Skip to content

[MLSys 2024 Best Paper Award] AWQ: Activation-aware Weight Quantization for LLM Compression and Acceleration

License

Notifications You must be signed in to change notification settings

mit-han-lab/llm-awq

Folders and files

NameName
Last commit message
Last commit date

Latest commit

Β 
Β 
Β 
Β 
Β 
Β 
Β 
Β 
Β 
Β 
Β 
Β 
Β 
Β 
Β 
Β 
Β 
Β 
Β 

Repository files navigation

AWQ: Activation-aware Weight Quantization for LLM Compression and Acceleration

[Paper][Slides][Video]

Efficient and accurate low-bit weight quantization (INT3/4) for LLMs, supporting instruction-tuned models and multi-modal LMs.

overview

The current release supports:

  • [Beta] Chunk prefilling for faster prefilling in multi-round Q&A setting. Stable branch.
  • AWQ search for accurate quantization.
  • Pre-computed AWQ model zoo for LLMs (Llama-1/2/3, OPT, CodeLlama, StarCoder, Vicuna, VILA, 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 multi-modal LM (VILA).

Thanks to AWQ, TinyChat can deliver more efficient responses with LLM/VLM chatbots through 4-bit inference.

  • TinyChat on RTX 4090 (3.4x faster than FP16):

TinyChat on RTX 4090: W4A16 is 3.4x faster than FP16

  • TinyChat on Jetson Orin (3.2x faster than FP16):

TinyChat on Orin: W4A16 is 3.2x faster than FP16

TinyChat also supports inference with vision language models (e.g., VILA, LLaVA). In the following examples, W4A16 quantized models from VILA family are launched with TinyChat.

  • TinyChat with VILA-13B on RTX 4090 (multi-image inputs supported):

TinyChat with VILA on 4090

  • TinyChat with VILA-7B/13B on Jetson Orin:

TinyChat with VILA on Orin

Check out TinyChat, which offers a turn-key solution for on-device inference of LLMs and VLMs on resource-constrained edge platforms. With TinyChat, it is now possible to efficiently run large models on small and low-power devices even without Internet connection!

News

  • [2024/10] πŸ”₯ [Beta] We supported Chunk Prefilling in TinyChat, leading to an order of magnitude faster prefilling in multi-round Q&A (over 1k history tokens). Details are here. The original stable branch is here.
  • [2024/05] πŸ† AWQ receives the Best Paper Award at MLSys 2024. πŸŽ‰
  • [2024/05] πŸ”₯ The VILA-1.5 model family which features video understanding is now supported in AWQ and TinyChat. Check out out online demo powered by TinyChat here. Example is here.
  • [2024/05] πŸ”₯ AMD adopts AWQ to improve LLM serving efficiency.
  • [2024/04] πŸ”₯ We released AWQ and TinyChat support for The Llama-3 model family! Check out our example here.
  • [2024/02] πŸ”₯ AWQ has been accepted to MLSys 2024!
  • [2024/02] πŸ”₯ We supported VILA Vision Languague Models in AWQ & TinyChat! Check our latest demos with multi-image inputs!
  • [2024/02] πŸ”₯ We released new version of quantized GEMM/GEMV kernels in TinyChat, leading to 38 tokens/second inference speed on NVIDIA Jetson Orin!
  • [2024/01] πŸ”₯ AWQ has been integrated by Google Vertex AI!
  • [2023/11] πŸ”₯ AWQ has been integrated by Amazon Sagemaker Containers!
  • [2023/11] πŸ”₯ We added AWQ support and pre-computed search results for CodeLlama, StarCoder, StableCode models. Checkout our model zoo here!
  • [2023/11] πŸ”₯ AWQ is now integrated natively in Hugging Face transformers through from_pretrained. You can either load quantized models from the Hub or your own HF quantized models.
  • [2023/10] AWQ is integrated into NVIDIA TensorRT-LLM
  • [2023/09] AWQ is integrated into Intel Neural Compressor, FastChat, vLLM, HuggingFace TGI, and LMDeploy.
  • [2023/09] ⚑ Check out our latest TinyChat, which is ~2x faster than the first release on Orin!
  • [2023/09] ⚑ Check out AutoAWQ, a third-party implementation to make AWQ easier to expand to new models, improve inference speed, and integrate into Huggingface.
  • [2023/07] πŸ”₯ We released TinyChat, an efficient and lightweight chatbot interface based on AWQ. TinyChat enables efficient LLM inference on both cloud and edge GPUs. Llama-2-chat models are supported! Check out our implementation here.
  • [2023/07] πŸ”₯ We added AWQ support and pre-computed search results for Llama-2 models (7B & 13B). Checkout our model zoo here!
  • [2023/07] We extended the support for more LLM models including MPT, Falcon, and BLOOM.

Contents

Helpful Links

  • VILA online demo: Visual Language Models efficiently supported by AWQ & TinyChat.
  • LLM on the Edge: AWQ and TinyChat support edge GPUs such as NVIDIA Jetson Orin.
  • VLMs on Laptop: Follow the instructions to deploy VLMs on NVIDIA Laptops with TinyChat.
  • Gradio Server: Try to build your own VLM online demo with AWQ and TinyChat!
  • QServe: πŸ”₯ [New] Efficient and accurate serving system for large-scale LLM inference.

Install

  1. Clone this repository and navigate to AWQ folder
git clone https://github.com/mit-han-lab/llm-awq
cd llm-awq
  1. Install Package
conda create -n awq python=3.10 -y
conda activate awq
pip install --upgrade pip  # enable PEP 660 support
pip install -e .
  • For edge devices like Orin, before running the commands above, please:

    1. Modify pyproject.toml by commenting out this line.
    2. Set this line to transformers==4.32.0.
    3. Manually install precompiled PyTorch binaries (>=2.0.0) from NVIDIA.
    4. Set the appropriate Python version for conda environment (e.g., conda create -n awq python=3.8 -y for JetPack 5).
  1. Install efficient W4A16 (4-bit weight, 16-bit activation) CUDA kernel and optimized FP16 kernels (e.g. layernorm, positional encodings).
cd awq/kernels
python setup.py install
  1. In order to run AWQ and TinyChat with VILA-1.5 model family, please install VILA:
git clone [email protected]:Efficient-Large-Model/VILA.git
cd VILA
pip install -e .

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:

# 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
VILA-1.5 3B/8B/13B/40B βœ… βœ…
Llama3 8B/70B βœ… βœ…
VILA 7B/13B βœ…
Llama2 7B/13B/70B βœ… βœ…
LLaMA 7B/13B/30B/65B βœ… βœ…
OPT 125m/1.3B/2.7B/6.7B/13B/30B βœ… βœ…
CodeLlama 7B/13B/34B βœ… βœ…
StarCoder 15.5B βœ… βœ…
Vicuna-v1.1 7B/13B βœ…
LLaVA-v0 13B βœ…

Note: We only list models that we have prepare the AWQ searching results in the table above. AWQ also supports models such as LLaVA-v1.5 7B, and you may need to run the AWQ search on your own to quantize these models.

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

Usage

We provide several sample script to run AWQ (please refer to ./scripts). We use Llama3-8B as an example.

  1. Perform AWQ search and save search results (we already did it for you):
python -m awq.entry --model_path /PATH/TO/LLAMA3/llama3-8b \
    --w_bit 4 --q_group_size 128 \
    --run_awq --dump_awq awq_cache/llama3-8b-w4-g128.pt
  1. Evaluate the AWQ quantized model on WikiText-2 (simulated pseudo quantization)
python -m awq.entry --model_path /PATH/TO/LLAMA3/llama3-8b \
    --tasks wikitext \
    --w_bit 4 --q_group_size 128 \
    --load_awq awq_cache/llama3-8b-w4-g128.pt \
    --q_backend fake
  1. Generate real quantized weights (INT4)
mkdir quant_cache
python -m awq.entry --model_path /PATH/TO/LLAMA3/llama3-8b \
    --w_bit 4 --q_group_size 128 \
    --load_awq awq_cache/llama3-8b-w4-g128.pt \
    --q_backend real --dump_quant quant_cache/llama3-8b-w4-g128-awq.pt
  1. Load and evaluate the real quantized model (now you can see smaller gpu memory usage)
python -m awq.entry --model_path /PATH/TO/LLAMA3/llama3-8b \
    --tasks wikitext \
    --w_bit 4 --q_group_size 128 \
    --load_quant quant_cache/llama3-8b-w4-g128-awq.pt

Results on Vision-Language Models (VILA-1.5)

AWQ also seamlessly supports large multi-modal models (LMMs). We demonstrate the results on the recent VILA-1.5 model family.

VILA-1.5-3B VQA-v2 GQA VizWiz ScienceQA TextVQA POPE MME MMBench MMBench-CN SEED
FP16 80.4 61.5 53.5 69.0 60.4 85.9 1442.4 63.4 52.7 60.9
AWQ-INT4 80.0 61.1 53.8 67.8 60.4 85.9 1437.3 63.3 51.4 59.8
VILA-1.5-8B VQA-v2 GQA VizWiz ScienceQA TextVQA POPE MME MMBench MMBench-CN SEED
FP16 80.9 61.9 58.7 79.9 66.3 84.4 1577.01 72.3 66.2 64.2
AWQ-INT4 80.3 61.7 59.3 79.0 65.4 82.9 1593.65 71.0 64.9 64.0
VILA-1.5-13B VQA-v2 GQA VizWiz ScienceQA TextVQA POPE MME MMBench MMBench-CN SEED
FP16 82.8 64.3 62.6 80.1 65.0 86.3 1569.55 74.9 66.3 65.1
AWQ-INT4 82.7 64.5 63.3 79.7 64.7 86.7 1531.35 74.7 66.7 65.1
VILA-1.5-40B VQA-v2 GQA VizWiz ScienceQA TextVQA POPE MME MMBench MMBench-CN SEED
FP16 84.3 64.6 62.2 87.2 73.6 87.3 1726.82 82.4 80.2 69.1
AWQ-INT4 84.1 64.4 61.3 86.7 73.2 88.2 1714.79 83.2 79.6 68.9

Inference speed ( Token/sec )

$~~~~~~$ Precision A100 4090 Orin
VILA1.5-3B fp16 104.6 137.6 25.4
VILA1.5-3B-AWQ int4 182.8 215.5 42.5
VILA1.5-3B-S2 fp16 104.3 137.2 24.6
VILA1.5-3B-S2-AWQ int4 180.2 219.3 40.1
Llama-3-VILA1.5-8B fp16 74.9 57.4 10.2
Llama-3-VILA1.5-8B-AWQ int4 168.9 150.2 28.7
VILA1.5-13B fp16 50.9 OOM 6.1
VILA1.5-13B-AWQ int4 115.9 105.7 20.6
VILA1.5-40B fp16 OOM OOM --
VILA1.5-40B-AWQ int4 57.0 OOM --

Reference

If you find AWQ useful or relevant to your research, please kindly cite our paper:

@inproceedings{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 Chen, Wei-Ming and Wang, Wei-Chen and Xiao, Guangxuan and Dang, Xingyu and Gan, Chuang and Han, Song},
  booktitle={MLSys},
  year={2024}
}

Related Projects

SmoothQuant: Accurate and Efficient Post-Training Quantization for Large Language Models

GPTQ: Accurate Post-training Compression for Generative Pretrained Transformers

Vicuna and FastChat

LLaVA: Large Language and Vision Assistant

VILA: On Pre-training for Visual Language Models

About

[MLSys 2024 Best Paper Award] AWQ: Activation-aware Weight Quantization for LLM Compression and Acceleration

Resources

License

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published