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qwopqwop200 / GPTQ-for-LLaMa

4 bits quantization of LLaMA using GPTQ
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GPTQ-for-LLaMA

I am currently focusing on AutoGPTQ and recommend using AutoGPTQ instead of GPTQ for Llama.

4 bits quantization of LLaMA using GPTQ

GPTQ is SOTA one-shot weight quantization method

It can be used universally, but it is not the fastest and only supports linux.

Triton only supports Linux, so if you are a Windows user, please use WSL2.

News or Update

AutoGPTQ-triton, a packaged version of GPTQ with triton, has been integrated into 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, As the size of the model increases, the difference in performance between FP16 and GPTQ decreases.

GPTQ vs 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, 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

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

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

Triton GPTQ kernel code is based on GPTQ-triton