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https://github.com/qwopqwop200/gptq-for-llama
4 bits quantization of LLaMA using GPTQ
https://github.com/qwopqwop200/gptq-for-llama
Last synced: 25 days ago
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4 bits quantization of LLaMA using GPTQ
- Host: GitHub
- URL: https://github.com/qwopqwop200/gptq-for-llama
- Owner: qwopqwop200
- License: apache-2.0
- Created: 2023-03-06T14:09:39.000Z (over 1 year ago)
- Default Branch: triton
- Last Pushed: 2024-07-13T04:45:28.000Z (4 months ago)
- Last Synced: 2024-10-01T12:23:05.875Z (about 1 month ago)
- Language: Python
- Homepage:
- Size: 2.66 MB
- Stars: 2,986
- Watchers: 42
- Forks: 458
- Open Issues: 61
-
Metadata Files:
- Readme: README.md
- License: LICENSE.txt
Awesome Lists containing this project
- awesome-ChatGPT-repositories - GPTQ-for-LLaMa - 4 bits quantization of LLaMA using GPTQ (Langchain)
README
# GPTQ-for-LLaMA
**I am currently focusing on [AutoGPTQ](https://github.com/PanQiWei/AutoGPTQ) and recommend using [AutoGPTQ](https://github.com/PanQiWei/AutoGPTQ) instead of GPTQ for Llama.**
4 bits quantization of [LLaMA](https://arxiv.org/abs/2302.13971) using [GPTQ](https://arxiv.org/abs/2210.17323)
GPTQ is SOTA one-shot weight quantization method
**It can be used universally, but it is not the [fastest](https://github.com/qwopqwop200/GPTQ-for-LLaMa/tree/old-cuda) and only supports linux.**
**Triton only supports Linux, so if you are a Windows user, please use [WSL2](https://learn.microsoft.com/en-us/windows/wsl/install).**
## News or Update
**AutoGPTQ-triton, a packaged version of GPTQ with triton, has been integrated into [AutoGPTQ](https://github.com/PanQiWei/AutoGPTQ).**
## Result
LLaMA-7B(click me)
| [LLaMA-7B](https://arxiv.org/abs/2302.13971) | Bits | group-size | memory(MiB) | Wikitext2 | checkpoint size(GB) |
| -------------------------------------------------- | ---- | ---------- | ----------- | --------- | ------------------- |
| FP16 | 16 | - | 13940 | 5.68 | 12.5 |
| RTN | 4 | - | - | 6.29 | - |
| [GPTQ](https://arxiv.org/abs/2210.17323) | 4 | - | 4740 | 6.09 | 3.5 |
| [GPTQ](https://arxiv.org/abs/2210.17323) | 4 | 128 | 4891 | 5.85 | 3.6 |
| RTN | 3 | - | - | 25.54 | - |
| [GPTQ](https://arxiv.org/abs/2210.17323) | 3 | - | 3852 | 8.07 | 2.7 |
| [GPTQ](https://arxiv.org/abs/2210.17323) | 3 | 128 | 4116 | 6.61 | 3.0 |
LLaMA-13B
| [LLaMA-13B](https://arxiv.org/abs/2302.13971) | Bits | group-size | memory(MiB) | Wikitext2 | checkpoint size(GB) |
| -------------------------------------------------- | ---- | ---------- | ----------- | --------- | ------------------- |
| FP16 | 16 | - | OOM | 5.09 | 24.2 |
| RTN | 4 | - | - | 5.53 | - |
| [GPTQ](https://arxiv.org/abs/2210.17323) | 4 | - | 8410 | 5.36 | 6.5 |
| [GPTQ](https://arxiv.org/abs/2210.17323) | 4 | 128 | 8747 | 5.20 | 6.7 |
| RTN | 3 | - | - | 11.40 | - |
| [GPTQ](https://arxiv.org/abs/2210.17323) | 3 | - | 6870 | 6.63 | 5.1 |
| [GPTQ](https://arxiv.org/abs/2210.17323) | 3 | 128 | 7277 | 5.62 | 5.4 |
LLaMA-33B
| [LLaMA-33B](https://arxiv.org/abs/2302.13971) | Bits | group-size | memory(MiB) | Wikitext2 | checkpoint size(GB) |
| -------------------------------------------------- | ---- | ---------- | ----------- | --------- | ------------------- |
| FP16 | 16 | - | OOM | 4.10 | 60.5 |
| RTN | 4 | - | - | 4.54 | - |
| [GPTQ](https://arxiv.org/abs/2210.17323) | 4 | - | 19493 | 4.45 | 15.7 |
| [GPTQ](https://arxiv.org/abs/2210.17323) | 4 | 128 | 20570 | 4.23 | 16.3 |
| RTN | 3 | - | - | 14.89 | - |
| [GPTQ](https://arxiv.org/abs/2210.17323) | 3 | - | 15493 | 5.69 | 12.0 |
| [GPTQ](https://arxiv.org/abs/2210.17323) | 3 | 128 | 16566 | 4.80 | 13.0 |
LLaMA-65B
| [LLaMA-65B](https://arxiv.org/abs/2302.13971) | Bits | group-size | memory(MiB) | Wikitext2 | checkpoint size(GB) |
| -------------------------------------------------- | ---- | ---------- | ----------- | --------- | ------------------- |
| FP16 | 16 | - | OOM | 3.53 | 121.0 |
| RTN | 4 | - | - | 3.92 | - |
| [GPTQ](https://arxiv.org/abs/2210.17323) | 4 | - | OOM | 3.84 | 31.1 |
| [GPTQ](https://arxiv.org/abs/2210.17323) | 4 | 128 | OOM | 3.65 | 32.3 |
| RTN | 3 | - | - | 10.59 | - |
| [GPTQ](https://arxiv.org/abs/2210.17323) | 3 | - | OOM | 5.04 | 23.6 |
| [GPTQ](https://arxiv.org/abs/2210.17323) | 3 | 128 | OOM | 4.17 | 25.6 |
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.(https://github.com/IST-DASLab/gptq/issues/1)
According to [GPTQ paper](https://arxiv.org/abs/2210.17323), As the size of the model increases, the difference in performance between FP16 and GPTQ decreases.
## GPTQ vs [bitsandbytes](https://github.com/TimDettmers/bitsandbytes)
LLaMA-7B(click me)
| [LLaMA-7B(seqlen=2048)](https://arxiv.org/abs/2302.13971) | Bits Per Weight(BPW)| memory(MiB) | c4(ppl) |
| --------------------------------------------------------------- | ------------------- | ----------- | --------- |
| FP16 | 16 | 13948 | 5.22 |
| [GPTQ-128g](https://arxiv.org/abs/2210.17323) | 4.15 | 4781 | 5.30 |
| [nf4-double_quant](https://arxiv.org/abs/2305.14314) | 4.127 | 4804 | 5.30 |
| [nf4](https://arxiv.org/abs/2305.14314) | 4.5 | 5102 | 5.30 |
| [fp4](https://arxiv.org/abs/2212.09720) | 4.5 | 5102 | 5.33 |
LLaMA-13B
| [LLaMA-13B(seqlen=2048)](https://arxiv.org/abs/2302.13971) | Bits Per Weight(BPW)| memory(MiB) | c4(ppl) |
| ---------------------------------------------------------------- | ------------------- | ----------- | --------- |
| FP16 | 16 | OOM | - |
| [GPTQ-128g](https://arxiv.org/abs/2210.17323) | 4.15 | 8589 | 5.02 |
| [nf4-double_quant](https://arxiv.org/abs/2305.14314) | 4.127 | 8581 | 5.04 |
| [nf4](https://arxiv.org/abs/2305.14314) | 4.5 | 9170 | 5.04 |
| [fp4](https://arxiv.org/abs/2212.09720) | 4.5 | 9170 | 5.11 |
LLaMA-33B
| [LLaMA-33B(seqlen=1024)](https://arxiv.org/abs/2302.13971) | Bits Per Weight(BPW)| memory(MiB) | c4(ppl) |
| ---------------------------------------------------------------- | ------------------- | ----------- | --------- |
| FP16 | 16 | OOM | - |
| [GPTQ-128g](https://arxiv.org/abs/2210.17323) | 4.15 | 18441 | 3.71 |
| [nf4-double_quant](https://arxiv.org/abs/2305.14314) | 4.127 | 18313 | 3.76 |
| [nf4](https://arxiv.org/abs/2305.14314) | 4.5 | 19729 | 3.75 |
| [fp4](https://arxiv.org/abs/2212.09720) | 4.5 | 19729 | 3.75 |
## Installation
If you don't have [conda](https://docs.conda.io/en/latest/miniconda.html), 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 https://github.com/qwopqwop200/GPTQ-for-LLaMa
cd GPTQ-for-LLaMa
pip install -r requirements.txt
```
## Dependencies
* `torch`: tested on v2.0.0+cu117
* `transformers`: tested on v4.28.0.dev0
* `datasets`: tested on v2.10.1
* `safetensors`: tested on v0.3.0
All experiments were run on a single NVIDIA RTX3090.
# Language Generation
## LLaMA
```
#convert LLaMA to hf
python convert_llama_weights_to_hf.py --input_dir /path/to/downloaded/llama/weights --model_size 7B --output_dir ./llama-hf
# Benchmark language generation with 4-bit LLaMA-7B:
# Save compressed model
CUDA_VISIBLE_DEVICES=0 python llama.py ${MODEL_DIR} c4 --wbits 4 --true-sequential --act-order --groupsize 128 --save llama7b-4bit-128g.pt
# Or save compressed `.safetensors` model
CUDA_VISIBLE_DEVICES=0 python llama.py ${MODEL_DIR} c4 --wbits 4 --true-sequential --act-order --groupsize 128 --save_safetensors llama7b-4bit-128g.safetensors
# Benchmark generating a 2048 token sequence with the saved model
CUDA_VISIBLE_DEVICES=0 python llama.py ${MODEL_DIR} c4 --wbits 4 --groupsize 128 --load llama7b-4bit-128g.pt --benchmark 2048 --check
# Benchmark FP16 baseline, note that the model will be split across all listed GPUs
CUDA_VISIBLE_DEVICES=0,1,2,3,4 python llama.py ${MODEL_DIR} c4 --benchmark 2048 --check
# model inference with the saved model
CUDA_VISIBLE_DEVICES=0 python llama_inference.py ${MODEL_DIR} --wbits 4 --groupsize 128 --load llama7b-4bit-128g.pt --text "this is llama"
# model inference with the saved model using safetensors loaded direct to gpu
CUDA_VISIBLE_DEVICES=0 python llama_inference.py ${MODEL_DIR} --wbits 4 --groupsize 128 --load llama7b-4bit-128g.safetensors --text "this is llama" --device=0
# model inference with the saved model with offload(This is very slow).
CUDA_VISIBLE_DEVICES=0 python llama_inference_offload.py ${MODEL_DIR} --wbits 4 --groupsize 128 --load llama7b-4bit-128g.pt --text "this is llama" --pre_layer 16
It takes about 180 seconds to generate 45 tokens(5->50 tokens) on single RTX3090 based on LLaMa-65B. pre_layer is set to 50.
```
Basically, 4-bit quantization and 128 groupsize are recommended.
You can also export quantization parameters with toml+numpy format.
```
CUDA_VISIBLE_DEVICES=0 python llama.py ${MODEL_DIR} c4 --wbits 4 --true-sequential --act-order --groupsize 128 --quant-directory ${TOML_DIR}
```
# Acknowledgements
This code is based on [GPTQ](https://github.com/IST-DASLab/gptq)
Thanks to Meta AI for releasing [LLaMA](https://arxiv.org/abs/2302.13971), a powerful LLM.
Triton GPTQ kernel code is based on [GPTQ-triton](https://github.com/fpgaminer/GPTQ-triton)