, + int32_t depth, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, int64_t attn_stride_4, - int kernel_size, - int kernel_size_depth, - int dilation, - int dilation_depth) { + const std::tuple& kernel_size, + const std::tuple& dilation) { dim = Kernel::get_dim(dim); int64_t problem_size = batch_size * heads * depth * height * width * dim; auto grid = Kernel::Base::get_grid(problem_size); @@ -339,10 +312,12 @@ struct NeighborhoodNeighborhood3D { height, width, heads, - kernel_size, - dilation, - kernel_size_depth, - dilation_depth, + std::get<0>(kernel_size), + std::get<1>(kernel_size), + std::get<2>(kernel_size), + std::get<0>(dilation), + std::get<1>(dilation), + std::get<2>(dilation), dim, attn_stride_0, attn_stride_1, diff --git a/csrc/include/natten/cuda/naive/pointwise_neighborhood_1d.cuh b/csrc/include/natten/cuda/naive/pointwise_neighborhood_1d.cuh index 2901367..e7e0aeb 100644 --- a/csrc/include/natten/cuda/naive/pointwise_neighborhood_1d.cuh +++ b/csrc/include/natten/cuda/naive/pointwise_neighborhood_1d.cuh @@ -32,6 +32,8 @@ #include #include +#include +#include #include namespace natten { @@ -41,42 +43,45 @@ namespace naive { template struct PointwiseNeighborhood1DBase { struct Params { + bool is_grad; scalar_t* query; // query / d_out scalar_t* key; // key / value scalar_t* bias = nullptr; // optional: bias scalar_t* attn; // attn / d_attn - const int length; - const int heads; - const int kernel_size_in; - const int dilation_in; - const int dim; - const int batch_size; - const int64_t attn_stride_0, attn_stride_1, attn_stride_2; - const int64_t query_stride_0, query_stride_1, query_stride_2; - const int64_t bias_stride_0; + int32_t length; + int32_t heads; + int32_t kernel_size; + int32_t dilation; + int32_t dim; + int32_t batch_size; + int64_t attn_stride_0, attn_stride_1, attn_stride_2; + int64_t query_stride_0, query_stride_1, query_stride_2; + int64_t bias_stride_0; __device__ __host__ Params() {} __device__ __host__ Params( + bool is_grad, scalar_t* query, scalar_t* key, scalar_t* attn, - const int length, - const int heads, - const int kernel_size_in, - const int dilation_in, - const int dim, - const int batch_size, - const int64_t attn_stride_0, - const int64_t attn_stride_1, - const int64_t attn_stride_2) - : query(query), + int32_t length, + int32_t heads, + int32_t kernel_size, + int32_t dilation, + int32_t dim, + int32_t batch_size, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2) + : is_grad(is_grad), + query(query), key(key), attn(attn), length(length), heads(heads), - kernel_size_in(kernel_size_in), - dilation_in(dilation_in), + kernel_size(kernel_size), + dilation(dilation), dim(dim), batch_size(batch_size), bias_stride_0(0), @@ -93,26 +98,27 @@ struct PointwiseNeighborhood1DBase { scalar_t* key, // value / key scalar_t* bias, // relative positional bias tensor scalar_t* attn, // output / d_query - const int length, - const int heads, - const int kernel_size_in, - const int dilation_in, - const int dim, - const int batch_size, - const int64_t attn_stride_0, - const int64_t attn_stride_1, - const int64_t attn_stride_2) - : query(query), + int32_t length, + int32_t heads, + int32_t kernel_size, + int32_t dilation, + int32_t dim, + int32_t batch_size, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2) + : is_grad(false), + query(query), key(key), bias(bias), attn(attn), length(length), heads(heads), - kernel_size_in(kernel_size_in), - dilation_in(dilation_in), + kernel_size(kernel_size), + dilation(dilation), dim(dim), batch_size(batch_size), - bias_stride_0(2 * kernel_size_in - 1), + bias_stride_0(2 * kernel_size - 1), attn_stride_2(attn_stride_2), attn_stride_1(attn_stride_1), attn_stride_0(attn_stride_0), @@ -124,12 +130,12 @@ struct PointwiseNeighborhood1DBase { __device__ __host__ PointwiseNeighborhood1DBase() {} static LaunchParams get_launch_params( - int batch_dim, - int length, - int kernel_size) { - int KERNELTHREADS = min(CUDA_NUM_THREADS, kernel_size); - int TOKENTHREADS = min(CUDA_NUM_THREADS / KERNELTHREADS, length); - int BATCHTHREADS = + int32_t batch_dim, + int32_t length, + int32_t kernel_size) { + int32_t KERNELTHREADS = min(CUDA_NUM_THREADS, kernel_size); + int32_t TOKENTHREADS = min(CUDA_NUM_THREADS / KERNELTHREADS, length); + int32_t BATCHTHREADS = min(64, max(1, CUDA_NUM_THREADS / (TOKENTHREADS * KERNELTHREADS))); dim3 grid( (length + TOKENTHREADS - 1) / TOKENTHREADS, @@ -140,48 +146,53 @@ struct PointwiseNeighborhood1DBase { } }; -template +template struct PointwiseNeighborhood1DFull : PointwiseNeighborhood1DBase { using Base = PointwiseNeighborhood1DBase; using Params = typename Base::Params; + using CausalMask = CausalMask_; static constexpr bool IsBF16Kernel = false; static constexpr bool IsHalfKernel = false; static constexpr bool UsesSmem = false; __device__ __host__ PointwiseNeighborhood1DFull() : Base() {} - static __host__ int get_dim(int dim) { + static __host__ int32_t get_dim(int32_t dim) { return dim; } __device__ void launch(Params p) { - const int KERNEL_SIZE = (KS > 1) ? KS : p.kernel_size_in; - const int NEIGHBORHOOD_SIZE = (NS > 0) ? NS : KERNEL_SIZE / 2; - const int dilation = (DILATION > 0) ? DILATION : p.dilation_in; - const int z = blockIdx.z * blockDim.z + threadIdx.z; + auto mask_value = AttnMask::value(p.is_grad); + auto mask_0 = + NeighborhoodMask(p.length, p.kernel_size, p.dilation); + int32_t z = blockIdx.z * blockDim.z + threadIdx.z; if (z < p.batch_size * p.heads) { - const int i = blockIdx.x * blockDim.x + threadIdx.x; + int32_t i = blockIdx.x * blockDim.x + threadIdx.x; if (i < p.length) { - const int ki = blockIdx.y * blockDim.y + threadIdx.y; - if (ki < KERNEL_SIZE) { - const int b = z / p.heads; - const int h = z - b * p.heads; - const int ni = get_window_start( - i, p.length, KERNEL_SIZE, NEIGHBORHOOD_SIZE, dilation); - scalar_t updt = scalar_t(0); - const int64_t batchHeadOffset = - b * p.query_stride_0 + h * p.query_stride_1; - const int64_t queryOffset = batchHeadOffset + i * p.query_stride_2; - const int64_t keyOffset = - batchHeadOffset + (ki * dilation + ni) * p.query_stride_2; -#pragma unroll - for (int dimOffset = 0; dimOffset < p.dim; ++dimOffset) - updt += - p.query[queryOffset + dimOffset] * p.key[keyOffset + dimOffset]; - if (p.bias) { - const int pi = get_pb_start( - i, p.length, KERNEL_SIZE, NEIGHBORHOOD_SIZE, dilation); - updt += p.bias[h * p.bias_stride_0 + (pi + ki)]; + int32_t ki = blockIdx.y * blockDim.y + threadIdx.y; + if (ki < p.kernel_size) { + auto b = z / p.heads; + auto h = z - b * p.heads; + + auto ni = mask_0.get_window_start(i); + auto ei = mask_0.get_window_end(i, ni); + + scalar_t updt = scalar_t(0.0); + auto key_idx = ki * p.dilation + ni; + if (key_idx < ei) { + int64_t batchHeadOffset = + b * p.query_stride_0 + h * p.query_stride_1; + int64_t queryOffset = batchHeadOffset + i * p.query_stride_2; + int64_t keyOffset = batchHeadOffset + key_idx * p.query_stride_2; + for (int32_t dimOffset = 0; dimOffset < p.dim; ++dimOffset) + updt += p.query[queryOffset + dimOffset] * + p.key[keyOffset + dimOffset]; + if (p.bias) { + auto pi = mask_0.get_pb_start(i); + updt += p.bias[h * p.bias_stride_0 + (pi + ki)]; + } + } else { + updt = mask_value; } p.attn [b * p.attn_stride_0 + h * p.attn_stride_1 + i * p.attn_stride_2 + @@ -192,10 +203,11 @@ struct PointwiseNeighborhood1DFull : PointwiseNeighborhood1DBase { } }; -template +template struct PointwiseNeighborhood1DHalf : PointwiseNeighborhood1DBase { using Base = PointwiseNeighborhood1DBase; using Params = typename Base::Params; + using CausalMask = CausalMask_; static constexpr bool IsBF16Kernel = IsBF16::value; static constexpr bool IsHalfKernel = true; static constexpr bool UsesSmem = false; @@ -204,7 +216,7 @@ struct PointwiseNeighborhood1DHalf : PointwiseNeighborhood1DBase { using HalfHelper = typename HalfArray::Base; - static __host__ int get_dim(int dim) { + static __host__ int32_t get_dim(int32_t dim) { if (dim % 2 != 0) { std::cerr << "Naive NATTEN half-precision kernels only support 32-bit alignment. " @@ -216,38 +228,44 @@ struct PointwiseNeighborhood1DHalf : PointwiseNeighborhood1DBase { } __device__ void launch(Params p) { - const int KERNEL_SIZE = (KS > 1) ? KS : p.kernel_size_in; - const int NEIGHBORHOOD_SIZE = (NS > 0) ? NS : KERNEL_SIZE / 2; - const int dilation = (DILATION > 0) ? DILATION : p.dilation_in; - const int z = blockIdx.z * blockDim.z + threadIdx.z; + auto mask_value = AttnMask::value(p.is_grad); + auto mask_0 = + NeighborhoodMask(p.length, p.kernel_size, p.dilation); + int32_t z = blockIdx.z * blockDim.z + threadIdx.z; if (z < p.batch_size * p.heads) { - const int i = blockIdx.x * blockDim.x + threadIdx.x; + int32_t i = blockIdx.x * blockDim.x + threadIdx.x; if (i < p.length) { - const int ki = blockIdx.y * blockDim.y + threadIdx.y; - if (ki < KERNEL_SIZE) { + int32_t ki = blockIdx.y * blockDim.y + threadIdx.y; + if (ki < p.kernel_size) { auto query2 = HalfHelper::typecast(p.query); auto key2 = HalfHelper::typecast(p.key); - const int b = z / p.heads; - const int h = z - b * p.heads; - const int ni = get_window_start( - i, p.length, KERNEL_SIZE, NEIGHBORHOOD_SIZE, dilation); - auto updt = HalfHelper::zero(); - const int64_t batchHeadOffset = - b * p.query_stride_0 + h * p.query_stride_1; - const int64_t queryOffset = batchHeadOffset + i * p.query_stride_2; - const int64_t keyOffset = - batchHeadOffset + (ki * dilation + ni) * p.query_stride_2; -#pragma unroll - for (int dimOffset = 0; dimOffset < p.dim; ++dimOffset) - updt = HalfHelper::fma( - query2[queryOffset + dimOffset], - key2[keyOffset + dimOffset], - updt); - scalar_t acc = HalfHelper::cast_back(HalfHelper::add(updt.x, updt.y)); - if (p.bias) { - const int pi = get_pb_start( - i, p.length, KERNEL_SIZE, NEIGHBORHOOD_SIZE, dilation); - acc = HalfHelper::add(acc, p.bias[h * p.bias_stride_0 + (pi + ki)]); + auto b = z / p.heads; + auto h = z - b * p.heads; + + auto ni = mask_0.get_window_start(i); + auto ei = mask_0.get_window_end(i, ni); + + scalar_t acc = HalfHelper::zero(); + auto key_idx = ki * p.dilation + ni; + if (key_idx < ei) { + auto updt = HalfHelper::zeros(); + int64_t batchHeadOffset = + b * p.query_stride_0 + h * p.query_stride_1; + int64_t queryOffset = batchHeadOffset + i * p.query_stride_2; + int64_t keyOffset = batchHeadOffset + key_idx * p.query_stride_2; + for (int32_t dimOffset = 0; dimOffset < p.dim; ++dimOffset) + updt = HalfHelper::fma( + query2[queryOffset + dimOffset], + key2[keyOffset + dimOffset], + updt); + acc = HalfHelper::cast_back(HalfHelper::add(updt.x, updt.y)); + if (p.bias) { + auto pi = mask_0.get_pb_start(i); + acc = + HalfHelper::add(acc, p.bias[h * p.bias_stride_0 + (pi + ki)]); + } + } else { + acc = mask_value; } p.attn [b * p.attn_stride_0 + h * p.attn_stride_1 + i * p.attn_stride_2 + @@ -261,42 +279,42 @@ struct PointwiseNeighborhood1DHalf : PointwiseNeighborhood1DBase { template struct PointwiseNeighborhood1D { using Args = Args_; - static constexpr int KS = Args::KernelSize; - static constexpr int NS = Args::NeighborhoodSize; - static constexpr int DILATION = Args::Dilation; using scalar_t = typename Args::Dtype; + using CausalMask = typename Args::CausalMask; using Kernel = typename std::conditional< sizeof(scalar_t) >= 4, - PointwiseNeighborhood1DFull, - PointwiseNeighborhood1DHalf>::type; + PointwiseNeighborhood1DFull, + PointwiseNeighborhood1DHalf>::type; using Params = typename Kernel::Params; void operator()( - const int cc, + int32_t cc, cudaStream_t stream, + bool is_grad, void* query_ptr, void* key_ptr, void* attn_ptr, - int batch_size, - int heads, - int length, - int dim, + int32_t batch_size, + int32_t heads, + int32_t length, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, - int kernel_size, - int dilation) { + const std::tuple& kernel_size, + const std::tuple& dilation) { dim = Kernel::get_dim(dim); LaunchParams lp = Kernel::Base::get_launch_params( - batch_size * heads, length, kernel_size); + batch_size * heads, length, natten::flatten(kernel_size)); auto params = Params( + is_grad, reinterpret_cast(query_ptr), reinterpret_cast(key_ptr), reinterpret_cast(attn_ptr), length, heads, - kernel_size, - dilation, + std::get<0>(kernel_size), + std::get<0>(dilation), dim, batch_size, attn_stride_0, @@ -309,35 +327,35 @@ struct PointwiseNeighborhood1D { template struct PointwiseNeighborhood1DWithBias { using Args = Args_; - static constexpr int KS = Args::KernelSize; - static constexpr int NS = Args::NeighborhoodSize; - static constexpr int DILATION = Args::Dilation; using scalar_t = typename Args::Dtype; + using CausalMask = typename Args::CausalMask; using Kernel = typename std::conditional< sizeof(scalar_t) >= 4, - PointwiseNeighborhood1DFull, - PointwiseNeighborhood1DHalf>::type; + PointwiseNeighborhood1DFull, + PointwiseNeighborhood1DHalf>::type; using Params = typename Kernel::Params; + static_assert(!CausalMask::Dim0, "PN+Bias does not support causal masking."); + void operator()( - const int cc, + int32_t cc, cudaStream_t stream, void* query_ptr, void* key_ptr, void* bias_ptr, void* attn_ptr, - int batch_size, - int heads, - int length, - int dim, + int32_t batch_size, + int32_t heads, + int32_t length, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, - int kernel_size, - int dilation) { + const std::tuple& kernel_size, + const std::tuple& dilation) { dim = Kernel::get_dim(dim); LaunchParams lp = Kernel::Base::get_launch_params( - batch_size * heads, length, kernel_size); + batch_size * heads, length, natten::flatten(kernel_size)); auto params = Params( reinterpret_cast(query_ptr), reinterpret_cast(key_ptr), @@ -345,8 +363,8 @@ struct PointwiseNeighborhood1DWithBias { reinterpret_cast(attn_ptr), length, heads, - kernel_size, - dilation, + std::get<0>(kernel_size), + std::get<0>(dilation), dim, batch_size, attn_stride_0, diff --git a/csrc/include/natten/cuda/naive/pointwise_neighborhood_2d.cuh b/csrc/include/natten/cuda/naive/pointwise_neighborhood_2d.cuh index e72efe1..94b0394 100644 --- a/csrc/include/natten/cuda/naive/pointwise_neighborhood_2d.cuh +++ b/csrc/include/natten/cuda/naive/pointwise_neighborhood_2d.cuh @@ -36,6 +36,8 @@ #include #include +#include +#include #include #include #include @@ -52,63 +54,69 @@ namespace naive { ///////////////////////////// Main kernels //////////////////////////////////// /////////////////////////////////////////////////////////////////////////////// -template +template struct PointwiseNeighborhood2DFull : PointwiseNeighborhood2DBase { using Base = PointwiseNeighborhood2DBase; using Params = typename Base::Params; + using CausalMask = CausalMask_; static constexpr bool IsBF16Kernel = false; static constexpr bool IsHalfKernel = false; static constexpr bool UsesSmem = false; __device__ __host__ PointwiseNeighborhood2DFull() : Base() {} - static __host__ int get_dim(int dim) { + static __host__ int32_t get_dim(int32_t dim) { return dim; } __device__ void launch(Params p) { - const int KERNEL_SIZE = (KS > 1) ? KS : p.kernel_size_in; - const int NEIGHBORHOOD_SIZE = (NS > 0) ? NS : KERNEL_SIZE / 2; - const int dilation = (DILATION > 0) ? DILATION : p.dilation_in; - const int z = blockIdx.z * blockDim.z + threadIdx.z; + auto mask_value = AttnMask::value(p.is_grad); + auto mask_0 = NeighborhoodMask( + p.height, p.kernel_size_0, p.dilation_0); + auto mask_1 = NeighborhoodMask( + p.width, p.kernel_size_1, p.dilation_1); + int32_t z = blockIdx.z * blockDim.z + threadIdx.z; if (z < p.batch_size * p.heads) { - const int x = blockIdx.x * blockDim.x + threadIdx.x; + int32_t x = blockIdx.x * blockDim.x + threadIdx.x; if (x < p.height * p.width) { - const int y = blockIdx.y * blockDim.y + threadIdx.y; - if (y < KERNEL_SIZE * KERNEL_SIZE) { - const int b = z / p.heads; - const int h = z - b * p.heads; - const int ki = y / KERNEL_SIZE; - const int kj = y - ki * KERNEL_SIZE; - const int i = x / p.width; - const int j = x - i * p.width; + int32_t y = blockIdx.y * blockDim.y + threadIdx.y; + if (y < p.kernel_size_0 * p.kernel_size_1) { + auto b = z / p.heads; + auto h = z - b * p.heads; + auto ki = y / p.kernel_size_1; + auto kj = y - ki * p.kernel_size_1; + auto i = x / p.width; + auto j = x - i * p.width; - const int ni = get_window_start( - i, p.height, KERNEL_SIZE, NEIGHBORHOOD_SIZE, dilation); - const int nj = get_window_start( - j, p.width, KERNEL_SIZE, NEIGHBORHOOD_SIZE, dilation); + auto ni = mask_0.get_window_start(i); + auto nj = mask_1.get_window_start(j); + auto ei = mask_0.get_window_end(i, ni); + auto ej = mask_1.get_window_end(j, nj); - scalar_t updt = scalar_t(0); - const int64_t batchHeadOffset = - b * p.query_stride_0 + h * p.query_stride_1; - const int64_t queryOffset = - batchHeadOffset + i * p.query_stride_2 + j * p.query_stride_3; - const int64_t keyOffset = batchHeadOffset + - (ki * dilation + ni) * p.query_stride_2 + - (kj * dilation + nj) * p.query_stride_3; - for (int dimOffset = 0; dimOffset < p.dim; ++dimOffset) - updt += - p.query[queryOffset + dimOffset] * p.key[keyOffset + dimOffset]; - if (p.bias) { - const int pi = get_pb_start( - i, p.height, KERNEL_SIZE, NEIGHBORHOOD_SIZE, dilation); - const int pj = get_pb_start( - j, p.width, KERNEL_SIZE, NEIGHBORHOOD_SIZE, dilation); - const int64_t biasIndex = - h * p.bias_stride_0 + (pi + ki) * p.bias_stride_1 + (pj + kj); - updt += p.bias[biasIndex]; + auto key_idx_i = ki * p.dilation_0 + ni; + auto key_idx_j = kj * p.dilation_1 + nj; + scalar_t updt = scalar_t(0.0); + if (key_idx_i < ei && key_idx_j < ej) { + int64_t batchHeadOffset = + b * p.query_stride_0 + h * p.query_stride_1; + int64_t queryOffset = + batchHeadOffset + i * p.query_stride_2 + j * p.query_stride_3; + int64_t keyOffset = batchHeadOffset + key_idx_i * p.query_stride_2 + + key_idx_j * p.query_stride_3; + for (int32_t dimOffset = 0; dimOffset < p.dim; ++dimOffset) + updt += p.query[queryOffset + dimOffset] * + p.key[keyOffset + dimOffset]; + if (p.bias) { + auto pi = mask_0.get_pb_start(i); + auto pj = mask_1.get_pb_start(j); + int64_t biasIndex = + h * p.bias_stride_0 + (pi + ki) * p.bias_stride_1 + (pj + kj); + updt += p.bias[biasIndex]; + } + } else { + updt = mask_value; } - const int64_t index = b * p.attn_stride_0 + h * p.attn_stride_1 + + int64_t index = b * p.attn_stride_0 + h * p.attn_stride_1 + i * p.attn_stride_2 + j * p.attn_stride_3 + y; p.attn[index] = updt; } @@ -117,10 +125,11 @@ struct PointwiseNeighborhood2DFull : PointwiseNeighborhood2DBase { } }; -template +template struct PointwiseNeighborhood2DHalf : PointwiseNeighborhood2DBase { using Base = PointwiseNeighborhood2DBase; using Params = typename Base::Params; + using CausalMask = CausalMask_; static constexpr bool IsBF16Kernel = IsBF16::value; static constexpr bool IsHalfKernel = true; static constexpr bool UsesSmem = false; @@ -129,7 +138,7 @@ struct PointwiseNeighborhood2DHalf : PointwiseNeighborhood2DBase { using HalfHelper = typename HalfArray::Base; - static __host__ int get_dim(int dim) { + static __host__ int32_t get_dim(int32_t dim) { if (dim % 2 != 0) { std::cerr << "Naive NATTEN half-precision kernels only support 32-bit alignment. " @@ -141,54 +150,61 @@ struct PointwiseNeighborhood2DHalf : PointwiseNeighborhood2DBase { } __device__ void launch(Params p) { - const int KERNEL_SIZE = (KS > 1) ? KS : p.kernel_size_in; - const int NEIGHBORHOOD_SIZE = (NS > 0) ? NS : KERNEL_SIZE / 2; - const int dilation = (DILATION > 0) ? DILATION : p.dilation_in; - const int z = blockIdx.z * blockDim.z + threadIdx.z; + auto mask_value = AttnMask::value(p.is_grad); + auto mask_0 = NeighborhoodMask( + p.height, p.kernel_size_0, p.dilation_0); + auto mask_1 = NeighborhoodMask( + p.width, p.kernel_size_1, p.dilation_1); + int32_t z = blockIdx.z * blockDim.z + threadIdx.z; if (z < p.batch_size * p.heads) { - const int x = blockIdx.x * blockDim.x + threadIdx.x; + int32_t x = blockIdx.x * blockDim.x + threadIdx.x; if (x < p.height * p.width) { - const int y = blockIdx.y * blockDim.y + threadIdx.y; - if (y < KERNEL_SIZE * KERNEL_SIZE) { + int32_t y = blockIdx.y * blockDim.y + threadIdx.y; + if (y < p.kernel_size_0 * p.kernel_size_1) { auto query2 = HalfHelper::typecast(p.query); auto key2 = HalfHelper::typecast(p.key); - const int b = z / p.heads; - const int h = z - b * p.heads; - const int ki = y / KERNEL_SIZE; - const int kj = y - ki * KERNEL_SIZE; - const int i = x / p.width; - const int j = x - i * p.width; + auto b = z / p.heads; + auto h = z - b * p.heads; + auto ki = y / p.kernel_size_1; + auto kj = y - ki * p.kernel_size_1; + auto i = x / p.width; + auto j = x - i * p.width; - const int ni = get_window_start( - i, p.height, KERNEL_SIZE, NEIGHBORHOOD_SIZE, dilation); - const int nj = get_window_start( - j, p.width, KERNEL_SIZE, NEIGHBORHOOD_SIZE, dilation); + auto ni = mask_0.get_window_start(i); + auto nj = mask_1.get_window_start(j); + auto ei = mask_0.get_window_end(i, ni); + auto ej = mask_1.get_window_end(j, nj); - auto updt = HalfHelper::zero(); - const int64_t batchHeadOffset = - b * p.query_stride_0 + h * p.query_stride_1; - const int64_t queryOffset = - batchHeadOffset + i * p.query_stride_2 + j * p.query_stride_3; - const int64_t keyOffset = batchHeadOffset + - (ki * dilation + ni) * p.query_stride_2 + - (kj * dilation + nj) * p.query_stride_3; - for (int dimOffset = 0; dimOffset < p.dim; ++dimOffset) - updt = HalfHelper::fma( - query2[queryOffset + dimOffset], - key2[keyOffset + dimOffset], - updt); - const int64_t index = b * p.attn_stride_0 + h * p.attn_stride_1 + - i * p.attn_stride_2 + j * p.attn_stride_3 + y; - scalar_t acc = HalfHelper::cast_back(HalfHelper::add(updt.x, updt.y)); - if (p.bias) { - const int pi = get_pb_start( - i, p.height, KERNEL_SIZE, NEIGHBORHOOD_SIZE, dilation); - const int pj = get_pb_start( - j, p.width, KERNEL_SIZE, NEIGHBORHOOD_SIZE, dilation); - const int64_t biasIndex = - h * p.bias_stride_0 + (pi + ki) * p.bias_stride_1 + (pj + kj); - acc = HalfHelper::add(acc, p.bias[biasIndex]); + auto key_idx_i = ki * p.dilation_0 + ni; + auto key_idx_j = kj * p.dilation_1 + nj; + scalar_t acc = HalfHelper::zero(); + + if (key_idx_i < ei && key_idx_j < ej) { + auto updt = HalfHelper::zeros(); + int64_t batchHeadOffset = + b * p.query_stride_0 + h * p.query_stride_1; + int64_t queryOffset = + batchHeadOffset + i * p.query_stride_2 + j * p.query_stride_3; + int64_t keyOffset = batchHeadOffset + key_idx_i * p.query_stride_2 + + key_idx_j * p.query_stride_3; + for (int32_t dimOffset = 0; dimOffset < p.dim; ++dimOffset) + updt = HalfHelper::fma( + query2[queryOffset + dimOffset], + key2[keyOffset + dimOffset], + updt); + acc = HalfHelper::cast_back(HalfHelper::add(updt.x, updt.y)); + if (p.bias) { + auto pi = mask_0.get_pb_start(i); + auto pj = mask_1.get_pb_start(j); + int64_t biasIndex = + h * p.bias_stride_0 + (pi + ki) * p.bias_stride_1 + (pj + kj); + acc = HalfHelper::add(acc, p.bias[biasIndex]); + } + } else { + acc = mask_value; } + int64_t index = b * p.attn_stride_0 + h * p.attn_stride_1 + + i * p.attn_stride_2 + j * p.attn_stride_3 + y; p.attn[index] = acc; } } @@ -203,469 +219,294 @@ struct PointwiseNeighborhood2DHalf : PointwiseNeighborhood2DBase { template struct PointwiseNeighborhood2D { using Args = Args_; - static constexpr int KS = Args::KernelSize; - static constexpr int NS = Args::NeighborhoodSize; - static constexpr int DILATION = Args::Dilation; using scalar_t = typename Args::Dtype; + using CausalMask = typename Args::CausalMask; using Kernel = typename std::conditional< sizeof(scalar_t) >= 4, - PointwiseNeighborhood2DFull, - PointwiseNeighborhood2DHalf>::type; + PointwiseNeighborhood2DFull, + PointwiseNeighborhood2DHalf>::type; + using Kernel3x3 = typename std::conditional< sizeof(scalar_t) >= 4, - PointwiseNeighborhood2DFull3x3, - PointwiseNeighborhood2DHalf3x3>::type; + PointwiseNeighborhood2DFull3x3, + PointwiseNeighborhood2DHalf3x3>::type; using Kernel5x5 = typename std::conditional< sizeof(scalar_t) >= 4, - PointwiseNeighborhood2DFull5x5, - PointwiseNeighborhood2DHalf5x5>::type; + PointwiseNeighborhood2DFull5x5, + PointwiseNeighborhood2DHalf5x5>::type; using Kernel7x7 = typename std::conditional< sizeof(scalar_t) >= 4, - PointwiseNeighborhood2DFull7x7, - PointwiseNeighborhood2DHalf7x7>::type; + PointwiseNeighborhood2DFull7x7, + PointwiseNeighborhood2DHalf7x7>::type; using Kernel9x9 = typename std::conditional< sizeof(scalar_t) >= 4, - PointwiseNeighborhood2DFull9x9, - PointwiseNeighborhood2DHalf9x9>::type; + PointwiseNeighborhood2DFull9x9, + PointwiseNeighborhood2DHalf9x9>::type; using Kernel11x11 = typename std::conditional< sizeof(scalar_t) >= 4, - PointwiseNeighborhood2DFull11x11, - PointwiseNeighborhood2DHalf11x11>::type; + PointwiseNeighborhood2DFull11x11, + PointwiseNeighborhood2DHalf11x11>::type; using Kernel13x13 = typename std::conditional< sizeof(scalar_t) >= 4, - PointwiseNeighborhood2DFull13x13, - PointwiseNeighborhood2DHalf13x13>::type; + PointwiseNeighborhood2DFull13x13, + PointwiseNeighborhood2DHalf13x13>::type; using Params = typename Kernel::Params; void operator()( - const int cc, + int32_t cc, cudaStream_t stream, + bool is_grad, void* query_ptr, void* key_ptr, void* attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, + int32_t batch_size, + int32_t heads, + int32_t height, + int32_t width, + int32_t original_dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, - int kernel_size, - int dilation) { - if (dim == 32 && natten::kEnableTiledNA && cc >= 60) { - if (kernel_size == 3) { - LaunchParams lp = Kernel3x3::get_launch_params( - batch_size * heads, - height, - width, - kernel_size * kernel_size, - dilation); - dim = Kernel3x3::get_dim(dim); - auto params = Params( - reinterpret_cast(query_ptr), - reinterpret_cast(key_ptr), - reinterpret_cast(attn_ptr), - height, - width, - heads, - kernel_size, - dilation, - dim, - batch_size, - attn_stride_0, - attn_stride_1, - attn_stride_2, - attn_stride_3); - launch_cuda_kernel<<>>(params); - return; - } else if (kernel_size == 5) { - LaunchParams lp = Kernel5x5::get_launch_params( - batch_size * heads, - height, - width, - kernel_size * kernel_size, - dilation); - dim = Kernel5x5::get_dim(dim); - auto params = Params( - reinterpret_cast(query_ptr), - reinterpret_cast(key_ptr), - reinterpret_cast(attn_ptr), - height, - width, - heads, - kernel_size, - dilation, - dim, - batch_size, - attn_stride_0, - attn_stride_1, - attn_stride_2, - attn_stride_3); - launch_cuda_kernel<<>>(params); - return; - } else if (kernel_size == 7) { - LaunchParams lp = Kernel7x7::get_launch_params( - batch_size * heads, - height, - width, - kernel_size * kernel_size, - dilation); - dim = Kernel7x7::get_dim(dim); - auto params = Params( - reinterpret_cast(query_ptr), - reinterpret_cast(key_ptr), - reinterpret_cast(attn_ptr), - height, - width, - heads, - kernel_size, - dilation, - dim, - batch_size, - attn_stride_0, - attn_stride_1, - attn_stride_2, - attn_stride_3); - launch_cuda_kernel<<>>(params); - return; - } else if (kernel_size == 9) { - LaunchParams lp = Kernel9x9::get_launch_params( - batch_size * heads, - height, - width, - kernel_size * kernel_size, - dilation); - dim = Kernel9x9::get_dim(dim); - auto params = Params( - reinterpret_cast(query_ptr), - reinterpret_cast(key_ptr), - reinterpret_cast(attn_ptr), - height, - width, - heads, - kernel_size, - dilation, - dim, - batch_size, - attn_stride_0, - attn_stride_1, - attn_stride_2, - attn_stride_3); - launch_cuda_kernel<<>>(params); - return; - } else if (kernel_size == 11) { - LaunchParams lp = Kernel11x11::get_launch_params( - batch_size * heads, - height, - width, - kernel_size * kernel_size, - dilation); - dim = Kernel11x11::get_dim(dim); - auto params = Params( - reinterpret_cast(query_ptr), - reinterpret_cast(key_ptr), - reinterpret_cast(attn_ptr), - height, - width, - heads, - kernel_size, - dilation, - dim, - batch_size, - attn_stride_0, - attn_stride_1, - attn_stride_2, - attn_stride_3); - launch_cuda_kernel - <<>>(params); - return; - } else if (kernel_size == 13) { - LaunchParams lp = Kernel13x13::get_launch_params( - batch_size * heads, - height, - width, - kernel_size * kernel_size, - dilation); - dim = Kernel13x13::get_dim(dim); - auto params = Params( - reinterpret_cast(query_ptr), - reinterpret_cast(key_ptr), - reinterpret_cast(attn_ptr), - height, - width, - heads, - kernel_size, - dilation, - dim, - batch_size, - attn_stride_0, - attn_stride_1, - attn_stride_2, - attn_stride_3); - launch_cuda_kernel - <<>>(params); - return; - } - } - LaunchParams lp = Kernel::get_launch_params( - batch_size * heads, height * width, kernel_size * kernel_size); - dim = Kernel::get_dim(dim); + const std::tuple& kernel_size, + const std::tuple& dilation) { + auto dim = Kernel::get_dim(original_dim); auto params = Params( + is_grad, reinterpret_cast(query_ptr), reinterpret_cast(key_ptr), reinterpret_cast(attn_ptr), height, width, heads, - kernel_size, - dilation, + std::get<0>(kernel_size), + std::get<1>(kernel_size), + std::get<0>(dilation), + std::get<1>(dilation), dim, batch_size, attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3); - launch_cuda_kernel<<>>(params); + if constexpr (!CausalMask::Dim0 && !CausalMask::Dim1) { + if (natten::kEnableTiledNA && all_dims_match(kernel_size) && + all_dims_match(dilation) && original_dim == 32 && cc >= 50) { + auto kernel_size_ = std::get<0>(kernel_size); + auto dilation_ = std::get<0>(dilation); + if (kernel_size_ == 3) { + LaunchParams lp = Kernel3x3::get_launch_params( + batch_size * heads, + height, + width, + natten::flatten(kernel_size), + dilation_); + launch_cuda_kernel + <<>>(params); + return; + } else if (kernel_size_ == 5) { + LaunchParams lp = Kernel5x5::get_launch_params( + batch_size * heads, + height, + width, + natten::flatten(kernel_size), + dilation_); + launch_cuda_kernel + <<>>(params); + return; + } else if (kernel_size_ == 7) { + LaunchParams lp = Kernel7x7::get_launch_params( + batch_size * heads, + height, + width, + natten::flatten(kernel_size), + dilation_); + launch_cuda_kernel + <<>>(params); + return; + } else if (kernel_size_ == 9) { + LaunchParams lp = Kernel9x9::get_launch_params( + batch_size * heads, + height, + width, + natten::flatten(kernel_size), + dilation_); + launch_cuda_kernel + <<>>(params); + return; + } else if (kernel_size_ == 11) { + LaunchParams lp = Kernel11x11::get_launch_params( + batch_size * heads, + height, + width, + natten::flatten(kernel_size), + dilation_); + launch_cuda_kernel + <<>>(params); + return; + } else if (kernel_size_ == 13) { + LaunchParams lp = Kernel13x13::get_launch_params( + batch_size * heads, + height, + width, + natten::flatten(kernel_size), + dilation_); + launch_cuda_kernel + <<>>(params); + return; + } + } + LaunchParams lp = Kernel::get_launch_params( + batch_size * heads, height * width, natten::flatten(kernel_size)); + launch_cuda_kernel<<>>(params); + } else { + LaunchParams lp = Kernel::get_launch_params( + batch_size * heads, height * width, natten::flatten(kernel_size)); + launch_cuda_kernel<<>>(params); + } } }; template struct PointwiseNeighborhood2DWithBias { using Args = Args_; - static constexpr int KS = Args::KernelSize; - static constexpr int NS = Args::NeighborhoodSize; - static constexpr int DILATION = Args::Dilation; using scalar_t = typename Args::Dtype; + using CausalMask = typename Args::CausalMask; using Kernel = typename std::conditional< sizeof(scalar_t) >= 4, - PointwiseNeighborhood2DFull, - PointwiseNeighborhood2DHalf>::type; + PointwiseNeighborhood2DFull, + PointwiseNeighborhood2DHalf>::type; using Kernel3x3 = typename std::conditional< sizeof(scalar_t) >= 4, - PointwiseNeighborhood2DFull3x3, - PointwiseNeighborhood2DHalf3x3>::type; + PointwiseNeighborhood2DFull3x3, + PointwiseNeighborhood2DHalf3x3>::type; using Kernel5x5 = typename std::conditional< sizeof(scalar_t) >= 4, - PointwiseNeighborhood2DFull5x5, - PointwiseNeighborhood2DHalf5x5>::type; + PointwiseNeighborhood2DFull5x5, + PointwiseNeighborhood2DHalf5x5>::type; using Kernel7x7 = typename std::conditional< sizeof(scalar_t) >= 4, - PointwiseNeighborhood2DFull7x7, - PointwiseNeighborhood2DHalf7x7>::type; + PointwiseNeighborhood2DFull7x7, + PointwiseNeighborhood2DHalf7x7>::type; using Kernel9x9 = typename std::conditional< sizeof(scalar_t) >= 4, - PointwiseNeighborhood2DFull9x9, - PointwiseNeighborhood2DHalf9x9>::type; + PointwiseNeighborhood2DFull9x9, + PointwiseNeighborhood2DHalf9x9>::type; using Kernel11x11 = typename std::conditional< sizeof(scalar_t) >= 4, - PointwiseNeighborhood2DFull11x11, - PointwiseNeighborhood2DHalf11x11>::type; + PointwiseNeighborhood2DFull11x11, + PointwiseNeighborhood2DHalf11x11>::type; using Kernel13x13 = typename std::conditional< sizeof(scalar_t) >= 4, - PointwiseNeighborhood2DFull13x13, - PointwiseNeighborhood2DHalf13x13>::type; + PointwiseNeighborhood2DFull13x13, + PointwiseNeighborhood2DHalf13x13>::type; using Params = typename Kernel::Params; + static_assert( + !CausalMask::Dim0 && !CausalMask::Dim1, + "PN+Bias does not support causal masking."); + void operator()( - const int cc, + int32_t cc, cudaStream_t stream, void* query_ptr, void* key_ptr, void* bias_ptr, void* attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, + int32_t batch_size, + int32_t heads, + int32_t height, + int32_t width, + int32_t original_dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, - int kernel_size, - int dilation) { - if (dim == 32 && natten::kEnableTiledNA && cc >= 60) { - if (kernel_size == 3) { + const std::tuple& kernel_size, + const std::tuple& dilation) { + auto dim = Kernel::get_dim(original_dim); + auto params = Params( + reinterpret_cast(query_ptr), + reinterpret_cast(key_ptr), + reinterpret_cast(bias_ptr), + reinterpret_cast(attn_ptr), + height, + width, + heads, + std::get<0>(kernel_size), + std::get<1>(kernel_size), + std::get<0>(dilation), + std::get<1>(dilation), + dim, + batch_size, + attn_stride_0, + attn_stride_1, + attn_stride_2, + attn_stride_3); + if (natten::kEnableTiledNA && all_dims_match(kernel_size) && + all_dims_match(dilation) && original_dim == 32 && cc >= 50) { + auto kernel_size_ = std::get<0>(kernel_size); + auto dilation_ = std::get<0>(dilation); + if (kernel_size_ == 3) { LaunchParams lp = Kernel3x3::get_launch_params( batch_size * heads, height, width, - kernel_size * kernel_size, - dilation); - dim = Kernel3x3::get_dim(dim); - auto params = Params( - reinterpret_cast(query_ptr), - reinterpret_cast(key_ptr), - reinterpret_cast(bias_ptr), - reinterpret_cast(attn_ptr), - height, - width, - heads, - kernel_size, - dilation, - dim, - batch_size, - attn_stride_0, - attn_stride_1, - attn_stride_2, - attn_stride_3); + kernel_size_ * kernel_size_, + dilation_); launch_cuda_kernel<<>>(params); return; - } else if (kernel_size == 5) { + } else if (kernel_size_ == 5) { LaunchParams lp = Kernel5x5::get_launch_params( batch_size * heads, height, width, - kernel_size * kernel_size, - dilation); - dim = Kernel5x5::get_dim(dim); - auto params = Params( - reinterpret_cast(query_ptr), - reinterpret_cast(key_ptr), - reinterpret_cast(bias_ptr), - reinterpret_cast(attn_ptr), - height, - width, - heads, - kernel_size, - dilation, - dim, - batch_size, - attn_stride_0, - attn_stride_1, - attn_stride_2, - attn_stride_3); + natten::flatten(kernel_size), + dilation_); launch_cuda_kernel<<>>(params); return; - } else if (kernel_size == 7) { + } else if (kernel_size_ == 7) { LaunchParams lp = Kernel7x7::get_launch_params( batch_size * heads, height, width, - kernel_size * kernel_size, - dilation); - dim = Kernel7x7::get_dim(dim); - auto params = Params( - reinterpret_cast(query_ptr), - reinterpret_cast(key_ptr), - reinterpret_cast(bias_ptr), - reinterpret_cast(attn_ptr), - height, - width, - heads, - kernel_size, - dilation, - dim, - batch_size, - attn_stride_0, - attn_stride_1, - attn_stride_2, - attn_stride_3); + natten::flatten(kernel_size), + dilation_); launch_cuda_kernel<<>>(params); return; - } else if (kernel_size == 9) { + } else if (kernel_size_ == 9) { LaunchParams lp = Kernel9x9::get_launch_params( batch_size * heads, height, width, - kernel_size * kernel_size, - dilation); - dim = Kernel9x9::get_dim(dim); - auto params = Params( - reinterpret_cast(query_ptr), - reinterpret_cast(key_ptr), - reinterpret_cast(bias_ptr), - reinterpret_cast(attn_ptr), - height, - width, - heads, - kernel_size, - dilation, - dim, - batch_size, - attn_stride_0, - attn_stride_1, - attn_stride_2, - attn_stride_3); + natten::flatten(kernel_size), + dilation_); launch_cuda_kernel<<>>(params); return; - } else if (kernel_size == 11) { + } else if (kernel_size_ == 11) { LaunchParams lp = Kernel11x11::get_launch_params( batch_size * heads, height, width, - kernel_size * kernel_size, - dilation); - dim = Kernel11x11::get_dim(dim); - auto params = Params( - reinterpret_cast(query_ptr), - reinterpret_cast(key_ptr), - reinterpret_cast(bias_ptr), - reinterpret_cast(attn_ptr), - height, - width, - heads, - kernel_size, - dilation, - dim, - batch_size, - attn_stride_0, - attn_stride_1, - attn_stride_2, - attn_stride_3); + natten::flatten(kernel_size), + dilation_); launch_cuda_kernel <<>>(params); return; - } else if (kernel_size == 13) { + } else if (kernel_size_ == 13) { LaunchParams lp = Kernel13x13::get_launch_params( batch_size * heads, height, width, - kernel_size * kernel_size, - dilation); - dim = Kernel13x13::get_dim(dim); - auto params = Params( - reinterpret_cast(query_ptr), - reinterpret_cast(key_ptr), - reinterpret_cast(bias_ptr), - reinterpret_cast(attn_ptr), - height, - width, - heads, - kernel_size, - dilation, - dim, - batch_size, - attn_stride_0, - attn_stride_1, - attn_stride_2, - attn_stride_3); + natten::flatten(kernel_size), + dilation_); launch_cuda_kernel <<>>(params); return; } } LaunchParams lp = Kernel::get_launch_params( - batch_size * heads, height * width, kernel_size * kernel_size); - dim = Kernel::get_dim(dim); - auto params = Params( - reinterpret_cast(query_ptr), - reinterpret_cast(key_ptr), - reinterpret_cast(bias_ptr), - reinterpret_cast(attn_ptr), - height, - width, - heads, - kernel_size, - dilation, - dim, - batch_size, - attn_stride_0, - attn_stride_1, - attn_stride_2, - attn_stride_3); + batch_size * heads, height * width, natten::flatten(kernel_size)); launch_cuda_kernel<<>>(params); } }; diff --git a/csrc/include/natten/cuda/naive/pointwise_neighborhood_3d.cuh b/csrc/include/natten/cuda/naive/pointwise_neighborhood_3d.cuh index bf87106..2aaa9a3 100644 --- a/csrc/include/natten/cuda/naive/pointwise_neighborhood_3d.cuh +++ b/csrc/include/natten/cuda/naive/pointwise_neighborhood_3d.cuh @@ -32,6 +32,8 @@ #include #include +#include +#include #include namespace natten { @@ -41,58 +43,63 @@ namespace naive { template struct PointwiseNeighborhood3DBase { struct Params { + bool is_grad; scalar_t* query; // query / d_out scalar_t* key; // key / value scalar_t* bias = nullptr; // optional: bias scalar_t* attn; // attn / d_attn - const int depth; - const int height; - const int width; - const int heads; - const int kernel_size_in; - const int dilation_in; - const int depth_kernel_size_in; - const int depth_dilation_in; - const int dim; - const int batch_size; - const int64_t attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, + int32_t depth; + int32_t height; + int32_t width; + int32_t heads; + int32_t kernel_size_0, kernel_size_1, kernel_size_2; + int32_t dilation_0, dilation_1, dilation_2; + int32_t dim; + int32_t batch_size; + int64_t attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4; - const int64_t query_stride_0, query_stride_1, query_stride_2, query_stride_3, + int64_t query_stride_0, query_stride_1, query_stride_2, query_stride_3, query_stride_4; - const int64_t bias_stride_0, bias_stride_1, bias_stride_2; + int64_t bias_stride_0, bias_stride_1, bias_stride_2; __device__ __host__ Params() {} __device__ __host__ Params( + bool is_grad, scalar_t* query, scalar_t* key, scalar_t* attn, - const int depth, - const int height, - const int width, - const int heads, - const int kernel_size_in, - const int dilation_in, - const int depth_kernel_size_in, - const int depth_dilation_in, - const int dim, - const int batch_size, - const int64_t attn_stride_0, - const int64_t attn_stride_1, - const int64_t attn_stride_2, - const int64_t attn_stride_3, - const int64_t attn_stride_4) - : query(query), + int32_t depth, + int32_t height, + int32_t width, + int32_t heads, + int32_t kernel_size_0, + int32_t kernel_size_1, + int32_t kernel_size_2, + int32_t dilation_0, + int32_t dilation_1, + int32_t dilation_2, + int32_t dim, + int32_t batch_size, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int64_t attn_stride_4) + : is_grad(is_grad), + query(query), key(key), attn(attn), depth(depth), height(height), width(width), heads(heads), - kernel_size_in(kernel_size_in), - dilation_in(dilation_in), - depth_kernel_size_in(depth_kernel_size_in), - depth_dilation_in(depth_dilation_in), + kernel_size_0(kernel_size_0), + kernel_size_1(kernel_size_1), + kernel_size_2(kernel_size_2), + dilation_0(dilation_0), + dilation_1(dilation_1), + dilation_2(dilation_2), dim(dim), batch_size(batch_size), bias_stride_2(0), @@ -115,22 +122,25 @@ struct PointwiseNeighborhood3DBase { scalar_t* key, // value / key scalar_t* bias, // relative positional bias tensor scalar_t* attn, // output / d_query - const int depth, - const int height, - const int width, - const int heads, - const int kernel_size_in, - const int dilation_in, - const int depth_kernel_size_in, - const int depth_dilation_in, - const int dim, - const int batch_size, - const int64_t attn_stride_0, - const int64_t attn_stride_1, - const int64_t attn_stride_2, - const int64_t attn_stride_3, - const int64_t attn_stride_4) - : query(query), + int32_t depth, + int32_t height, + int32_t width, + int32_t heads, + int32_t kernel_size_0, + int32_t kernel_size_1, + int32_t kernel_size_2, + int32_t dilation_0, + int32_t dilation_1, + int32_t dilation_2, + int32_t dim, + int32_t batch_size, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int64_t attn_stride_4) + : is_grad(false), + query(query), key(key), bias(bias), attn(attn), @@ -138,17 +148,19 @@ struct PointwiseNeighborhood3DBase { height(height), width(width), heads(heads), - kernel_size_in(kernel_size_in), - dilation_in(dilation_in), - depth_kernel_size_in(depth_kernel_size_in), - depth_dilation_in(depth_dilation_in), + kernel_size_0(kernel_size_0), + kernel_size_1(kernel_size_1), + kernel_size_2(kernel_size_2), + dilation_0(dilation_0), + dilation_1(dilation_1), + dilation_2(dilation_2), dim(dim), batch_size(batch_size), - bias_stride_2(2 * kernel_size_in - 1), - bias_stride_1((2 * kernel_size_in - 1) * (2 * kernel_size_in - 1)), + bias_stride_2(2 * kernel_size_2 - 1), + bias_stride_1((2 * kernel_size_1 - 1) * (2 * kernel_size_2 - 1)), bias_stride_0( - (2 * kernel_size_in - 1) * (2 * kernel_size_in - 1) * - (2 * depth_kernel_size_in - 1)), + (2 * kernel_size_0 - 1) * (2 * kernel_size_1 - 1) * + (2 * kernel_size_2 - 1)), attn_stride_4(attn_stride_4), attn_stride_3(attn_stride_3), attn_stride_2(attn_stride_2), @@ -164,14 +176,19 @@ struct PointwiseNeighborhood3DBase { __device__ __host__ PointwiseNeighborhood3DBase() {} static LaunchParams get_launch_params( - int batch_dim, - int spatial_size, - int attention_span) { - int KERNELTHREADS = - min(CUDA_NUM_THREADS, attention_span /* == kernel_size^3 */); - int PIXELTHREADS = min(int(CUDA_NUM_THREADS / KERNELTHREADS), spatial_size); - int BATCHTHREADS = - min(64, max(1, CUDA_NUM_THREADS / (PIXELTHREADS * KERNELTHREADS))); + int32_t batch_dim, + int32_t spatial_size, + int32_t attention_span) { + // NOTE: was 1024 as of 01/30/2024, but since adding causal masks, + // runs out of registers with certain causal masks (F, T, T) with + // 1024 threads per CTA. + int32_t num_threads = 512; + int32_t KERNELTHREADS = + min(num_threads, attention_span /* == kernel_size^3 */); + int32_t PIXELTHREADS = + min(int32_t(num_threads / KERNELTHREADS), spatial_size); + int32_t BATCHTHREADS = + min(64, max(1, num_threads / (PIXELTHREADS * KERNELTHREADS))); dim3 grid( (spatial_size + PIXELTHREADS - 1) / PIXELTHREADS, (attention_span + KERNELTHREADS - 1) / KERNELTHREADS, @@ -181,88 +198,84 @@ struct PointwiseNeighborhood3DBase { } }; -template < - typename scalar_t, - int KS, - int NS, - int DILATION, - int DKS, - int DNS, - int DDILATION> +template struct PointwiseNeighborhood3DFull : PointwiseNeighborhood3DBase { using Base = PointwiseNeighborhood3DBase; using Params = typename Base::Params; + using CausalMask = CausalMask_; static constexpr bool IsBF16Kernel = false; static constexpr bool IsHalfKernel = false; static constexpr bool UsesSmem = false; __device__ __host__ PointwiseNeighborhood3DFull() : Base() {} - static __host__ int get_dim(int dim) { + static __host__ int32_t get_dim(int32_t dim) { return dim; } __device__ void launch(Params p) { - const int KERNEL_SIZE = (KS > 1) ? KS : p.kernel_size_in; - const int KERNEL_SIZE_D = (DKS > 1) ? DKS : p.depth_kernel_size_in; - const int NEIGHBORHOOD_SIZE = (NS > 0) ? NS : KERNEL_SIZE / 2; - const int NEIGHBORHOOD_SIZE_D = (DNS > 0) ? DNS : KERNEL_SIZE_D / 2; - const int dilation = (DILATION > 0) ? DILATION : p.dilation_in; - const int dilation_d = (DDILATION > 0) ? DDILATION : p.depth_dilation_in; - const int z = blockIdx.z * blockDim.z + threadIdx.z; + auto mask_value = AttnMask::value(p.is_grad); + auto mask_0 = NeighborhoodMask( + p.depth, p.kernel_size_0, p.dilation_0); + auto mask_1 = NeighborhoodMask( + p.height, p.kernel_size_1, p.dilation_1); + auto mask_2 = NeighborhoodMask( + p.width, p.kernel_size_2, p.dilation_2); + int32_t z = blockIdx.z * blockDim.z + threadIdx.z; if (z < p.batch_size * p.heads) { - const int x = blockIdx.x * blockDim.x + threadIdx.x; + int32_t x = blockIdx.x * blockDim.x + threadIdx.x; if (x < p.depth * p.height * p.width) { - const int y = blockIdx.y * blockDim.y + threadIdx.y; - if (y < KERNEL_SIZE_D * KERNEL_SIZE * KERNEL_SIZE) { - int indtmp1 = y / KERNEL_SIZE; - const int kk = indtmp1 / KERNEL_SIZE; - const int kj = y - indtmp1 * KERNEL_SIZE; - const int ki = indtmp1 - kk * KERNEL_SIZE; + int32_t y = blockIdx.y * blockDim.y + threadIdx.y; + if (y < p.kernel_size_0 * p.kernel_size_1 * p.kernel_size_2) { + int32_t indtmp1 = y / p.kernel_size_2; + auto kk = indtmp1 / p.kernel_size_1; + auto kj = y - indtmp1 * p.kernel_size_2; + auto ki = indtmp1 - kk * p.kernel_size_1; indtmp1 = x / p.width; - const int k = indtmp1 / p.height; - const int j = x - indtmp1 * p.width; - const int i = indtmp1 - k * p.height; - - const int b = z / p.heads; - const int h = z - b * p.heads; - - const int ni = get_window_start( - i, p.height, KERNEL_SIZE, NEIGHBORHOOD_SIZE, dilation); - const int nj = get_window_start( - j, p.width, KERNEL_SIZE, NEIGHBORHOOD_SIZE, dilation); - const int nk = get_window_start( - k, p.depth, KERNEL_SIZE_D, NEIGHBORHOOD_SIZE_D, dilation_d); - + auto k = indtmp1 / p.height; + auto j = x - indtmp1 * p.width; + auto i = indtmp1 - k * p.height; + + auto b = z / p.heads; + auto h = z - b * p.heads; + + auto nk = mask_0.get_window_start(k); + auto ni = mask_1.get_window_start(i); + auto nj = mask_2.get_window_start(j); + auto ek = mask_0.get_window_end(k, nk); + auto ei = mask_1.get_window_end(i, ni); + auto ej = mask_2.get_window_end(j, nj); + + auto key_idx_k = kk * p.dilation_0 + nk; + auto key_idx_i = ki * p.dilation_1 + ni; + auto key_idx_j = kj * p.dilation_2 + nj; scalar_t updt = scalar_t(0); - const int64_t batchHeadOffset = - b * p.query_stride_0 + h * p.query_stride_1; - const int64_t queryOffset = batchHeadOffset + k * p.query_stride_2 + - i * p.query_stride_3 + j * p.query_stride_4; - const int64_t keyOffset = batchHeadOffset + - (kk * dilation_d + nk) * p.query_stride_2 + - (ki * dilation + ni) * p.query_stride_3 + - (kj * dilation + nj) * p.query_stride_4; -#pragma unroll - for (int dimOffset = 0; dimOffset < p.dim; ++dimOffset) - updt += - p.query[queryOffset + dimOffset] * p.key[keyOffset + dimOffset]; - const int64_t index = b * p.attn_stride_0 + h * p.attn_stride_1 + + if (key_idx_i < ei && key_idx_j < ej && key_idx_k < ek) { + int64_t batchHeadOffset = + b * p.query_stride_0 + h * p.query_stride_1; + int64_t queryOffset = batchHeadOffset + k * p.query_stride_2 + + i * p.query_stride_3 + j * p.query_stride_4; + int64_t keyOffset = batchHeadOffset + key_idx_k * p.query_stride_2 + + key_idx_i * p.query_stride_3 + key_idx_j * p.query_stride_4; + for (int32_t dimOffset = 0; dimOffset < p.dim; ++dimOffset) + updt += p.query[queryOffset + dimOffset] * + p.key[keyOffset + dimOffset]; + if (p.bias) { + auto pk = mask_0.get_pb_start(k); + auto pi = mask_1.get_pb_start(i); + auto pj = mask_2.get_pb_start(j); + int64_t biasIndex = h * p.bias_stride_0 + + (pk + kk) * p.bias_stride_1 + (pi + ki) * p.bias_stride_2 + + (pj + kj); + updt += p.bias[biasIndex]; + } + } else { + updt = mask_value; + } + int64_t index = b * p.attn_stride_0 + h * p.attn_stride_1 + k * p.attn_stride_2 + i * p.attn_stride_3 + j * p.attn_stride_4 + y; - if (p.bias) { - const int pi = get_pb_start( - i, p.height, KERNEL_SIZE, NEIGHBORHOOD_SIZE, dilation); - const int pj = get_pb_start( - j, p.width, KERNEL_SIZE, NEIGHBORHOOD_SIZE, dilation); - const int pk = get_pb_start( - k, p.depth, KERNEL_SIZE_D, NEIGHBORHOOD_SIZE_D, dilation_d); - const int64_t biasIndex = h * p.bias_stride_0 + - (pk + kk) * p.bias_stride_1 + (pi + ki) * p.bias_stride_2 + - (pj + kj); - updt += p.bias[biasIndex]; - } p.attn[index] = updt; } } @@ -270,17 +283,11 @@ struct PointwiseNeighborhood3DFull : PointwiseNeighborhood3DBase { } }; -template < - typename scalar_t, - int KS, - int NS, - int DILATION, - int DKS, - int DNS, - int DDILATION> +template struct PointwiseNeighborhood3DHalf : PointwiseNeighborhood3DBase { using Base = PointwiseNeighborhood3DBase; using Params = typename Base::Params; + using CausalMask = CausalMask_; static constexpr bool IsBF16Kernel = IsBF16::value; static constexpr bool IsHalfKernel = true; static constexpr bool UsesSmem = false; @@ -289,7 +296,7 @@ struct PointwiseNeighborhood3DHalf : PointwiseNeighborhood3DBase { using HalfHelper = typename HalfArray::Base; - static __host__ int get_dim(int dim) { + static __host__ int32_t get_dim(int32_t dim) { if (dim % 2 != 0) { std::cerr << "Naive NATTEN half-precision kernels only support 32-bit alignment. " @@ -301,71 +308,75 @@ struct PointwiseNeighborhood3DHalf : PointwiseNeighborhood3DBase { } __device__ void launch(Params p) { - const int KERNEL_SIZE = (KS > 1) ? KS : p.kernel_size_in; - const int KERNEL_SIZE_D = (DKS > 1) ? DKS : p.depth_kernel_size_in; - const int NEIGHBORHOOD_SIZE = (NS > 0) ? NS : KERNEL_SIZE / 2; - const int NEIGHBORHOOD_SIZE_D = (DNS > 0) ? DNS : KERNEL_SIZE_D / 2; - const int dilation = (DILATION > 0) ? DILATION : p.dilation_in; - const int dilation_d = (DDILATION > 0) ? DDILATION : p.depth_dilation_in; - const int z = blockIdx.z * blockDim.z + threadIdx.z; + auto mask_value = AttnMask::value(p.is_grad); + auto mask_0 = NeighborhoodMask( + p.depth, p.kernel_size_0, p.dilation_0); + auto mask_1 = NeighborhoodMask( + p.height, p.kernel_size_1, p.dilation_1); + auto mask_2 = NeighborhoodMask( + p.width, p.kernel_size_2, p.dilation_2); + int32_t z = blockIdx.z * blockDim.z + threadIdx.z; if (z < p.batch_size * p.heads) { - const int x = blockIdx.x * blockDim.x + threadIdx.x; + int32_t x = blockIdx.x * blockDim.x + threadIdx.x; if (x < p.depth * p.height * p.width) { - const int y = blockIdx.y * blockDim.y + threadIdx.y; - if (y < KERNEL_SIZE_D * KERNEL_SIZE * KERNEL_SIZE) { + int32_t y = blockIdx.y * blockDim.y + threadIdx.y; + if (y < p.kernel_size_0 * p.kernel_size_1 * p.kernel_size_2) { auto query2 = HalfHelper::typecast(p.query); auto key2 = HalfHelper::typecast(p.key); - int indtmp1 = y / KERNEL_SIZE; - const int kk = indtmp1 / KERNEL_SIZE; - const int kj = y - indtmp1 * KERNEL_SIZE; - const int ki = indtmp1 - kk * KERNEL_SIZE; + int32_t indtmp1 = y / p.kernel_size_2; + auto kk = indtmp1 / p.kernel_size_1; + auto kj = y - indtmp1 * p.kernel_size_2; + auto ki = indtmp1 - kk * p.kernel_size_1; indtmp1 = x / p.width; - const int k = indtmp1 / p.height; - const int j = x - indtmp1 * p.width; - const int i = indtmp1 - k * p.height; - - const int b = z / p.heads; - const int h = z - b * p.heads; - - const int ni = get_window_start( - i, p.height, KERNEL_SIZE, NEIGHBORHOOD_SIZE, dilation); - const int nj = get_window_start( - j, p.width, KERNEL_SIZE, NEIGHBORHOOD_SIZE, dilation); - const int nk = get_window_start( - k, p.depth, KERNEL_SIZE_D, NEIGHBORHOOD_SIZE_D, dilation_d); - - auto updt = HalfHelper::zero(); - const int64_t batchHeadOffset = - b * p.query_stride_0 + h * p.query_stride_1; - const int64_t queryOffset = batchHeadOffset + k * p.query_stride_2 + - i * p.query_stride_3 + j * p.query_stride_4; - const int64_t keyOffset = batchHeadOffset + - (kk * dilation_d + nk) * p.query_stride_2 + - (ki * dilation + ni) * p.query_stride_3 + - (kj * dilation + nj) * p.query_stride_4; -#pragma unroll - for (int dimOffset = 0; dimOffset < p.dim; ++dimOffset) - updt = HalfHelper::fma( - query2[queryOffset + dimOffset], - key2[keyOffset + dimOffset], - updt); - const int64_t index = b * p.attn_stride_0 + h * p.attn_stride_1 + + auto k = indtmp1 / p.height; + auto j = x - indtmp1 * p.width; + auto i = indtmp1 - k * p.height; + + auto b = z / p.heads; + auto h = z - b * p.heads; + + auto nk = mask_0.get_window_start(k); + auto ni = mask_1.get_window_start(i); + auto nj = mask_2.get_window_start(j); + auto ek = mask_0.get_window_end(k, nk); + auto ei = mask_1.get_window_end(i, ni); + auto ej = mask_2.get_window_end(j, nj); + + auto key_idx_k = kk * p.dilation_0 + nk; + auto key_idx_i = ki * p.dilation_1 + ni; + auto key_idx_j = kj * p.dilation_2 + nj; + scalar_t acc = HalfHelper::zero(); + + if (key_idx_i < ei && key_idx_j < ej && key_idx_k < ek) { + auto updt = HalfHelper::zeros(); + int64_t batchHeadOffset = + b * p.query_stride_0 + h * p.query_stride_1; + int64_t queryOffset = batchHeadOffset + k * p.query_stride_2 + + i * p.query_stride_3 + j * p.query_stride_4; + int64_t keyOffset = batchHeadOffset + key_idx_k * p.query_stride_2 + + key_idx_i * p.query_stride_3 + key_idx_j * p.query_stride_4; + for (int32_t dimOffset = 0; dimOffset < p.dim; ++dimOffset) + updt = HalfHelper::fma( + query2[queryOffset + dimOffset], + key2[keyOffset + dimOffset], + updt); + acc = HalfHelper::cast_back(HalfHelper::add(updt.x, updt.y)); + if (p.bias) { + auto pk = mask_0.get_pb_start(k); + auto pi = mask_1.get_pb_start(i); + auto pj = mask_2.get_pb_start(j); + int64_t biasIndex = h * p.bias_stride_0 + + (pk + kk) * p.bias_stride_1 + (pi + ki) * p.bias_stride_2 + + (pj + kj); + acc = HalfHelper::add(acc, p.bias[biasIndex]); + } + } else { + acc = mask_value; + } + int64_t index = b * p.attn_stride_0 + h * p.attn_stride_1 + k * p.attn_stride_2 + i * p.attn_stride_3 + j * p.attn_stride_4 + y; - scalar_t acc = HalfHelper::cast_back(HalfHelper::add(updt.x, updt.y)); - if (p.bias) { - const int pi = get_pb_start( - i, p.height, KERNEL_SIZE, NEIGHBORHOOD_SIZE, dilation); - const int pj = get_pb_start( - j, p.width, KERNEL_SIZE, NEIGHBORHOOD_SIZE, dilation); - const int pk = get_pb_start( - k, p.depth, KERNEL_SIZE_D, NEIGHBORHOOD_SIZE_D, dilation_d); - const int64_t biasIndex = h * p.bias_stride_0 + - (pk + kk) * p.bias_stride_1 + (pi + ki) * p.bias_stride_2 + - (pj + kj); - acc = HalfHelper::add(acc, p.bias[biasIndex]); - } p.attn[index] = acc; } } @@ -376,60 +387,41 @@ struct PointwiseNeighborhood3DHalf : PointwiseNeighborhood3DBase { template struct PointwiseNeighborhood3D { using Args = Args_; - static constexpr int KS = Args::KernelSize; - static constexpr int NS = Args::NeighborhoodSize; - static constexpr int DKS = Args::DepthKernelSize; - static constexpr int DNS = Args::DepthNeighborhoodSize; - static constexpr int DILATION = Args::Dilation; - static constexpr int DDILATION = Args::DepthDilation; using scalar_t = typename Args::Dtype; + using CausalMask = typename Args::CausalMask; using Kernel = typename std::conditional< sizeof(scalar_t) >= 4, - PointwiseNeighborhood3DFull< - scalar_t, - KS, - NS, - DILATION, - DKS, - DNS, - DDILATION>, - PointwiseNeighborhood3DHalf< - scalar_t, - KS, - NS, - DILATION, - DKS, - DNS, - DDILATION>>::type; + PointwiseNeighborhood3DFull, + PointwiseNeighborhood3DHalf>::type; using Params = typename Kernel::Params; void operator()( - const int cc, + int32_t cc, cudaStream_t stream, + bool is_grad, void* query_ptr, void* key_ptr, void* attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, + int32_t batch_size, + int32_t heads, + int32_t depth, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, int64_t attn_stride_4, - int kernel_size, - int kernel_size_depth, - int dilation, - int dilation_depth) { + const std::tuple& kernel_size, + const std::tuple& dilation) { dim = Kernel::get_dim(dim); LaunchParams lp = Kernel::Base::get_launch_params( batch_size * heads, depth * height * width, - kernel_size_depth * kernel_size * kernel_size); + natten::flatten(kernel_size)); auto params = Params( + is_grad, reinterpret_cast(query_ptr), reinterpret_cast(key_ptr), reinterpret_cast(attn_ptr), @@ -437,10 +429,12 @@ struct PointwiseNeighborhood3D { height, width, heads, - kernel_size, - dilation, - kernel_size_depth, - dilation_depth, + std::get<0>(kernel_size), + std::get<1>(kernel_size), + std::get<2>(kernel_size), + std::get<0>(dilation), + std::get<1>(dilation), + std::get<2>(dilation), dim, batch_size, attn_stride_0, @@ -455,60 +449,43 @@ struct PointwiseNeighborhood3D { template struct PointwiseNeighborhood3DWithBias { using Args = Args_; - static constexpr int KS = Args::KernelSize; - static constexpr int NS = Args::NeighborhoodSize; - static constexpr int DKS = Args::DepthKernelSize; - static constexpr int DNS = Args::DepthNeighborhoodSize; - static constexpr int DILATION = Args::Dilation; - static constexpr int DDILATION = Args::DepthDilation; using scalar_t = typename Args::Dtype; + using CausalMask = typename Args::CausalMask; using Kernel = typename std::conditional< sizeof(scalar_t) >= 4, - PointwiseNeighborhood3DFull< - scalar_t, - KS, - NS, - DILATION, - DKS, - DNS, - DDILATION>, - PointwiseNeighborhood3DHalf< - scalar_t, - KS, - NS, - DILATION, - DKS, - DNS, - DDILATION>>::type; + PointwiseNeighborhood3DFull, + PointwiseNeighborhood3DHalf>::type; using Params = typename Kernel::Params; + static_assert( + !CausalMask::Dim0 && !CausalMask::Dim1 && !CausalMask::Dim2, + "PN+Bias does not support causal masking."); + void operator()( - const int cc, + int32_t cc, cudaStream_t stream, void* query_ptr, void* key_ptr, void* bias_ptr, void* attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, + int32_t batch_size, + int32_t heads, + int32_t depth, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, int64_t attn_stride_4, - int kernel_size, - int kernel_size_depth, - int dilation, - int dilation_depth) { + const std::tuple& kernel_size, + const std::tuple& dilation) { dim = Kernel::get_dim(dim); LaunchParams lp = Kernel::Base::get_launch_params( batch_size * heads, depth * height * width, - kernel_size_depth * kernel_size * kernel_size); + natten::flatten(kernel_size)); auto params = Params( reinterpret_cast(query_ptr), reinterpret_cast(key_ptr), @@ -518,10 +495,12 @@ struct PointwiseNeighborhood3DWithBias { height, width, heads, - kernel_size, - dilation, - kernel_size_depth, - dilation_depth, + std::get<0>(kernel_size), + std::get<1>(kernel_size), + std::get<2>(kernel_size), + std::get<0>(dilation), + std::get<1>(dilation), + std::get<2>(dilation), dim, batch_size, attn_stride_0, diff --git a/csrc/include/natten/cuda/naive/rel_pos_bias_1d.cuh b/csrc/include/natten/cuda/naive/rel_pos_bias_1d.cuh index 8dda269..ae1a2d4 100644 --- a/csrc/include/natten/cuda/naive/rel_pos_bias_1d.cuh +++ b/csrc/include/natten/cuda/naive/rel_pos_bias_1d.cuh @@ -28,6 +28,7 @@ #include #include +#include #include // TODO: We're still using ATen's atomic add! @@ -45,41 +46,41 @@ struct RelPosBiasGradient1DBase { struct Params { acc_t* d_bias; scalar_t* d_attn; - const int length; - const int heads; - const int kernel_size_in; - const int dilation_in; - const int batch_size; - const int num_threads; - const int64_t problem_size; - const int64_t attn_stride_0, attn_stride_1, attn_stride_2; - const int64_t bias_stride_0; + int32_t length; + int32_t heads; + int32_t kernel_size; + int32_t dilation; + int32_t batch_size; + int32_t num_threads; + int64_t problem_size; + int64_t attn_stride_0, attn_stride_1, attn_stride_2; + int64_t bias_stride_0; __device__ __host__ Params() {} __device__ __host__ Params( acc_t* d_bias, scalar_t* d_attn, - const int length, - const int heads, - const int kernel_size_in, - const int dilation_in, - const int batch_size, - const int64_t attn_stride_0, - const int64_t attn_stride_1, - const int64_t attn_stride_2, - const int64_t problem_size, - const int num_threads) + int32_t length, + int32_t heads, + int32_t kernel_size, + int32_t dilation, + int32_t batch_size, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t problem_size, + int32_t num_threads) : d_bias(d_bias), d_attn(d_attn), length(length), heads(heads), - kernel_size_in(kernel_size_in), - dilation_in(dilation_in), + kernel_size(kernel_size), + dilation(dilation), batch_size(batch_size), problem_size(problem_size), num_threads(num_threads), - bias_stride_0(2 * kernel_size_in - 1), + bias_stride_0(2 * kernel_size - 1), attn_stride_2(attn_stride_2), attn_stride_1(attn_stride_1), attn_stride_0(attn_stride_0) {} @@ -87,14 +88,14 @@ struct RelPosBiasGradient1DBase { __device__ __host__ RelPosBiasGradient1DBase() {} - static LaunchParams get_launch_params(int n_threads) { + static LaunchParams get_launch_params(int32_t n_threads) { dim3 grid(GET_BLOCKS(n_threads, 64)); dim3 block(64); return LaunchParams(grid, block); } }; -template +template struct RelPosBiasGradient1DFull : RelPosBiasGradient1DBase { using Base = RelPosBiasGradient1DBase; using Params = typename Base::Params; @@ -105,32 +106,30 @@ struct RelPosBiasGradient1DFull : RelPosBiasGradient1DBase { __device__ __host__ RelPosBiasGradient1DFull() : Base() {} __device__ void launch(Params p) { - const int KERNEL_SIZE = (KS > 1) ? KS : p.kernel_size_in; - const int NEIGHBORHOOD_SIZE = (NS > 0) ? NS : KERNEL_SIZE / 2; - const int dilation = (DILATION > 0) ? DILATION : p.dilation_in; - const int64_t linearIndex = blockIdx.x * blockDim.x + threadIdx.x; + auto kernel_size_half = p.kernel_size / 2; + int64_t linearIndex = blockIdx.x * blockDim.x + threadIdx.x; if (linearIndex < p.num_threads) { - int indtmp1 = linearIndex / KERNEL_SIZE; - const int ki = linearIndex - indtmp1 * KERNEL_SIZE; - const int h = indtmp1 / p.length; - const int i = indtmp1 - h * p.length; - const int pi = - get_pb_start(i, p.length, KERNEL_SIZE, NEIGHBORHOOD_SIZE, dilation); + int32_t indtmp1 = linearIndex / p.kernel_size; + auto ki = linearIndex - indtmp1 * p.kernel_size; + auto h = indtmp1 / p.length; + auto i = indtmp1 - h * p.length; + auto pi = get_pb_start( + i, p.length, p.kernel_size, kernel_size_half, p.dilation); acc_t d_rpb_update = acc_t(0); int64_t attnOffset = h * p.attn_stride_1 + i * p.attn_stride_2 + ki; #pragma unroll - for (int b = 0; b < p.batch_size; ++b) { + for (int32_t b = 0; b < p.batch_size; ++b) { d_rpb_update += static_cast(p.d_attn[attnOffset]); attnOffset += p.attn_stride_0; } - const int64_t index = h * p.bias_stride_0 + (pi + ki); + int64_t index = h * p.bias_stride_0 + (pi + ki); at::native::fastAtomicAdd( p.d_bias, index, p.problem_size, d_rpb_update, true); } } }; -template +template struct RelPosBiasGradient1DHalf : RelPosBiasGradient1DBase { using Base = RelPosBiasGradient1DBase; using Params = typename Base::Params; @@ -143,25 +142,23 @@ struct RelPosBiasGradient1DHalf : RelPosBiasGradient1DBase { using HalfHelper = typename HalfArray::Base; __device__ void launch(Params p) { - const int KERNEL_SIZE = (KS > 1) ? KS : p.kernel_size_in; - const int NEIGHBORHOOD_SIZE = (NS > 0) ? NS : KERNEL_SIZE / 2; - const int dilation = (DILATION > 0) ? DILATION : p.dilation_in; - const int64_t linearIndex = blockIdx.x * blockDim.x + threadIdx.x; + auto kernel_size_half = p.kernel_size / 2; + int64_t linearIndex = blockIdx.x * blockDim.x + threadIdx.x; if (linearIndex < p.num_threads) { - int indtmp1 = linearIndex / KERNEL_SIZE; - const int ki = linearIndex - indtmp1 * KERNEL_SIZE; - const int h = indtmp1 / p.length; - const int i = indtmp1 - h * p.length; - const int pi = - get_pb_start(i, p.length, KERNEL_SIZE, NEIGHBORHOOD_SIZE, dilation); + int32_t indtmp1 = linearIndex / p.kernel_size; + auto ki = linearIndex - indtmp1 * p.kernel_size; + auto h = indtmp1 / p.length; + auto i = indtmp1 - h * p.length; + auto pi = get_pb_start( + i, p.length, p.kernel_size, kernel_size_half, p.dilation); acc_t d_rpb_update = acc_t(0); int64_t attnOffset = h * p.attn_stride_1 + i * p.attn_stride_2 + ki; #pragma unroll - for (int b = 0; b < p.batch_size; ++b) { + for (int32_t b = 0; b < p.batch_size; ++b) { d_rpb_update += HalfHelper::to_float(p.d_attn[attnOffset]); attnOffset += p.attn_stride_0; } - const int64_t index = h * p.bias_stride_0 + (pi + ki); + int64_t index = h * p.bias_stride_0 + (pi + ki); at::native::fastAtomicAdd( p.d_bias, index, p.problem_size, d_rpb_update, true); } @@ -171,42 +168,42 @@ struct RelPosBiasGradient1DHalf : RelPosBiasGradient1DBase { template struct RelPosBiasGradient1D { using Args = Args_; - static constexpr int KS = Args::KernelSize; - static constexpr int NS = Args::NeighborhoodSize; - static constexpr int DILATION = Args::Dilation; using scalar_t = typename Args::Dtype; + using CausalMask = typename Args::CausalMask; using acc_t = typename std::conditional= 4, scalar_t, float>::type; using Kernel = typename std::conditional< sizeof(scalar_t) >= 4, - RelPosBiasGradient1DFull, - RelPosBiasGradient1DHalf>::type; + RelPosBiasGradient1DFull, + RelPosBiasGradient1DHalf>::type; using Params = typename Kernel::Params; + static_assert(!CausalMask::Dim0, "PN+Bias does not support causal masking."); + void operator()( - const int cc, + int32_t cc, cudaStream_t stream, void* d_bias_ptr, void* d_attn_ptr, - int batch_size, - int heads, - int length, - int dim, + int32_t batch_size, + int32_t heads, + int32_t length, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, - int kernel_size, - int dilation) { - int num_threads = heads * length * kernel_size; - int64_t problem_size = heads * (2 * kernel_size - 1); + const std::tuple& kernel_size, + const std::tuple& dilation) { + int32_t num_threads = heads * length * natten::flatten(kernel_size); + int64_t problem_size = heads * (2 * std::get<0>(kernel_size) - 1); LaunchParams lp = Kernel::Base::get_launch_params(num_threads); auto params = Params( reinterpret_cast(d_bias_ptr), reinterpret_cast(d_attn_ptr), length, heads, - kernel_size, - dilation, + std::get<0>(kernel_size), + std::get<0>(dilation), batch_size, attn_stride_0, attn_stride_1, diff --git a/csrc/include/natten/cuda/naive/rel_pos_bias_2d.cuh b/csrc/include/natten/cuda/naive/rel_pos_bias_2d.cuh index 184894e..4a67622 100644 --- a/csrc/include/natten/cuda/naive/rel_pos_bias_2d.cuh +++ b/csrc/include/natten/cuda/naive/rel_pos_bias_2d.cuh @@ -28,6 +28,7 @@ #include #include +#include #include // TODO: We're still using ATen's atomic add! @@ -45,46 +46,50 @@ struct RelPosBiasGradient2DBase { struct Params { acc_t* d_bias; scalar_t* d_attn; - const int height; - const int width; - const int heads; - const int kernel_size_in; - const int dilation_in; - const int batch_size; - const int num_threads; - const int64_t problem_size; - const int64_t attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3; - const int64_t bias_stride_0, bias_stride_1; + int32_t height; + int32_t width; + int32_t heads; + int32_t kernel_size_0, kernel_size_1; + int32_t dilation_0, dilation_1; + int32_t batch_size; + int32_t num_threads; + int64_t problem_size; + int64_t attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3; + int64_t bias_stride_0, bias_stride_1; __device__ __host__ Params() {} __device__ __host__ Params( acc_t* d_bias, scalar_t* d_attn, - const int height, - const int width, - const int heads, - const int kernel_size_in, - const int dilation_in, - const int batch_size, - const int64_t attn_stride_0, - const int64_t attn_stride_1, - const int64_t attn_stride_2, - const int64_t attn_stride_3, - const int64_t problem_size, - const int num_threads) + int32_t height, + int32_t width, + int32_t heads, + int32_t kernel_size_0, + int32_t kernel_size_1, + int32_t dilation_0, + int32_t dilation_1, + int32_t batch_size, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int64_t problem_size, + int32_t num_threads) : d_bias(d_bias), d_attn(d_attn), height(height), width(width), heads(heads), - kernel_size_in(kernel_size_in), - dilation_in(dilation_in), + kernel_size_0(kernel_size_0), + kernel_size_1(kernel_size_1), + dilation_0(dilation_0), + dilation_1(dilation_1), batch_size(batch_size), problem_size(problem_size), num_threads(num_threads), - bias_stride_1(2 * kernel_size_in - 1), - bias_stride_0((2 * kernel_size_in - 1) * (2 * kernel_size_in - 1)), + bias_stride_1(2 * kernel_size_1 - 1), + bias_stride_0((2 * kernel_size_1 - 1) * (2 * kernel_size_0 - 1)), attn_stride_3(attn_stride_3), attn_stride_2(attn_stride_2), attn_stride_1(attn_stride_1), @@ -93,14 +98,14 @@ struct RelPosBiasGradient2DBase { __device__ __host__ RelPosBiasGradient2DBase() {} - static LaunchParams get_launch_params(int n_threads) { + static LaunchParams get_launch_params(int32_t n_threads) { dim3 grid(GET_BLOCKS(n_threads, 64)); dim3 block(64); return LaunchParams(grid, block); } }; -template +template struct RelPosBiasGradient2DFull : RelPosBiasGradient2DBase { using Base = RelPosBiasGradient2DBase; using Params = typename Base::Params; @@ -111,35 +116,34 @@ struct RelPosBiasGradient2DFull : RelPosBiasGradient2DBase { __device__ __host__ RelPosBiasGradient2DFull() : Base() {} __device__ void launch(Params p) { - const int KERNEL_SIZE = (KS > 1) ? KS : p.kernel_size_in; - const int NEIGHBORHOOD_SIZE = (NS > 0) ? NS : KERNEL_SIZE / 2; - const int dilation = (DILATION > 0) ? DILATION : p.dilation_in; - const int64_t linearIndex = blockIdx.x * blockDim.x + threadIdx.x; + auto kernel_size_half_0 = p.kernel_size_0 / 2; + auto kernel_size_half_1 = p.kernel_size_1 / 2; + int64_t linearIndex = blockIdx.x * blockDim.x + threadIdx.x; if (linearIndex < p.num_threads) { - int indtmp1 = linearIndex / KERNEL_SIZE; - const int kj = linearIndex - indtmp1 * KERNEL_SIZE; - int indtmp2 = indtmp1 / KERNEL_SIZE; - const int ki = indtmp1 - indtmp2 * KERNEL_SIZE; + int32_t indtmp1 = linearIndex / p.kernel_size_1; + auto kj = linearIndex - indtmp1 * p.kernel_size_1; + int32_t indtmp2 = indtmp1 / p.kernel_size_0; + auto ki = indtmp1 - indtmp2 * p.kernel_size_0; indtmp1 = indtmp2; indtmp2 = indtmp1 / p.width; - const int j = indtmp1 - indtmp2 * p.width; + auto j = indtmp1 - indtmp2 * p.width; indtmp1 = indtmp2; indtmp2 = indtmp1 / p.height; - const int i = indtmp1 - indtmp2 * p.height; - const int h = indtmp2; - const int pi = - get_pb_start(i, p.height, KERNEL_SIZE, NEIGHBORHOOD_SIZE, dilation); - const int pj = - get_pb_start(j, p.width, KERNEL_SIZE, NEIGHBORHOOD_SIZE, dilation); + auto i = indtmp1 - indtmp2 * p.height; + auto& h = indtmp2; + auto pi = get_pb_start( + i, p.height, p.kernel_size_0, kernel_size_half_0, p.dilation_0); + auto pj = get_pb_start( + j, p.width, p.kernel_size_1, kernel_size_half_1, p.dilation_1); acc_t d_rpb_update = acc_t(0); int64_t attnOffset = h * p.attn_stride_1 + i * p.attn_stride_2 + - j * p.attn_stride_3 + (ki * KERNEL_SIZE + kj); + j * p.attn_stride_3 + (ki * p.kernel_size_1 + kj); #pragma unroll - for (int b = 0; b < p.batch_size; ++b) { + for (int32_t b = 0; b < p.batch_size; ++b) { d_rpb_update += static_cast(p.d_attn[attnOffset]); attnOffset += p.attn_stride_0; } - const int64_t index = + int64_t index = h * p.bias_stride_0 + (pi + ki) * p.bias_stride_1 + (pj + kj); at::native::fastAtomicAdd( p.d_bias, index, p.problem_size, d_rpb_update, true); @@ -147,7 +151,7 @@ struct RelPosBiasGradient2DFull : RelPosBiasGradient2DBase { } }; -template +template struct RelPosBiasGradient2DHalf : RelPosBiasGradient2DBase { using Base = RelPosBiasGradient2DBase; using Params = typename Base::Params; @@ -160,35 +164,34 @@ struct RelPosBiasGradient2DHalf : RelPosBiasGradient2DBase { using HalfHelper = typename HalfArray::Base; __device__ void launch(Params p) { - const int KERNEL_SIZE = (KS > 1) ? KS : p.kernel_size_in; - const int NEIGHBORHOOD_SIZE = (NS > 0) ? NS : KERNEL_SIZE / 2; - const int dilation = (DILATION > 0) ? DILATION : p.dilation_in; - const int64_t linearIndex = blockIdx.x * blockDim.x + threadIdx.x; + auto kernel_size_half_0 = p.kernel_size_0 / 2; + auto kernel_size_half_1 = p.kernel_size_1 / 2; + int64_t linearIndex = blockIdx.x * blockDim.x + threadIdx.x; if (linearIndex < p.num_threads) { - int indtmp1 = linearIndex / KERNEL_SIZE; - const int kj = linearIndex - indtmp1 * KERNEL_SIZE; - int indtmp2 = indtmp1 / KERNEL_SIZE; - const int ki = indtmp1 - indtmp2 * KERNEL_SIZE; + int32_t indtmp1 = linearIndex / p.kernel_size_1; + auto kj = linearIndex - indtmp1 * p.kernel_size_1; + int32_t indtmp2 = indtmp1 / p.kernel_size_0; + auto ki = indtmp1 - indtmp2 * p.kernel_size_0; indtmp1 = indtmp2; indtmp2 = indtmp1 / p.width; - const int j = indtmp1 - indtmp2 * p.width; + auto j = indtmp1 - indtmp2 * p.width; indtmp1 = indtmp2; indtmp2 = indtmp1 / p.height; - const int i = indtmp1 - indtmp2 * p.height; - const int h = indtmp2; - const int pi = - get_pb_start(i, p.height, KERNEL_SIZE, NEIGHBORHOOD_SIZE, dilation); - const int pj = - get_pb_start(j, p.width, KERNEL_SIZE, NEIGHBORHOOD_SIZE, dilation); + auto i = indtmp1 - indtmp2 * p.height; + auto& h = indtmp2; + auto pi = get_pb_start( + i, p.height, p.kernel_size_0, kernel_size_half_0, p.dilation_0); + auto pj = get_pb_start( + j, p.width, p.kernel_size_1, kernel_size_half_1, p.dilation_1); acc_t d_rpb_update = acc_t(0); int64_t attnOffset = h * p.attn_stride_1 + i * p.attn_stride_2 + - j * p.attn_stride_3 + (ki * KERNEL_SIZE + kj); + j * p.attn_stride_3 + (ki * p.kernel_size_1 + kj); #pragma unroll - for (int b = 0; b < p.batch_size; ++b) { + for (int32_t b = 0; b < p.batch_size; ++b) { d_rpb_update += HalfHelper::to_float(p.d_attn[attnOffset]); attnOffset += p.attn_stride_0; } - const int64_t index = + int64_t index = h * p.bias_stride_0 + (pi + ki) * p.bias_stride_1 + (pj + kj); at::native::fastAtomicAdd( p.d_bias, index, p.problem_size, d_rpb_update, true); @@ -199,36 +202,39 @@ struct RelPosBiasGradient2DHalf : RelPosBiasGradient2DBase { template struct RelPosBiasGradient2D { using Args = Args_; - static constexpr int KS = Args::KernelSize; - static constexpr int NS = Args::NeighborhoodSize; - static constexpr int DILATION = Args::Dilation; using scalar_t = typename Args::Dtype; + using CausalMask = typename Args::CausalMask; using acc_t = typename std::conditional= 4, scalar_t, float>::type; using Kernel = typename std::conditional< sizeof(scalar_t) >= 4, - RelPosBiasGradient2DFull, - RelPosBiasGradient2DHalf>::type; + RelPosBiasGradient2DFull, + RelPosBiasGradient2DHalf>::type; using Params = typename Kernel::Params; + static_assert( + !CausalMask::Dim0 && !CausalMask::Dim1, + "PN+Bias does not support causal masking."); + void operator()( - const int cc, + int32_t cc, cudaStream_t stream, void* d_bias_ptr, void* d_attn_ptr, - int batch_size, - int heads, - int height, - int width, - int dim, + int32_t batch_size, + int32_t heads, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, - int kernel_size, - int dilation) { - int num_threads = heads * height * width * kernel_size * kernel_size; - int64_t problem_size = heads * (2 * kernel_size - 1) * (2 * kernel_size - 1); + const std::tuple& kernel_size, + const std::tuple& dilation) { + int32_t num_threads = heads * height * width * natten::flatten(kernel_size); + int64_t problem_size = heads * (2 * std::get<0>(kernel_size) - 1) * + (2 * std::get<1>(kernel_size) - 1); LaunchParams lp = Kernel::Base::get_launch_params(num_threads); auto params = Params( reinterpret_cast(d_bias_ptr), @@ -236,8 +242,10 @@ struct RelPosBiasGradient2D { height, width, heads, - kernel_size, - dilation, + std::get<0>(kernel_size), + std::get<1>(kernel_size), + std::get<0>(dilation), + std::get<1>(dilation), batch_size, attn_stride_0, attn_stride_1, diff --git a/csrc/include/natten/cuda/naive/rel_pos_bias_3d.cuh b/csrc/include/natten/cuda/naive/rel_pos_bias_3d.cuh index 8a44b79..d72167b 100644 --- a/csrc/include/natten/cuda/naive/rel_pos_bias_3d.cuh +++ b/csrc/include/natten/cuda/naive/rel_pos_bias_3d.cuh @@ -28,6 +28,7 @@ #include #include +#include #include // TODO: We're still using ATen's atomic add! @@ -45,60 +46,62 @@ struct RelPosBiasGradient3DBase { struct Params { acc_t* d_bias; scalar_t* d_attn; - const int depth; - const int height; - const int width; - const int heads; - const int kernel_size_in; - const int dilation_in; - const int depth_kernel_size_in; - const int depth_dilation_in; - const int batch_size; - const int num_threads; - const int64_t problem_size; - const int64_t attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, + int32_t depth; + int32_t height; + int32_t width; + int32_t heads; + int32_t kernel_size_0, kernel_size_1, kernel_size_2; + int32_t dilation_0, dilation_1, dilation_2; + int32_t batch_size; + int32_t num_threads; + int64_t problem_size; + int64_t attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3, attn_stride_4; - const int64_t bias_stride_0, bias_stride_1, bias_stride_2; + int64_t bias_stride_0, bias_stride_1, bias_stride_2; __device__ __host__ Params() {} __device__ __host__ Params( acc_t* d_bias, scalar_t* d_attn, - const int depth, - const int height, - const int width, - const int heads, - const int kernel_size_in, - const int dilation_in, - const int depth_kernel_size_in, - const int depth_dilation_in, - const int batch_size, - const int64_t attn_stride_0, - const int64_t attn_stride_1, - const int64_t attn_stride_2, - const int64_t attn_stride_3, - const int64_t attn_stride_4, - const int64_t problem_size, - const int num_threads) + int32_t depth, + int32_t height, + int32_t width, + int32_t heads, + int32_t kernel_size_0, + int32_t kernel_size_1, + int32_t kernel_size_2, + int32_t dilation_0, + int32_t dilation_1, + int32_t dilation_2, + int32_t batch_size, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3, + int64_t attn_stride_4, + int64_t problem_size, + int32_t num_threads) : d_bias(d_bias), d_attn(d_attn), depth(depth), height(height), width(width), heads(heads), - kernel_size_in(kernel_size_in), - dilation_in(dilation_in), - depth_kernel_size_in(depth_kernel_size_in), - depth_dilation_in(depth_dilation_in), + kernel_size_0(kernel_size_0), + kernel_size_1(kernel_size_1), + kernel_size_2(kernel_size_2), + dilation_0(dilation_0), + dilation_1(dilation_1), + dilation_2(dilation_2), batch_size(batch_size), problem_size(problem_size), num_threads(num_threads), - bias_stride_2(2 * kernel_size_in - 1), - bias_stride_1((2 * kernel_size_in - 1) * (2 * kernel_size_in - 1)), + bias_stride_2(2 * kernel_size_2 - 1), + bias_stride_1((2 * kernel_size_1 - 1) * (2 * kernel_size_2 - 1)), bias_stride_0( - (2 * kernel_size_in - 1) * (2 * kernel_size_in - 1) * - (2 * depth_kernel_size_in - 1)), + (2 * kernel_size_0 - 1) * (2 * kernel_size_1 - 1) * + (2 * kernel_size_2 - 1)), attn_stride_4(attn_stride_4), attn_stride_3(attn_stride_3), attn_stride_2(attn_stride_2), @@ -108,22 +111,14 @@ struct RelPosBiasGradient3DBase { __device__ __host__ RelPosBiasGradient3DBase() {} - static LaunchParams get_launch_params(int n_threads) { + static LaunchParams get_launch_params(int32_t n_threads) { dim3 grid(GET_BLOCKS(n_threads, 128)); dim3 block(128); return LaunchParams(grid, block); } }; -template < - typename scalar_t, - typename acc_t, - int KS, - int NS, - int DILATION, - int DKS, - int DNS, - int DDILATION> +template struct RelPosBiasGradient3DFull : RelPosBiasGradient3DBase { using Base = RelPosBiasGradient3DBase; using Params = typename Base::Params; @@ -134,47 +129,45 @@ struct RelPosBiasGradient3DFull : RelPosBiasGradient3DBase { __device__ __host__ RelPosBiasGradient3DFull() : Base() {} __device__ void launch(Params p) { - const int KERNEL_SIZE = (KS > 1) ? KS : p.kernel_size_in; - const int KERNEL_SIZE_D = (DKS > 1) ? DKS : p.depth_kernel_size_in; - const int NEIGHBORHOOD_SIZE = (NS > 0) ? NS : KERNEL_SIZE / 2; - const int NEIGHBORHOOD_SIZE_D = (DNS > 0) ? DNS : KERNEL_SIZE_D / 2; - const int dilation = (DILATION > 0) ? DILATION : p.dilation_in; - const int dilation_d = (DDILATION > 0) ? DDILATION : p.depth_dilation_in; - const int64_t linearIndex = blockIdx.x * blockDim.x + threadIdx.x; + auto kernel_size_half_0 = p.kernel_size_0 / 2; + auto kernel_size_half_1 = p.kernel_size_1 / 2; + auto kernel_size_half_2 = p.kernel_size_2 / 2; + int64_t linearIndex = blockIdx.x * blockDim.x + threadIdx.x; if (linearIndex < p.num_threads) { - int indtmp1 = linearIndex / KERNEL_SIZE; - const int kj = linearIndex - indtmp1 * KERNEL_SIZE; - int indtmp2 = indtmp1 / KERNEL_SIZE; - const int ki = indtmp1 - indtmp2 * KERNEL_SIZE; + int32_t indtmp1 = linearIndex / p.kernel_size_2; + auto kj = linearIndex - indtmp1 * p.kernel_size_2; + int32_t indtmp2 = indtmp1 / p.kernel_size_1; + auto ki = indtmp1 - indtmp2 * p.kernel_size_1; indtmp1 = indtmp2; - indtmp2 = indtmp1 / KERNEL_SIZE_D; - const int kk = indtmp1 - indtmp2 * KERNEL_SIZE_D; + indtmp2 = indtmp1 / p.kernel_size_0; + auto kk = indtmp1 - indtmp2 * p.kernel_size_0; indtmp1 = indtmp2; indtmp2 = indtmp1 / p.width; - const int j = indtmp1 - indtmp2 * p.width; + auto j = indtmp1 - indtmp2 * p.width; indtmp1 = indtmp2; indtmp2 = indtmp1 / p.height; - const int i = indtmp1 - indtmp2 * p.height; + auto i = indtmp1 - indtmp2 * p.height; indtmp1 = indtmp2; indtmp2 = indtmp1 / p.depth; - const int k = indtmp1 - indtmp2 * p.depth; - const int h = indtmp2; - const int pi = - get_pb_start(i, p.height, KERNEL_SIZE, NEIGHBORHOOD_SIZE, dilation); - const int pj = - get_pb_start(j, p.width, KERNEL_SIZE, NEIGHBORHOOD_SIZE, dilation); - const int pk = get_pb_start( - k, p.depth, KERNEL_SIZE_D, NEIGHBORHOOD_SIZE_D, dilation_d); + auto k = indtmp1 - indtmp2 * p.depth; + auto& h = indtmp2; + auto pk = get_pb_start( + k, p.depth, p.kernel_size_0, kernel_size_half_0, p.dilation_0); + auto pi = get_pb_start( + i, p.height, p.kernel_size_1, kernel_size_half_1, p.dilation_1); + auto pj = get_pb_start( + j, p.width, p.kernel_size_2, kernel_size_half_2, p.dilation_2); acc_t d_rpb_update = acc_t(0); int64_t attnOffset = h * p.attn_stride_1 + k * p.attn_stride_2 + i * p.attn_stride_3 + j * p.attn_stride_4 + - ((kk * KERNEL_SIZE * KERNEL_SIZE) + (ki * KERNEL_SIZE) + kj); + ((kk * p.kernel_size_1 * p.kernel_size_2) + (ki * p.kernel_size_2) + + kj); #pragma unroll - for (int b = 0; b < p.batch_size; ++b) { + for (int32_t b = 0; b < p.batch_size; ++b) { d_rpb_update += static_cast(p.d_attn[attnOffset]); attnOffset += p.attn_stride_0; } - const int64_t index = h * p.bias_stride_0 + (pk + kk) * p.bias_stride_1 + + int64_t index = h * p.bias_stride_0 + (pk + kk) * p.bias_stride_1 + (pi + ki) * p.bias_stride_2 + (pj + kj); at::native::fastAtomicAdd( p.d_bias, index, p.problem_size, d_rpb_update, true); @@ -182,15 +175,7 @@ struct RelPosBiasGradient3DFull : RelPosBiasGradient3DBase { } }; -template < - typename scalar_t, - typename acc_t, - int KS, - int NS, - int DILATION, - int DKS, - int DNS, - int DDILATION> +template struct RelPosBiasGradient3DHalf : RelPosBiasGradient3DBase { using Base = RelPosBiasGradient3DBase; using Params = typename Base::Params; @@ -203,47 +188,45 @@ struct RelPosBiasGradient3DHalf : RelPosBiasGradient3DBase { using HalfHelper = typename HalfArray::Base; __device__ void launch(Params p) { - const int KERNEL_SIZE = (KS > 1) ? KS : p.kernel_size_in; - const int KERNEL_SIZE_D = (DKS > 1) ? DKS : p.depth_kernel_size_in; - const int NEIGHBORHOOD_SIZE = (NS > 0) ? NS : KERNEL_SIZE / 2; - const int NEIGHBORHOOD_SIZE_D = (DNS > 0) ? DNS : KERNEL_SIZE_D / 2; - const int dilation = (DILATION > 0) ? DILATION : p.dilation_in; - const int dilation_d = (DDILATION > 0) ? DDILATION : p.depth_dilation_in; - const int64_t linearIndex = blockIdx.x * blockDim.x + threadIdx.x; + auto kernel_size_half_0 = p.kernel_size_0 / 2; + auto kernel_size_half_1 = p.kernel_size_1 / 2; + auto kernel_size_half_2 = p.kernel_size_2 / 2; + int64_t linearIndex = blockIdx.x * blockDim.x + threadIdx.x; if (linearIndex < p.num_threads) { - int indtmp1 = linearIndex / KERNEL_SIZE; - const int kj = linearIndex - indtmp1 * KERNEL_SIZE; - int indtmp2 = indtmp1 / KERNEL_SIZE; - const int ki = indtmp1 - indtmp2 * KERNEL_SIZE; + int32_t indtmp1 = linearIndex / p.kernel_size_2; + auto kj = linearIndex - indtmp1 * p.kernel_size_2; + int32_t indtmp2 = indtmp1 / p.kernel_size_1; + auto ki = indtmp1 - indtmp2 * p.kernel_size_1; indtmp1 = indtmp2; - indtmp2 = indtmp1 / KERNEL_SIZE_D; - const int kk = indtmp1 - indtmp2 * KERNEL_SIZE_D; + indtmp2 = indtmp1 / p.kernel_size_0; + auto kk = indtmp1 - indtmp2 * p.kernel_size_0; indtmp1 = indtmp2; indtmp2 = indtmp1 / p.width; - const int j = indtmp1 - indtmp2 * p.width; + auto j = indtmp1 - indtmp2 * p.width; indtmp1 = indtmp2; indtmp2 = indtmp1 / p.height; - const int i = indtmp1 - indtmp2 * p.height; + auto i = indtmp1 - indtmp2 * p.height; indtmp1 = indtmp2; indtmp2 = indtmp1 / p.depth; - const int k = indtmp1 - indtmp2 * p.depth; - const int h = indtmp2; - const int pi = - get_pb_start(i, p.height, KERNEL_SIZE, NEIGHBORHOOD_SIZE, dilation); - const int pj = - get_pb_start(j, p.width, KERNEL_SIZE, NEIGHBORHOOD_SIZE, dilation); - const int pk = get_pb_start( - k, p.depth, KERNEL_SIZE_D, NEIGHBORHOOD_SIZE_D, dilation_d); + auto k = indtmp1 - indtmp2 * p.depth; + auto& h = indtmp2; + auto pk = get_pb_start( + k, p.depth, p.kernel_size_0, kernel_size_half_0, p.dilation_0); + auto pi = get_pb_start( + i, p.height, p.kernel_size_1, kernel_size_half_1, p.dilation_1); + auto pj = get_pb_start( + j, p.width, p.kernel_size_2, kernel_size_half_2, p.dilation_2); acc_t d_rpb_update = acc_t(0); int64_t attnOffset = h * p.attn_stride_1 + k * p.attn_stride_2 + i * p.attn_stride_3 + j * p.attn_stride_4 + - ((kk * KERNEL_SIZE * KERNEL_SIZE) + (ki * KERNEL_SIZE) + kj); + ((kk * p.kernel_size_1 * p.kernel_size_2) + (ki * p.kernel_size_2) + + kj); #pragma unroll - for (int b = 0; b < p.batch_size; ++b) { + for (int32_t b = 0; b < p.batch_size; ++b) { d_rpb_update += HalfHelper::to_float(p.d_attn[attnOffset]); attnOffset += p.attn_stride_0; } - const int64_t index = h * p.bias_stride_0 + (pk + kk) * p.bias_stride_1 + + int64_t index = h * p.bias_stride_0 + (pk + kk) * p.bias_stride_1 + (pi + ki) * p.bias_stride_2 + (pj + kj); at::native::fastAtomicAdd( p.d_bias, index, p.problem_size, d_rpb_update, true); @@ -254,60 +237,42 @@ struct RelPosBiasGradient3DHalf : RelPosBiasGradient3DBase { template struct RelPosBiasGradient3D { using Args = Args_; - static constexpr int KS = Args::KernelSize; - static constexpr int NS = Args::NeighborhoodSize; - static constexpr int DKS = Args::DepthKernelSize; - static constexpr int DNS = Args::DepthNeighborhoodSize; - static constexpr int DILATION = Args::Dilation; - static constexpr int DDILATION = Args::DepthDilation; using scalar_t = typename Args::Dtype; + using CausalMask = typename Args::CausalMask; using acc_t = typename std::conditional= 4, scalar_t, float>::type; using Kernel = typename std::conditional< sizeof(scalar_t) >= 4, - RelPosBiasGradient3DFull< - scalar_t, - acc_t, - KS, - NS, - DILATION, - DKS, - DNS, - DDILATION>, - RelPosBiasGradient3DHalf< - scalar_t, - acc_t, - KS, - NS, - DILATION, - DKS, - DNS, - DDILATION>>::type; + RelPosBiasGradient3DFull, + RelPosBiasGradient3DHalf>::type; using Params = typename Kernel::Params; + static_assert( + !CausalMask::Dim0 && !CausalMask::Dim1 && !CausalMask::Dim2, + "PN+Bias does not support causal masking."); + void operator()( - const int cc, + int32_t cc, cudaStream_t stream, void* d_bias_ptr, void* d_attn_ptr, - int batch_size, - int heads, - int depth, - int height, - int width, - int dim, + int32_t batch_size, + int32_t heads, + int32_t depth, + int32_t height, + int32_t width, + int32_t dim, int64_t attn_stride_0, int64_t attn_stride_1, int64_t attn_stride_2, int64_t attn_stride_3, int64_t attn_stride_4, - int kernel_size, - int kernel_size_depth, - int dilation, - int dilation_depth) { - int num_threads = heads * depth * height * width * kernel_size_depth * - kernel_size * kernel_size; - int64_t problem_size = heads * (2 * kernel_size_depth - 1) * (2 * kernel_size - 1) * (2 * kernel_size - 1); + const std::tuple& kernel_size, + const std::tuple& dilation) { + int32_t num_threads = + heads * depth * height * width * natten::flatten(kernel_size); + int64_t problem_size = heads * (2 * std::get<0>(kernel_size) - 1) * + (2 * std::get<1>(kernel_size) - 1) * (2 * std::get<2>(kernel_size) - 1); LaunchParams lp = Kernel::Base::get_launch_params(num_threads); auto params = Params( reinterpret_cast(d_bias_ptr), @@ -316,10 +281,12 @@ struct RelPosBiasGradient3D { height, width, heads, - kernel_size, - dilation, - kernel_size_depth, - dilation_depth, + std::get<0>(kernel_size), + std::get<1>(kernel_size), + std::get<2>(kernel_size), + std::get<0>(dilation), + std::get<1>(dilation), + std::get<2>(dilation), batch_size, attn_stride_0, attn_stride_1, diff --git a/csrc/include/natten/cuda/naive/tiled/base.cuh b/csrc/include/natten/cuda/naive/tiled/base.cuh index 5ed4ad0..d8e948b 100644 --- a/csrc/include/natten/cuda/naive/tiled/base.cuh +++ b/csrc/include/natten/cuda/naive/tiled/base.cuh @@ -45,46 +45,53 @@ namespace naive { template struct PointwiseNeighborhood2DBase { struct Params { + bool is_grad; scalar_t* query; // query / d_out scalar_t* key; // key / value scalar_t* bias = nullptr; // optional: bias scalar_t* attn; // attn / d_attn - const int height; - const int width; - const int heads; - const int kernel_size_in; - const int dilation_in; - const int dim; - const int batch_size; - const int64_t attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3; - const int64_t query_stride_0, query_stride_1, query_stride_2, query_stride_3; - const int64_t bias_stride_0, bias_stride_1; + int32_t height; + int32_t width; + int32_t heads; + int32_t kernel_size_0, kernel_size_1; + int32_t dilation_0, dilation_1; + int32_t dim; + int32_t batch_size; + int64_t attn_stride_0, attn_stride_1, attn_stride_2, attn_stride_3; + int64_t query_stride_0, query_stride_1, query_stride_2, query_stride_3; + int64_t bias_stride_0, bias_stride_1; __device__ __host__ Params() {} __device__ __host__ Params( + bool is_grad, scalar_t* query, scalar_t* key, scalar_t* attn, - const int height, - const int width, - const int heads, - const int kernel_size_in, - const int dilation_in, - const int dim, - const int batch_size, - const int64_t attn_stride_0, - const int64_t attn_stride_1, - const int64_t attn_stride_2, - const int64_t attn_stride_3) - : query(query), + int32_t height, + int32_t width, + int32_t heads, + int32_t kernel_size_0, + int32_t kernel_size_1, + int32_t dilation_0, + int32_t dilation_1, + int32_t dim, + int32_t batch_size, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3) + : is_grad(is_grad), + query(query), key(key), attn(attn), height(height), width(width), heads(heads), - kernel_size_in(kernel_size_in), - dilation_in(dilation_in), + kernel_size_0(kernel_size_0), + kernel_size_1(kernel_size_1), + dilation_0(dilation_0), + dilation_1(dilation_1), dim(dim), batch_size(batch_size), bias_stride_1(0), @@ -104,30 +111,35 @@ struct PointwiseNeighborhood2DBase { scalar_t* key, // value / key scalar_t* bias, // relative positional bias tensor scalar_t* attn, // output / d_query - const int height, - const int width, - const int heads, - const int kernel_size_in, - const int dilation_in, - const int dim, - const int batch_size, - const int64_t attn_stride_0, - const int64_t attn_stride_1, - const int64_t attn_stride_2, - const int64_t attn_stride_3) - : query(query), + int32_t height, + int32_t width, + int32_t heads, + int32_t kernel_size_0, + int32_t kernel_size_1, + int32_t dilation_0, + int32_t dilation_1, + int32_t dim, + int32_t batch_size, + int64_t attn_stride_0, + int64_t attn_stride_1, + int64_t attn_stride_2, + int64_t attn_stride_3) + : is_grad(false), + query(query), key(key), bias(bias), attn(attn), height(height), width(width), heads(heads), - kernel_size_in(kernel_size_in), - dilation_in(dilation_in), + kernel_size_0(kernel_size_0), + kernel_size_1(kernel_size_1), + dilation_0(dilation_0), + dilation_1(dilation_1), dim(dim), batch_size(batch_size), - bias_stride_1(2 * kernel_size_in - 1), - bias_stride_0((2 * kernel_size_in - 1) * (2 * kernel_size_in - 1)), + bias_stride_1(2 * kernel_size_1 - 1), + bias_stride_0((2 * kernel_size_0 - 1) * (2 * kernel_size_1 - 1)), attn_stride_3(attn_stride_3), attn_stride_2(attn_stride_2), attn_stride_1(attn_stride_1), @@ -141,13 +153,14 @@ struct PointwiseNeighborhood2DBase { __device__ __host__ PointwiseNeighborhood2DBase() {} static LaunchParams get_launch_params( - int batch_dim, - int spatial_size, - int attention_span) { - int KERNELTHREADS = + int32_t batch_dim, + int32_t spatial_size, + int32_t attention_span) { + int32_t KERNELTHREADS = min(CUDA_NUM_THREADS, attention_span /* == kernel_size^2 */); - int PIXELTHREADS = min(int(CUDA_NUM_THREADS / KERNELTHREADS), spatial_size); - int BATCHTHREADS = + int32_t PIXELTHREADS = + min(int32_t(CUDA_NUM_THREADS / KERNELTHREADS), spatial_size); + int32_t BATCHTHREADS = min(64, max(1, CUDA_NUM_THREADS / (PIXELTHREADS * KERNELTHREADS))); dim3 grid( (spatial_size + PIXELTHREADS - 1) / PIXELTHREADS, diff --git a/csrc/include/natten/cuda/naive/tiled/pointwise_neighborhood_2d_tiled_11x11_13x13.cuh b/csrc/include/natten/cuda/naive/tiled/pointwise_neighborhood_2d_tiled_11x11_13x13.cuh index 9add001..1d83382 100644 --- a/csrc/include/natten/cuda/naive/tiled/pointwise_neighborhood_2d_tiled_11x11_13x13.cuh +++ b/csrc/include/natten/cuda/naive/tiled/pointwise_neighborhood_2d_tiled_11x11_13x13.cuh @@ -53,16 +53,15 @@ namespace naive { template < typename scalar_t, typename memscalar_t, - int TILEX, - int TILEY, - int KTILEX, - int KTILEY, - int XTHREADS, - int YTHREADS, - int BATCHTHREADS, - int KERNEL_SIZE, - int NEIGHBORHOOD_SIZE, - int DILATION> + int32_t TILEX, + int32_t TILEY, + int32_t KTILEX, + int32_t KTILEY, + int32_t XTHREADS, + int32_t YTHREADS, + int32_t BATCHTHREADS, + int32_t KERNEL_SIZE, + int32_t NEIGHBORHOOD_SIZE> struct PointwiseNeighborhood2DFull11x11_13x13 : PointwiseNeighborhood2DBase { using Base = PointwiseNeighborhood2DBase; @@ -73,40 +72,37 @@ struct PointwiseNeighborhood2DFull11x11_13x13 __device__ __host__ PointwiseNeighborhood2DFull11x11_13x13() : Base() {} - static __host__ int get_dim(int dim) { - return 32; - } - __device__ void launch(Params p) { - const int dilation = (DILATION > 0) ? DILATION : p.dilation_in; // Because batch heads have stride 1 per threadblock, we can just use // blockIdx since blockDim will be 1 and threadIdx will always be 0. const - // int z = blockIdx.z * blockDim.z + threadIdx.z; - const int z = blockIdx.z; - const int b = z / p.heads; - const int h = z - b * p.heads; + // int32_t z = blockIdx.z * blockDim.z + threadIdx.z; + // TODO: remove when/if tiled kernels allow varying dilations + auto dilation = p.dilation_0; + int32_t z = blockIdx.z; + int32_t b = z / p.heads; + int32_t h = z - b * p.heads; // Not needed again because it will always be true. // if (z < batch_size * heads) // { - const int lti = threadIdx.y * (TILEY * KERNEL_SIZE) + threadIdx.x; - const int64_t batchHeadOffset = b * p.query_stride_0 + h * p.query_stride_1; - const int si = int(blockIdx.y / dilation) * (TILEX * dilation) + + int32_t lti = threadIdx.y * (TILEY * KERNEL_SIZE) + threadIdx.x; + int64_t batchHeadOffset = b * p.query_stride_0 + h * p.query_stride_1; + int32_t si = int32_t(blockIdx.y / dilation) * (TILEX * dilation) + (blockIdx.y % dilation); - const int sj = int(blockIdx.x / dilation) * (TILEY * dilation) + + int32_t sj = int32_t(blockIdx.x / dilation) * (TILEY * dilation) + (blockIdx.x % dilation); - const int sni = get_window_start( + int32_t sni = get_window_start( si, p.height, KERNEL_SIZE, NEIGHBORHOOD_SIZE, dilation); - const int snj = + int32_t snj = get_window_start(sj, p.width, KERNEL_SIZE, NEIGHBORHOOD_SIZE, dilation); __shared__ memscalar_t tile[TILEX * TILEY][DIM_32 + 3]; __shared__ memscalar_t kTile[KTILEX * KTILEY][DIM_32 + 3]; /* query tile */ - const int qtx = lti / DIM_32; - const int qty = lti - qtx * DIM_32; + int32_t qtx = lti / DIM_32; + int32_t qty = lti - qtx * DIM_32; if (qtx < TILEX * TILEY) { - int qi = qtx / TILEY; - const int qj = (qtx - qi * TILEY) * dilation + sj; + int32_t qi = qtx / TILEY; + int32_t qj = (qtx - qi * TILEY) * dilation + sj; qi = qi * dilation + si; if (qi < p.height && qj < p.width) tile[qtx][qty] = p.query @@ -114,48 +110,48 @@ struct PointwiseNeighborhood2DFull11x11_13x13 qj * p.query_stride_3 + qty]; } /* key tile */ - const int ktx = lti / KSTRIDE_32; - const int kty = (lti - ktx * KSTRIDE_32) * KITERS_32; + int32_t ktx = lti / KSTRIDE_32; + int32_t kty = (lti - ktx * KSTRIDE_32) * KITERS_32; if (ktx < KTILEX * KTILEY) { - int bi = ktx / KTILEY; - const int bj = (ktx - bi * KTILEY) * dilation + snj; + int32_t bi = ktx / KTILEY; + int32_t bj = (ktx - bi * KTILEY) * dilation + snj; bi = bi * dilation + sni; if (bi < p.height && bj < p.width) { - const int64_t keyOffset = batchHeadOffset + bi * p.query_stride_2 + + int64_t keyOffset = batchHeadOffset + bi * p.query_stride_2 + bj * p.query_stride_3 + kty; #pragma unroll - for (int ti = 0; ti < KITERS_32; ++ti) + for (int32_t ti = 0; ti < KITERS_32; ++ti) kTile[ktx][kty + ti] = p.key[keyOffset + ti]; } } __syncthreads(); - const int ii = threadIdx.y / KERNEL_SIZE; - const int ki = threadIdx.y - ii * KERNEL_SIZE; - const int jj = threadIdx.x / KERNEL_SIZE; - const int kj = threadIdx.x - jj * KERNEL_SIZE; - const int i = si + ii * dilation, j = sj + jj * dilation; + int32_t ii = threadIdx.y / KERNEL_SIZE; + int32_t ki = threadIdx.y - ii * KERNEL_SIZE; + int32_t jj = threadIdx.x / KERNEL_SIZE; + int32_t kj = threadIdx.x - jj * KERNEL_SIZE; + int32_t i = si + ii * dilation, j = sj + jj * dilation; if (i < p.height && j < p.width) { - const int ni = get_window_start( + int32_t ni = get_window_start( i, p.height, KERNEL_SIZE, NEIGHBORHOOD_SIZE, dilation); - const int nj = get_window_start( + int32_t nj = get_window_start( j, p.width, KERNEL_SIZE, NEIGHBORHOOD_SIZE, dilation); scalar_t updt = scalar_t(0); - const int queryIdx = ii * TILEY + jj; - const int keyIdx = int((ni + ki * dilation - sni) / dilation) * KTILEY + - int((nj + kj * dilation - snj) / dilation); + int32_t queryIdx = ii * TILEY + jj; + int32_t keyIdx = int32_t((ni + ki * dilation - sni) / dilation) * KTILEY + + int32_t((nj + kj * dilation - snj) / dilation); #pragma unroll - for (int dimOffset = 0; dimOffset < DIM_32; ++dimOffset) + for (int32_t dimOffset = 0; dimOffset < DIM_32; ++dimOffset) updt += tile[queryIdx][dimOffset] * kTile[keyIdx][dimOffset]; - const int64_t index = b * p.attn_stride_0 + h * p.attn_stride_1 + + int64_t index = b * p.attn_stride_0 + h * p.attn_stride_1 + i * p.attn_stride_2 + j * p.attn_stride_3 + ki * KERNEL_SIZE + kj; if (p.bias) { - const int pi = + int32_t pi = get_pb_start(i, p.height, KERNEL_SIZE, NEIGHBORHOOD_SIZE, dilation); - const int pj = + int32_t pj = get_pb_start(j, p.width, KERNEL_SIZE, NEIGHBORHOOD_SIZE, dilation); - const int64_t biasIndex = + int64_t biasIndex = h * p.bias_stride_0 + (pi + ki) * p.bias_stride_1 + (pj + kj); updt += p.bias[biasIndex]; } @@ -165,13 +161,13 @@ struct PointwiseNeighborhood2DFull11x11_13x13 } static LaunchParams get_launch_params( - int batch_dim, - int height, - int width, - int attention_span, - int dilation) { - int xsize = width * KERNEL_SIZE; - int ysize = height * KERNEL_SIZE; + int32_t batch_dim, + int32_t height, + int32_t width, + int32_t attention_span, + int32_t dilation) { + int32_t xsize = width * KERNEL_SIZE; + int32_t ysize = height * KERNEL_SIZE; const dim3 grid( (xsize + XTHREADS * dilation - 1) / XTHREADS, (ysize + YTHREADS * dilation - 1) / YTHREADS, @@ -184,16 +180,15 @@ struct PointwiseNeighborhood2DFull11x11_13x13 template < typename scalar_t, typename memscalar_t, - int TILEX, - int TILEY, - int KTILEX, - int KTILEY, - int XTHREADS, - int YTHREADS, - int BATCHTHREADS, - int KERNEL_SIZE, - int NEIGHBORHOOD_SIZE, - int DILATION> + int32_t TILEX, + int32_t TILEY, + int32_t KTILEX, + int32_t KTILEY, + int32_t XTHREADS, + int32_t YTHREADS, + int32_t BATCHTHREADS, + int32_t KERNEL_SIZE, + int32_t NEIGHBORHOOD_SIZE> struct PointwiseNeighborhood2DHalf11x11_13x13 : PointwiseNeighborhood2DBase { using Base = PointwiseNeighborhood2DBase; @@ -207,31 +202,28 @@ struct PointwiseNeighborhood2DHalf11x11_13x13 using HalfHelper = typename HalfArray::Base; using HalfType = typename HalfHelper::ElementVector; - static __host__ int get_dim(int dim) { - return 16; - } - __device__ void launch(Params p) { - const int dilation = (DILATION > 0) ? DILATION : p.dilation_in; // Because batch heads have stride 1 per threadblock, we can just use // blockIdx since blockDim will be 1 and threadIdx will always be 0. const - // int z = blockIdx.z * blockDim.z + threadIdx.z; - const int z = blockIdx.z; - const int b = z / p.heads; - const int h = z - b * p.heads; + // int32_t z = blockIdx.z * blockDim.z + threadIdx.z; + // TODO: remove when/if tiled kernels allow varying dilations + auto dilation = p.dilation_0; + int32_t z = blockIdx.z; + int32_t b = z / p.heads; + int32_t h = z - b * p.heads; // Not needed again because it will always be true. // if (z < batch_size * heads) // { - const int lti = threadIdx.y * (TILEY * KERNEL_SIZE) + threadIdx.x; - const int stride2 = DIMHALF_32 * p.width; - const int64_t batchHeadOffset = b * p.query_stride_0 + h * p.query_stride_1; - const int si = int(blockIdx.y / dilation) * (TILEX * dilation) + + int32_t lti = threadIdx.y * (TILEY * KERNEL_SIZE) + threadIdx.x; + int32_t stride2 = DIMHALF_32 * p.width; + int64_t batchHeadOffset = b * p.query_stride_0 + h * p.query_stride_1; + int32_t si = int32_t(blockIdx.y / dilation) * (TILEX * dilation) + (blockIdx.y % dilation); - const int sj = int(blockIdx.x / dilation) * (TILEY * dilation) + + int32_t sj = int32_t(blockIdx.x / dilation) * (TILEY * dilation) + (blockIdx.x % dilation); - const int sni = get_window_start( + int32_t sni = get_window_start( si, p.height, KERNEL_SIZE, NEIGHBORHOOD_SIZE, dilation); - const int snj = + int32_t snj = get_window_start(sj, p.width, KERNEL_SIZE, NEIGHBORHOOD_SIZE, dilation); __shared__ HalfType tile[TILEX * TILEY][DIM_32 + 3]; __shared__ HalfType kTile[KTILEX * KTILEY][DIM_32 + 3]; @@ -239,68 +231,68 @@ struct PointwiseNeighborhood2DHalf11x11_13x13 auto key2 = HalfHelper::typecast(p.key); /* query tile */ - const int qtx = lti / DIM_32; - const int qtyp = lti - qtx * DIM_32; - const int qdi = qtyp / KHALFITERS_32; - const int qdj = qtyp - qdi * KHALFITERS_32; - const int qty = qdi * KITERS_32 + qdj; + int32_t qtx = lti / DIM_32; + int32_t qtyp = lti - qtx * DIM_32; + int32_t qdi = qtyp / KHALFITERS_32; + int32_t qdj = qtyp - qdi * KHALFITERS_32; + int32_t qty = qdi * KITERS_32 + qdj; if (qtx < TILEX * TILEY && qtyp < DIMHALF_32) { - int qi = qtx / TILEY; - const int qj = (qtx - qi * TILEY) * dilation + sj; + int32_t qi = qtx / TILEY; + int32_t qj = (qtx - qi * TILEY) * dilation + sj; qi = qi * dilation + si; if (qi < p.height && qj < p.width) tile[qtx][qty] = query2[batchHeadOffset + qi * stride2 + qj * DIMHALF_32 + qtyp]; } /* key tile */ - const int ktx = lti / KSTRIDE_32; - const int kty = (lti - ktx * KSTRIDE_32) * KHALFITERS_32; + int32_t ktx = lti / KSTRIDE_32; + int32_t kty = (lti - ktx * KSTRIDE_32) * KHALFITERS_32; if (ktx < KTILEX * KTILEY) { - int bi = ktx / KTILEY; - const int bj = (ktx - bi * KTILEY) * dilation + snj; + int32_t bi = ktx / KTILEY; + int32_t bj = (ktx - bi * KTILEY) * dilation + snj; bi = bi * dilation + sni; if (bi < p.height && bj < p.width) { - const int64_t keyOffset = + int64_t keyOffset = batchHeadOffset + bi * stride2 + bj * DIMHALF_32 + kty; #pragma unroll - for (int ti = 0; ti < KHALFITERS_32; ++ti) + for (int32_t ti = 0; ti < KHALFITERS_32; ++ti) kTile[ktx][kty * 2 + ti] = key2[keyOffset + ti]; } } __syncthreads(); - const int ii = threadIdx.y / KERNEL_SIZE; - const int ki = threadIdx.y - ii * KERNEL_SIZE; - const int jj = threadIdx.x / KERNEL_SIZE; - const int kj = threadIdx.x - jj * KERNEL_SIZE; - const int i = si + ii * dilation, j = sj + jj * dilation; + int32_t ii = threadIdx.y / KERNEL_SIZE; + int32_t ki = threadIdx.y - ii * KERNEL_SIZE; + int32_t jj = threadIdx.x / KERNEL_SIZE; + int32_t kj = threadIdx.x - jj * KERNEL_SIZE; + int32_t i = si + ii * dilation, j = sj + jj * dilation; if (i < p.height && j < p.width) { - const int ni = get_window_start( + int32_t ni = get_window_start( i, p.height, KERNEL_SIZE, NEIGHBORHOOD_SIZE, dilation); - const int nj = get_window_start( + int32_t nj = get_window_start( j, p.width, KERNEL_SIZE, NEIGHBORHOOD_SIZE, dilation); - auto updt = HalfHelper::zero(); - const int queryIdx = ii * TILEY + jj; - const int keyIdx = int((ni + ki * dilation - sni) / dilation) * KTILEY + - int((nj + kj * dilation - snj) / dilation); + auto updt = HalfHelper::zeros(); + int32_t queryIdx = ii * TILEY + jj; + int32_t keyIdx = int32_t((ni + ki * dilation - sni) / dilation) * KTILEY + + int32_t((nj + kj * dilation - snj) / dilation); #pragma unroll - for (int di = 0; di < KSTRIDE_32; ++di) + for (int32_t di = 0; di < KSTRIDE_32; ++di) #pragma unroll - for (int dj = 0; dj < KHALFITERS_32; ++dj) + for (int32_t dj = 0; dj < KHALFITERS_32; ++dj) updt = HalfHelper::fma( tile[queryIdx][di * KITERS_32 + dj], kTile[keyIdx][di * KITERS_32 + dj], updt); - const int64_t index = b * p.attn_stride_0 + h * p.attn_stride_1 + + int64_t index = b * p.attn_stride_0 + h * p.attn_stride_1 + i * p.attn_stride_2 + j * p.attn_stride_3 + ki * KERNEL_SIZE + kj; scalar_t acc = HalfHelper::cast_back(HalfHelper::add(updt.x, updt.y)); if (p.bias) { - const int pi = + int32_t pi = get_pb_start(i, p.height, KERNEL_SIZE, NEIGHBORHOOD_SIZE, dilation); - const int pj = + int32_t pj = get_pb_start(j, p.width, KERNEL_SIZE, NEIGHBORHOOD_SIZE, dilation); - const int64_t biasIndex = + int64_t biasIndex = h * p.bias_stride_0 + (pi + ki) * p.bias_stride_1 + (pj + kj); acc = HalfHelper::add(acc, p.bias[biasIndex]); } @@ -310,13 +302,13 @@ struct PointwiseNeighborhood2DHalf11x11_13x13 } static LaunchParams get_launch_params( - int batch_dim, - int height, - int width, - int attention_span, - int dilation) { - int xsize = width * KERNEL_SIZE; - int ysize = height * KERNEL_SIZE; + int32_t batch_dim, + int32_t height, + int32_t width, + int32_t attention_span, + int32_t dilation) { + int32_t xsize = width * KERNEL_SIZE; + int32_t ysize = height * KERNEL_SIZE; const dim3 grid( (xsize + XTHREADS * dilation - 1) / XTHREADS, (ysize + YTHREADS * dilation - 1) / YTHREADS, @@ -326,7 +318,7 @@ struct PointwiseNeighborhood2DHalf11x11_13x13 } }; -template +template struct PointwiseNeighborhood2DFull11x11 : PointwiseNeighborhood2DFull11x11_13x13< scalar_t, @@ -339,8 +331,7 @@ struct PointwiseNeighborhood2DFull11x11 YTHREADS_11, BATCHTHREADS_11, 11, - 5, - DILATION> { + 5> { using Base = PointwiseNeighborhood2DFull11x11_13x13< scalar_t, scalar_t, @@ -352,13 +343,12 @@ struct PointwiseNeighborhood2DFull11x11 YTHREADS_11, BATCHTHREADS_11, 11, - 5, - DILATION>; + 5>; __device__ __host__ PointwiseNeighborhood2DFull11x11() : Base() {} }; -template +template struct PointwiseNeighborhood2DHalf11x11 : PointwiseNeighborhood2DHalf11x11_13x13< scalar_t, @@ -371,8 +361,7 @@ struct PointwiseNeighborhood2DHalf11x11 YTHREADS_11, BATCHTHREADS_11, 11, - 5, - DILATION> { + 5> { using Base = PointwiseNeighborhood2DHalf11x11_13x13< scalar_t, scalar_t, @@ -384,13 +373,12 @@ struct PointwiseNeighborhood2DHalf11x11 YTHREADS_11, BATCHTHREADS_11, 11, - 5, - DILATION>; + 5>; __device__ __host__ PointwiseNeighborhood2DHalf11x11() : Base() {} }; -template +template struct PointwiseNeighborhood2DFull13x13 : PointwiseNeighborhood2DFull11x11_13x13< scalar_t, @@ -403,8 +391,7 @@ struct PointwiseNeighborhood2DFull13x13 YTHREADS_13, BATCHTHREADS_13, 13, - 6, - DILATION> { + 6> { using Base = PointwiseNeighborhood2DFull11x11_13x13< scalar_t, float, @@ -416,13 +403,12 @@ struct PointwiseNeighborhood2DFull13x13 YTHREADS_13, BATCHTHREADS_13, 13, - 6, - DILATION>; + 6>; __device__ __host__ PointwiseNeighborhood2DFull13x13() : Base() {} }; -template +template struct PointwiseNeighborhood2DHalf13x13 : PointwiseNeighborhood2DHalf11x11_13x13< scalar_t, @@ -435,8 +421,7 @@ struct PointwiseNeighborhood2DHalf13x13 YTHREADS_13, BATCHTHREADS_13, 13, - 6, - DILATION> { + 6> { using Base = PointwiseNeighborhood2DHalf11x11_13x13< scalar_t, float, @@ -448,8 +433,7 @@ struct PointwiseNeighborhood2DHalf13x13 YTHREADS_13, BATCHTHREADS_13, 13, - 6, - DILATION>; + 6>; __device__ __host__ PointwiseNeighborhood2DHalf13x13() : Base() {} }; diff --git a/csrc/include/natten/cuda/naive/tiled/pointwise_neighborhood_2d_tiled_3x3.cuh b/csrc/include/natten/cuda/naive/tiled/pointwise_neighborhood_2d_tiled_3x3.cuh index b044470..089763b 100644 --- a/csrc/include/natten/cuda/naive/tiled/pointwise_neighborhood_2d_tiled_3x3.cuh +++ b/csrc/include/natten/cuda/naive/tiled/pointwise_neighborhood_2d_tiled_3x3.cuh @@ -50,7 +50,7 @@ namespace naive { /////////////////////////////////////////////////////////////////////////////// /////////////// 3x3 -template +template struct PointwiseNeighborhood2DFull3x3 : PointwiseNeighborhood2DBase { using Base = PointwiseNeighborhood2DBase; using Params = typename Base::Params; @@ -60,92 +60,90 @@ struct PointwiseNeighborhood2DFull3x3 : PointwiseNeighborhood2DBase { __device__ __host__ PointwiseNeighborhood2DFull3x3() : Base() {} - static __host__ int get_dim(int dim) { - return 32; - } - __device__ void launch(Params p) { - const int dilation = (DILATION > 0) ? DILATION : p.dilation_in; // Because batch heads have stride 1 per threadblock, we can just use // blockIdx since blockDim will be 1 and threadIdx will always be 0. const - // int z = blockIdx.z * blockDim.z + threadIdx.z; - const int z = blockIdx.z; - const int b = z / p.heads; - const int h = z - b * p.heads; + // int32_t z = blockIdx.z * blockDim.z + threadIdx.z; + // TODO: remove when/if tiled kernels allow varying dilations + auto dilation = p.dilation_0; + int32_t z = blockIdx.z; + int32_t b = z / p.heads; + int32_t h = z - b * p.heads; // Not needed again because it will always be true. // if (z < batch_size * heads) // { - const int lti = threadIdx.y * (TILE_3 * KERNEL_SIZE_3) + threadIdx.x; - const int64_t batchHeadOffset = b * p.query_stride_0 + h * p.query_stride_1; - const int si = int(blockIdx.y / dilation) * (TILE_3 * dilation) + + int32_t lti = threadIdx.y * (TILE_3 * KERNEL_SIZE_3) + threadIdx.x; + int64_t batchHeadOffset = b * p.query_stride_0 + h * p.query_stride_1; + int32_t si = int32_t(blockIdx.y / dilation) * (TILE_3 * dilation) + (blockIdx.y % dilation); - const int sj = int(blockIdx.x / dilation) * (TILE_3 * dilation) + + int32_t sj = int32_t(blockIdx.x / dilation) * (TILE_3 * dilation) + (blockIdx.x % dilation); - const int sni = get_window_start( + int32_t sni = get_window_start( si, p.height, KERNEL_SIZE_3, NEIGHBORHOOD_SIZE_3, dilation); - const int snj = get_window_start( + int32_t snj = get_window_start( sj, p.width, KERNEL_SIZE_3, NEIGHBORHOOD_SIZE_3, dilation); __shared__ scalar_t tile[TILE_3 * TILE_3][DIM_32 + 3]; __shared__ scalar_t kTile[KTILE_3 * KTILE_3][DIM_32 + 3]; /* query tile */ - const int qtx = lti / QSTRIDE_3; - const int qty = (lti - qtx * QSTRIDE_3) * QITERS_3; + int32_t qtx = lti / QSTRIDE_3; + int32_t qty = (lti - qtx * QSTRIDE_3) * QITERS_3; if (qtx < TILE_3 * TILE_3) { - int qi = qtx / TILE_3; - const int qj = (qtx - qi * TILE_3) * dilation + sj; + int32_t qi = qtx / TILE_3; + int32_t qj = (qtx - qi * TILE_3) * dilation + sj; qi = qi * dilation + si; if (qi < p.height && qj < p.width) { #pragma unroll - for (int ti = 0; ti < QITERS_3; ++ti) + for (int32_t ti = 0; ti < QITERS_3; ++ti) tile[qtx][qty + ti] = p.query [batchHeadOffset + qi * p.query_stride_2 + qj * p.query_stride_3 + qty + ti]; } } /* key tile */ - const int ktx = lti / KSTRIDE_32; - const int kty = (lti - ktx * KSTRIDE_32) * KITERS_32; + int32_t ktx = lti / KSTRIDE_32; + int32_t kty = (lti - ktx * KSTRIDE_32) * KITERS_32; if (ktx < KTILE_3 * KTILE_3) { - int bi = ktx / KTILE_3; - const int bj = (ktx - bi * KTILE_3) * dilation + snj; + int32_t bi = ktx / KTILE_3; + int32_t bj = (ktx - bi * KTILE_3) * dilation + snj; bi = bi * dilation + sni; if (bi < p.height && bj < p.width) { - const int64_t keyOffset = batchHeadOffset + bi * p.query_stride_2 + + int64_t keyOffset = batchHeadOffset + bi * p.query_stride_2 + bj * p.query_stride_3 + kty; #pragma unroll - for (int ti = 0; ti < KITERS_32; ++ti) + for (int32_t ti = 0; ti < KITERS_32; ++ti) kTile[ktx][kty + ti] = p.key[keyOffset + ti]; } } __syncthreads(); - const int ii = threadIdx.y / KERNEL_SIZE_3; - const int ki = threadIdx.y - ii * KERNEL_SIZE_3; - const int jj = threadIdx.x / KERNEL_SIZE_3; - const int kj = threadIdx.x - jj * KERNEL_SIZE_3; - const int i = si + ii * dilation, j = sj + jj * dilation; + int32_t ii = threadIdx.y / KERNEL_SIZE_3; + int32_t ki = threadIdx.y - ii * KERNEL_SIZE_3; + int32_t jj = threadIdx.x / KERNEL_SIZE_3; + int32_t kj = threadIdx.x - jj * KERNEL_SIZE_3; + int32_t i = si + ii * dilation, j = sj + jj * dilation; if (i < p.height && j < p.width) { - const int ni = get_window_start( + int32_t ni = get_window_start( i, p.height, KERNEL_SIZE_3, NEIGHBORHOOD_SIZE_3, dilation); - const int nj = get_window_start( + int32_t nj = get_window_start( j, p.width, KERNEL_SIZE_3, NEIGHBORHOOD_SIZE_3, dilation); scalar_t updt = scalar_t(0); - const int queryIdx = ii * TILE_3 + jj; - const int keyIdx = int((ni + ki * dilation - sni) / dilation) * KTILE_3 + - int((nj + kj * dilation - snj) / dilation); + int32_t queryIdx = ii * TILE_3 + jj; + int32_t keyIdx = + int32_t((ni + ki * dilation - sni) / dilation) * KTILE_3 + + int32_t((nj + kj * dilation - snj) / dilation); #pragma unroll - for (int dimOffset = 0; dimOffset < DIM_32; ++dimOffset) + for (int32_t dimOffset = 0; dimOffset < DIM_32; ++dimOffset) updt += tile[queryIdx][dimOffset] * kTile[keyIdx][dimOffset]; - const int64_t index = b * p.attn_stride_0 + h * p.attn_stride_1 + + int64_t index = b * p.attn_stride_0 + h * p.attn_stride_1 + i * p.attn_stride_2 + j * p.attn_stride_3 + ki * KERNEL_SIZE_3 + kj; if (p.bias) { - const int pi = get_pb_start( + int32_t pi = get_pb_start( i, p.height, KERNEL_SIZE_3, NEIGHBORHOOD_SIZE_3, dilation); - const int pj = get_pb_start( + int32_t pj = get_pb_start( j, p.width, KERNEL_SIZE_3, NEIGHBORHOOD_SIZE_3, dilation); - const int64_t biasIndex = + int64_t biasIndex = h * p.bias_stride_0 + (pi + ki) * p.bias_stride_1 + (pj + kj); updt += p.bias[biasIndex]; } @@ -155,16 +153,16 @@ struct PointwiseNeighborhood2DFull3x3 : PointwiseNeighborhood2DBase { } static LaunchParams get_launch_params( - int batch_dim, - int height, - int width, - int attention_span, - int dilation) { - int xsize = width * KERNEL_SIZE_3; - int ysize = height * KERNEL_SIZE_3; - int XTHREADS = XYTHREADS_3; - int YTHREADS = XYTHREADS_3; - int BATCHTHREADS = BATCHTHREADS_3; + int32_t batch_dim, + int32_t height, + int32_t width, + int32_t attention_span, + int32_t dilation) { + int32_t xsize = width * KERNEL_SIZE_3; + int32_t ysize = height * KERNEL_SIZE_3; + int32_t XTHREADS = XYTHREADS_3; + int32_t YTHREADS = XYTHREADS_3; + int32_t BATCHTHREADS = BATCHTHREADS_3; const dim3 grid( (xsize + XTHREADS * dilation - 1) / XTHREADS, (ysize + YTHREADS * dilation - 1) / YTHREADS, @@ -174,7 +172,7 @@ struct PointwiseNeighborhood2DFull3x3 : PointwiseNeighborhood2DBase { } }; -template +template struct PointwiseNeighborhood2DHalf3x3 : PointwiseNeighborhood2DBase { using Base = PointwiseNeighborhood2DBase; using Params = typename Base::Params; @@ -187,31 +185,28 @@ struct PointwiseNeighborhood2DHalf3x3 : PointwiseNeighborhood2DBase { using HalfHelper = typename HalfArray::Base; using HalfType = typename HalfHelper::ElementVector; - static __host__ int get_dim(int dim) { - return 16; - } - __device__ void launch(Params p) { - const int dilation = (DILATION > 0) ? DILATION : p.dilation_in; // Because batch heads have stride 1 per threadblock, we can just use // blockIdx since blockDim will be 1 and threadIdx will always be 0. const - // int z = blockIdx.z * blockDim.z + threadIdx.z; - const int z = blockIdx.z; - const int b = z / p.heads; - const int h = z - b * p.heads; + // int32_t z = blockIdx.z * blockDim.z + threadIdx.z; + // TODO: remove when/if tiled kernels allow varying dilations + auto dilation = p.dilation_0; + int32_t z = blockIdx.z; + int32_t b = z / p.heads; + int32_t h = z - b * p.heads; // Not needed again because it will always be true. // if (z < batch_size * heads) // { - const int lti = threadIdx.y * (TILE_3 * KERNEL_SIZE_3) + threadIdx.x; - const int stride2 = DIMHALF_32 * p.width; - const int64_t batchHeadOffset = b * p.query_stride_0 + h * p.query_stride_1; - const int si = int(blockIdx.y / dilation) * (TILE_3 * dilation) + + int32_t lti = threadIdx.y * (TILE_3 * KERNEL_SIZE_3) + threadIdx.x; + int32_t stride2 = DIMHALF_32 * p.width; + int64_t batchHeadOffset = b * p.query_stride_0 + h * p.query_stride_1; + int32_t si = int32_t(blockIdx.y / dilation) * (TILE_3 * dilation) + (blockIdx.y % dilation); - const int sj = int(blockIdx.x / dilation) * (TILE_3 * dilation) + + int32_t sj = int32_t(blockIdx.x / dilation) * (TILE_3 * dilation) + (blockIdx.x % dilation); - const int sni = get_window_start( + int32_t sni = get_window_start( si, p.height, KERNEL_SIZE_3, NEIGHBORHOOD_SIZE_3, dilation); - const int snj = get_window_start( + int32_t snj = get_window_start( sj, p.width, KERNEL_SIZE_3, NEIGHBORHOOD_SIZE_3, dilation); __shared__ HalfType tile[TILE_3 * TILE_3][DIM_32 + 3]; __shared__ HalfType kTile[KTILE_3 * KTILE_3][DIM_32 + 3]; @@ -219,64 +214,65 @@ struct PointwiseNeighborhood2DHalf3x3 : PointwiseNeighborhood2DBase { auto key2 = HalfHelper::typecast(p.key); /* query tile */ - const int qtx = lti / QSTRIDE_3_HALF; - const int qty = (lti - qtx * QSTRIDE_3_HALF) * QITERS_3_HALF; + int32_t qtx = lti / QSTRIDE_3_HALF; + int32_t qty = (lti - qtx * QSTRIDE_3_HALF) * QITERS_3_HALF; if (qtx < TILE_3 * TILE_3) { - int qi = qtx / TILE_3; - const int qj = (qtx - qi * TILE_3) * dilation + sj; + int32_t qi = qtx / TILE_3; + int32_t qj = (qtx - qi * TILE_3) * dilation + sj; qi = qi * dilation + si; if (qi < p.height && qj < p.width) { #pragma unroll - for (int ti = 0; ti < QITERS_3_HALF; ++ti) + for (int32_t ti = 0; ti < QITERS_3_HALF; ++ti) tile[qtx][qty + ti] = query2 [batchHeadOffset + qi * stride2 + qj * DIMHALF_32 + qty + ti]; } } /* key tile */ - const int ktx = lti / KSTRIDE_32; - const int kty = (lti - ktx * KSTRIDE_32) * KHALFITERS_32; + int32_t ktx = lti / KSTRIDE_32; + int32_t kty = (lti - ktx * KSTRIDE_32) * KHALFITERS_32; if (ktx < KTILE_3 * KTILE_3) { - int bi = ktx / KTILE_3; - const int bj = (ktx - bi * KTILE_3) * dilation + snj; + int32_t bi = ktx / KTILE_3; + int32_t bj = (ktx - bi * KTILE_3) * dilation + snj; bi = bi * dilation + sni; if (bi < p.height && bj < p.width) { - const int64_t keyOffset = + int64_t keyOffset = batchHeadOffset + bi * stride2 + bj * DIMHALF_32 + kty; #pragma unroll - for (int ti = 0; ti < KHALFITERS_32; ++ti) + for (int32_t ti = 0; ti < KHALFITERS_32; ++ti) kTile[ktx][kty + ti] = key2[keyOffset + ti]; } } __syncthreads(); - const int ii = threadIdx.y / KERNEL_SIZE_3; - const int ki = threadIdx.y - ii * KERNEL_SIZE_3; - const int jj = threadIdx.x / KERNEL_SIZE_3; - const int kj = threadIdx.x - jj * KERNEL_SIZE_3; - const int i = si + ii * dilation, j = sj + jj * dilation; + int32_t ii = threadIdx.y / KERNEL_SIZE_3; + int32_t ki = threadIdx.y - ii * KERNEL_SIZE_3; + int32_t jj = threadIdx.x / KERNEL_SIZE_3; + int32_t kj = threadIdx.x - jj * KERNEL_SIZE_3; + int32_t i = si + ii * dilation, j = sj + jj * dilation; if (i < p.height && j < p.width) { - const int ni = get_window_start( + int32_t ni = get_window_start( i, p.height, KERNEL_SIZE_3, NEIGHBORHOOD_SIZE_3, dilation); - const int nj = get_window_start( + int32_t nj = get_window_start( j, p.width, KERNEL_SIZE_3, NEIGHBORHOOD_SIZE_3, dilation); - auto updt = HalfHelper::zero(); - const int queryIdx = ii * TILE_3 + jj; - const int keyIdx = int((ni + ki * dilation - sni) / dilation) * KTILE_3 + - int((nj + kj * dilation - snj) / dilation); + auto updt = HalfHelper::zeros(); + int32_t queryIdx = ii * TILE_3 + jj; + int32_t keyIdx = + int32_t((ni + ki * dilation - sni) / dilation) * KTILE_3 + + int32_t((nj + kj * dilation - snj) / dilation); #pragma unroll - for (int dimOffset = 0; dimOffset < DIMHALF_32; ++dimOffset) + for (int32_t dimOffset = 0; dimOffset < DIMHALF_32; ++dimOffset) updt = HalfHelper::fma( tile[queryIdx][dimOffset], kTile[keyIdx][dimOffset], updt); - const int64_t index = b * p.attn_stride_0 + h * p.attn_stride_1 + + int64_t index = b * p.attn_stride_0 + h * p.attn_stride_1 + i * p.attn_stride_2 + j * p.attn_stride_3 + ki * KERNEL_SIZE_3 + kj; scalar_t acc = HalfHelper::cast_back(HalfHelper::add(updt.x, updt.y)); if (p.bias) { - const int pi = get_pb_start( + int32_t pi = get_pb_start( i, p.height, KERNEL_SIZE_3, NEIGHBORHOOD_SIZE_3, dilation); - const int pj = get_pb_start( + int32_t pj = get_pb_start( j, p.width, KERNEL_SIZE_3, NEIGHBORHOOD_SIZE_3, dilation); - const int64_t biasIndex = + int64_t biasIndex = h * p.bias_stride_0 + (pi + ki) * p.bias_stride_1 + (pj + kj); acc = HalfHelper::add(acc, p.bias[biasIndex]); } @@ -286,16 +282,16 @@ struct PointwiseNeighborhood2DHalf3x3 : PointwiseNeighborhood2DBase { } static LaunchParams get_launch_params( - int batch_dim, - int height, - int width, - int attention_span, - int dilation) { - int xsize = width * KERNEL_SIZE_3; - int ysize = height * KERNEL_SIZE_3; - int XTHREADS = XYTHREADS_3; - int YTHREADS = XYTHREADS_3; - int BATCHTHREADS = BATCHTHREADS_3; + int32_t batch_dim, + int32_t height, + int32_t width, + int32_t attention_span, + int32_t dilation) { + int32_t xsize = width * KERNEL_SIZE_3; + int32_t ysize = height * KERNEL_SIZE_3; + int32_t XTHREADS = XYTHREADS_3; + int32_t YTHREADS = XYTHREADS_3; + int32_t BATCHTHREADS = BATCHTHREADS_3; const dim3 grid( (xsize + XTHREADS * dilation - 1) / XTHREADS, (ysize + YTHREADS * dilation - 1) / YTHREADS, diff --git a/csrc/include/natten/cuda/naive/tiled/pointwise_neighborhood_2d_tiled_5x5.cuh b/csrc/include/natten/cuda/naive/tiled/pointwise_neighborhood_2d_tiled_5x5.cuh index 3063bec..8a3a63d 100644 --- a/csrc/include/natten/cuda/naive/tiled/pointwise_neighborhood_2d_tiled_5x5.cuh +++ b/csrc/include/natten/cuda/naive/tiled/pointwise_neighborhood_2d_tiled_5x5.cuh @@ -50,7 +50,7 @@ namespace naive { /////////////////////////////////////////////////////////////////////////////// /////////////// 5x5 -template +template struct PointwiseNeighborhood2DFull5x5 : PointwiseNeighborhood2DBase { using Base = PointwiseNeighborhood2DBase; using Params = typename Base::Params; @@ -60,40 +60,37 @@ struct PointwiseNeighborhood2DFull5x5 : PointwiseNeighborhood2DBase { __device__ __host__ PointwiseNeighborhood2DFull5x5() : Base() {} - static __host__ int get_dim(int dim) { - return 32; - } - __device__ void launch(Params p) { - const int dilation = (DILATION > 0) ? DILATION : p.dilation_in; // Because batch heads have stride 1 per threadblock, we can just use // blockIdx since blockDim will be 1 and threadIdx will always be 0. const // int z = blockIdx.z * blockDim.z + threadIdx.z; - const int z = blockIdx.z; - const int b = z / p.heads; - const int h = z - b * p.heads; + // TODO: remove when/if tiled kernels allow varying dilations + auto dilation = p.dilation_0; + int z = blockIdx.z; + int b = z / p.heads; + int h = z - b * p.heads; // Not needed again because it will always be true. // if (z < batch_size * heads) // { - const int lti = threadIdx.y * (TILE_5 * KERNEL_SIZE_5) + threadIdx.x; - const int64_t batchHeadOffset = b * p.query_stride_0 + h * p.query_stride_1; - const int si = int(blockIdx.y / dilation) * (TILE_5 * dilation) + + int lti = threadIdx.y * (TILE_5 * KERNEL_SIZE_5) + threadIdx.x; + int64_t batchHeadOffset = b * p.query_stride_0 + h * p.query_stride_1; + int si = int(blockIdx.y / dilation) * (TILE_5 * dilation) + (blockIdx.y % dilation); - const int sj = int(blockIdx.x / dilation) * (TILE_5 * dilation) + + int sj = int(blockIdx.x / dilation) * (TILE_5 * dilation) + (blockIdx.x % dilation); - const int sni = get_window_start( + int sni = get_window_start( si, p.height, KERNEL_SIZE_5, NEIGHBORHOOD_SIZE_5, dilation); - const int snj = get_window_start( + int snj = get_window_start( sj, p.width, KERNEL_SIZE_5, NEIGHBORHOOD_SIZE_5, dilation); __shared__ scalar_t tile[TILE_5 * TILE_5][DIM_32 + 3]; __shared__ scalar_t kTile[KTILE_5 * KTILE_5][DIM_32 + 3]; /* query tile */ - const int qtx = lti / QSTRIDE_5; - const int qty = (lti - qtx * QSTRIDE_5) * QITERS_5; + int qtx = lti / QSTRIDE_5; + int qty = (lti - qtx * QSTRIDE_5) * QITERS_5; if (qtx < TILE_5 * TILE_5) { int qi = qtx / TILE_5; - const int qj = (qtx - qi * TILE_5) * dilation + sj; + int qj = (qtx - qi * TILE_5) * dilation + sj; qi = qi * dilation + si; if (qi < p.height && qj < p.width) { #pragma unroll @@ -104,14 +101,14 @@ struct PointwiseNeighborhood2DFull5x5 : PointwiseNeighborhood2DBase { } } /* key tile */ - const int ktx = lti / KSTRIDE_32; - const int kty = (lti - ktx * KSTRIDE_32) * KITERS_32; + int ktx = lti / KSTRIDE_32; + int kty = (lti - ktx * KSTRIDE_32) * KITERS_32; if (ktx < KTILE_5 * KTILE_5) { int bi = ktx / KTILE_5; - const int bj = (ktx - bi * KTILE_5) * dilation + snj; + int bj = (ktx - bi * KTILE_5) * dilation + snj; bi = bi * dilation + sni; if (bi < p.height && bj < p.width) { - const int64_t keyOffset = batchHeadOffset + bi * p.query_stride_2 + + int64_t keyOffset = batchHeadOffset + bi * p.query_stride_2 + bj * p.query_stride_3 + kty; #pragma unroll for (int ti = 0; ti < KITERS_32; ++ti) @@ -119,33 +116,33 @@ struct PointwiseNeighborhood2DFull5x5 : PointwiseNeighborhood2DBase { } } __syncthreads(); - const int ii = threadIdx.y / KERNEL_SIZE_5; - const int ki = threadIdx.y - ii * KERNEL_SIZE_5; - const int jj = threadIdx.x / KERNEL_SIZE_5; - const int kj = threadIdx.x - jj * KERNEL_SIZE_5; - const int i = si + ii * dilation, j = sj + jj * dilation; + int ii = threadIdx.y / KERNEL_SIZE_5; + int ki = threadIdx.y - ii * KERNEL_SIZE_5; + int jj = threadIdx.x / KERNEL_SIZE_5; + int kj = threadIdx.x - jj * KERNEL_SIZE_5; + int i = si + ii * dilation, j = sj + jj * dilation; if (i < p.height && j < p.width) { - const int ni = get_window_start( + int ni = get_window_start( i, p.height, KERNEL_SIZE_5, NEIGHBORHOOD_SIZE_5, dilation); - const int nj = get_window_start( + int nj = get_window_start( j, p.width, KERNEL_SIZE_5, NEIGHBORHOOD_SIZE_5, dilation); scalar_t updt = scalar_t(0); - const int queryIdx = ii * TILE_5 + jj; - const int keyIdx = int((ni + ki * dilation - sni) / dilation) * KTILE_5 + + int queryIdx = ii * TILE_5 + jj; + int keyIdx = int((ni + ki * dilation - sni) / dilation) * KTILE_5 + int((nj + kj * dilation - snj) / dilation); #pragma unroll for (int dimOffset = 0; dimOffset < DIM_32; ++dimOffset) updt += tile[queryIdx][dimOffset] * kTile[keyIdx][dimOffset]; - const int64_t index = b * p.attn_stride_0 + h * p.attn_stride_1 + + int64_t index = b * p.attn_stride_0 + h * p.attn_stride_1 + i * p.attn_stride_2 + j * p.attn_stride_3 + ki * KERNEL_SIZE_5 + kj; if (p.bias) { - const int pi = get_pb_start( + int pi = get_pb_start( i, p.height, KERNEL_SIZE_5, NEIGHBORHOOD_SIZE_5, dilation); - const int pj = get_pb_start( + int pj = get_pb_start( j, p.width, KERNEL_SIZE_5, NEIGHBORHOOD_SIZE_5, dilation); - const int64_t biasIndex = + int64_t biasIndex = h * p.bias_stride_0 + (pi + ki) * p.bias_stride_1 + (pj + kj); updt += p.bias[biasIndex]; } @@ -174,7 +171,7 @@ struct PointwiseNeighborhood2DFull5x5 : PointwiseNeighborhood2DBase { } }; -template +template struct PointwiseNeighborhood2DHalf5x5 : PointwiseNeighborhood2DBase { using Base = PointwiseNeighborhood2DBase; using Params = typename Base::Params; @@ -187,31 +184,28 @@ struct PointwiseNeighborhood2DHalf5x5 : PointwiseNeighborhood2DBase { using HalfHelper = typename HalfArray::Base; using HalfType = typename HalfHelper::ElementVector; - static __host__ int get_dim(int dim) { - return 16; - } - __device__ void launch(Params p) { - const int dilation = (DILATION > 0) ? DILATION : p.dilation_in; // Because batch heads have stride 1 per threadblock, we can just use // blockIdx since blockDim will be 1 and threadIdx will always be 0. const // int z = blockIdx.z * blockDim.z + threadIdx.z; - const int z = blockIdx.z; - const int b = z / p.heads; - const int h = z - b * p.heads; + // TODO: remove when/if tiled kernels allow varying dilations + auto dilation = p.dilation_0; + int z = blockIdx.z; + int b = z / p.heads; + int h = z - b * p.heads; // Not needed again because it will always be true. // if (z < batch_size * heads) // { - const int lti = threadIdx.y * (TILE_5 * KERNEL_SIZE_5) + threadIdx.x; - const int stride2 = DIMHALF_32 * p.width; - const int64_t batchHeadOffset = b * p.query_stride_0 + h * p.query_stride_1; - const int si = int(blockIdx.y / dilation) * (TILE_5 * dilation) + + int lti = threadIdx.y * (TILE_5 * KERNEL_SIZE_5) + threadIdx.x; + int stride2 = DIMHALF_32 * p.width; + int64_t batchHeadOffset = b * p.query_stride_0 + h * p.query_stride_1; + int si = int(blockIdx.y / dilation) * (TILE_5 * dilation) + (blockIdx.y % dilation); - const int sj = int(blockIdx.x / dilation) * (TILE_5 * dilation) + + int sj = int(blockIdx.x / dilation) * (TILE_5 * dilation) + (blockIdx.x % dilation); - const int sni = get_window_start( + int sni = get_window_start( si, p.height, KERNEL_SIZE_5, NEIGHBORHOOD_SIZE_5, dilation); - const int snj = get_window_start( + int snj = get_window_start( sj, p.width, KERNEL_SIZE_5, NEIGHBORHOOD_SIZE_5, dilation); __shared__ HalfType tile[TILE_5 * TILE_5][DIM_32 + 3]; __shared__ HalfType kTile[KTILE_5 * KTILE_5][DIM_32 + 3]; @@ -219,11 +213,11 @@ struct PointwiseNeighborhood2DHalf5x5 : PointwiseNeighborhood2DBase { auto key2 = HalfHelper::typecast(p.key); /* query tile */ - const int qtx = lti / DIMHALF_32; - const int qty = lti - qtx * DIMHALF_32; + int qtx = lti / DIMHALF_32; + int qty = lti - qtx * DIMHALF_32; if (qtx < TILE_5 * TILE_5) { int qi = qtx / TILE_5; - const int qj = (qtx - qi * TILE_5) * dilation + sj; + int qj = (qtx - qi * TILE_5) * dilation + sj; qi = qi * dilation + si; if (qi < p.height && qj < p.width) { tile[qtx][qty] = @@ -231,14 +225,14 @@ struct PointwiseNeighborhood2DHalf5x5 : PointwiseNeighborhood2DBase { } } /* key tile */ - const int ktx = lti / KSTRIDE_32; - const int kty = (lti - ktx * KSTRIDE_32) * KHALFITERS_32; + int ktx = lti / KSTRIDE_32; + int kty = (lti - ktx * KSTRIDE_32) * KHALFITERS_32; if (ktx < KTILE_5 * KTILE_5) { int bi = ktx / KTILE_5; - const int bj = (ktx - bi * KTILE_5) * dilation + snj; + int bj = (ktx - bi * KTILE_5) * dilation + snj; bi = bi * dilation + sni; if (bi < p.height && bj < p.width) { - const int64_t keyOffset = + int64_t keyOffset = batchHeadOffset + bi * stride2 + bj * DIMHALF_32 + kty; #pragma unroll for (int ti = 0; ti < KHALFITERS_32; ++ti) @@ -246,19 +240,19 @@ struct PointwiseNeighborhood2DHalf5x5 : PointwiseNeighborhood2DBase { } } __syncthreads(); - const int ii = threadIdx.y / KERNEL_SIZE_5; - const int ki = threadIdx.y - ii * KERNEL_SIZE_5; - const int jj = threadIdx.x / KERNEL_SIZE_5; - const int kj = threadIdx.x - jj * KERNEL_SIZE_5; - const int i = si + ii * dilation, j = sj + jj * dilation; + int ii = threadIdx.y / KERNEL_SIZE_5; + int ki = threadIdx.y - ii * KERNEL_SIZE_5; + int jj = threadIdx.x / KERNEL_SIZE_5; + int kj = threadIdx.x - jj * KERNEL_SIZE_5; + int i = si + ii * dilation, j = sj + jj * dilation; if (i < p.height && j < p.width) { - const int ni = get_window_start( + int ni = get_window_start( i, p.height, KERNEL_SIZE_5, NEIGHBORHOOD_SIZE_5, dilation); - const int nj = get_window_start( + int nj = get_window_start( j, p.width, KERNEL_SIZE_5, NEIGHBORHOOD_SIZE_5, dilation); - auto updt = HalfHelper::zero(); - const int queryIdx = ii * TILE_5 + jj; - const int keyIdx = int((ni + ki * dilation - sni) / dilation) * KTILE_5 + + auto updt = HalfHelper::zeros(); + int queryIdx = ii * TILE_5 + jj; + int keyIdx = int((ni + ki * dilation - sni) / dilation) * KTILE_5 + int((nj + kj * dilation - snj) / dilation); #pragma unroll @@ -266,15 +260,15 @@ struct PointwiseNeighborhood2DHalf5x5 : PointwiseNeighborhood2DBase { updt = HalfHelper::fma( tile[queryIdx][dimOffset], kTile[keyIdx][dimOffset], updt); - const int64_t index = b * p.attn_stride_0 + h * p.attn_stride_1 + + int64_t index = b * p.attn_stride_0 + h * p.attn_stride_1 + i * p.attn_stride_2 + j * p.attn_stride_3 + ki * KERNEL_SIZE_5 + kj; scalar_t acc = HalfHelper::cast_back(HalfHelper::add(updt.x, updt.y)); if (p.bias) { - const int pi = get_pb_start( + int pi = get_pb_start( i, p.height, KERNEL_SIZE_5, NEIGHBORHOOD_SIZE_5, dilation); - const int pj = get_pb_start( + int pj = get_pb_start( j, p.width, KERNEL_SIZE_5, NEIGHBORHOOD_SIZE_5, dilation); - const int64_t biasIndex = + int64_t biasIndex = h * p.bias_stride_0 + (pi + ki) * p.bias_stride_1 + (pj + kj); acc = HalfHelper::add(acc, p.bias[biasIndex]); } diff --git a/csrc/include/natten/cuda/naive/tiled/pointwise_neighborhood_2d_tiled_7x7_9x9.cuh b/csrc/include/natten/cuda/naive/tiled/pointwise_neighborhood_2d_tiled_7x7_9x9.cuh index 9ddf2f6..0386c8b 100644 --- a/csrc/include/natten/cuda/naive/tiled/pointwise_neighborhood_2d_tiled_7x7_9x9.cuh +++ b/csrc/include/natten/cuda/naive/tiled/pointwise_neighborhood_2d_tiled_7x7_9x9.cuh @@ -52,13 +52,12 @@ namespace naive { /////////////// 7x7 and 9x9 template < typename scalar_t, - int TILE, - int KTILE, - int XYTHREADS, - int BATCHTHREADS, - int KERNEL_SIZE, - int NEIGHBORHOOD_SIZE, - int DILATION> + int32_t TILE, + int32_t KTILE, + int32_t XYTHREADS, + int32_t BATCHTHREADS, + int32_t KERNEL_SIZE, + int32_t NEIGHBORHOOD_SIZE> struct PointwiseNeighborhood2DFull7x7_9x9 : PointwiseNeighborhood2DBase { using Base = PointwiseNeighborhood2DBase; @@ -69,40 +68,37 @@ struct PointwiseNeighborhood2DFull7x7_9x9 __device__ __host__ PointwiseNeighborhood2DFull7x7_9x9() : Base() {} - static __host__ int get_dim(int dim) { - return 32; - } - __device__ void launch(Params p) { - const int dilation = (DILATION > 0) ? DILATION : p.dilation_in; // Because batch heads have stride 1 per threadblock, we can just use // blockIdx since blockDim will be 1 and threadIdx will always be 0. const - // int z = blockIdx.z * blockDim.z + threadIdx.z; - const int z = blockIdx.z; - const int b = z / p.heads; - const int h = z - b * p.heads; + // int32_t z = blockIdx.z * blockDim.z + threadIdx.z; + // TODO: remove when/if tiled kernels allow varying dilations + auto dilation = p.dilation_0; + int32_t z = blockIdx.z; + int32_t b = z / p.heads; + int32_t h = z - b * p.heads; // Not needed again because it will always be true. // if (z < batch_size * heads) // { - const int lti = threadIdx.y * (TILE * KERNEL_SIZE) + threadIdx.x; - const int64_t batchHeadOffset = b * p.query_stride_0 + h * p.query_stride_1; - const int si = int(blockIdx.y / dilation) * (TILE * dilation) + + int32_t lti = threadIdx.y * (TILE * KERNEL_SIZE) + threadIdx.x; + int64_t batchHeadOffset = b * p.query_stride_0 + h * p.query_stride_1; + int32_t si = int32_t(blockIdx.y / dilation) * (TILE * dilation) + (blockIdx.y % dilation); - const int sj = int(blockIdx.x / dilation) * (TILE * dilation) + + int32_t sj = int32_t(blockIdx.x / dilation) * (TILE * dilation) + (blockIdx.x % dilation); - const int sni = get_window_start( + int32_t sni = get_window_start( si, p.height, KERNEL_SIZE, NEIGHBORHOOD_SIZE, dilation); - const int snj = + int32_t snj = get_window_start(sj, p.width, KERNEL_SIZE, NEIGHBORHOOD_SIZE, dilation); __shared__ scalar_t tile[TILE * TILE][DIM_32 + 3]; __shared__ scalar_t kTile[KTILE * KTILE][DIM_32 + 3]; /* query tile */ - const int qtx = lti / DIM_32; - const int qty = lti - qtx * DIM_32; + int32_t qtx = lti / DIM_32; + int32_t qty = lti - qtx * DIM_32; if (qtx < TILE * TILE) { - int qi = qtx / TILE; - const int qj = (qtx - qi * TILE) * dilation + sj; + int32_t qi = qtx / TILE; + int32_t qj = (qtx - qi * TILE) * dilation + sj; qi = qi * dilation + si; if (qi < p.height && qj < p.width) tile[qtx][qty] = p.query @@ -110,48 +106,48 @@ struct PointwiseNeighborhood2DFull7x7_9x9 qj * p.query_stride_3 + qty]; } /* key tile */ - const int ktx = lti / KSTRIDE_32; - const int kty = (lti - ktx * KSTRIDE_32) * KITERS_32; + int32_t ktx = lti / KSTRIDE_32; + int32_t kty = (lti - ktx * KSTRIDE_32) * KITERS_32; if (ktx < KTILE * KTILE) { - int bi = ktx / KTILE; - const int bj = (ktx - bi * KTILE) * dilation + snj; + int32_t bi = ktx / KTILE; + int32_t bj = (ktx - bi * KTILE) * dilation + snj; bi = bi * dilation + sni; if (bi < p.height && bj < p.width) { - const int64_t keyOffset = batchHeadOffset + bi * p.query_stride_2 + + int64_t keyOffset = batchHeadOffset + bi * p.query_stride_2 + bj * p.query_stride_3 + kty; #pragma unroll - for (int ti = 0; ti < KITERS_32; ++ti) + for (int32_t ti = 0; ti < KITERS_32; ++ti) kTile[ktx][kty + ti] = p.key[keyOffset + ti]; } } __syncthreads(); - const int ii = threadIdx.y / KERNEL_SIZE; - const int ki = threadIdx.y - ii * KERNEL_SIZE; - const int jj = threadIdx.x / KERNEL_SIZE; - const int kj = threadIdx.x - jj * KERNEL_SIZE; - const int i = si + ii * dilation, j = sj + jj * dilation; + int32_t ii = threadIdx.y / KERNEL_SIZE; + int32_t ki = threadIdx.y - ii * KERNEL_SIZE; + int32_t jj = threadIdx.x / KERNEL_SIZE; + int32_t kj = threadIdx.x - jj * KERNEL_SIZE; + int32_t i = si + ii * dilation, j = sj + jj * dilation; if (i < p.height && j < p.width) { - const int ni = get_window_start( + int32_t ni = get_window_start( i, p.height, KERNEL_SIZE, NEIGHBORHOOD_SIZE, dilation); - const int nj = get_window_start( + int32_t nj = get_window_start( j, p.width, KERNEL_SIZE, NEIGHBORHOOD_SIZE, dilation); scalar_t updt = scalar_t(0); - const int queryIdx = ii * TILE + jj; - const int keyIdx = int((ni + ki * dilation - sni) / dilation) * KTILE + - int((nj + kj * dilation - snj) / dilation); + int32_t queryIdx = ii * TILE + jj; + int32_t keyIdx = int32_t((ni + ki * dilation - sni) / dilation) * KTILE + + int32_t((nj + kj * dilation - snj) / dilation); #pragma unroll - for (int dimOffset = 0; dimOffset < DIM_32; ++dimOffset) + for (int32_t dimOffset = 0; dimOffset < DIM_32; ++dimOffset) updt += tile[queryIdx][dimOffset] * kTile[keyIdx][dimOffset]; - const int64_t index = b * p.attn_stride_0 + h * p.attn_stride_1 + + int64_t index = b * p.attn_stride_0 + h * p.attn_stride_1 + i * p.attn_stride_2 + j * p.attn_stride_3 + ki * KERNEL_SIZE + kj; if (p.bias) { - const int pi = + int32_t pi = get_pb_start(i, p.height, KERNEL_SIZE, NEIGHBORHOOD_SIZE, dilation); - const int pj = + int32_t pj = get_pb_start(j, p.width, KERNEL_SIZE, NEIGHBORHOOD_SIZE, dilation); - const int64_t biasIndex = + int64_t biasIndex = h * p.bias_stride_0 + (pi + ki) * p.bias_stride_1 + (pj + kj); updt += p.bias[biasIndex]; } @@ -161,15 +157,15 @@ struct PointwiseNeighborhood2DFull7x7_9x9 } static LaunchParams get_launch_params( - int batch_dim, - int height, - int width, - int attention_span, - int dilation) { - int xsize = width * KERNEL_SIZE; - int ysize = height * KERNEL_SIZE; - int XTHREADS = XYTHREADS; - int YTHREADS = XYTHREADS; + int32_t batch_dim, + int32_t height, + int32_t width, + int32_t attention_span, + int32_t dilation) { + int32_t xsize = width * KERNEL_SIZE; + int32_t ysize = height * KERNEL_SIZE; + int32_t XTHREADS = XYTHREADS; + int32_t YTHREADS = XYTHREADS; const dim3 grid( (xsize + XTHREADS * dilation - 1) / XTHREADS, (ysize + YTHREADS * dilation - 1) / YTHREADS, @@ -181,13 +177,12 @@ struct PointwiseNeighborhood2DFull7x7_9x9 template < typename scalar_t, - int TILE, - int KTILE, - int XYTHREADS, - int BATCHTHREADS, - int KERNEL_SIZE, - int NEIGHBORHOOD_SIZE, - int DILATION> + int32_t TILE, + int32_t KTILE, + int32_t XYTHREADS, + int32_t BATCHTHREADS, + int32_t KERNEL_SIZE, + int32_t NEIGHBORHOOD_SIZE> struct PointwiseNeighborhood2DHalf7x7_9x9 : PointwiseNeighborhood2DBase { using Base = PointwiseNeighborhood2DBase; @@ -201,31 +196,28 @@ struct PointwiseNeighborhood2DHalf7x7_9x9 using HalfHelper = typename HalfArray::Base; using HalfType = typename HalfHelper::ElementVector; - static __host__ int get_dim(int dim) { - return 16; - } - __device__ void launch(Params p) { - const int dilation = (DILATION > 0) ? DILATION : p.dilation_in; // Because batch heads have stride 1 per threadblock, we can just use // blockIdx since blockDim will be 1 and threadIdx will always be 0. const - // int z = blockIdx.z * blockDim.z + threadIdx.z; - const int z = blockIdx.z; - const int b = z / p.heads; - const int h = z - b * p.heads; + // int32_t z = blockIdx.z * blockDim.z + threadIdx.z; + // TODO: remove when/if tiled kernels allow varying dilations + auto dilation = p.dilation_0; + int32_t z = blockIdx.z; + int32_t b = z / p.heads; + int32_t h = z - b * p.heads; // Not needed again because it will always be true. // if (z < batch_size * heads) // { - const int lti = threadIdx.y * (TILE * KERNEL_SIZE) + threadIdx.x; - const int stride2 = DIMHALF_32 * p.width; - const int64_t batchHeadOffset = b * p.query_stride_0 + h * p.query_stride_1; - const int si = int(blockIdx.y / dilation) * (TILE * dilation) + + int32_t lti = threadIdx.y * (TILE * KERNEL_SIZE) + threadIdx.x; + int32_t stride2 = DIMHALF_32 * p.width; + int64_t batchHeadOffset = b * p.query_stride_0 + h * p.query_stride_1; + int32_t si = int32_t(blockIdx.y / dilation) * (TILE * dilation) + (blockIdx.y % dilation); - const int sj = int(blockIdx.x / dilation) * (TILE * dilation) + + int32_t sj = int32_t(blockIdx.x / dilation) * (TILE * dilation) + (blockIdx.x % dilation); - const int sni = get_window_start( + int32_t sni = get_window_start( si, p.height, KERNEL_SIZE, NEIGHBORHOOD_SIZE, dilation); - const int snj = + int32_t snj = get_window_start(sj, p.width, KERNEL_SIZE, NEIGHBORHOOD_SIZE, dilation); __shared__ HalfType tile[TILE * TILE][DIM_32 + 3]; __shared__ HalfType kTile[KTILE * KTILE][DIM_32 + 3]; @@ -233,68 +225,68 @@ struct PointwiseNeighborhood2DHalf7x7_9x9 auto key2 = HalfHelper::typecast(p.key); /* query tile */ - const int qtx = lti / DIM_32; - const int qtyp = lti - qtx * DIM_32; - const int qdi = qtyp / KHALFITERS_32; - const int qdj = qtyp - qdi * KHALFITERS_32; - const int qty = qdi * KITERS_32 + qdj; + int32_t qtx = lti / DIM_32; + int32_t qtyp = lti - qtx * DIM_32; + int32_t qdi = qtyp / KHALFITERS_32; + int32_t qdj = qtyp - qdi * KHALFITERS_32; + int32_t qty = qdi * KITERS_32 + qdj; if (qtx < TILE * TILE && qtyp < DIMHALF_32) { - int qi = qtx / TILE; - const int qj = (qtx - qi * TILE) * dilation + sj; + int32_t qi = qtx / TILE; + int32_t qj = (qtx - qi * TILE) * dilation + sj; qi = qi * dilation + si; if (qi < p.height && qj < p.width) tile[qtx][qty] = query2[batchHeadOffset + qi * stride2 + qj * DIMHALF_32 + qtyp]; } /* key tile */ - const int ktx = lti / KSTRIDE_32; - const int kty = (lti - ktx * KSTRIDE_32) * KHALFITERS_32; + int32_t ktx = lti / KSTRIDE_32; + int32_t kty = (lti - ktx * KSTRIDE_32) * KHALFITERS_32; if (ktx < KTILE * KTILE) { - int bi = ktx / KTILE; - const int bj = (ktx - bi * KTILE) * dilation + snj; + int32_t bi = ktx / KTILE; + int32_t bj = (ktx - bi * KTILE) * dilation + snj; bi = bi * dilation + sni; if (bi < p.height && bj < p.width) { - const int64_t keyOffset = + int64_t keyOffset = batchHeadOffset + bi * stride2 + bj * DIMHALF_32 + kty; #pragma unroll - for (int ti = 0; ti < KHALFITERS_32; ++ti) + for (int32_t ti = 0; ti < KHALFITERS_32; ++ti) kTile[ktx][kty * 2 + ti] = key2[keyOffset + ti]; } } __syncthreads(); - const int ii = threadIdx.y / KERNEL_SIZE; - const int ki = threadIdx.y - ii * KERNEL_SIZE; - const int jj = threadIdx.x / KERNEL_SIZE; - const int kj = threadIdx.x - jj * KERNEL_SIZE; - const int i = si + ii * dilation, j = sj + jj * dilation; + int32_t ii = threadIdx.y / KERNEL_SIZE; + int32_t ki = threadIdx.y - ii * KERNEL_SIZE; + int32_t jj = threadIdx.x / KERNEL_SIZE; + int32_t kj = threadIdx.x - jj * KERNEL_SIZE; + int32_t i = si + ii * dilation, j = sj + jj * dilation; if (i < p.height && j < p.width) { - const int ni = get_window_start( + int32_t ni = get_window_start( i, p.height, KERNEL_SIZE, NEIGHBORHOOD_SIZE, dilation); - const int nj = get_window_start( + int32_t nj = get_window_start( j, p.width, KERNEL_SIZE, NEIGHBORHOOD_SIZE, dilation); - auto updt = HalfHelper::zero(); - const int queryIdx = ii * TILE + jj; - const int keyIdx = int((ni + ki * dilation - sni) / dilation) * KTILE + - int((nj + kj * dilation - snj) / dilation); + auto updt = HalfHelper::zeros(); + int32_t queryIdx = ii * TILE + jj; + int32_t keyIdx = int32_t((ni + ki * dilation - sni) / dilation) * KTILE + + int32_t((nj + kj * dilation - snj) / dilation); #pragma unroll - for (int di = 0; di < KSTRIDE_32; ++di) + for (int32_t di = 0; di < KSTRIDE_32; ++di) #pragma unroll - for (int dj = 0; dj < KHALFITERS_32; ++dj) + for (int32_t dj = 0; dj < KHALFITERS_32; ++dj) updt = HalfHelper::fma( tile[queryIdx][di * KITERS_32 + dj], kTile[keyIdx][di * KITERS_32 + dj], updt); - const int64_t index = b * p.attn_stride_0 + h * p.attn_stride_1 + + int64_t index = b * p.attn_stride_0 + h * p.attn_stride_1 + i * p.attn_stride_2 + j * p.attn_stride_3 + ki * KERNEL_SIZE + kj; scalar_t acc = HalfHelper::cast_back(HalfHelper::add(updt.x, updt.y)); if (p.bias) { - const int pi = + int32_t pi = get_pb_start(i, p.height, KERNEL_SIZE, NEIGHBORHOOD_SIZE, dilation); - const int pj = + int32_t pj = get_pb_start(j, p.width, KERNEL_SIZE, NEIGHBORHOOD_SIZE, dilation); - const int64_t biasIndex = + int64_t biasIndex = h * p.bias_stride_0 + (pi + ki) * p.bias_stride_1 + (pj + kj); acc = HalfHelper::add(acc, p.bias[biasIndex]); } @@ -304,15 +296,15 @@ struct PointwiseNeighborhood2DHalf7x7_9x9 } static LaunchParams get_launch_params( - int batch_dim, - int height, - int width, - int attention_span, - int dilation) { - int xsize = width * KERNEL_SIZE; - int ysize = height * KERNEL_SIZE; - int XTHREADS = XYTHREADS; - int YTHREADS = XYTHREADS; + int32_t batch_dim, + int32_t height, + int32_t width, + int32_t attention_span, + int32_t dilation) { + int32_t xsize = width * KERNEL_SIZE; + int32_t ysize = height * KERNEL_SIZE; + int32_t XTHREADS = XYTHREADS; + int32_t YTHREADS = XYTHREADS; const dim3 grid( (xsize + XTHREADS * dilation - 1) / XTHREADS, (ysize + YTHREADS * dilation - 1) / YTHREADS, @@ -322,7 +314,7 @@ struct PointwiseNeighborhood2DHalf7x7_9x9 } }; -template +template struct PointwiseNeighborhood2DFull7x7 : PointwiseNeighborhood2DFull7x7_9x9< scalar_t, TILE_7, @@ -330,8 +322,7 @@ struct PointwiseNeighborhood2DFull7x7 : PointwiseNeighborhood2DFull7x7_9x9< XYTHREADS_7, BATCHTHREADS_7, 7, - 3, - DILATION> { + 3> { using Base = PointwiseNeighborhood2DFull7x7_9x9< scalar_t, TILE_7, @@ -339,13 +330,12 @@ struct PointwiseNeighborhood2DFull7x7 : PointwiseNeighborhood2DFull7x7_9x9< XYTHREADS_7, BATCHTHREADS_7, 7, - 3, - DILATION>; + 3>; __device__ __host__ PointwiseNeighborhood2DFull7x7() : Base() {} }; -template +template struct PointwiseNeighborhood2DHalf7x7 : PointwiseNeighborhood2DHalf7x7_9x9< scalar_t, TILE_7, @@ -353,8 +343,7 @@ struct PointwiseNeighborhood2DHalf7x7 : PointwiseNeighborhood2DHalf7x7_9x9< XYTHREADS_7, BATCHTHREADS_7, 7, - 3, - DILATION> { + 3> { using Base = PointwiseNeighborhood2DHalf7x7_9x9< scalar_t, TILE_7, @@ -362,13 +351,12 @@ struct PointwiseNeighborhood2DHalf7x7 : PointwiseNeighborhood2DHalf7x7_9x9< XYTHREADS_7, BATCHTHREADS_7, 7, - 3, - DILATION>; + 3>; __device__ __host__ PointwiseNeighborhood2DHalf7x7() : Base() {} }; -template +template struct PointwiseNeighborhood2DFull9x9 : PointwiseNeighborhood2DFull7x7_9x9< scalar_t, TILE_9, @@ -376,8 +364,7 @@ struct PointwiseNeighborhood2DFull9x9 : PointwiseNeighborhood2DFull7x7_9x9< XYTHREADS_9, BATCHTHREADS_9, 9, - 4, - DILATION> { + 4> { using Base = PointwiseNeighborhood2DFull7x7_9x9< scalar_t, TILE_9, @@ -385,13 +372,12 @@ struct PointwiseNeighborhood2DFull9x9 : PointwiseNeighborhood2DFull7x7_9x9< XYTHREADS_9, BATCHTHREADS_9, 9, - 4, - DILATION>; + 4>; __device__ __host__ PointwiseNeighborhood2DFull9x9() : Base() {} }; -template +template struct PointwiseNeighborhood2DHalf9x9 : PointwiseNeighborhood2DHalf7x7_9x9< scalar_t, TILE_9, @@ -399,8 +385,7 @@ struct PointwiseNeighborhood2DHalf9x9 : PointwiseNeighborhood2DHalf7x7_9x9< XYTHREADS_9, BATCHTHREADS_9, 9, - 4, - DILATION> { + 4> { using Base = PointwiseNeighborhood2DHalf7x7_9x9< scalar_t, TILE_9, @@ -408,8 +393,7 @@ struct PointwiseNeighborhood2DHalf9x9 : PointwiseNeighborhood2DHalf7x7_9x9< XYTHREADS_9, BATCHTHREADS_9, 9, - 4, - DILATION>; + 4>; __device__ __host__ PointwiseNeighborhood2DHalf9x9() : Base() {} }; diff --git a/csrc/include/natten/cuda/utils/cuda.h b/csrc/include/natten/cuda/utils/cuda.h new file mode 100644 index 0000000..8ce4b17 --- /dev/null +++ b/csrc/include/natten/cuda/utils/cuda.h @@ -0,0 +1,35 @@ +/*************************************************************************************************** + * Copyright (c) 2022-2024 Ali Hassani. + * + * Permission is hereby granted, free of charge, to any person obtaining a copy + * of this software and associated documentation files (the "Software"), to deal + * in the Software without restriction, including without limitation the rights + * to use, copy, modify, merge, publish, distribute, sublicense, and/or sell + * copies of the Software, and to permit persons to whom the Software is + * furnished to do so, subject to the following conditions: + * + * The above copyright notice and this permission notice shall be included in + *all copies or substantial portions of the Software. + * + * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, + * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE + * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER + * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, + * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE + * SOFTWARE. + * + **************************************************************************************************/ + +#pragma once + +#define NATTEN_CUDA_CHECK(status) \ + [&] { \ + cudaError_t error = status; \ + if (error != cudaSuccess) { \ + std::cerr << "NATTEN failure: CUDA runtime error: " \ + << cudaGetErrorString(error) << " at: " << __LINE__ \ + << std::endl; \ + exit(EXIT_FAILURE); \ + } \ + }(); diff --git a/csrc/include/natten/cuda/gemm/utils.cuh b/csrc/include/natten/cuda/utils/cutlass.h similarity index 97% rename from csrc/include/natten/cuda/gemm/utils.cuh rename to csrc/include/natten/cuda/utils/cutlass.h index 19690a5..70872a5 100644 --- a/csrc/include/natten/cuda/gemm/utils.cuh +++ b/csrc/include/natten/cuda/utils/cutlass.h @@ -33,9 +33,11 @@ * **************************************************************************************************/ +#pragma once + #include -#define CUTLASS_CHECK(status) \ +#define NATTEN_CUTLASS_CHECK(status) \ [&] { \ cutlass::Status error = status; \ if (error != cutlass::Status::kSuccess) { \ diff --git a/csrc/include/natten/naive_argpack.h b/csrc/include/natten/naive_argpack.h index 2d421bd..91c9fec 100644 --- a/csrc/include/natten/naive_argpack.h +++ b/csrc/include/natten/naive_argpack.h @@ -26,21 +26,37 @@ namespace natten { namespace naive { -template -struct ArgumentPack { - static constexpr int KernelSize = (KernelSize_ <= 0) ? -1 : KernelSize_; - static constexpr int DepthKernelSize = -1; +// TODO: - static_assert(KernelSize <= 0 || (KernelSize > 1 && KernelSize % 2 == 1)); - - static constexpr int NeighborhoodSize = - (KernelSize <= 0) ? -1 : KernelSize / 2; - static constexpr int DepthNeighborhoodSize = -1; +template +struct ArgumentPack1D { + using Dtype = scalar_t; + struct CausalMask { + static constexpr bool Dim0 = CausalMask0_; + }; +}; - static constexpr int Dilation = (Dilation_ <= 0) ? -1 : Dilation_; - static constexpr int DepthDilation = -1; +template +struct ArgumentPack2D { + using Dtype = scalar_t; + struct CausalMask { + static constexpr bool Dim0 = CausalMask0_; + static constexpr bool Dim1 = CausalMask1_; + }; +}; +template < + typename scalar_t, + bool CausalMask0_, + bool CausalMask1_, + bool CausalMask2_> +struct ArgumentPack3D { using Dtype = scalar_t; + struct CausalMask { + static constexpr bool Dim0 = CausalMask0_; + static constexpr bool Dim1 = CausalMask1_; + static constexpr bool Dim2 = CausalMask2_; + }; }; } // namespace naive diff --git a/csrc/include/natten/natten.h b/csrc/include/natten/natten.h new file mode 100644 index 0000000..cafce5a --- /dev/null +++ b/csrc/include/natten/natten.h @@ -0,0 +1,130 @@ +/*************************************************************************************************** + * Copyright (c) 2022-2024 Ali Hassani. + * + * Permission is hereby granted, free of charge, to any person obtaining a copy + * of this software and associated documentation files (the "Software"), to deal + * in the Software without restriction, including without limitation the rights + * to use, copy, modify, merge, publish, distribute, sublicense, and/or sell + * copies of the Software, and to permit persons to whom the Software is + * furnished to do so, subject to the following conditions: + * + * The above copyright notice and this permission notice shall be included in + *all copies or substantial portions of the Software. + * + * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, + * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE + * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER + * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, + * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE + * SOFTWARE. + * + **************************************************************************************************/ + +#pragma once + +namespace natten { + +#define NATTEN_FAILURE(msg) \ + std::cerr << #msg << std::endl; \ + exit(EXIT_FAILURE); + +#define NATTEN_CHECK(cond, msg) \ + if (!(cond)) { \ + std::cerr << #msg << std::endl; \ + exit(EXIT_FAILURE); \ + } + +// Causal mask helpers +template +bool any_true(TupleType v); + +template <> +inline bool any_true(std::tuple v) { + return std::get<0>(v); +} + +template <> +inline bool any_true(std::tuple v) { + return std::get<0>(v) || std::get<1>(v); +} + +template <> +inline bool any_true(std::tuple v) { + return std::get<0>(v) || std::get<1>(v) || std::get<2>(v); +} + +// Kernel size helpers +template +int32_t flatten(TupleType v); + +template <> +inline int32_t flatten(std::tuple v) { + return std::get<0>(v); +} + +template <> +inline int32_t flatten(std::tuple v) { + return std::get<0>(v) * std::get<1>(v); +} + +template <> +inline int32_t flatten(std::tuple v) { + return std::get<0>(v) * std::get<1>(v) * std::get<2>(v); +} + +template +bool all_dims_match(TupleType v); + +template <> +inline bool all_dims_match(std::tuple v) { + return true; +} + +template <> +inline bool all_dims_match(std::tuple v) { + return std::get<0>(v) == std::get<1>(v); +} + +template <> +inline bool all_dims_match(std::tuple v) { + return std::get<0>(v) == std::get<1>(v) && std::get<1>(v) == std::get<2>(v); +} + +// Runtime accessors + +template +int32_t get_from_tuple(TupleType v, size_t index); + +template <> +inline int32_t get_from_tuple(std::tuple v, size_t index) { + NATTEN_CHECK(index == 0, "Got invalid index for tuple of size 1."); + return std::get<0>(v); +} + +template <> +inline int32_t get_from_tuple(std::tuple v, size_t index) { + NATTEN_CHECK( + index == 0 || index == 1, "Got invalid index for tuple of size 2."); + if (index == 0) { + return std::get<0>(v); + } + return std::get<1>(v); +} + +template <> +inline int32_t get_from_tuple( + std::tuple v, + size_t index) { + NATTEN_CHECK( + index == 0 || index == 1 || index == 2, + "Got invalid index for tuple of size 3."); + if (index == 0) { + return std::get<0>(v); + } else if (index == 1) { + return std::get<1>(v); + } + return std::get<2>(v); +} + +} // namespace natten diff --git a/csrc/include/natten/pytorch/cpu/na1d.h b/csrc/include/natten/pytorch/cpu/na1d.h index ef956d9..bd829eb 100644 --- a/csrc/include/natten/pytorch/cpu/na1d.h +++ b/csrc/include/natten/pytorch/cpu/na1d.h @@ -27,21 +27,41 @@ #pragma once #include +#include + namespace natten { namespace pytorch { namespace cpu { +void na1d_forward( + const at::Tensor& query, + const at::Tensor& key, + const at::Tensor& value, + at::Tensor& out, + const at::optional& rpb, + int32_t batch_size, + int32_t length, + int32_t heads, + int32_t dim, + const std::tuple& kernel_size, + const std::tuple& dilation, + const std::tuple& is_causal, + float attn_scale, + const std::tuple& query_tile_size, + const std::tuple& key_tile_size); + void na1d_qk_forward( const at::Tensor& query, const at::Tensor& key, const at::optional& bias, at::Tensor& attn, - const int batch_size, - const int heads, - const int length, - const int dim, - const int kernel_size, - const int dilation); + int32_t batch_size, + int32_t heads, + int32_t length, + int32_t dim, + const std::tuple& kernel_size, + const std::tuple& dilation, + const std::tuple& is_causal); void na1d_qk_backward( const at::Tensor& d_attn, @@ -50,23 +70,25 @@ void na1d_qk_backward( at::Tensor& d_query, at::Tensor& d_key, at::optional& d_bias, - const int batch_size, - const int heads, - const int length, - const int dim, - const int kernel_size, - const int dilation); + int32_t batch_size, + int32_t heads, + int32_t length, + int32_t dim, + const std::tuple& kernel_size, + const std::tuple& dilation, + const std::tuple& is_causal); void na1d_av_forward( const at::Tensor& attn, const at::Tensor& value, at::Tensor& output, - const int batch_size, - const int heads, - const int length, - const int dim, - const int kernel_size, - const int dilation); + int32_t batch_size, + int32_t heads, + int32_t length, + int32_t dim, + const std::tuple& kernel_size, + const std::tuple& dilation, + const std::tuple& is_causal); void na1d_av_backward( const at::Tensor& d_out, @@ -74,12 +96,13 @@ void na1d_av_backward( const at::Tensor& value, at::Tensor& d_attn, at::Tensor& d_value, - const int batch_size, - const int heads, - const int length, - const int dim, - const int kernel_size, - const int dilation); + int32_t batch_size, + int32_t heads, + int32_t length, + int32_t dim, + const std::tuple& kernel_size, + const std::tuple& dilation, + const std::tuple& is_causal); } // namespace cpu } // namespace pytorch diff --git a/csrc/include/natten/pytorch/cpu/na2d.h b/csrc/include/natten/pytorch/cpu/na2d.h index ea628e7..ee73cb8 100644 --- a/csrc/include/natten/pytorch/cpu/na2d.h +++ b/csrc/include/natten/pytorch/cpu/na2d.h @@ -27,22 +27,43 @@ #pragma once #include +#include + namespace natten { namespace pytorch { namespace cpu { +void na2d_forward( + const at::Tensor& query, + const at::Tensor& key, + const at::Tensor& value, + at::Tensor& out, + const at::optional& rpb, + int32_t batch_size, + int32_t height, + int32_t width, + int32_t heads, + int32_t dim, + const std::tuple& kernel_size, + const std::tuple& dilation, + const std::tuple& is_causal, + float attn_scale, + const std::tuple& query_tile_size, + const std::tuple& key_tile_size); + void na2d_qk_forward( const at::Tensor& query, const at::Tensor& key, const at::optional& bias, at::Tensor& attn, - const int batch_size, - const int heads, - const int height, - const int width, - const int dim, - const int kernel_size, - const int dilation); + int32_t batch_size, + int32_t heads, + int32_t height, + int32_t width, + int32_t dim, + const std::tuple& kernel_size, + const std::tuple& dilation, + const std::tuple& is_causal); void na2d_qk_backward( const at::Tensor& d_attn, @@ -51,25 +72,27 @@ void na2d_qk_backward( at::Tensor& d_query, at::Tensor& d_key, at::optional& d_bias, - const int batch_size, - const int heads, - const int height, - const int width, - const int dim, - const int kernel_size, - const int dilation); + int32_t batch_size, + int32_t heads, + int32_t height, + int32_t width, + int32_t dim, + const std::tuple& kernel_size, + const std::tuple& dilation, + const std::tuple& is_causal); void na2d_av_forward( const at::Tensor& attn, const at::Tensor& value, at::Tensor& output, - const int batch_size, - const int heads, - const int height, - const int width, - const int dim, - const int kernel_size, - const int dilation); + int32_t batch_size, + int32_t heads, + int32_t height, + int32_t width, + int32_t dim, + const std::tuple& kernel_size, + const std::tuple& dilation, + const std::tuple& is_causal); void na2d_av_backward( const at::Tensor& d_out, @@ -77,13 +100,14 @@ void na2d_av_backward( const at::Tensor& value, at::Tensor& d_attn, at::Tensor& d_value, - const int batch_size, - const int heads, - const int height, - const int width, - const int dim, - const int kernel_size, - const int dilation); + int32_t batch_size, + int32_t heads, + int32_t height, + int32_t width, + int32_t dim, + const std::tuple& kernel_size, + const std::tuple& dilation, + const std::tuple& is_causal); } // namespace cpu } // namespace pytorch diff --git a/csrc/include/natten/pytorch/cpu/na3d.h b/csrc/include/natten/pytorch/cpu/na3d.h index 39e23a0..44589fd 100644 --- a/csrc/include/natten/pytorch/cpu/na3d.h +++ b/csrc/include/natten/pytorch/cpu/na3d.h @@ -27,25 +27,45 @@ #pragma once #include +#include + namespace natten { namespace pytorch { namespace cpu { +void na3d_forward( + const at::Tensor& query, + const at::Tensor& key, + const at::Tensor& value, + at::Tensor& out, + const at::optional& rpb, + int32_t batch_size, + int32_t depth, + int32_t height, + int32_t width, + int32_t heads, + int32_t dim, + const std::tuple& kernel_size, + const std::tuple& dilation, + const std::tuple& is_causal, + float attn_scale, + const std::tuple& query_tile_size, + const std::tuple& key_tile_size); + void na3d_qk_forward( const at::Tensor& query, const at::Tensor& key, const at::optional& bias, at::Tensor& attn, - const int batch_size, - const int heads, - const int depth, - const int height, - const int width, - const int dim, - const int kernel_size, - const int dilation, - const int depth_kernel_size, - const int depth_dilation); + int32_t batch_size, + int32_t heads, + int32_t depth, + int32_t height, + int32_t width, + int32_t dim, + const std::tuple& kernel_size, + const std::tuple& dilation, + const std::tuple& is_causal); void na3d_qk_backward( const at::Tensor& d_attn, @@ -54,31 +74,29 @@ void na3d_qk_backward( at::Tensor& d_query, at::Tensor& d_key, at::optional& d_bias, - const int batch_size, - const int heads, - const int depth, - const int height, - const int width, - const int dim, - const int kernel_size, - const int dilation, - const int depth_kernel_size, - const int depth_dilation); + int32_t batch_size, + int32_t heads, + int32_t depth, + int32_t height, + int32_t width, + int32_t dim, + const std::tuple& kernel_size, + const std::tuple& dilation, + const std::tuple& is_causal); void na3d_av_forward( const at::Tensor& attn, const at::Tensor& value, at::Tensor& output, - const int batch_size, - const int heads, - const int depth, - const int height, - const int width, - const int dim, - const int kernel_size, - const int dilation, - const int depth_kernel_size, - const int depth_dilation); + int32_t batch_size, + int32_t heads, + int32_t depth, + int32_t height, + int32_t width, + int32_t dim, + const std::tuple& kernel_size, + const std::tuple& dilation, + const std::tuple& is_causal); void na3d_av_backward( const at::Tensor& d_out, @@ -86,16 +104,15 @@ void na3d_av_backward( const at::Tensor& value, at::Tensor& d_attn, at::Tensor& d_value, - const int batch_size, - const int heads, - const int depth, - const int height, - const int width, - const int dim, - const int kernel_size, - const int dilation, - const int depth_kernel_size, - const int depth_dilation); + int32_t batch_size, + int32_t heads, + int32_t depth, + int32_t height, + int32_t width, + int32_t dim, + const std::tuple& kernel_size, + const std::tuple& dilation, + const std::tuple& is_causal); } // namespace cpu } // namespace pytorch diff --git a/csrc/include/natten/pytorch/cuda/helpers.cuh b/csrc/include/natten/pytorch/cuda/helpers.cuh index 686c4ed..97836a8 100644 --- a/csrc/include/natten/pytorch/cuda/helpers.cuh +++ b/csrc/include/natten/pytorch/cuda/helpers.cuh @@ -30,14 +30,15 @@ cudaDeviceProp* device_props = \ at::cuda::getDeviceProperties(device_index); \ const int cc = device_props->major * 10 + device_props->minor; \ + const size_t max_smem = device_props->sharedMemPerBlockOptin; \ if (c10_dtype == torch::kFloat) { \ - fn_name(cc, stream, __VA_ARGS__); \ + fn_name(cc, max_smem, stream, __VA_ARGS__); \ } else if (c10_dtype == torch::kDouble) { \ - fn_name(cc, stream, __VA_ARGS__); \ + fn_name(cc, max_smem, stream, __VA_ARGS__); \ } else if (c10_dtype == torch::kFloat16 && cc >= 60) { \ - fn_name(cc, stream, __VA_ARGS__); \ + fn_name(cc, max_smem, stream, __VA_ARGS__); \ } else if (c10_dtype == torch::kBFloat16 && cc >= 80) { \ - fn_name(cc, stream, __VA_ARGS__); \ + fn_name(cc, max_smem, stream, __VA_ARGS__); \ } else { \ std::cerr << "NATTEN (CUDA) does not support data type " << c10_dtype \ << " on device with index " << device_index << " (SM " << cc \ diff --git a/csrc/include/natten/pytorch/cuda/na1d.cuh b/csrc/include/natten/pytorch/cuda/na1d.cuh index 083e9fb..cb9ea1f 100644 --- a/csrc/include/natten/pytorch/cuda/na1d.cuh +++ b/csrc/include/natten/pytorch/cuda/na1d.cuh @@ -27,21 +27,41 @@ #pragma once #include +#include + namespace natten { namespace pytorch { namespace cuda { +void na1d_forward( + const at::Tensor& query, + const at::Tensor& key, + const at::Tensor& value, + at::Tensor& out, + const at::optional& rpb, + int32_t batch_size, + int32_t length, + int32_t heads, + int32_t dim, + const std::tuple& kernel_size, + const std::tuple& dilation, + const std::tuple& is_causal, + float attn_scale, + const std::tuple& query_tile_size, + const std::tuple& key_tile_size); + void na1d_qk_forward( const at::Tensor& query, const at::Tensor& key, const at::optional& bias, at::Tensor& attn, - const int batch_size, - const int heads, - const int length, - const int dim, - const int kernel_size, - const int dilation); + int32_t batch_size, + int32_t heads, + int32_t length, + int32_t dim, + const std::tuple& kernel_size, + const std::tuple& dilation, + const std::tuple& is_causal); void na1d_qk_backward( const at::Tensor& d_attn, @@ -50,23 +70,25 @@ void na1d_qk_backward( at::Tensor& d_query, at::Tensor& d_key, at::optional& d_bias, - const int batch_size, - const int heads, - const int length, - const int dim, - const int kernel_size, - const int dilation); + int32_t batch_size, + int32_t heads, + int32_t length, + int32_t dim, + const std::tuple& kernel_size, + const std::tuple& dilation, + const std::tuple& is_causal); void na1d_av_forward( const at::Tensor& attn, const at::Tensor& value, at::Tensor& output, - const int batch_size, - const int heads, - const int length, - const int dim, - const int kernel_size, - const int dilation); + int32_t batch_size, + int32_t heads, + int32_t length, + int32_t dim, + const std::tuple& kernel_size, + const std::tuple& dilation, + const std::tuple& is_causal); void na1d_av_backward( const at::Tensor& d_out, @@ -74,12 +96,13 @@ void na1d_av_backward( const at::Tensor& value, at::Tensor& d_attn, at::Tensor& d_value, - const int batch_size, - const int heads, - const int length, - const int dim, - const int kernel_size, - const int dilation); + int32_t batch_size, + int32_t heads, + int32_t length, + int32_t dim, + const std::tuple& kernel_size, + const std::tuple& dilation, + const std::tuple& is_causal); } // namespace cuda } // namespace pytorch diff --git a/csrc/include/natten/pytorch/cuda/na2d.cuh b/csrc/include/natten/pytorch/cuda/na2d.cuh index e42529d..92cc3b8 100644 --- a/csrc/include/natten/pytorch/cuda/na2d.cuh +++ b/csrc/include/natten/pytorch/cuda/na2d.cuh @@ -27,22 +27,43 @@ #pragma once #include +#include + namespace natten { namespace pytorch { namespace cuda { +void na2d_forward( + const at::Tensor& query, + const at::Tensor& key, + const at::Tensor& value, + at::Tensor& out, + const at::optional& rpb, + int32_t batch_size, + int32_t height, + int32_t width, + int32_t heads, + int32_t dim, + const std::tuple& kernel_size, + const std::tuple& dilation, + const std::tuple& is_causal, + float attn_scale, + const std::tuple& query_tile_size, + const std::tuple& key_tile_size); + void na2d_qk_forward( const at::Tensor& query, const at::Tensor& key, const at::optional& bias, at::Tensor& attn, - const int batch_size, - const int heads, - const int height, - const int width, - const int dim, - const int kernel_size, - const int dilation); + int32_t batch_size, + int32_t heads, + int32_t height, + int32_t width, + int32_t dim, + const std::tuple& kernel_size, + const std::tuple& dilation, + const std::tuple& is_causal); void na2d_qk_backward( const at::Tensor& d_attn, @@ -51,25 +72,27 @@ void na2d_qk_backward( at::Tensor& d_query, at::Tensor& d_key, at::optional& d_bias, - const int batch_size, - const int heads, - const int height, - const int width, - const int dim, - const int kernel_size, - const int dilation); + int32_t batch_size, + int32_t heads, + int32_t height, + int32_t width, + int32_t dim, + const std::tuple& kernel_size, + const std::tuple& dilation, + const std::tuple& is_causal); void na2d_av_forward( const at::Tensor& attn, const at::Tensor& value, at::Tensor& output, - const int batch_size, - const int heads, - const int height, - const int width, - const int dim, - const int kernel_size, - const int dilation); + int32_t batch_size, + int32_t heads, + int32_t height, + int32_t width, + int32_t dim, + const std::tuple& kernel_size, + const std::tuple& dilation, + const std::tuple& is_causal); void na2d_av_backward( const at::Tensor& d_out, @@ -77,13 +100,14 @@ void na2d_av_backward( const at::Tensor& value, at::Tensor& d_attn, at::Tensor& d_value, - const int batch_size, - const int heads, - const int height, - const int width, - const int dim, - const int kernel_size, - const int dilation); + int32_t batch_size, + int32_t heads, + int32_t height, + int32_t width, + int32_t dim, + const std::tuple& kernel_size, + const std::tuple& dilation, + const std::tuple& is_causal); } // namespace cuda } // namespace pytorch diff --git a/csrc/include/natten/pytorch/cuda/na3d.cuh b/csrc/include/natten/pytorch/cuda/na3d.cuh index c9acb0a..6e1e818 100644 --- a/csrc/include/natten/pytorch/cuda/na3d.cuh +++ b/csrc/include/natten/pytorch/cuda/na3d.cuh @@ -27,25 +27,45 @@ #pragma once #include +#include + namespace natten { namespace pytorch { namespace cuda { +void na3d_forward( + const at::Tensor& query, + const at::Tensor& key, + const at::Tensor& value, + at::Tensor& out, + const at::optional& rpb, + int32_t batch_size, + int32_t depth, + int32_t height, + int32_t width, + int32_t heads, + int32_t dim, + const std::tuple& kernel_size, + const std::tuple& dilation, + const std::tuple& is_causal, + float attn_scale, + const std::tuple& query_tile_size, + const std::tuple& key_tile_size); + void na3d_qk_forward( const at::Tensor& query, const at::Tensor& key, const at::optional& bias, at::Tensor& attn, - const int batch_size, - const int heads, - const int depth, - const int height, - const int width, - const int dim, - const int kernel_size, - const int dilation, - const int depth_kernel_size, - const int depth_dilation); + int32_t batch_size, + int32_t heads, + int32_t depth, + int32_t height, + int32_t width, + int32_t dim, + const std::tuple& kernel_size, + const std::tuple& dilation, + const std::tuple& is_causal); void na3d_qk_backward( const at::Tensor& d_attn, @@ -54,31 +74,29 @@ void na3d_qk_backward( at::Tensor& d_query, at::Tensor& d_key, at::optional& d_bias, - const int batch_size, - const int heads, - const int depth, - const int height, - const int width, - const int dim, - const int kernel_size, - const int dilation, - const int depth_kernel_size, - const int depth_dilation); + int32_t batch_size, + int32_t heads, + int32_t depth, + int32_t height, + int32_t width, + int32_t dim, + const std::tuple& kernel_size, + const std::tuple& dilation, + const std::tuple& is_causal); void na3d_av_forward( const at::Tensor& attn, const at::Tensor& value, at::Tensor& output, - const int batch_size, - const int heads, - const int depth, - const int height, - const int width, - const int dim, - const int kernel_size, - const int dilation, - const int depth_kernel_size, - const int depth_dilation); + int32_t batch_size, + int32_t heads, + int32_t depth, + int32_t height, + int32_t width, + int32_t dim, + const std::tuple& kernel_size, + const std::tuple& dilation, + const std::tuple& is_causal); void na3d_av_backward( const at::Tensor& d_out, @@ -86,16 +104,15 @@ void na3d_av_backward( const at::Tensor& value, at::Tensor& d_attn, at::Tensor& d_value, - const int batch_size, - const int heads, - const int depth, - const int height, - const int width, - const int dim, - const int kernel_size, - const int dilation, - const int depth_kernel_size, - const int depth_dilation); + int32_t batch_size, + int32_t heads, + int32_t depth, + int32_t height, + int32_t width, + int32_t dim, + const std::tuple& kernel_size, + const std::tuple& dilation, + const std::tuple& is_causal); } // namespace cuda } // namespace pytorch diff --git a/csrc/include/natten/pytorch/helpers.h b/csrc/include/natten/pytorch/helpers.h index 64999eb..59432e2 100644 --- a/csrc/include/natten/pytorch/helpers.h +++ b/csrc/include/natten/pytorch/helpers.h @@ -24,8 +24,9 @@ #pragma once #include -#define CHECK_CONTIGUOUS(x) \ - TORCH_CHECK(x.is_contiguous(), #x " must be contiguous") +#define CHECK_CONTIGUOUS(x) \ + TORCH_CHECK(!x.is_sparse(), #x " must be a dense tensor"); \ + TORCH_CHECK(x.is_contiguous(), #x " must be contiguous"); #ifdef NATTEN_WITH_CUDA #define DISPATCH_DEVICE(c10_type, kernel_name, ...) \ @@ -59,7 +60,7 @@ namespace natten { namespace pytorch { -inline void CheckArgs(int kernel_size, int dilation) { +inline void CheckArgs(int32_t kernel_size, int32_t dilation) { TORCH_CHECK( kernel_size > 1 && kernel_size % 2 == 1, "Kernel size must be an odd number greater than 1, got ", @@ -72,7 +73,31 @@ inline void CheckArgs(int kernel_size, int dilation) { "."); } -inline void CheckArgsAgainstDim(int dim, int kernel_size, int dilation) { +inline void CheckArgs( + const std::tuple& kernel_size, + const std::tuple& dilation) { + CheckArgs(std::get<0>(kernel_size), std::get<0>(dilation)); +} + +inline void CheckArgs( + const std::tuple& kernel_size, + const std::tuple& dilation) { + CheckArgs(std::get<0>(kernel_size), std::get<0>(dilation)); + CheckArgs(std::get<1>(kernel_size), std::get<1>(dilation)); +} + +inline void CheckArgs( + const std::tuple& kernel_size, + const std::tuple& dilation) { + CheckArgs(std::get<0>(kernel_size), std::get<0>(dilation)); + CheckArgs(std::get<1>(kernel_size), std::get<1>(dilation)); + CheckArgs(std::get<2>(kernel_size), std::get<2>(dilation)); +} + +inline void CheckArgsAgainstDim( + int32_t dim, + int32_t kernel_size, + int32_t dilation) { TORCH_CHECK( kernel_size * dilation <= dim, "Input axes must be less than or equal to the product of kernel size and dilation. " @@ -85,6 +110,36 @@ inline void CheckArgsAgainstDim(int dim, int kernel_size, int dilation) { "."); } +inline void CheckArgsAgainstDim( + const std::tuple& dim, + const std::tuple& kernel_size, + const std::tuple& dilation) { + CheckArgsAgainstDim( + std::get<0>(dim), std::get<0>(kernel_size), std::get<0>(dilation)); +} + +inline void CheckArgsAgainstDim( + const std::tuple& dim, + const std::tuple& kernel_size, + const std::tuple& dilation) { + CheckArgsAgainstDim( + std::get<0>(dim), std::get<0>(kernel_size), std::get<0>(dilation)); + CheckArgsAgainstDim( + std::get<1>(dim), std::get<1>(kernel_size), std::get<1>(dilation)); +} + +inline void CheckArgsAgainstDim( + const std::tuple& dim, + const std::tuple& kernel_size, + const std::tuple& dilation) { + CheckArgsAgainstDim( + std::get<0>(dim), std::get<0>(kernel_size), std::get<0>(dilation)); + CheckArgsAgainstDim( + std::get<1>(dim), std::get<1>(kernel_size), std::get<1>(dilation)); + CheckArgsAgainstDim( + std::get<2>(dim), std::get<2>(kernel_size), std::get<2>(dilation)); +} + inline void CheckIfPropertiesMatch(const at::Tensor& a, const at::Tensor& b) { TORCH_CHECK( a.device().is_cuda() == b.device().is_cuda(), @@ -124,11 +179,11 @@ void CheckIfTensorShapesMatch(const at::Tensor& a, const at::Tensor& b) { } } -template +template void CheckAttnShape( const at::Tensor& input, const at::Tensor& attn, - int kernel_size) { + KernelType kernel_size) { static_assert(NaDim >= 1 && NaDim < 4); TORCH_CHECK(attn.dim() == NaDim + 3, "Expected ", NaDim + 3, "-D tensors."); for (size_t i = 0; i < NaDim + 2; ++i) { @@ -141,7 +196,7 @@ void CheckAttnShape( " != ", input.size(i)); } - auto expected_kernel_size = std::pow(kernel_size, NaDim); + auto expected_kernel_size = natten::flatten(kernel_size); TORCH_CHECK( attn.size(NaDim + 2) == expected_kernel_size, "Expected attention dim was ", @@ -150,11 +205,12 @@ void CheckAttnShape( attn.size(NaDim + 2)); } -template +template void CheckBias( const at::Tensor& input, const at::Tensor& bias, - int kernel_size) { + int32_t num_heads, + KernelType kernel_size) { static_assert(NaDim >= 1 && NaDim < 4); TORCH_CHECK( input.scalar_type() == bias.scalar_type(), @@ -163,80 +219,9 @@ void CheckBias( bias.device().is_cuda() == input.device().is_cuda(), "Expected positional bias to be on the same device as the inputs."); CHECK_CONTIGUOUS(bias); - TORCH_CHECK( - bias.size(0) == input.size(1), - "Expected bias.shape[0] == input.shape[1] == heads."); + TORCH_CHECK(bias.size(0) == num_heads, "Expected bias.shape[0] == heads."); for (size_t i = 0; i < NaDim; ++i) { - auto expected_bias_dim = kernel_size * 2 - 1; - TORCH_CHECK( - bias.size(i + 1) == expected_bias_dim, - "Invalid bias shape at dim ", - i + 1, - "; " - "expected ", - expected_bias_dim, - ", got ", - bias.size(i + 1), - "."); - } -} - -// TODO: I resent this; please do it the right way. -template -void CheckAttnShape( - const at::Tensor& input, - const at::Tensor& attn, - int kernel_size, - int kernel_size_d) { - static_assert(NaDim == 3); - TORCH_CHECK(attn.dim() == NaDim + 3, "Expected ", NaDim + 3, "-D tensors."); - for (size_t i = 0; i < NaDim + 2; ++i) { - TORCH_CHECK( - input.size(i) == attn.size(i), - "Tensor shape mismatch at dimension ", - i, - ": ", - input.size(i), - " != ", - input.size(i)); - } - auto expected_kernel_size = kernel_size * kernel_size * kernel_size_d; - TORCH_CHECK( - attn.size(NaDim + 2) == expected_kernel_size, - "Expected attention dim was ", - expected_kernel_size, - ", got ", - attn.size(NaDim + 2)); -} - -template -void CheckBias( - const at::Tensor& input, - const at::Tensor& bias, - int kernel_size, - int kernel_size_d) { - static_assert(NaDim == 3); - TORCH_CHECK( - input.scalar_type() == bias.scalar_type(), - "Inputs and bias must match in dtype."); - TORCH_CHECK( - bias.device().is_cuda() == input.device().is_cuda(), - "Expected positional bias to be on the same device as the inputs."); - CHECK_CONTIGUOUS(bias); - TORCH_CHECK( - bias.size(0) == input.size(1), - "Expected bias.shape[0] == input.shape[1] == heads."); - - auto expected_bias_dim_0 = kernel_size_d * 2 - 1; - TORCH_CHECK( - bias.size(1) == expected_bias_dim_0, - "Invalid bias shape at dim 1; expected ", - expected_bias_dim_0, - ", got ", - bias.size(1), - "."); - for (size_t i = 1; i < NaDim; ++i) { - auto expected_bias_dim = kernel_size * 2 - 1; + auto expected_bias_dim = natten::get_from_tuple(kernel_size, i) * 2 - 1; TORCH_CHECK( bias.size(i + 1) == expected_bias_dim, "Invalid bias shape at dim ", diff --git a/csrc/include/natten/pytorch/na1d.h b/csrc/include/natten/pytorch/na1d.h index 0e866ee..5fd678b 100644 --- a/csrc/include/natten/pytorch/na1d.h +++ b/csrc/include/natten/pytorch/na1d.h @@ -27,16 +27,32 @@ #pragma once #include +#include + namespace natten { namespace pytorch { +void na1d_forward( + at::Tensor& out, + const at::Tensor& query, + const at::Tensor& key, + const at::Tensor& value, + const at::optional& rpb, + const std::tuple& kernel_size, + const std::tuple& dilation, + const std::tuple& is_causal, + float attn_scale, + const std::tuple& query_tile_size, + const std::tuple& key_tile_size); + void na1d_qk_forward( at::Tensor& attn, const at::Tensor& query, const at::Tensor& key, const at::optional& bias, - const int kernel_size, - const int dilation); + const std::tuple& kernel_size, + const std::tuple& dilation, + const std::tuple& is_causal); void na1d_qk_backward( at::Tensor& d_query, @@ -45,15 +61,17 @@ void na1d_qk_backward( const at::Tensor& d_attn, const at::Tensor& query, const at::Tensor& key, - const int kernel_size, - const int dilation); + const std::tuple& kernel_size, + const std::tuple& dilation, + const std::tuple& is_causal); void na1d_av_forward( at::Tensor& out, const at::Tensor& attn, const at::Tensor& value, - const int kernel_size, - const int dilation); + const std::tuple& kernel_size, + const std::tuple& dilation, + const std::tuple& is_causal); void na1d_av_backward( at::Tensor& d_attn, @@ -61,8 +79,9 @@ void na1d_av_backward( const at::Tensor& d_out, const at::Tensor& attn, const at::Tensor& value, - const int kernel_size, - const int dilation); + const std::tuple& kernel_size, + const std::tuple& dilation, + const std::tuple& is_causal); } // namespace pytorch } // namespace natten diff --git a/csrc/include/natten/pytorch/na2d.h b/csrc/include/natten/pytorch/na2d.h index 8c480ee..8f39cec 100644 --- a/csrc/include/natten/pytorch/na2d.h +++ b/csrc/include/natten/pytorch/na2d.h @@ -27,16 +27,32 @@ #pragma once #include +#include + namespace natten { namespace pytorch { +void na2d_forward( + at::Tensor& out, + const at::Tensor& query, + const at::Tensor& key, + const at::Tensor& value, + const at::optional& rpb, + const std::tuple& kernel_size, + const std::tuple& dilation, + const std::tuple& is_causal, + float attn_scale, + const std::tuple& query_tile_size, + const std::tuple& key_tile_size); + void na2d_qk_forward( at::Tensor& attn, const at::Tensor& query, const at::Tensor& key, const at::optional& bias, - const int kernel_size, - const int dilation); + const std::tuple& kernel_size, + const std::tuple& dilation, + const std::tuple& is_causal); void na2d_qk_backward( at::Tensor& d_query, @@ -45,15 +61,17 @@ void na2d_qk_backward( const at::Tensor& d_attn, const at::Tensor& query, const at::Tensor& key, - const int kernel_size, - const int dilation); + const std::tuple& kernel_size, + const std::tuple& dilation, + const std::tuple& is_causal); void na2d_av_forward( at::Tensor& out, const at::Tensor& attn, const at::Tensor& value, - const int kernel_size, - const int dilation); + const std::tuple& kernel_size, + const std::tuple& dilation, + const std::tuple& is_causal); void na2d_av_backward( at::Tensor& d_attn, @@ -61,8 +79,9 @@ void na2d_av_backward( const at::Tensor& d_out, const at::Tensor& attn, const at::Tensor& value, - const int kernel_size, - const int dilation); + const std::tuple& kernel_size, + const std::tuple& dilation, + const std::tuple& is_causal); } // namespace pytorch } // namespace natten diff --git a/csrc/include/natten/pytorch/na3d.h b/csrc/include/natten/pytorch/na3d.h index ee4f16d..ac1a6c3 100644 --- a/csrc/include/natten/pytorch/na3d.h +++ b/csrc/include/natten/pytorch/na3d.h @@ -27,18 +27,32 @@ #pragma once #include +#include + namespace natten { namespace pytorch { +void na3d_forward( + at::Tensor& out, + const at::Tensor& query, + const at::Tensor& key, + const at::Tensor& value, + const at::optional& rpb, + const std::tuple& kernel_size, + const std::tuple& dilation, + const std::tuple& is_causal, + float attn_scale, + const std::tuple& query_tile_size, + const std::tuple& key_tile_size); + void na3d_qk_forward( at::Tensor& attn, const at::Tensor& query, const at::Tensor& key, const at::optional& bias, - const int kernel_size, - const int dilation, - const int depth_kernel_size, - const int depth_dilation); + const std::tuple& kernel_size, + const std::tuple& dilation, + const std::tuple& is_causal); void na3d_qk_backward( at::Tensor& d_query, @@ -47,19 +61,17 @@ void na3d_qk_backward( const at::Tensor& d_attn, const at::Tensor& query, const at::Tensor& key, - const int kernel_size, - const int dilation, - const int depth_kernel_size, - const int depth_dilation); + const std::tuple& kernel_size, + const std::tuple& dilation, + const std::tuple& is_causal); void na3d_av_forward( at::Tensor& out, const at::Tensor& attn, const at::Tensor& value, - const int kernel_size, - const int dilation, - const int depth_kernel_size, - const int depth_dilation); + const std::tuple& kernel_size, + const std::tuple& dilation, + const std::tuple& is_causal); void na3d_av_backward( at::Tensor& d_attn, @@ -67,10 +79,9 @@ void na3d_av_backward( const at::Tensor& d_out, const at::Tensor& attn, const at::Tensor& value, - const int kernel_size, - const int dilation, - const int depth_kernel_size, - const int depth_dilation); + const std::tuple& kernel_size, + const std::tuple& dilation, + const std::tuple& is_causal); } // namespace pytorch } // namespace natten diff --git a/csrc/natten.cpp b/csrc/natten.cpp index ddd6e81..461123c 100644 --- a/csrc/natten.cpp +++ b/csrc/natten.cpp @@ -32,6 +32,15 @@ namespace natten { PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) { + m.def( + "na1d_forward", &natten::pytorch::na1d_forward, "NA1D forward (fused)"); + + m.def( + "na2d_forward", &natten::pytorch::na2d_forward, "NA2D forward (fused)"); + + m.def( + "na3d_forward", &natten::pytorch::na3d_forward, "NA3D forward (fused)"); + m.def( "na1d_qk_forward", &natten::pytorch::na1d_qk_forward, "NA1D QK forward"); m.def( diff --git a/csrc/src/pytorch/cpu/na1d.cpp b/csrc/src/pytorch/cpu/na1d.cpp index aa83d98..fbdf303 100644 --- a/csrc/src/pytorch/cpu/na1d.cpp +++ b/csrc/src/pytorch/cpu/na1d.cpp @@ -27,25 +27,47 @@ #include #include -#include "natten/cpu/na1d.h" -#include "natten/dtypes.h" -#include "natten/pytorch/cpu/helpers.h" +#include +#include +#include +#include namespace natten { namespace pytorch { namespace cpu { +void na1d_forward( + const at::Tensor& query, + const at::Tensor& key, + const at::Tensor& value, + at::Tensor& out, + const at::optional& rpb, + int32_t batch_size, + int32_t length, + int32_t heads, + int32_t dim, + const std::tuple& kernel_size, + const std::tuple& dilation, + const std::tuple& is_causal, + float attn_scale, + const std::tuple& query_tile_size, + const std::tuple& key_tile_size) { + // TODO: implement CPU reference + NATTEN_FAILURE("Fused kernels are not available on CPU yet."); +} + void na1d_qk_forward( const at::Tensor& query, const at::Tensor& key, const at::optional& bias, at::Tensor& attn, - const int batch_size, - const int heads, - const int length, - const int dim, - const int kernel_size, - const int dilation) { + int32_t batch_size, + int32_t heads, + int32_t length, + int32_t dim, + const std::tuple& kernel_size, + const std::tuple& dilation, + const std::tuple& is_causal) { DISPATCH_DTYPE( query.scalar_type(), natten::cpu::na1d_qk_forward, @@ -61,7 +83,8 @@ void na1d_qk_forward( attn.stride(1), attn.stride(2), kernel_size, - dilation); + dilation, + is_causal); } void na1d_qk_backward( @@ -71,12 +94,13 @@ void na1d_qk_backward( at::Tensor& d_query, at::Tensor& d_key, at::optional& d_bias, - const int batch_size, - const int heads, - const int length, - const int dim, - const int kernel_size, - const int dilation) { + int32_t batch_size, + int32_t heads, + int32_t length, + int32_t dim, + const std::tuple& kernel_size, + const std::tuple& dilation, + const std::tuple& is_causal) { DISPATCH_DTYPE( d_attn.scalar_type(), natten::cpu::na1d_qk_backward, @@ -95,19 +119,21 @@ void na1d_qk_backward( d_attn.stride(1), d_attn.stride(2), kernel_size, - dilation); + dilation, + is_causal); } void na1d_av_forward( const at::Tensor& attn, const at::Tensor& value, at::Tensor& output, - const int batch_size, - const int heads, - const int length, - const int dim, - const int kernel_size, - const int dilation) { + int32_t batch_size, + int32_t heads, + int32_t length, + int32_t dim, + const std::tuple& kernel_size, + const std::tuple& dilation, + const std::tuple& is_causal) { DISPATCH_DTYPE( attn.scalar_type(), natten::cpu::na1d_av_forward, @@ -122,7 +148,8 @@ void na1d_av_forward( attn.stride(1), attn.stride(2), kernel_size, - dilation); + dilation, + is_causal); } void na1d_av_backward( @@ -131,12 +158,13 @@ void na1d_av_backward( const at::Tensor& value, at::Tensor& d_attn, at::Tensor& d_value, - const int batch_size, - const int heads, - const int length, - const int dim, - const int kernel_size, - const int dilation) { + int32_t batch_size, + int32_t heads, + int32_t length, + int32_t dim, + const std::tuple& kernel_size, + const std::tuple& dilation, + const std::tuple& is_causal) { DISPATCH_DTYPE( d_out.scalar_type(), natten::cpu::na1d_av_backward, @@ -153,7 +181,8 @@ void na1d_av_backward( attn.stride(1), attn.stride(2), kernel_size, - dilation); + dilation, + is_causal); } } // namespace cpu diff --git a/csrc/src/pytorch/cpu/na2d.cpp b/csrc/src/pytorch/cpu/na2d.cpp index b30ac3c..dc8e796 100644 --- a/csrc/src/pytorch/cpu/na2d.cpp +++ b/csrc/src/pytorch/cpu/na2d.cpp @@ -27,26 +27,49 @@ #include #include -#include "natten/cpu/na2d.h" -#include "natten/dtypes.h" -#include "natten/pytorch/cpu/helpers.h" +#include +#include +#include +#include namespace natten { namespace pytorch { namespace cpu { +void na2d_forward( + const at::Tensor& query, + const at::Tensor& key, + const at::Tensor& value, + at::Tensor& out, + const at::optional& rpb, + int32_t batch_size, + int32_t height, + int32_t width, + int32_t heads, + int32_t dim, + const std::tuple& kernel_size, + const std::tuple& dilation, + const std::tuple& is_causal, + float attn_scale, + const std::tuple& query_tile_size, + const std::tuple& key_tile_size) { + // TODO: implement CPU reference + NATTEN_FAILURE("Fused kernels are not available on CPU yet."); +} + void na2d_qk_forward( const at::Tensor& query, const at::Tensor& key, const at::optional& bias, at::Tensor& attn, - const int batch_size, - const int heads, - const int height, - const int width, - const int dim, - const int kernel_size, - const int dilation) { + int32_t batch_size, + int32_t heads, + int32_t height, + int32_t width, + int32_t dim, + const std::tuple& kernel_size, + const std::tuple& dilation, + const std::tuple& is_causal) { DISPATCH_DTYPE( query.scalar_type(), natten::cpu::na2d_qk_forward, @@ -64,7 +87,8 @@ void na2d_qk_forward( attn.stride(2), attn.stride(3), kernel_size, - dilation); + dilation, + is_causal); } void na2d_qk_backward( @@ -74,13 +98,14 @@ void na2d_qk_backward( at::Tensor& d_query, at::Tensor& d_key, at::optional& d_bias, - const int batch_size, - const int heads, - const int height, - const int width, - const int dim, - const int kernel_size, - const int dilation) { + int32_t batch_size, + int32_t heads, + int32_t height, + int32_t width, + int32_t dim, + const std::tuple& kernel_size, + const std::tuple& dilation, + const std::tuple& is_causal) { DISPATCH_DTYPE( d_attn.scalar_type(), natten::cpu::na2d_qk_backward, @@ -101,20 +126,22 @@ void na2d_qk_backward( d_attn.stride(2), d_attn.stride(3), kernel_size, - dilation); + dilation, + is_causal); } void na2d_av_forward( const at::Tensor& attn, const at::Tensor& value, at::Tensor& output, - const int batch_size, - const int heads, - const int height, - const int width, - const int dim, - const int kernel_size, - const int dilation) { + int32_t batch_size, + int32_t heads, + int32_t height, + int32_t width, + int32_t dim, + const std::tuple& kernel_size, + const std::tuple& dilation, + const std::tuple& is_causal) { DISPATCH_DTYPE( attn.scalar_type(), natten::cpu::na2d_av_forward, @@ -131,7 +158,8 @@ void na2d_av_forward( attn.stride(2), attn.stride(3), kernel_size, - dilation); + dilation, + is_causal); } void na2d_av_backward( @@ -140,13 +168,14 @@ void na2d_av_backward( const at::Tensor& value, at::Tensor& d_attn, at::Tensor& d_value, - const int batch_size, - const int heads, - const int height, - const int width, - const int dim, - const int kernel_size, - const int dilation) { + int32_t batch_size, + int32_t heads, + int32_t height, + int32_t width, + int32_t dim, + const std::tuple& kernel_size, + const std::tuple& dilation, + const std::tuple& is_causal) { DISPATCH_DTYPE( d_out.scalar_type(), natten::cpu::na2d_av_backward, @@ -165,7 +194,8 @@ void na2d_av_backward( attn.stride(2), attn.stride(3), kernel_size, - dilation); + dilation, + is_causal); } } // namespace cpu diff --git a/csrc/src/pytorch/cpu/na3d.cpp b/csrc/src/pytorch/cpu/na3d.cpp index 2d48a14..6c899cd 100644 --- a/csrc/src/pytorch/cpu/na3d.cpp +++ b/csrc/src/pytorch/cpu/na3d.cpp @@ -27,29 +27,51 @@ #include #include -#include "natten/cpu/na3d.h" -#include "natten/dtypes.h" -#include "natten/pytorch/cpu/helpers.h" +#include +#include +#include +#include namespace natten { namespace pytorch { namespace cpu { +void na3d_forward( + const at::Tensor& query, + const at::Tensor& key, + const at::Tensor& value, + at::Tensor& out, + const at::optional& rpb, + int32_t batch_size, + int32_t depth, + int32_t height, + int32_t width, + int32_t heads, + int32_t dim, + const std::tuple& kernel_size, + const std::tuple& dilation, + const std::tuple& is_causal, + float attn_scale, + const std::tuple& query_tile_size, + const std::tuple& key_tile_size) { + // TODO: implement CPU reference + NATTEN_FAILURE("Fused kernels are not available on CPU yet."); +} + void na3d_qk_forward( const at::Tensor& query, const at::Tensor& key, const at::optional& bias, at::Tensor& attn, - const int batch_size, - const int heads, - const int depth, - const int height, - const int width, - const int dim, - const int kernel_size, - const int dilation, - const int depth_kernel_size, - const int depth_dilation) { + int32_t batch_size, + int32_t heads, + int32_t depth, + int32_t height, + int32_t width, + int32_t dim, + const std::tuple& kernel_size, + const std::tuple& dilation, + const std::tuple& is_causal) { DISPATCH_DTYPE( query.scalar_type(), natten::cpu::na3d_qk_forward, @@ -70,8 +92,7 @@ void na3d_qk_forward( attn.stride(4), kernel_size, dilation, - depth_kernel_size, - depth_dilation); + is_causal); } void na3d_qk_backward( @@ -81,16 +102,15 @@ void na3d_qk_backward( at::Tensor& d_query, at::Tensor& d_key, at::optional& d_bias, - const int batch_size, - const int heads, - const int depth, - const int height, - const int width, - const int dim, - const int kernel_size, - const int dilation, - const int depth_kernel_size, - const int depth_dilation) { + int32_t batch_size, + int32_t heads, + int32_t depth, + int32_t height, + int32_t width, + int32_t dim, + const std::tuple& kernel_size, + const std::tuple& dilation, + const std::tuple& is_causal) { DISPATCH_DTYPE( d_attn.scalar_type(), natten::cpu::na3d_qk_backward, @@ -114,24 +134,22 @@ void na3d_qk_backward( d_attn.stride(4), kernel_size, dilation, - depth_kernel_size, - depth_dilation); + is_causal); } void na3d_av_forward( const at::Tensor& attn, const at::Tensor& value, at::Tensor& output, - const int batch_size, - const int heads, - const int depth, - const int height, - const int width, - const int dim, - const int kernel_size, - const int dilation, - const int depth_kernel_size, - const int depth_dilation) { + int32_t batch_size, + int32_t heads, + int32_t depth, + int32_t height, + int32_t width, + int32_t dim, + const std::tuple& kernel_size, + const std::tuple& dilation, + const std::tuple& is_causal) { DISPATCH_DTYPE( attn.scalar_type(), natten::cpu::na3d_av_forward, @@ -151,8 +169,7 @@ void na3d_av_forward( attn.stride(4), kernel_size, dilation, - depth_kernel_size, - depth_dilation); + is_causal); } void na3d_av_backward( @@ -161,16 +178,15 @@ void na3d_av_backward( const at::Tensor& value, at::Tensor& d_attn, at::Tensor& d_value, - const int batch_size, - const int heads, - const int depth, - const int height, - const int width, - const int dim, - const int kernel_size, - const int dilation, - const int depth_kernel_size, - const int depth_dilation) { + int32_t batch_size, + int32_t heads, + int32_t depth, + int32_t height, + int32_t width, + int32_t dim, + const std::tuple& kernel_size, + const std::tuple& dilation, + const std::tuple& is_causal) { DISPATCH_DTYPE( d_out.scalar_type(), natten::cpu::na3d_av_backward, @@ -192,8 +208,7 @@ void na3d_av_backward( attn.stride(4), kernel_size, dilation, - depth_kernel_size, - depth_dilation); + is_causal); } } // namespace cpu diff --git a/csrc/src/pytorch/cuda/na1d.cu b/csrc/src/pytorch/cuda/na1d.cu index 1c6a188..21b5991 100644 --- a/csrc/src/pytorch/cuda/na1d.cu +++ b/csrc/src/pytorch/cuda/na1d.cu @@ -28,6 +28,7 @@ #include #include +#include #include #include #include @@ -36,20 +37,59 @@ namespace natten { namespace pytorch { namespace cuda { +void na1d_forward( + const at::Tensor& query, + const at::Tensor& key, + const at::Tensor& value, + at::Tensor& out, + const at::optional& rpb, + int32_t batch_size, + int32_t length, + int32_t heads, + int32_t dim, + const std::tuple& kernel_size, + const std::tuple& dilation, + const std::tuple& is_causal, + float attn_scale, + const std::tuple& query_tile_size, + const std::tuple& key_tile_size) { + DISPATCH_DTYPE( + query.device().index(), + at::cuda::getCurrentCUDAStream(query.device().index()), + query.scalar_type(), + natten::cuda::na1d_forward, + static_cast(query.data_ptr()), + static_cast(key.data_ptr()), + static_cast(value.data_ptr()), + static_cast(out.data_ptr()), + rpb.has_value() ? static_cast(rpb.value().data_ptr()) : nullptr, + batch_size, + length, + heads, + dim, + kernel_size, + dilation, + is_causal, + attn_scale, + query_tile_size, + key_tile_size); +} + void na1d_qk_forward( const at::Tensor& query, const at::Tensor& key, const at::optional& bias, at::Tensor& attn, - const int batch_size, - const int heads, - const int length, - const int dim, - const int kernel_size, - const int dilation) { + int32_t batch_size, + int32_t heads, + int32_t length, + int32_t dim, + const std::tuple& kernel_size, + const std::tuple& dilation, + const std::tuple& is_causal) { DISPATCH_DTYPE( query.device().index(), - at::cuda::getCurrentCUDAStream(), + at::cuda::getCurrentCUDAStream(query.device().index()), query.scalar_type(), natten::cuda::na1d_qk_forward, static_cast(query.data_ptr()), @@ -64,7 +104,8 @@ void na1d_qk_forward( attn.stride(1), attn.stride(2), kernel_size, - dilation); + dilation, + is_causal); } void na1d_qk_backward( @@ -74,15 +115,16 @@ void na1d_qk_backward( at::Tensor& d_query, at::Tensor& d_key, at::optional& d_bias, - const int batch_size, - const int heads, - const int length, - const int dim, - const int kernel_size, - const int dilation) { + int32_t batch_size, + int32_t heads, + int32_t length, + int32_t dim, + const std::tuple& kernel_size, + const std::tuple& dilation, + const std::tuple& is_causal) { DISPATCH_DTYPE( d_attn.device().index(), - at::cuda::getCurrentCUDAStream(), + at::cuda::getCurrentCUDAStream(query.device().index()), d_attn.scalar_type(), natten::cuda::na1d_qk_backward, static_cast(query.data_ptr()), @@ -100,22 +142,24 @@ void na1d_qk_backward( d_attn.stride(1), d_attn.stride(2), kernel_size, - dilation); + dilation, + is_causal); } void na1d_av_forward( const at::Tensor& attn, const at::Tensor& value, at::Tensor& output, - const int batch_size, - const int heads, - const int length, - const int dim, - const int kernel_size, - const int dilation) { + int32_t batch_size, + int32_t heads, + int32_t length, + int32_t dim, + const std::tuple& kernel_size, + const std::tuple& dilation, + const std::tuple& is_causal) { DISPATCH_DTYPE( attn.device().index(), - at::cuda::getCurrentCUDAStream(), + at::cuda::getCurrentCUDAStream(attn.device().index()), attn.scalar_type(), natten::cuda::na1d_av_forward, static_cast(attn.data_ptr()), @@ -129,7 +173,8 @@ void na1d_av_forward( attn.stride(1), attn.stride(2), kernel_size, - dilation); + dilation, + is_causal); } void na1d_av_backward( @@ -138,15 +183,16 @@ void na1d_av_backward( const at::Tensor& value, at::Tensor& d_attn, at::Tensor& d_value, - const int batch_size, - const int heads, - const int length, - const int dim, - const int kernel_size, - const int dilation) { + int32_t batch_size, + int32_t heads, + int32_t length, + int32_t dim, + const std::tuple& kernel_size, + const std::tuple& dilation, + const std::tuple& is_causal) { DISPATCH_DTYPE( d_out.device().index(), - at::cuda::getCurrentCUDAStream(), + at::cuda::getCurrentCUDAStream(attn.device().index()), d_out.scalar_type(), natten::cuda::na1d_av_backward, static_cast(attn.data_ptr()), @@ -162,7 +208,8 @@ void na1d_av_backward( attn.stride(1), attn.stride(2), kernel_size, - dilation); + dilation, + is_causal); } } // namespace cuda diff --git a/csrc/src/pytorch/cuda/na2d.cu b/csrc/src/pytorch/cuda/na2d.cu index bfa5031..aea1cb0 100644 --- a/csrc/src/pytorch/cuda/na2d.cu +++ b/csrc/src/pytorch/cuda/na2d.cu @@ -28,6 +28,7 @@ #include #include +#include #include #include #include @@ -36,21 +37,62 @@ namespace natten { namespace pytorch { namespace cuda { +void na2d_forward( + const at::Tensor& query, + const at::Tensor& key, + const at::Tensor& value, + at::Tensor& out, + const at::optional& rpb, + int32_t batch_size, + int32_t height, + int32_t width, + int32_t heads, + int32_t dim, + const std::tuple& kernel_size, + const std::tuple& dilation, + const std::tuple& is_causal, + float attn_scale, + const std::tuple& query_tile_size, + const std::tuple& key_tile_size) { + DISPATCH_DTYPE( + query.device().index(), + at::cuda::getCurrentCUDAStream(query.device().index()), + query.scalar_type(), + natten::cuda::na2d_forward, + static_cast(query.data_ptr()), + static_cast(key.data_ptr()), + static_cast(value.data_ptr()), + static_cast(out.data_ptr()), + rpb.has_value() ? static_cast(rpb.value().data_ptr()) : nullptr, + batch_size, + height, + width, + heads, + dim, + kernel_size, + dilation, + is_causal, + attn_scale, + query_tile_size, + key_tile_size); +} + void na2d_qk_forward( const at::Tensor& query, const at::Tensor& key, const at::optional& bias, at::Tensor& attn, - const int batch_size, - const int heads, - const int height, - const int width, - const int dim, - const int kernel_size, - const int dilation) { + int32_t batch_size, + int32_t heads, + int32_t height, + int32_t width, + int32_t dim, + const std::tuple& kernel_size, + const std::tuple& dilation, + const std::tuple& is_causal) { DISPATCH_DTYPE( query.device().index(), - at::cuda::getCurrentCUDAStream(), + at::cuda::getCurrentCUDAStream(query.device().index()), query.scalar_type(), natten::cuda::na2d_qk_forward, static_cast(query.data_ptr()), @@ -67,7 +109,8 @@ void na2d_qk_forward( attn.stride(2), attn.stride(3), kernel_size, - dilation); + dilation, + is_causal); } void na2d_qk_backward( @@ -77,16 +120,17 @@ void na2d_qk_backward( at::Tensor& d_query, at::Tensor& d_key, at::optional& d_bias, - const int batch_size, - const int heads, - const int height, - const int width, - const int dim, - const int kernel_size, - const int dilation) { + int32_t batch_size, + int32_t heads, + int32_t height, + int32_t width, + int32_t dim, + const std::tuple& kernel_size, + const std::tuple& dilation, + const std::tuple& is_causal) { DISPATCH_DTYPE( d_attn.device().index(), - at::cuda::getCurrentCUDAStream(), + at::cuda::getCurrentCUDAStream(query.device().index()), d_attn.scalar_type(), natten::cuda::na2d_qk_backward, static_cast(query.data_ptr()), @@ -106,23 +150,25 @@ void na2d_qk_backward( d_attn.stride(2), d_attn.stride(3), kernel_size, - dilation); + dilation, + is_causal); } void na2d_av_forward( const at::Tensor& attn, const at::Tensor& value, at::Tensor& output, - const int batch_size, - const int heads, - const int height, - const int width, - const int dim, - const int kernel_size, - const int dilation) { + int32_t batch_size, + int32_t heads, + int32_t height, + int32_t width, + int32_t dim, + const std::tuple& kernel_size, + const std::tuple& dilation, + const std::tuple& is_causal) { DISPATCH_DTYPE( attn.device().index(), - at::cuda::getCurrentCUDAStream(), + at::cuda::getCurrentCUDAStream(attn.device().index()), attn.scalar_type(), natten::cuda::na2d_av_forward, static_cast(attn.data_ptr()), @@ -138,7 +184,8 @@ void na2d_av_forward( attn.stride(2), attn.stride(3), kernel_size, - dilation); + dilation, + is_causal); } void na2d_av_backward( @@ -147,16 +194,17 @@ void na2d_av_backward( const at::Tensor& value, at::Tensor& d_attn, at::Tensor& d_value, - const int batch_size, - const int heads, - const int height, - const int width, - const int dim, - const int kernel_size, - const int dilation) { + int32_t batch_size, + int32_t heads, + int32_t height, + int32_t width, + int32_t dim, + const std::tuple& kernel_size, + const std::tuple& dilation, + const std::tuple& is_causal) { DISPATCH_DTYPE( d_out.device().index(), - at::cuda::getCurrentCUDAStream(), + at::cuda::getCurrentCUDAStream(attn.device().index()), d_out.scalar_type(), natten::cuda::na2d_av_backward, static_cast(attn.data_ptr()), @@ -174,7 +222,8 @@ void na2d_av_backward( attn.stride(2), attn.stride(3), kernel_size, - dilation); + dilation, + is_causal); } } // namespace cuda diff --git a/csrc/src/pytorch/cuda/na3d.cu b/csrc/src/pytorch/cuda/na3d.cu index ac17349..ce6b6b5 100644 --- a/csrc/src/pytorch/cuda/na3d.cu +++ b/csrc/src/pytorch/cuda/na3d.cu @@ -28,6 +28,7 @@ #include #include +#include #include #include #include @@ -36,24 +37,65 @@ namespace natten { namespace pytorch { namespace cuda { +void na3d_forward( + const at::Tensor& query, + const at::Tensor& key, + const at::Tensor& value, + at::Tensor& out, + const at::optional& rpb, + int32_t batch_size, + int32_t depth, + int32_t height, + int32_t width, + int32_t heads, + int32_t dim, + const std::tuple& kernel_size, + const std::tuple& dilation, + const std::tuple& is_causal, + float attn_scale, + const std::tuple& query_tile_size, + const std::tuple& key_tile_size) { + DISPATCH_DTYPE( + query.device().index(), + at::cuda::getCurrentCUDAStream(query.device().index()), + query.scalar_type(), + natten::cuda::na3d_forward, + static_cast(query.data_ptr()), + static_cast(key.data_ptr()), + static_cast(value.data_ptr()), + static_cast(out.data_ptr()), + rpb.has_value() ? static_cast(rpb.value().data_ptr()) : nullptr, + batch_size, + depth, + height, + width, + heads, + dim, + kernel_size, + dilation, + is_causal, + attn_scale, + query_tile_size, + key_tile_size); +} + void na3d_qk_forward( const at::Tensor& query, const at::Tensor& key, const at::optional& bias, at::Tensor& attn, - const int batch_size, - const int heads, - const int depth, - const int height, - const int width, - const int dim, - const int kernel_size, - const int dilation, - const int depth_kernel_size, - const int depth_dilation) { + int32_t batch_size, + int32_t heads, + int32_t depth, + int32_t height, + int32_t width, + int32_t dim, + const std::tuple& kernel_size, + const std::tuple& dilation, + const std::tuple& is_causal) { DISPATCH_DTYPE( query.device().index(), - at::cuda::getCurrentCUDAStream(), + at::cuda::getCurrentCUDAStream(query.device().index()), query.scalar_type(), natten::cuda::na3d_qk_forward, static_cast(query.data_ptr()), @@ -73,8 +115,7 @@ void na3d_qk_forward( attn.stride(4), kernel_size, dilation, - depth_kernel_size, - depth_dilation); + is_causal); } void na3d_qk_backward( @@ -84,19 +125,18 @@ void na3d_qk_backward( at::Tensor& d_query, at::Tensor& d_key, at::optional& d_bias, - const int batch_size, - const int heads, - const int depth, - const int height, - const int width, - const int dim, - const int kernel_size, - const int dilation, - const int depth_kernel_size, - const int depth_dilation) { + int32_t batch_size, + int32_t heads, + int32_t depth, + int32_t height, + int32_t width, + int32_t dim, + const std::tuple& kernel_size, + const std::tuple& dilation, + const std::tuple& is_causal) { DISPATCH_DTYPE( d_attn.device().index(), - at::cuda::getCurrentCUDAStream(), + at::cuda::getCurrentCUDAStream(query.device().index()), d_attn.scalar_type(), natten::cuda::na3d_qk_backward, static_cast(query.data_ptr()), @@ -119,27 +159,25 @@ void na3d_qk_backward( d_attn.stride(4), kernel_size, dilation, - depth_kernel_size, - depth_dilation); + is_causal); } void na3d_av_forward( const at::Tensor& attn, const at::Tensor& value, at::Tensor& output, - const int batch_size, - const int heads, - const int depth, - const int height, - const int width, - const int dim, - const int kernel_size, - const int dilation, - const int depth_kernel_size, - const int depth_dilation) { + int32_t batch_size, + int32_t heads, + int32_t depth, + int32_t height, + int32_t width, + int32_t dim, + const std::tuple& kernel_size, + const std::tuple& dilation, + const std::tuple& is_causal) { DISPATCH_DTYPE( attn.device().index(), - at::cuda::getCurrentCUDAStream(), + at::cuda::getCurrentCUDAStream(attn.device().index()), attn.scalar_type(), natten::cuda::na3d_av_forward, static_cast(attn.data_ptr()), @@ -158,8 +196,7 @@ void na3d_av_forward( attn.stride(4), kernel_size, dilation, - depth_kernel_size, - depth_dilation); + is_causal); } void na3d_av_backward( @@ -168,19 +205,18 @@ void na3d_av_backward( const at::Tensor& value, at::Tensor& d_attn, at::Tensor& d_value, - const int batch_size, - const int heads, - const int depth, - const int height, - const int width, - const int dim, - const int kernel_size, - const int dilation, - const int depth_kernel_size, - const int depth_dilation) { + int32_t batch_size, + int32_t heads, + int32_t depth, + int32_t height, + int32_t width, + int32_t dim, + const std::tuple& kernel_size, + const std::tuple& dilation, + const std::tuple& is_causal) { DISPATCH_DTYPE( d_out.device().index(), - at::cuda::getCurrentCUDAStream(), + at::cuda::getCurrentCUDAStream(attn.device().index()), d_out.scalar_type(), natten::cuda::na3d_av_backward, static_cast(attn.data_ptr()), @@ -201,8 +237,7 @@ void na3d_av_backward( attn.stride(4), kernel_size, dilation, - depth_kernel_size, - depth_dilation); + is_causal); } } // namespace cuda diff --git a/csrc/src/pytorch/na1d.cpp b/csrc/src/pytorch/na1d.cpp index 3f82865..597530d 100644 --- a/csrc/src/pytorch/na1d.cpp +++ b/csrc/src/pytorch/na1d.cpp @@ -24,39 +24,92 @@ \brief Neighborhood Attention 1D Torch interface */ -#include "natten/pytorch/cpu/na1d.h" #include #include + +#include +#include #ifdef NATTEN_WITH_CUDA -#include "natten/pytorch/cuda/na1d.cuh" +#include #endif - -#include "natten/pytorch/helpers.h" +#include namespace natten { namespace pytorch { +void na1d_forward( + at::Tensor& out, + const at::Tensor& query, + const at::Tensor& key, + const at::Tensor& value, + const at::optional& rpb, + const std::tuple& kernel_size, + const std::tuple& dilation, + const std::tuple& is_causal, + float attn_scale, + const std::tuple& query_tile_size, + const std::tuple& key_tile_size) { + CHECK_CONTIGUOUS(query); + CHECK_CONTIGUOUS(key); + CHECK_CONTIGUOUS(value); + CheckArgs(kernel_size, dilation); + CheckIfPropertiesMatch(query, key, value); + CheckIfTensorShapesMatch<1>(query, key); + CheckIfTensorShapesMatch<1>(query, value); + CheckIfTensorShapesMatch<1>(out, value); + int32_t batch_size = query.size(0); + int32_t length = query.size(1); + int32_t heads = query.size(2); + int32_t dim = query.size(3); + CheckArgsAgainstDim({length}, kernel_size, dilation); + if (rpb.has_value()) { + CheckBias<1>(query, rpb.value(), heads, kernel_size); + } + DISPATCH_DEVICE( + query.device(), + na1d_forward, + query, + key, + value, + out, + rpb, + batch_size, + length, + heads, + dim, + kernel_size, + dilation, + is_causal, + attn_scale, + query_tile_size, + key_tile_size); +} + void na1d_qk_forward( at::Tensor& attn, const at::Tensor& query, const at::Tensor& key, const at::optional& bias, - const int kernel_size, - const int dilation) { + const std::tuple& kernel_size, + const std::tuple& dilation, + const std::tuple& is_causal) { + NATTEN_CHECK( + !any_true(is_causal) || !bias.has_value(), + "Neighborhood attention with causal masking does not support positional biases yet."); CHECK_CONTIGUOUS(query); CHECK_CONTIGUOUS(key); CheckArgs(kernel_size, dilation); CheckIfPropertiesMatch(query, key, attn); CheckIfTensorShapesMatch<1>(query, key); CheckAttnShape<1>(query, attn, kernel_size); + int32_t batch_size = query.size(0); + int32_t heads = query.size(1); + int32_t length = query.size(2); + int32_t dim = query.size(3); + CheckArgsAgainstDim({length}, kernel_size, dilation); if (bias.has_value()) { - CheckBias<1>(query, bias.value(), kernel_size); + CheckBias<1>(query, bias.value(), heads, kernel_size); } - int batch_size = query.size(0); - int heads = query.size(1); - int length = query.size(2); - int dim = query.size(3); - CheckArgsAgainstDim(length, kernel_size, dilation); DISPATCH_DEVICE( query.device(), na1d_qk_forward, @@ -69,7 +122,8 @@ void na1d_qk_forward( length, dim, kernel_size, - dilation); + dilation, + is_causal); } void na1d_qk_backward( @@ -79,8 +133,12 @@ void na1d_qk_backward( const at::Tensor& d_attn, const at::Tensor& query, const at::Tensor& key, - const int kernel_size, - const int dilation) { + const std::tuple& kernel_size, + const std::tuple& dilation, + const std::tuple& is_causal) { + NATTEN_CHECK( + !any_true(is_causal) || !d_bias.has_value(), + "Neighborhood attention with causal masking does not support positional biases yet."); CHECK_CONTIGUOUS(d_query); CHECK_CONTIGUOUS(d_key); CHECK_CONTIGUOUS(query); @@ -92,14 +150,14 @@ void na1d_qk_backward( CheckIfTensorShapesMatch<1>(d_query, d_key); CheckIfTensorShapesMatch<1>(query, d_key); CheckAttnShape<1>(query, d_attn, kernel_size); + int32_t batch_size = query.size(0); + int32_t heads = query.size(1); + int32_t length = query.size(2); + int32_t dim = query.size(3); + CheckArgsAgainstDim({length}, kernel_size, dilation); if (d_bias.has_value()) { - CheckBias<1>(query, d_bias.value(), kernel_size); + CheckBias<1>(query, d_bias.value(), heads, kernel_size); } - int batch_size = query.size(0); - int heads = query.size(1); - int length = query.size(2); - int dim = query.size(3); - CheckArgsAgainstDim(length, kernel_size, dilation); DISPATCH_DEVICE( d_attn.device(), na1d_qk_backward, @@ -114,26 +172,28 @@ void na1d_qk_backward( length, dim, kernel_size, - dilation); + dilation, + is_causal); } void na1d_av_forward( at::Tensor& out, const at::Tensor& attn, const at::Tensor& value, - const int kernel_size, - const int dilation) { + const std::tuple& kernel_size, + const std::tuple& dilation, + const std::tuple& is_causal) { CHECK_CONTIGUOUS(out); CHECK_CONTIGUOUS(value); CheckArgs(kernel_size, dilation); CheckIfPropertiesMatch(out, value, attn); CheckIfTensorShapesMatch<1>(out, value); CheckAttnShape<1>(value, attn, kernel_size); - int batch_size = value.size(0); - int heads = value.size(1); - int length = value.size(2); - int dim = value.size(3); - CheckArgsAgainstDim(length, kernel_size, dilation); + int32_t batch_size = value.size(0); + int32_t heads = value.size(1); + int32_t length = value.size(2); + int32_t dim = value.size(3); + CheckArgsAgainstDim({length}, kernel_size, dilation); DISPATCH_DEVICE( attn.device(), na1d_av_forward, @@ -145,7 +205,8 @@ void na1d_av_forward( length, dim, kernel_size, - dilation); + dilation, + is_causal); } void na1d_av_backward( @@ -154,8 +215,9 @@ void na1d_av_backward( const at::Tensor& d_out, const at::Tensor& attn, const at::Tensor& value, - const int kernel_size, - const int dilation) { + const std::tuple& kernel_size, + const std::tuple& dilation, + const std::tuple& is_causal) { CHECK_CONTIGUOUS(d_out); CHECK_CONTIGUOUS(d_value); CHECK_CONTIGUOUS(value); @@ -166,11 +228,11 @@ void na1d_av_backward( CheckIfTensorShapesMatch<1>(attn, d_attn); CheckIfTensorShapesMatch<1>(value, d_out); CheckAttnShape<1>(value, attn, kernel_size); - int batch_size = value.size(0); - int heads = value.size(1); - int length = value.size(2); - int dim = value.size(3); - CheckArgsAgainstDim(length, kernel_size, dilation); + int32_t batch_size = value.size(0); + int32_t heads = value.size(1); + int32_t length = value.size(2); + int32_t dim = value.size(3); + CheckArgsAgainstDim({length}, kernel_size, dilation); DISPATCH_DEVICE( attn.device(), na1d_av_backward, @@ -184,7 +246,8 @@ void na1d_av_backward( length, dim, kernel_size, - dilation); + dilation, + is_causal); } } // namespace pytorch diff --git a/csrc/src/pytorch/na2d.cpp b/csrc/src/pytorch/na2d.cpp index 37382f1..d1bc8e5 100644 --- a/csrc/src/pytorch/na2d.cpp +++ b/csrc/src/pytorch/na2d.cpp @@ -24,41 +24,95 @@ \brief Neighborhood Attention 2D Torch interface */ -#include "natten/pytorch/cpu/na2d.h" #include #include + +#include +#include #ifdef NATTEN_WITH_CUDA -#include "natten/pytorch/cuda/na2d.cuh" +#include #endif - -#include "natten/pytorch/helpers.h" +#include namespace natten { namespace pytorch { +void na2d_forward( + at::Tensor& out, + const at::Tensor& query, + const at::Tensor& key, + const at::Tensor& value, + const at::optional& rpb, + const std::tuple& kernel_size, + const std::tuple& dilation, + const std::tuple& is_causal, + float attn_scale, + const std::tuple& query_tile_size, + const std::tuple& key_tile_size) { + CHECK_CONTIGUOUS(query); + CHECK_CONTIGUOUS(key); + CHECK_CONTIGUOUS(value); + CheckArgs(kernel_size, dilation); + CheckIfPropertiesMatch(query, key, value); + CheckIfTensorShapesMatch<2>(query, key); + CheckIfTensorShapesMatch<2>(query, value); + CheckIfTensorShapesMatch<2>(out, value); + int32_t batch_size = query.size(0); + int32_t height = query.size(1); + int32_t width = query.size(2); + int32_t heads = query.size(3); + int32_t dim = query.size(4); + CheckArgsAgainstDim({height, width}, kernel_size, dilation); + if (rpb.has_value()) { + CheckBias<2>(query, rpb.value(), heads, kernel_size); + } + DISPATCH_DEVICE( + query.device(), + na2d_forward, + query, + key, + value, + out, + rpb, + batch_size, + height, + width, + heads, + dim, + kernel_size, + dilation, + is_causal, + attn_scale, + query_tile_size, + key_tile_size); +} + void na2d_qk_forward( at::Tensor& attn, const at::Tensor& query, const at::Tensor& key, const at::optional& bias, - const int kernel_size, - const int dilation) { + const std::tuple& kernel_size, + const std::tuple& dilation, + const std::tuple& is_causal) { + NATTEN_CHECK( + !any_true(is_causal) || !bias.has_value(), + "Neighborhood attention with causal masking does not support positional biases yet."); CHECK_CONTIGUOUS(query); CHECK_CONTIGUOUS(key); CheckArgs(kernel_size, dilation); CheckIfPropertiesMatch(query, key, attn); CheckIfTensorShapesMatch<2>(query, key); CheckAttnShape<2>(query, attn, kernel_size); + int32_t batch_size = query.size(0); + int32_t heads = query.size(1); + int32_t height = query.size(2); + int32_t width = query.size(3); + int32_t dim = query.size(4); + CheckArgsAgainstDim({height, width}, kernel_size, dilation); if (bias.has_value()) { - CheckBias<2>(query, bias.value(), kernel_size); + CheckBias<2>(query, bias.value(), heads, kernel_size); } - int batch_size = query.size(0); - int heads = query.size(1); - int height = query.size(2); - int width = query.size(3); - int dim = query.size(4); - CheckArgsAgainstDim(height, kernel_size, dilation); - CheckArgsAgainstDim(width, kernel_size, dilation); DISPATCH_DEVICE( query.device(), na2d_qk_forward, @@ -72,7 +126,8 @@ void na2d_qk_forward( width, dim, kernel_size, - dilation); + dilation, + is_causal); } void na2d_qk_backward( @@ -82,8 +137,12 @@ void na2d_qk_backward( const at::Tensor& d_attn, const at::Tensor& query, const at::Tensor& key, - const int kernel_size, - const int dilation) { + const std::tuple& kernel_size, + const std::tuple& dilation, + const std::tuple& is_causal) { + NATTEN_CHECK( + !any_true(is_causal) || !d_bias.has_value(), + "Neighborhood attention with causal masking does not support positional biases yet."); CHECK_CONTIGUOUS(d_query); CHECK_CONTIGUOUS(d_key); CHECK_CONTIGUOUS(query); @@ -95,16 +154,15 @@ void na2d_qk_backward( CheckIfTensorShapesMatch<2>(d_query, d_key); CheckIfTensorShapesMatch<2>(query, d_key); CheckAttnShape<2>(query, d_attn, kernel_size); + int32_t batch_size = query.size(0); + int32_t heads = query.size(1); + int32_t height = query.size(2); + int32_t width = query.size(3); + int32_t dim = query.size(4); + CheckArgsAgainstDim({height, width}, kernel_size, dilation); if (d_bias.has_value()) { - CheckBias<2>(query, d_bias.value(), kernel_size); + CheckBias<2>(query, d_bias.value(), heads, kernel_size); } - int batch_size = query.size(0); - int heads = query.size(1); - int height = query.size(2); - int width = query.size(3); - int dim = query.size(4); - CheckArgsAgainstDim(height, kernel_size, dilation); - CheckArgsAgainstDim(width, kernel_size, dilation); DISPATCH_DEVICE( d_attn.device(), na2d_qk_backward, @@ -120,28 +178,29 @@ void na2d_qk_backward( width, dim, kernel_size, - dilation); + dilation, + is_causal); } void na2d_av_forward( at::Tensor& out, const at::Tensor& attn, const at::Tensor& value, - const int kernel_size, - const int dilation) { + const std::tuple& kernel_size, + const std::tuple& dilation, + const std::tuple& is_causal) { CHECK_CONTIGUOUS(out); CHECK_CONTIGUOUS(value); CheckArgs(kernel_size, dilation); CheckIfPropertiesMatch(out, value, attn); CheckIfTensorShapesMatch<2>(out, value); CheckAttnShape<2>(value, attn, kernel_size); - int batch_size = value.size(0); - int heads = value.size(1); - int height = value.size(2); - int width = value.size(3); - int dim = value.size(4); - CheckArgsAgainstDim(height, kernel_size, dilation); - CheckArgsAgainstDim(width, kernel_size, dilation); + int32_t batch_size = value.size(0); + int32_t heads = value.size(1); + int32_t height = value.size(2); + int32_t width = value.size(3); + int32_t dim = value.size(4); + CheckArgsAgainstDim({height, width}, kernel_size, dilation); DISPATCH_DEVICE( attn.device(), na2d_av_forward, @@ -154,7 +213,8 @@ void na2d_av_forward( width, dim, kernel_size, - dilation); + dilation, + is_causal); } void na2d_av_backward( @@ -163,8 +223,9 @@ void na2d_av_backward( const at::Tensor& d_out, const at::Tensor& attn, const at::Tensor& value, - const int kernel_size, - const int dilation) { + const std::tuple& kernel_size, + const std::tuple& dilation, + const std::tuple& is_causal) { CHECK_CONTIGUOUS(d_out); CHECK_CONTIGUOUS(d_value); CHECK_CONTIGUOUS(value); @@ -175,13 +236,12 @@ void na2d_av_backward( CheckIfTensorShapesMatch<2>(attn, d_attn); CheckIfTensorShapesMatch<2>(value, d_out); CheckAttnShape<2>(value, attn, kernel_size); - int batch_size = d_out.size(0); - int heads = d_out.size(1); - int height = d_out.size(2); - int width = d_out.size(3); - int dim = d_out.size(4); - CheckArgsAgainstDim(height, kernel_size, dilation); - CheckArgsAgainstDim(width, kernel_size, dilation); + int32_t batch_size = d_out.size(0); + int32_t heads = d_out.size(1); + int32_t height = d_out.size(2); + int32_t width = d_out.size(3); + int32_t dim = d_out.size(4); + CheckArgsAgainstDim({height, width}, kernel_size, dilation); DISPATCH_DEVICE( attn.device(), na2d_av_backward, @@ -196,7 +256,8 @@ void na2d_av_backward( width, dim, kernel_size, - dilation); + dilation, + is_causal); } } // namespace pytorch diff --git a/csrc/src/pytorch/na3d.cpp b/csrc/src/pytorch/na3d.cpp index a185e3f..8124e2f 100644 --- a/csrc/src/pytorch/na3d.cpp +++ b/csrc/src/pytorch/na3d.cpp @@ -24,46 +24,98 @@ \brief Neighborhood Attention 3D Torch interface */ -#include "natten/pytorch/cpu/na3d.h" #include #include + +#include +#include #ifdef NATTEN_WITH_CUDA -#include "natten/pytorch/cuda/na3d.cuh" +#include #endif - -#include "natten/pytorch/helpers.h" +#include namespace natten { namespace pytorch { +void na3d_forward( + at::Tensor& out, + const at::Tensor& query, + const at::Tensor& key, + const at::Tensor& value, + const at::optional& rpb, + const std::tuple& kernel_size, + const std::tuple& dilation, + const std::tuple& is_causal, + float attn_scale, + const std::tuple& query_tile_size, + const std::tuple& key_tile_size) { + CHECK_CONTIGUOUS(query); + CHECK_CONTIGUOUS(key); + CHECK_CONTIGUOUS(value); + CheckArgs(kernel_size, dilation); + CheckIfPropertiesMatch(query, key, value); + CheckIfTensorShapesMatch<3>(query, key); + CheckIfTensorShapesMatch<3>(query, value); + CheckIfTensorShapesMatch<3>(out, value); + int32_t batch_size = query.size(0); + int32_t depth = query.size(1); + int32_t height = query.size(2); + int32_t width = query.size(3); + int32_t heads = query.size(4); + int32_t dim = query.size(5); + CheckArgsAgainstDim({depth, height, width}, kernel_size, dilation); + if (rpb.has_value()) { + CheckBias<3>(query, rpb.value(), heads, kernel_size); + } + DISPATCH_DEVICE( + query.device(), + na3d_forward, + query, + key, + value, + out, + rpb, + batch_size, + depth, + height, + width, + heads, + dim, + kernel_size, + dilation, + is_causal, + attn_scale, + query_tile_size, + key_tile_size); +} + void na3d_qk_forward( at::Tensor& attn, const at::Tensor& query, const at::Tensor& key, const at::optional& bias, - const int kernel_size, - const int dilation, - const int kernel_size_d, - const int dilation_d) { + const std::tuple& kernel_size, + const std::tuple& dilation, + const std::tuple& is_causal) { + NATTEN_CHECK( + !any_true(is_causal) || !bias.has_value(), + "Neighborhood attention with causal masking does not support positional biases yet."); CHECK_CONTIGUOUS(query); CHECK_CONTIGUOUS(key); CheckArgs(kernel_size, dilation); - CheckArgs(kernel_size_d, dilation_d); CheckIfPropertiesMatch(query, key, attn); CheckIfTensorShapesMatch<3>(query, key); - CheckAttnShape<3>(query, attn, kernel_size, kernel_size_d); + CheckAttnShape<3>(query, attn, kernel_size); + int32_t batch_size = query.size(0); + int32_t heads = query.size(1); + int32_t depth = query.size(2); + int32_t height = query.size(3); + int32_t width = query.size(4); + int32_t dim = query.size(5); + CheckArgsAgainstDim({depth, height, width}, kernel_size, dilation); if (bias.has_value()) { - CheckBias<3>(query, bias.value(), kernel_size, kernel_size_d); + CheckBias<3>(query, bias.value(), heads, kernel_size); } - int batch_size = query.size(0); - int heads = query.size(1); - int depth = query.size(2); - int height = query.size(3); - int width = query.size(4); - int dim = query.size(5); - CheckArgsAgainstDim(depth, kernel_size_d, dilation_d); - CheckArgsAgainstDim(height, kernel_size, dilation); - CheckArgsAgainstDim(width, kernel_size, dilation); DISPATCH_DEVICE( query.device(), na3d_qk_forward, @@ -79,8 +131,7 @@ void na3d_qk_forward( dim, kernel_size, dilation, - kernel_size_d, - dilation_d); + is_causal); } void na3d_qk_backward( @@ -90,34 +141,33 @@ void na3d_qk_backward( const at::Tensor& d_attn, const at::Tensor& query, const at::Tensor& key, - const int kernel_size, - const int dilation, - const int kernel_size_d, - const int dilation_d) { + const std::tuple& kernel_size, + const std::tuple& dilation, + const std::tuple& is_causal) { + NATTEN_CHECK( + !any_true(is_causal) || !d_bias.has_value(), + "Neighborhood attention with causal masking does not support positional biases yet."); CHECK_CONTIGUOUS(d_query); CHECK_CONTIGUOUS(d_key); CHECK_CONTIGUOUS(query); CHECK_CONTIGUOUS(key); CheckArgs(kernel_size, dilation); - CheckArgs(kernel_size_d, dilation_d); CheckIfPropertiesMatch(query, key); CheckIfPropertiesMatch(d_query, d_key, d_attn); CheckIfTensorShapesMatch<3>(query, key); CheckIfTensorShapesMatch<3>(d_query, d_key); CheckIfTensorShapesMatch<3>(query, d_key); - CheckAttnShape<3>(query, d_attn, kernel_size, kernel_size_d); + CheckAttnShape<3>(query, d_attn, kernel_size); + int32_t batch_size = query.size(0); + int32_t heads = query.size(1); + int32_t depth = query.size(2); + int32_t height = query.size(3); + int32_t width = query.size(4); + int32_t dim = query.size(5); + CheckArgsAgainstDim({depth, height, width}, kernel_size, dilation); if (d_bias.has_value()) { - CheckBias<3>(query, d_bias.value(), kernel_size, kernel_size_d); + CheckBias<3>(query, d_bias.value(), heads, kernel_size); } - int batch_size = query.size(0); - int heads = query.size(1); - int depth = query.size(2); - int height = query.size(3); - int width = query.size(4); - int dim = query.size(5); - CheckArgsAgainstDim(depth, kernel_size_d, dilation_d); - CheckArgsAgainstDim(height, kernel_size, dilation); - CheckArgsAgainstDim(width, kernel_size, dilation); DISPATCH_DEVICE( d_attn.device(), na3d_qk_backward, @@ -135,34 +185,29 @@ void na3d_qk_backward( dim, kernel_size, dilation, - kernel_size_d, - dilation_d); + is_causal); } void na3d_av_forward( at::Tensor& out, const at::Tensor& attn, const at::Tensor& value, - const int kernel_size, - const int dilation, - const int kernel_size_d, - const int dilation_d) { + const std::tuple& kernel_size, + const std::tuple& dilation, + const std::tuple& is_causal) { CHECK_CONTIGUOUS(out); CHECK_CONTIGUOUS(value); CheckArgs(kernel_size, dilation); - CheckArgs(kernel_size_d, dilation_d); CheckIfPropertiesMatch(out, value, attn); CheckIfTensorShapesMatch<3>(out, value); - CheckAttnShape<3>(value, attn, kernel_size, kernel_size_d); - int batch_size = value.size(0); - int heads = value.size(1); - int depth = value.size(2); - int height = value.size(3); - int width = value.size(4); - int dim = value.size(5); - CheckArgsAgainstDim(depth, kernel_size_d, dilation_d); - CheckArgsAgainstDim(height, kernel_size, dilation); - CheckArgsAgainstDim(width, kernel_size, dilation); + CheckAttnShape<3>(value, attn, kernel_size); + int32_t batch_size = value.size(0); + int32_t heads = value.size(1); + int32_t depth = value.size(2); + int32_t height = value.size(3); + int32_t width = value.size(4); + int32_t dim = value.size(5); + CheckArgsAgainstDim({depth, height, width}, kernel_size, dilation); DISPATCH_DEVICE( attn.device(), na3d_av_forward, @@ -177,8 +222,7 @@ void na3d_av_forward( dim, kernel_size, dilation, - kernel_size_d, - dilation_d); + is_causal); } void na3d_av_backward( @@ -187,30 +231,26 @@ void na3d_av_backward( const at::Tensor& d_out, const at::Tensor& attn, const at::Tensor& value, - const int kernel_size, - const int dilation, - const int kernel_size_d, - const int dilation_d) { + const std::tuple& kernel_size, + const std::tuple& dilation, + const std::tuple& is_causal) { CHECK_CONTIGUOUS(d_out); CHECK_CONTIGUOUS(d_value); CHECK_CONTIGUOUS(value); CheckArgs(kernel_size, dilation); - CheckArgs(kernel_size_d, dilation_d); CheckIfPropertiesMatch(attn, value); CheckIfPropertiesMatch(d_attn, d_value, d_out); CheckIfTensorShapesMatch<3>(value, d_value); CheckIfTensorShapesMatch<3>(attn, d_attn); CheckIfTensorShapesMatch<3>(value, d_out); - CheckAttnShape<3>(value, attn, kernel_size, kernel_size_d); - int batch_size = d_out.size(0); - int heads = d_out.size(1); - int depth = d_out.size(2); - int height = d_out.size(3); - int width = d_out.size(4); - int dim = d_out.size(5); - CheckArgsAgainstDim(depth, kernel_size_d, dilation_d); - CheckArgsAgainstDim(height, kernel_size, dilation); - CheckArgsAgainstDim(width, kernel_size, dilation); + CheckAttnShape<3>(value, attn, kernel_size); + int32_t batch_size = d_out.size(0); + int32_t heads = d_out.size(1); + int32_t depth = d_out.size(2); + int32_t height = d_out.size(3); + int32_t width = d_out.size(4); + int32_t dim = d_out.size(5); + CheckArgsAgainstDim({depth, height, width}, kernel_size, dilation); DISPATCH_DEVICE( attn.device(), na3d_av_backward, @@ -227,8 +267,7 @@ void na3d_av_backward( dim, kernel_size, dilation, - kernel_size_d, - dilation_d); + is_causal); } } // namespace pytorch diff --git a/docs/README.md b/docs/README.md new file mode 100644 index 0000000..d02307b --- /dev/null +++ b/docs/README.md @@ -0,0 +1,20 @@ +![NATTENLogo](../assets/natten_dark.png#gh-dark-mode-only) ![NATTENLogo](../assets/natten_light.png#gh-light-mode-only) + +Welcome to NATTEN docs! + +* [What is NATTEN and why does it exist?](history.md) +* [How is Neigborhood Attention implemented?](methodology/README.md) +* [How do I install NATTEN?](install.md) +* [Front-end](frontend.md) + * Describes how to check NATTEN's compatibility with your system, and its available features. + * Describes how to use torch modules `NeighborhoodAttention1D`, `NeighborhoodAttention2D`, and `NeighborhoodAttention3D`. + * Describes when and how to use ops directly instead of modules. +* [API](api.md) + * Describes the current C++ API structure. +* [Backend](backend.md) + * Overview of different implementations, and available framework extensions. + * Implementation details and limitations. + * Dispatch mechanism and auto-generated instantiations. +* [Build system](build.md) +* [Tests](tests.md) +* [Changelog](../CHANGELOG.md) diff --git a/docs/api.md b/docs/api.md new file mode 100644 index 0000000..2900a58 --- /dev/null +++ b/docs/api.md @@ -0,0 +1,95 @@ +## C++ API + +NOTE: this page is still a work in progress. + +### PyTorch Bindings + +#### FNA + +```cpp +// Forward pass +void na*d_forward( + Tensor output, + Tensor query, + Tensor key, + Tensor value, + optional rel_pos_bias, + Tuple kernel_size, + Tuple dilation, + BooleanTuple is_causal, + float attn_scale, + Tuple query_tile_size, + Tuple key_tile_size); +``` + +Notes: +* `Tuple` and `BooleanTuple` instances must have the same size as the NA rank (i.e. `tuple` and `tuple` + for NA-2D.) +* `query_tile_size` and `key_tile_size` will be REMOVED in the near future. Their sole purpose is to allow auto-tuner (written + in Python) to pick the tiling configuration. If feasible, auto-tuner should be moved into libnatten; otherwise the tiling + config must switch to enums instead of exposing the literal tile sizes to the users. If invalid tile sizes are input, the + final FNA dispatchers will fail to find a matching kernel, and raise an error. + +#### BMM-style NA + +##### QK operation + +```cpp +// Forward pass +void na*d_qk_forward( + Tensor attn, + Tensor query, + Tensor key, + optional rel_pos_bias, + Tuple kernel_size, + Tuple dilation, + BooleanTuple is_causal); + +// Backward pass +void na*d_qk_backward( + Tensor d_query, // Output + Tensor d_key, // Output + optional d_rel_pos_bias, // Optional output + Tensor d_attn, + Tensor query, + Tensor key, + Tuple kernel_size, + Tuple dilation, + BooleanTuple is_causal); +``` + +Notes: +* `Tuple` and `BooleanTuple` instances must have the same size as the NA rank (i.e. `tuple` and `tuple` + for NA-2D.) + +##### PV (AV) operation + +```cpp +// Forward pass +void na*d_av_forward( + Tensor output, + Tensor attn, + Tensor value, + Tuple kernel_size, + Tuple dilation, + BooleanTuple is_causal); + +// Backward pass +void na*d_av_backward( + Tensor d_attn, // Output + Tensor d_value, // Output + Tensor d_out, + Tensor attn, + Tensor value, + Tuple kernel_size, + Tuple dilation, + BooleanTuple is_causal); +``` + +Notes: +* `Tuple` and `BooleanTuple` instances must have the same size as the NA rank (i.e. `tuple` and `tuple` + for NA-2D.) + + +### Operators +TBD. diff --git a/docs/assets/batched_gemm_na_dark.png b/docs/assets/batched_gemm_na_dark.png new file mode 100644 index 0000000000000000000000000000000000000000..0cf702099e68033fe9f0dee0eeb2301b2bcfd4e0 GIT binary patch literal 253611 zcmeFac|6qX`#(Ny)DcoA%G%K>N6Avzm!T}BQYvNNDrDE#_fpXj>Zr(G5|O3s%b;u( z23baiu~j33p}}Ab-}^N)N}X4q&Zp1s&)@4j-qpOb6kn861c%)c|=Pcu?YQ_giehF->h>!WPk#H@1_4wKB?^kcpPz9 zUG21I*GNy3)1X%ZcA`)~G3$|k$}VN)9zW~iKUN!FEZ%o^;R-r;aW#+5JnQZ({{ENo z#S$Ag9yql2hK3=Bja5{9+oN>5e~RKd`MpchN93N7-$XQFrw3hzKihb#Y`SY^GF(M{ z7u#r+FY1jpC5j*fWMUf^F6z-@J4l@;0#54no^2P4_;d7jQ~%v2f~~ z%qulL+F<(d+4g|!bRi?8O^~md-eOWZCS$mQ-q?${V5UcF>WFwJ8DOZFoIZ!VKpYz& 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