DAQ: Density-Aware Post-Training Weight-Only Quantization For LLMs

Yingsong Luo; Ling Chen

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arXiv · 2410.12187

DAQ: Density-Aware Post-Training Weight-Only Quantization For LLMs

Yingsong Luo·Ling Chen

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Abstract

Large language models (LLMs) excel in various tasks but face deployment challenges due to hardware constraints. We propose density-aware post-training weight-only quantization (DAQ), which has two stages: 1) density-centric alignment, which identifies the center of high-density weights and centers the dynamic range on this point to align high-density weight regions with floating-point high-precision regions; 2) learnable dynamic range adjustment, which adjusts the dynamic range by optimizing quantization parameters (i.e., scale and zero-point) based on the impact of weights on the model output. Experiments on LLaMA and LLaMA-2 show that DAQ consistently outperforms the best baseline method, reducing perplexity loss by an average of 22.8% on LLaMA and 19.6% on LLaMA-2. Our code is available at https://github.com/LuoYingSong/DAQ.

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Yingsong Luo, Ling Chen. 2024-10-16. DAQ: Density-Aware Post-Training Weight-Only Quantization For LLMs. https://arxiv.org/abs/2410.12187

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