GPTQ-for-PULSE

4 bits quantization of LLaMA and Bloom using GPTQ

This repo is modified from GPTQ-for-LLaMa, the basic usage is the same as that repo, FYI.

GPTQ is SOTA one-shot weight quantization method

Supports the fastest speed, but uses both triton and cuda. Triton only supports Linux, so if you are a Windows user, please use WSL2.

News or Update

Support pulse model with lora finetuning 4-bit quantization.

Result

PULSE results are evaluated on the medical dataset.

Quantization requires a large amount of CPU memory. However, the memory required can be reduced by using swap memory.

Depending on the GPUs/drivers, there may be a difference in performance, which decreases as the model size increases.(IST-DASLab/gptq#1)

According to GPTQ paper, As the size of the model increases, the difference in performance between FP16 and GPTQ decreases.

Installation

If you don't have conda, install it first.

conda create --name gptq python=3.9 -y
conda activate gptq
conda install pytorch torchvision torchaudio pytorch-cuda=11.7 -c pytorch -c nvidia
# Or, if you're having trouble with conda, use pip with python3.9:
# pip3 install torch torchvision torchaudio

git clone -b pulse https://github.com/hanrui1sensetime/GPTQ-for-PULSE.git
cd GPTQ-for-PULSE
pip install -r requirements.txt
python setup_cuda.py install

Dependencies

• torch: tested on v2.0.0+cu117
• transformers: tested on v4.34.0
• datasets: tested on v2.13.1
• safetensors: tested on v0.3.1
• peft: tested on v0.7.0

All experiments were run on a single NVIDIA RTX3090.

Language Generation

PULSE

PULSE-7B model is implemented by bloomz.

# Generate 4-bit PULSE-7B model
CUDA_VISIBLE_DEVICES=0 python bloom.py ${MODEL_DIR} custom --wbits 4 --act-order --groupsize 128 --save pulse7b-4bit-128g.bin --calib_data ${CALIB_DATA_PATH}

# Generate 4-bit PULSE-7B with lora model
CUDA_VISIBLE_DEVICES=0 python bloom_lora.py ${MODEL_DIR} custom --wbits 4 --act-order --groupsize 128 --save pulse7b-4bit-128g.bin --calib_data ${CALIB_DATA_PATH} --peft_path ${PEFT_PATH}

Acknowledgements

This code is based on GPTQ-for-LLaMa

Thanks to Meta AI for releasing LLaMA, a powerful LLM.

Triton GPTQ kernel code is based on GPTQ-triton