AffineQuant: Affine Transformation Quantization for Large Language Models (Link)

AffineQuant is a simple and powerful quantization technique for LLMs.

Contents

• AffineQuant: Affine Transformation Quantization for Large Language Models
  • Contents
  • Install
  • Model Zoo
  • Usage
  • Results
  • Related Project
  • Citation

Install

conda create -n affinequant python=3.10 -y
conda activate affinequant
git clone https://github.com/bytedance/AffineQuant.git
cd AffineQuant
pip install --upgrade pip 
pip install -e .

We also leverage the kernel from AutoGPTQ to achieve real quantization. So you should also install the bug-fixed AutoGPTQ as follows:

pip install auto_gptq

Model Zoo

Coming Soon.

Usage

We provide full script to run AffineQuant in ./scripts/. We use LLaMa-7B as an example here:

1. Obtain the channel-wise scales and shifts required for initialization:

Optional, we also offer the script that you can generate channel-wise scales and shifts by yourself:

python generate_act_scale_shift.py --model /PATH/TO/LLaMA/llama-7b

2. Weight-only quantization

# W3A16
CUDA_VISIBLE_DEVICES=0 python main.py \
--model /PATH/TO/LLaMA/llama-7b  \
--epochs 20 --output_dir ./log/llama-7b-w3a16 \
--eval_ppl --wbits 3 --abits 16 --lwc --let --use_ln_matrix --sf 1e-2

# W3A16g128
CUDA_VISIBLE_DEVICES=0 python main.py \
--model /PATH/TO/LLaMA/llama-7b  \
--epochs 20 --output_dir ./log/llama-7b-w3a16g128 \
--eval_ppl --wbits 3 --abits 16 --group_size 128 --lwc --let --use_ln_matrix --sf 1e-2

3. weight-activation quantization

# W4A4
CUDA_VISIBLE_DEVICES=0 python main.py \
--model /PATH/TO/LLaMA/llama-7b  \
--epochs 20 --output_dir ./log/llama-7b-w4a4 \
--eval_ppl --wbits 4 --abits 4 --lwc --let --aug_loss --use_matrix --sf 0.1 \
--tasks hendrycksTest,piqa,arc_easy,arc_challenge,boolq,hellaswag,winogrande

More detailed and optional arguments:

• --model: the local model path or huggingface format.
• --wbits: weight quantization bits.
• --abits: activation quantization bits.
• --group_size: group size of weight quantization. If no set, use per-channel quantization for weight as default.
• --epochs: training epochs. You can set it as 0 to evaluate pre-trained AffineQuant checkpoints.
• --nsamples: number of calibration samples, 128 as default.
• --eval_ppl: evaluating the perplexity of quantized models.
• --tasks: evaluating zero-shot tasks.
• --resume: loading pre-trained AffineQuant parameters.
• --multigpu: to inference larger network on multiple GPUs
• --real_quant: real quantization, which can see memory reduce
• --save_dir: saving the quantization model for further exploration.
• --use_matrix: using qkt affine mateix or not.
• --use_ln_matrix: using layernorm affine matrix.
• --sf: stability factor for gradual mask.

Results

• AffineQuant achieve SoTA performance in weight-only quantization
• AffineQuant achieve SoTA performance in weight-activation quantization

Related Project

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

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

GPTQ: Accurate Post-training Compression for Generative Pretrained Transformers

RPTQ: Reorder-Based Post-Training Quantization for Large Language Models

OmniQuant: Omnidirectionally Calibrated Quantization for Large Language Models

MLC LLM

AutoGPTQ

Citation

@inproceedings{
ma2024affinequant,
title={AffineQuant: Affine Transformation Quantization for Large Language Models},
author={Yuexiao Ma and Huixia Li and Xiawu Zheng and Feng Ling and Xuefeng Xiao and Rui Wang and Shilei Wen and Fei Chao and Rongrong Ji},
booktitle={The Twelfth International Conference on Learning Representations},
year={2024},
url={https://openreview.net/forum?id=of2rhALq8l}
}