Tiny CUDA Neural Networks

This is a small, self-contained framework for training and querying neural networks. Most notably, it contains a lightning fast "fully fused" multi-layer perceptron as well as support for various advanced input encodings, losses, and optimizers.

This framework powers the following publication:

Real-time Neural Radiance Caching for Path Tracing
Thomas Müller, Fabrice Rousselle, Jan Novák, Alexander Keller
To appear: ACM Transactions on Graphics (SIGGRAPH) 2021

[ Paper ] [ GTC talk ] [ Video ] [ Interactive Results Viewer ] [ BibTex ]

For business inquiries, please contact [email protected].
For press and other inquiries, please contact Hector Marinez at [email protected].

Performance

Fully fused networks vs. TensorFlow v2.5.0 w/ XLA. Measured on 64 (solid line) and 128 (dashed line) neurons wide multi-layer perceptrons on an RTX 3090. Generated by benchmarks/bench_ours.cu and benchmarks/bench_tensorflow.py.

License and Citation

This framework is licensed under the BSD 3-clause license. Please see LICENSE.txt for details.

If you use it in your research, we would appreciate a citation via

@misc{tiny-cuda-nn,
    Author = {Thomas M\"uller},
    Year = {2021},
    Note = {https://github.com/nvlabs/tiny-cuda-nn},
    Title = {Tiny {CUDA} Neural Network Framework}
}

Special thanks go to the NRC authors for helpful discussions and to Nikolaus Binder for providing part of the infrastructure of this framework, as well as for help with utilizing TensorCores from within CUDA.

Usage

Tiny CUDA neural networks have a simple C++/CUDA API:

#include <tiny-cuda-nn/common.h>

// Configure the model
nlohmann::json config = {
	{"loss", {
		{"otype", "L2"}
	}},
	{"optimizer", {
		{"otype", "Adam"},
		{"learning_rate", 1e-3},
	}},
	{"encoding", {
		{"otype", "OneBlob"},
		{"n_bins", 32},
	}},
	{"network", {
		{"otype", "FullyFusedMLP"},
		{"n_neurons", 64},
		{"n_hidden_layers", 5},
		{"activation", "ReLU"},
		{"output_activation", "None"},
	}},
};

using namespace tcnn;

auto [loss, optimizer, network, trainer] =
	create_from_config(n_input_dims_to_encode, n_input_dims_to_pass_through, n_output_dims, config);

// Train the model
GPUMatrix<float, MatrixLayout::ColumnMajor> training_batch_inputs(n_input_dims, batch_size);
GPUMatrix<float, MatrixLayout::ColumnMajor> training_batch_targets(n_output_dims, batch_size);

for (int i = 0; i < n_training_steps; ++i) {
	generate_training_batch(&training_batch_inputs, &training_batch_targets); // <-- your code

	float loss;
	trainer->training_step(nullptr, training_batch_inputs, training_batch_targets, &loss);
	std::cout << "iteration=" << i << " loss=" << loss << std::endl;
}

// Use the model
GPUMatrix<float, MatrixLayout::ColumnMajor> inference_inputs(n_input_dims, batch_size);
generate_inputs(&inference_inputs); // <-- your code

GPUMatrix<float, MatrixLayout::ColumnMajor> inference_outputs(n_output_dims, batch_size);
network->inference(nullptr, inference_inputs, inference_outputs);

Example: learning a 2D image

We provide a sample application where an image function (x,y) -> (R,G,B) is learned. It can be run via

tiny-cuda-nn/build> ./mlp_learning_an_image ../data/images/albert.exr ../data/config.json

producing an image every 1000 training steps. Each 1000 steps should take roughly 0.8 seconds with the default configuration on an RTX 3090.

Learned image after 1,000 steps	Learned image after 10,000 steps	Reference image

Requirements

• CUDA v11.2 or higher.
• CMake v3.17 or higher.
• A C++14 capable compiler.
• A high-end NVIDIA GPU that supports TensorCores and has a large amount of shared memory. The framework was tested primarily with an RTX 3090.
  • Ampere GeForce GPUs: compiles out of the box.
  • Ampere A100: requires changing CMAKE_CUDA_ARCHITECTURE to 80 in CMakeLists.txt.
  • Turing GPUs: requires changing CMAKE_CUDA_ARCHITECTURE to 75 in CMakeLists.txt as well as changing SmArch in include/tiny-cuda-nn/cutlass_matmul.h to cutlass::arch::Sm75.

Compilation

Begin by cloning this repository and all its submodules using the following command:

> git clone --recursive https://github.com/nvlabs/tiny-cuda-nn
> cd tiny-cuda-nn
tiny-cuda-nn>

Then, use CMake to generate build files:

tiny-cuda-nn> mkdir build
tiny-cuda-nn> cd build
tiny-cuda-nn/build> cmake ..

Then, depending on your operating system

On Windows, open tiny-cuda-nn/build/tiny-cuda-nn.sln in Visual Studio and click the "Build" button. On Linux you can compile with

tiny-cuda-nn/build> make -j

Components

The following is a summary of all components of this framework that are currently released. Please consult the JSON documentation for how to configure them.

Networks
Fully fused MLP	src/fully_fused_mlp.cu	Lightning fast implementation of small multi-layer perceptrons (MLPs).
CUTLASS MLP	src/cutlass_mlp.cu	MLP based on CUTLASS' GEMM routines. Slower than fully-fused, but handles larger networks and still is reasonably fast.
CUTLASS ResNet	src/cutlass_resnet.cu	Fully connected residual network based on CUTLASS' GEMM routines.

Input encodings
Identity	include/tiny-cuda-nn/encodings/identity.h	Leaves values untouched.
Oneblob	include/tiny-cuda-nn/encodings/oneblob.h	From Neural Importance Sampling [Müller et al. 2019] and Neural Control Variates [Müller et al. 2020].
Frequency	include/tiny-cuda-nn/encodings/frequency.h	From NeRF [Mildenhall et al. 2020].
NRC	include/tiny-cuda-nn/encodings/nrc.h	Combined oneblob and frequency encoding used in Neural Radiance Caching [Müller et al. 2021].

Losses
L2	include/tiny-cuda-nn/losses/l2.h	Standard L2 loss.
Relative L2	include/tiny-cuda-nn/losses/relative_l2.h	Relative L2 loss normalized by the network prediction [Lehtinen et al. 2018].
Relative L2 Luminance	include/tiny-cuda-nn/losses/relative_l2_luminance.h	Same as above, but normalized by the luminance of the network prediction. Only applicable when network prediction is RGB. Used in Neural Radiance Caching [Müller et al. 2021].
Cross Entropy	include/tiny-cuda-nn/losses/cross_entropy.h	Standard cross entropy loss. Only applicable when the network prediction is a PDF.
Variance	include/tiny-cuda-nn/losses/variance_is.h	Standard variance loss. Only applicable when the network prediction is a PDF.

Optimizers
Adam	include/tiny-cuda-nn/optimizers/adam.h	Implementation of Adam [Kingma and Ba 2014], generalized to AdaBound [Luo et al. 2019].
Novograd	include/tiny-cuda-nn/optimizers/lookahead.h	Implementation of Novograd [Ginsburg et al. 2019].
SGD	include/tiny-cuda-nn/optimizers/sgd.h	Standard stochastic gradient descent (SGD).
Shampoo	include/tiny-cuda-nn/optimizers/shampoo.h	Implementation of the 2nd order Shampoo optimizer [Gupta et al. 2018] with home-grown optimizations as well as those by Anil et al. [2020].
Average	include/tiny-cuda-nn/optimizers/average.h	Wraps another optimizer and computes a linear average of the weights over the last N iterations. The average is used for inference only (does not feed back into training).
Batched	include/tiny-cuda-nn/optimizers/batched.h	Wraps another optimizer, invoking the nested optimizer once every N steps on the averaged gradient. Has the same effect as increasing the batch size but requires only a constant amount of memory.
EMA	include/tiny-cuda-nn/optimizers/average.h	Wraps another optimizer and computes an exponential moving average of the weights. The average is used for inference only (does not feed back into training).
Exponential Decay	include/tiny-cuda-nn/optimizers/exponential_decay.h	Wraps another optimizer and performs piecewise-constant exponential learning-rate decay.
Lookahead	include/tiny-cuda-nn/optimizers/lookahead.h	Wraps another optimizer, implementing the lookahead algorithm [Zhang et al. 2019].