arXivDaily arXiv每日学术速递 周一至周五更新
北美退伍军人节(Veterans Day),12 日无更新
arXiv 2502.05947cs.CVcs.CL
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基于动态树注意力的大语言模型多头解码加速

Acceleration Multiple Heads Decoding for LLM via Dynamic Tree Attention

Zhendong Zhang

AI总结:

针对大语言模型多头解码,在MEDUSA框架下用动态树注意力替代固定树注意力,以简单低复杂度策略构建动态树生成候选,实验显示在保持生成质量的同时提升了解码效率,证明了多头解码候选生成的改进潜力。

摘要:

多头解码通过同时预测后续多个token加速大语言模型(LLMs)的推理,它利用固定结构的树注意力并行生成和验证多个候选序列。本文在MEDUSA框架下将多头解码中的固定树注意力替换为动态树注意力,提出一种简单且低复杂度的策略来生成候选并构建动态树结构。初步实验表明,该方法在保持生成质量的同时提升了LLMs多头解码的效率,这一结果证明了多头解码在候选生成方面的改进潜力。

英文摘要:

Multiple heads decoding accelerates the inference of Large Language Models (LLMs) by predicting next several tokens simultaneously. It generates and verifies multiple candidate sequences in parallel via tree attention with a fixed structure. In this paper, we replace the fixed tree attention with dynamic tree attention on multiple head decoding, specifically in the context of MEDUSA. We propose a simple and low complexity strategy to generate candidates and construct the dynamic tree structure. Preliminary experiments show that the proposed method improves the decoding efficiency of multiple head decoding for LLMs while maintaining the generation quality. This result demonstrates the potential for improvement of multiple head decoding in candidate generation.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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