first commit

.gitignore

@@ -0,0 +1,170 @@
+.DS_Store
+
+data/
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+*.pyc
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+cover/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+.pybuilder/
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+#   For a library or package, you might want to ignore these files since the code is
+#   intended to run in multiple environments; otherwise, check them in:
+# .python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+#Pipfile.lock
+
+# poetry
+#   Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
+#   This is especially recommended for binary packages to ensure reproducibility, and is more
+#   commonly ignored for libraries.
+#   https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
+#poetry.lock
+
+# pdm
+#   Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
+#pdm.lock
+#   pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
+#   in version control.
+#   https://pdm.fming.dev/#use-with-ide
+.pdm.toml
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+
+# pytype static type analyzer
+.pytype/
+
+# Cython debug symbols
+cython_debug/
+
+# PyCharm
+#  JetBrains specific template is maintained in a separate JetBrains.gitignore that can
+#  be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
+#  and can be added to the global gitignore or merged into this file.  For a more nuclear
+#  option (not recommended) you can uncomment the following to ignore the entire idea folder.
+#.idea/
+
+*.pt
+**/*.pt
+**/*.pyc
+*.json
+__pycache__

README.md

@@ -0,0 +1,132 @@
+# AWQ: Activation-aware Weight Quantization for LLM Compression and Acceleration
+
+**Efficient and accurate** low-bit weight quantization (INT3/4) for LLMs, supporting **instruction-tuned** models and **multi-modal** LMs. 
+
+![overview](figures/overview.png)
+
+The current release supports: 
+
+- AWQ search for accurate quantization. 
+- Pre-computed AWQ model zoo for LLMs (LLaMA, OPT, Vicuna, LLaVA; load to generate quantized weights).
+- Memory-efficient 4-bit Linear in PyTorch.
+- Efficient CUDA kernel implementation for fast inference (support context and decoding stage).
+- Examples on 4-bit inference of an instruction-tuned model (Vicuna) and mult-modal LM (LLaVA).
+
+## Contents
+
+- [Install](#install)
+- [AWQ Model Zoo](#awq-model-zoo)
+- [Examples](#examples)
+- [Usage](#usage)
+- [Reference](#reference)
+
+## Install
+
+1. Clone this repository and navigate to AWQ folder
+```
+git clone https://github.com/mit-han-lab/llm-awq
+cd llm-awq
+```
+
+2. Install Package
+```
+conda create -n awq python=3.10 -y
+conda activate awq
+pip install --upgrade pip  # enable PEP 660 support
+pip install -e .
+```
+
+3. Install kernel implementation
+```
+cd awq/kernels
+python setup.py install
+```
+
+## AWQ Model Zoo
+
+We provide pre-computed AWQ search results for multiple model families, including LLaMA, OPT, Vicuna, and LLaVA. To get the pre-computed AWQ search results, run:
+
+```bash
+# git lfs install  # install git lfs if not already
+git clone https://huggingface.co/datasets/mit-han-lab/awq-model-zoo awq_cache
+```
+
+The detailed support list:
+
+| Models | Sizes                       | INT4-g128 | INT3-g128 |
+| ------ | --------------------------- | --------- | --------- |
+| LLaMA  | 7B/13B/30B/65B              | ✅         | ✅         |
+| OPT    | 125m/1.3B/2.7B/6.7B/13B/30B | ✅         | ✅         |
+| Vicuna | 7B/13B                      | ✅         |           |
+| LLaVA  | 13B                         | ✅         |           |
+
+## Examples
+
+AWQ can be easily applied to various LMs thanks to its good generalization, including instruction-tuned models and multi-modal LMs. It provides an easy-to-use tool to reduce the serving cost of LLMs.
+
+Here we provide two examples of AWQ application: Vicuna-7B (chatbot) and LLaVA-13B (visual reasoning) under `./examples` directory. AWQ can easily reduce the GPU memory of model serving and speed up token generation. It provides accurate quantization, providing reasoning outputs. You should be able to observe **memory savings** when running the models with 4-bit weights. 
+
+Note that we perform AWQ using only textual calibration data, depsite we are running on multi-modal input. Please refer to `./examples` for details.
+
+![overview](figures/example_vis.jpg)
+
+## Usage
+
+We provide several sample script to run AWQ (please refer to `./scripts`). We use OPT-6.7B as an example.
+
+1. Perform AWQ search and save search results (we already did it for you):
+```bash
+python -m awq.entry --model_path /PATH/TO/OPT/opt-6.7b \
+    --w_bit 4 --q_group_size 128 \
+    --run_awq --dump_awq awq_cache/opt-6.7b-w4-g128.pt
+```
+
+2. Evaluate the AWQ quantize model on WikiText-2 (simulated pseudo quantization)
+```bash
+python -m awq.entry --model_path /PATH/TO/OPT/opt-6.7b \
+    --tasks wikitext \
+    --w_bit 4 --q_group_size 128 \
+    --load_awq awq_cache/opt-6.7b-w4-g128.pt \
+    --q_backend fake
+```
+
+3. Generate real quantized weights (INT4)
+```bash
+mkdir quant_cache
+python -m awq.entry --model_path /PATH/TO/OPT/opt-6.7b \
+    --w_bit 4 --q_group_size 128 \
+    --load_awq awq_cache/opt-6.7b-w4-g128.pt \
+    --q_backend real --dump_quant quant_cache/opt-6.7b-w4-g128-awq.pt
+```
+
+4. Load and evaluate the real quantized model (now you can see smaller gpu memory usage)
+```bash
+python -m awq.entry --model_path /PATH/TO/OPT/opt-6.7b \
+    --tasks wikitext \
+    --w_bit 4 --q_group_size 128 \
+    --load_quant quant_cache/opt-6.7b-w4-g128-awq.pt
+```
+
+## Reference
+
+If you find AWQ useful or relevant to your research, please kindly cite our paper:
+
+```
+@article{lin2023awq,
+  title={AWQ: Activation-aware Weight Quantization for LLM Compression and Acceleration},
+  author={Lin, Ji and Tang, Jiaming and Tang, Haotian and Yang, Shang and Dang, Xingyu and Han, Song},
+  journal={arXiv},
+  year={2023}
+}
+```
+
+## Related Projects
+
+[SmoothQuant: Accurate and Efficient Post-Training Quantization for Large Language Models](https://github.com/mit-han-lab/smoothquant)
+
+[GPTQ: Accurate Post-training Compression for Generative Pretrained Transformers](https://arxiv.org/abs/2210.17323)
+
+[Vicuna and FastChat](https://github.com/lm-sys/FastChat#readme)
+
+[LLaVA: Large Language and Vision Assistant](https://github.com/haotian-liu/LLaVA)
+