GPTQ

This repository contains the code for the paper GPTQ: Accurate Post-training Compression for Generative Pretrained Transformers. The current release includes the following features:

• An efficient implementation of the GPTQ algorithm: gptq.py
• Compressing all models from the OPT and BLOOM families to 2/3/4 bits, including weight grouping: opt.py, bloom.py, zeroShot/
• Evaluating the perplexity of quantized models on several language generation tasks: opt.py, bloom.py
• Evaluating the performance of quantized models on several ZeroShot tasks: zeroShot/
• A 3-bit quantized matrix full-precision vector product CUDA kernel: quant_cuda_kernel.cu, quant_cuda.cpp, setup_cuda.py
• Benchmarking code for individual matrix-vector products and for language generation with quantized models: test_kernel.py, opt.py

Dependencies

• torch: tested on v1.10.1+cu111
• transformers: tested on v4.21.2
• datasets: tested on v1.17.0
• (to run 3-bit kernels: setup for compiling PyTorch CUDA extensions, see also https://pytorch.org/tutorials/advanced/cpp_extension.html, tested on CUDA 11.4)

All experiments were run on a single 80GB NVIDIA A100. However, most experiments will work on a GPU with a lot less memory as well.

Language Generation

OPT

# Compute full precision (FP16) results
CUDA_VISIBLE_DEVICES=0 python opt.py facebook/opt-125m c4
# Run RTN baseline and compute results
CUDA_VISIBLE_DEVICES=0 python opt.py facebook/opt-125m c4 --wbits 4 --nearest
# Run GPTQ and compute results
CUDA_VISIBLE_DEVICES=0 python opt.py facebook/opt-125m c4 --wbits 4 [--groupsize 1024]

To run other OPT models replace opt-125m with one of: opt-350m, opt-1.3b, opt-2.7b, opt-6.7b, opt-13b, opt-66b. For the 175B-parameter mode, you have to request access from Meta and then convert it to a local HuggingFace checkpoint using their scripts in metaseq. Once you have such a checkpoint, simply pass its path instead of facebook/opt-125m.

BLOOM

# Compute full precision (FP16) results
CUDA_VISIBLE_DEVICES=0 python bloom.py bigscience/bloom-560m c4
# Run RTN baseline and compute results
CUDA_VISIBLE_DEVICES=0 python bloom.py bigscience/bloom-560m c4 --wbits 4 --nearest
# Run GPTQ and compute results
CUDA_VISIBLE_DEVICES=0 python bloom.py bigscience/bloom-560m c4 --wbits 4 [--groupsize 1024]

To run other BLOOM models replace bloom-560m with one of: bloom-1b1, bloom-1b7, bloom-3b, bloom-7b1, bloom.

ZeroShot

See zeroShot/ folder.

3-bit CUDA Kernels

# Install kernels
python setup_cuda.py install

# Benchmark performance for FC2 layer of OPT-175B
CUDA_VISIBLE_DEVICES=0 python test_kernel.py

# Benchmark language generation with 3-bit OPT-175B:
# OPT175B denotes the name of the folder with the HuggingFace OPT-175b checkpoint (see above)

# Save compressed model
CUDA_VISIBLE_DEVICES=0 python opt.py OPT175B c4 --wbits 3 --save opt66-3bit.pt
# Benchmark generating a 128 token sequence with the saved model
CUDA_VISIBLE_DEVICES=0 python opt.py OPT175B c4 --load opt66-3bit.pt --benchmark 128
# Benchmark FP16 baseline, note that the model will be split across all listed GPUs
CUDA_VISIBLE_DEVICES=0,1,2,3,4 python opt.py OPT175B c4 --benchmark 128

Please note that our 3-bit kernels are currently only optimized for OPT-175B running on 1xA100 or 2xA6000 and may thus yield suboptimal performance on smaller models or on other GPUs.

Cite

If you found this work useful, please consider citing:

@article{frantar-gptq,
  title={{GPTQ}: Accurate Post-training Compression for Generative Pretrained Transformers}, 
  author={Elias Frantar and Saleh Ashkboos and Torsten Hoefler and Dan Alistarh},
  year={2022},
  journal={arXiv preprint arXiv:2210.17323}
}