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SliM-LLM: Salience-Driven Mixed-Precision Quantization for Large Language Models

The current release supports:

• SliM-LLM for accurate weight-only quantization (W=2bit/W=3bit).
• Support pre-trained model for LLMs (LLaMA-1&2&3/OPT).
• SliM-LLM is built on GPTQ and SliM-LLM+ is built on Omniquant, respectively.
• Experimental mixed-precision deployment on AutoGPTQ for LLaMA-7b/LLaMA-13b/LLaMA-2-7b.

News

• [2024/5] 🌟 Our SliM-LLM paper and code are open now! (We are still uploading and improving our code and huggingface repository, please be patient.)

Contents

• Install
• SliM-LLM Mixed-precision Zoo
• Usage
• Results
• Citation

Install

conda create -n slimllm python=3.10 -y
conda activate slimllm
git clone https://github.com/Aaronhuang-778/SliM-LLM
cd SliM-LLM
pip install --upgrade pip 
pip install -r requirements.txt

We also provide the fixed-AutoGPTQ which supports mixed-precision packing and computing in ./AutoGPTQ. Please download the needed package if using real quantization:

cd AutoGPTQ
pip install -r requirement.txt

SliM-LLM Mixed-Precision Zoo

We provide the generated group-wise bit-width for each model

You can download the pre-trained SliM-LLM mixed-precision you need at Huggingface.

We currently provide mixed-precision results for some models, and the remaining results are still being uploaded (SliM-LLM and SliM-LLM+ use the same set of group-wise mixed-precision).

Usage

Full running scripts of SliM-LLM and SliM-LLM+ are provided in each ./scripts/.

Here, We provide the running example of SliM-LLM and SliM-LLM+

1. For efficient quantization of SliM-LLM, you can obtain the group-wise bit-width from:

# Please run this script in ./SliM-LLM
conda install git git-lfs
git lfs install
git clone https://huggingface.co/AaronHuangWei/SliM-LLM_group-precision

If you want to download the group bit-width in other paths. Please check ./SliM-LLM/slim-llm/run.py (360 line) and ./SliM-LLM/slim-llm-plus/main.py (324 line).

2. Mixed-precision quantization with SliM-LLM

   Without pre-download group bit-width, SliM-LLM will run Salience-Determined Bit Allocation (SBA) to generate mixed-precision first.

cd slim-llm

# W2A16G128
python run.py \
{your top file for OPT}/facebook/opt-1.3b wikitext2 2bit --groupsize 128 \
--device "cuda:0" --tasks piqa,arc_easy,arc_challenge,boolq,hellaswag,winogrande

python run.py \
{your top file for LLaMA}/huggyllama/llama-7b wikitext2 2bit --groupsize 128 \
--device "cuda:0" --tasks piqa,arc_easy,arc_challenge,boolq,hellaswag,winogrande

python run.py \
{your top file for LLaMA-2}/llama2/Llama-2-7b-hf wikitext2 2bit --groupsize 128 \
--device "cuda:0" --tasks piqa,arc_easy,arc_challenge,boolq,hellaswag,winogrande

# W3A16G128
python run.py \
{your top file for OPT}/facebook/opt-1.3b wikitext2 3bit --groupsize 128 \
--device "cuda:0" --tasks piqa,arc_easy,arc_challenge,boolq,hellaswag,winogrande

python run.py \
{your top file for LLaMA}/huggyllama/llama-7b wikitext2 3bit --groupsize 128 \
--device "cuda:0" --tasks piqa,arc_easy,arc_challenge,boolq,hellaswag,winogrande

python run.py \
{your top file for LLaMA-2}/llama2/Llama-2-7b-hf wikitext2 3bit --groupsize 128 \
--device "cuda:0" --tasks piqa,arc_easy,arc_challenge,boolq,hellaswag,winogrande

3. For packing the quantization results with mixed-precision format: Example on LLaMA-13B

# First export the autogptq with mixed-precision version into python environment
bash:
export PYTHONPATH=$PYTHONPATH:/path/to/AutoGPTQ

# Then run the SliM-LLM script for LLaMA-13B

python run.py \
 huggyllama/llama-13b wikitext2 2bit --groupsize 128 \
--device "cuda:0" --tasks piqa,arc_easy,arc_challenge,boolq,hellaswag,winogrande 、
--save --pack

python run.py \
 huggyllama/llama-13b wikitext2 3bit --groupsize 128 \
--device "cuda:0" --tasks piqa,arc_easy,arc_challenge,boolq,hellaswag,winogrande 、
--save --pack

4. Mixed-precision quantization with SliM-LLM+

   Notably, SliM-LLM+ can only be run with group-wise precision configuration. Please download the bit-width first or use SliM-LLM to generate the bit-width.

cd slim-llm-plus

# W2A16G128
CUDA_VISIBLE_DEVICES=0 python main.py \
--model {your top file for OPT}/facebook/opt-1.3b --eval_ppl \
--epochs 50 --output_dir ./log/opt-1.3b-w2a16g128 \
--wbits 2 --abits 16 --group_size 128 --lwc \
--tasks piqa,arc_easy,arc_challenge,boolq,hellaswag,winogrande

CUDA_VISIBLE_DEVICES=0 python main.py \
--model {your top file for LLaMA}/huggyllama/llama-7b  --eval_ppl \
--epochs 50 --output_dir ./log/llama-7b-w2a16g128 \
--wbits 2 --abits 16 --group_size 128 --lwc --aug_loss

CUDA_VISIBLE_DEVICES=0 python main.py \
--model {your top file for LLaMA-2}/llama2/Llama-2-7b-hf  --eval_ppl \
--epochs 50 --output_dir ./log/llama-2-7b-w2a16g128 \
--wbits 2 --abits 16 --group_size 128 --lwc --aug_loss

# W3A16G128
CUDA_VISIBLE_DEVICES=0 python main.py \
--model {your top file for OPT}/facebook/opt-1.3b --eval_ppl \
--epochs 50 --output_dir ./log/opt-1.3b-w3a16g128 \
--wbits 3 --abits 16 --group_size 128 --lwc \
--tasks piqa,arc_easy,arc_challenge,boolq,hellaswag,winogrande

CUDA_VISIBLE_DEVICES=0 python main.py \
--model {your top file for LLaMA}/huggyllama/llama-7b  --eval_ppl \
--epochs 50 --output_dir ./log/llama-7b-w3a16g128 \
--wbits 3 --abits 16 --group_size 128 --lwc --aug_loss

CUDA_VISIBLE_DEVICES=0 python main.py \
--model {your top file for LLaMA-2}/llama2/Llama-2-7b-hf  --eval_ppl \
--epochs 50 --output_dir ./log/llama-2-7b-w3a16g128 \
--wbits 3 --abits 16 --group_size 128 --lwc --aug_loss

4. Due to the time consumption, we provide the pretained SliM-LLM+ parameters in huggingface to reproduce evaluation results of our paper

   1) download the pretrained SliM-LLM+ parameters you want through Huggingface (we are still uploading pretrained parameters).

   2) set epoch as 0 and inference with resume (following Omniquant):

CUDA_VISIBLE_DEVICES=0 python main.py \
--model /PATH/TO/LLaMA/llama-2-7b  \
--epochs 0 --output_dir ./log/test \
--eval_ppl --wbits 2 --abits 16 --group_size 128 --lwc \
--resume /PATH/TO/Pretrained/Parameters 

Results

• SliM-LLM results on the LLaMA family, compared with other gradient-free PTQ methods

• SliM-LLM+ results on the LLaMA family, compared with other gradient-optimized PTQ methods

• SliM-LLM results on zero-shot datasets

Related Project

OmniQuant: Omnidirectionally Calibrated Quantization for Large Language Models

GPTQ: Accurate Post-training Compression for Generative Pretrained Transformers

PB-LLM: Partially Binarized Large Language Models

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

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

AutoGPTQ

Citation

If you find SliM-LLM useful for your study or research, please cite our paper with:

@misc{huang2024slimllm,
      title={SliM-LLM: Salience-Driven Mixed-Precision Quantization for Large Language Models}, 
      author={Wei Huang and Haotong Qin and Yangdong Liu and Yawei Li and Xianglong Liu and Luca Benini and Michele Magno and Xiaojuan Qi},
      year={2024},
      eprint={2405.14917},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}