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Reorder-based post-training quantization for large language model

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

Large-scale language models (LLMs) have shown exceptional performance on various tasks. However, the deployment of LLMs is challenging due to their enormous size. One of the main challenges in quantizing LLMs is the different ranges between the channels, which affects the accuracy and compression ratio of the quantized model. In our paper, we propose a novel reorder-based quantization approach called RPTQ. The RPTQ approach involves rearranging the channels in the activations and then quantizing them in clusters, thereby reducing the impact of the range difference between channels. By implementing the RPTQ approach, we achieved a significant breakthrough by pushing LLM models to 3 bit activation for the first time.

Overview

Update

  • 2023.4.23 An bug in the calculation of the reorder index was identified in qkt_matmul (R2). This bug has been fixed, and the results have been updated accordingly.

Requirements

python packages

  • torch >= 2.0.0
  • transformers>=4.28.0
  • omegaconf pycountry sqlitedict lm-eval

Usage

The RPTQ approach can be applied to OPT models.

python main.py opt-1.3b --wbits 4 --abits 4 --eval_ppl --tasks lambada_openai,piqa,arc_easy,arc_challenge,openbookqa,boolq

Only quantize K/V cache:

python main.py opt-1.3b --wbits 4 --abits 4 --only_quant_kv --eval_ppl --tasks lambada_openai,piqa,arc_easy,arc_challenge,openbookqa,boolq

To quantize larger network please use --multigpu:

python main.py opt-66b --wbits 4 --abits 4 --only_quant_kv --eval_ppl --tasks lambada_openai,piqa,arc_easy,arc_challenge,openbookqa,boolq --multigpu

Results

Perplexity

Model OPT-1.3b OPT-6.7b OPT-13b OPT-30b OPT-66b OPT-175b
Task WIKI PT C4 WIKI PT C4 WIKI PT C4 WIKI PT C4 WIKI PT C4 WIKI PT C4
FP16 14.63 16.96 14.72 10.86 13.09 11.74 10.13 12.34 11.20 9.56 11.84 10.69 9.34 11.36 10.28 8.34 12.01 10.13
W4A16 14.78 17.21 14.92 11.18 13.62 12.07 10.29 12.45 11.27 9.55 11.91 10.74 9.30 11.42 10.31 8.37 12.31 10.26
W4A8 15.39 17.79 15.48 11.21 13.74 12.11 10.90 13.40 11.62 10.22 12.41 11.01 9.46 11.73 10.57 8.43 12.24 10.49
W4A4 16.88 19.23 16.55 12.00 15.17 12.85 12.74 15.76 14.71 11.15 14.11 13.48 12.23 18.87 15.93 10.60 15.59 12.28
W4A4KV 15.26 17.65 15.37 11.26 13.44 12.03 10.59 12.80 11.54 9.99 12.18 11.01 9.75 11.64 10.61 8.40 12.38 10.54
W4A3KV 17.22 19.94 16.92 11.92 14.13 12.61 11.15 13.90 12.04 11.62 14.95 11.96 10.88 14.69 11.36 9.39 13.45 11.27
W3A3KV 18.45 21.33 18.26 12.42 14.48 13.13 11.47 14.08 12.41 11.76 14.98 12.22 11.47 15.03 11.75 10.03 13.82 11.30

Zero-shot tasks

Task lambada_openai piqa
Model 1.3b 6.7b 13b 30b 66b 1.3b 6.7b 13b 30b 66b
FP16 57.98% 61.84% 68.60% 71.41% 67.14% 72.47% 74.53% 76.87% 78.01% 78.12%
W4A16 57.46% 60.78% 68.50% 71.37% 67.06% 71.59% 74.80% 76.93% 78.29% 78.18%
W4A8 52.39% 67.35% 62.44% 64.99% 67.02% 69.69% 75.89% 75.46% 76.93% 77.52%
W4A4 49.34% 64.93% 60.23% 63.92% 68.50% 68.66% 75.40% 73.55% 76.16% 77.14%
W4A4KV 52.90% 67.39% 62.77% 64.89% 69.99% 69.26% 76.00% 74.42% 76.65% 76.98%
W4A3KV 47.02% 64.97% 61.05% 59.20% 66.23% 68.22% 75.73% 73.23% 67.46% 74.21%
W3A3KV 42.84% 64.11% 60.02% 58.33% 65.28% 68.22% 74.64% 74.10% 67.51% 75.13%
Task arc_easy arc_challenge
Model 1.3b 6.7b 13b 30b 66b 1.3b 6.7b 13b 30b 66b
FP16 51.05% 58.03% 61.91% 65.31% 64.68% 29.69% 33.61% 35.66% 38.05% 38.99%
W4A16 51.17% 57.02% 61.82% 65.10% 64.89% 30.03% 32.59% 35.49% 37.96% 38.99%
W4A8 48.35% 60.18% 60.94% 63.46% 64.60% 26.36% 34.04% 35.58% 37.45% 38.82%
W4A4 47.55% 56.90% 58.41% 62.12% 63.76% 25.85% 34.30% 33.95% 36.17% 37.20%
W4A4KV 47.76% 57.74% 58.54% 63.59% 63.67% 27.64% 33.95% 34.21% 37.37% 37.71%
W4A3KV 46.29% 56.69% 56.10% 48.44% 59.00% 26.02% 33.95% 33.95% 30.71% 36.77%
W3A3KV 44.02% 55.59% 53.74% 50.42% 57.65% 26.53% 32.16% 32.50% 30.71% 34.98%
Task openbookqa boolq
Model 1.3b 6.7b 13b 30b 66b 1.3b 6.7b 13b 30b 66b
FP16 33.00% 38.00% 39.00% 40.20% 41.60% 57.73% 67.03% 65.90% 70.45% 70.85%
W4A16 31.80% 37.40% 39.20% 40.60% 42.00% 58.99% 59.72% 66.66% 70.70% 70.55%
W4A8 32.40% 38.00% 38.60% 39.40% 41.80% 46.88% 65.93% 66.57% 70.64% 71.07%
W4A4 32.60% 38.40% 38.00% 38.60% 42.00% 41.37% 65.44% 58.47% 67.70% 70.24%
W4A4KV 32.60% 38.40% 38.00% 39.80% 41.60% 43.33% 62.11% 62.47% 68.22% 70.79%
W4A3KV 32.80% 36.80% 37.00% 34.00% 39.40% 42.84% 61.31% 57.76% 61.74% 67.06%
W3A3KV 28.40% 35.20% 37.20% 32.40% 38.60% 46.23% 60.79% 65.07% 63.08% 67.49%

Citation

If you use our RPTQ approach in your research, please cite our paper:

@misc{yuan2023rptq,
      title={RPTQ: Reorder-based Post-training Quantization for Large Language Models}, 
      author={Zhihang Yuan and Lin Niu and Jiawei Liu and Wenyu Liu and Xinggang Wang and Yuzhang Shang and Guangyu Sun and Qiang Wu and Jiaxiang Wu and Bingzhe Wu},
      year={2023},
      eprint={2304.01089},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}

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