PrefixQuant: Static Quantization Beats Dynamic through Prefixed Outliers in LLMs

arXiv:2410.05265v1 Announce Kind: cross
Summary: Quantization is important for deploying Massive Language Fashions (LLMs) by enhancing reminiscence effectivity and inference velocity. Current strategies for activation quantization primarily handle channel-wise outliers, usually neglecting token-wise outliers, resulting in reliance on expensive per-token dynamic quantization. To deal with this, we introduce PrefixQuant, a novel approach that isolates outlier tokens offline with out re-training. Particularly, PrefixQuant identifies high-frequency outlier tokens and prefixes them within the KV cache, stopping the era of outlier tokens throughout inference and simplifying quantization. To our data, PrefixQuant is the primary to allow environment friendly per-tensor static quantization to outperform costly per-token dynamic quantization. For example, 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% common accuracy on 5 common sense reasoning duties, outperforming earlier per-token dynamic quantization strategies like QuaRot with 0.98 perplexity enchancment and +5.98 factors accuracy. Moreover, the inference velocity of W4A4 quantized fashions utilizing PrefixQuant is 1.60x to 2.81x sooner than FP16 fashions and exceeds QuaRot fashions by 1.2x to 1.3x. Our code is accessible at url{https://github.com/ChenMnZ/PrefixQuant}.

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