APTQ: Attention-aware Post-Training Mixed-Precision Quantization for Large Language Models

• URL: http://arxiv.org/abs/2402.14866v2
• Date: Tue, 16 Apr 2024 03:18:38 GMT
• Title: APTQ: Attention-aware Post-Training Mixed-Precision Quantization for Large Language Models
• Authors: Ziyi Guan, Hantao Huang, Yupeng Su, Hong Huang, Ngai Wong, Hao Yu,
• Abstract summary: We propose APTQ (Attention-aware Post-Training Mixed-Precision Quantization) for Large Language Models. We leverage the Hessian trace as a sensitivity metric for mixed-precision quantization, ensuring an informed precision reduction. Experiments show APTQ surpasses previous quantization methods, achieving an average of 4 bit width a 5.22 perplexity.
• Score: 12.006605064782567
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large Language Models (LLMs) have greatly advanced the natural language processing paradigm. However, the high computational load and huge model sizes pose a grand challenge for deployment on edge devices. To this end, we propose APTQ (Attention-aware Post-Training Mixed-Precision Quantization) for LLMs, which considers not only the second-order information of each layer's weights, but also, for the first time, the nonlinear effect of attention outputs on the entire model. We leverage the Hessian trace as a sensitivity metric for mixed-precision quantization, ensuring an informed precision reduction that retains model performance. Experiments show APTQ surpasses previous quantization methods, achieving an average of 4 bit width a 5.22 perplexity nearly equivalent to full precision in the C4 dataset. In addition, APTQ attains state-of-the-art zero-shot accuracy of 68.24\% and 70.48\% at an average bitwidth of 3.8 in LLaMa-7B and LLaMa-13B, respectively, demonstrating its effectiveness to produce high-quality quantized LLMs.

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