PrefixQuant: Static Quantization Beats Dynamic through Prefixed Outliers
  in LLMs

Mengzhao Chen; Yi Liu; Jiahao Wang; Yi Bin; Wenqi Shao; Ping Luo

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Paper · arXiv 2410.05265

PrefixQuant: Static Quantization Beats Dynamic through Prefixed Outliers in LLMs

Mengzhao Chen, Yi Liu, Jiahao Wang, Yi Bin, Wenqi Shao, Ping Luo

33 upvotesOctober 7, 2024arXiv 预印本

AI 摘要

PrefixQuant improves quantization efficiency in LLMs by isolating outlier tokens, enabling static quantization to outperform dynamic quantization in both speed and accuracy.

quantizationLarge Language Models (LLMs)memory efficiencyinference speedactivation quantizationchannel-wise outlierstoken-wise outliersPrefixQuantKV cacheper-tensor static quantizationper-token dynamic quantizationW4A4KV4Llama-3-8BWikiText2 perplexitycommon-sense reasoning tasksQuaRot

Abstract

Quantization is essential for deploying Large Language Models (LLMs) by enhancing memory efficiency and inference speed. Existing methods for activation quantization mainly address channel-wise outliers, often neglecting token-wise outliers, leading to reliance on costly per-token dynamic quantization. To address this, we introduce PrefixQuant, a novel technique that isolates outlier tokens offline without re-training. Specifically, PrefixQuant identifies high-frequency outlier tokens and prefixes them in the KV cache, preventing the generation of outlier tokens during inference and simplifying quantization. To our knowledge, PrefixQuant is the first to enable efficient per-tensor static quantization to outperform expensive per-token dynamic quantization. For instance, in W4A4KV4 (4- bit weight, 4-bit activation, and 4-bit KV cache) Llama-3-8B, PrefixQuant with per-tensor static quantization achieves a 7.43 WikiText2 perplexity and 71.08% average accuracy on 5 common-sense reasoning tasks, outperforming previous per-token dynamic quantization methods like QuaRot with 0.98 perplexity improvement and +5.98 points accuracy. Additionally, the inference speed of W4A4 quantized models using PrefixQuant is 1.60x to 2.81x faster than FP16 models and exceeds QuaRot models by 1.2x to 1.3x. Our code is available at https://github.com/ChenMnZ/PrefixQuant.