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Summary of Sampleattention: Near-lossless Acceleration Of Long Context Llm Inference with Adaptive Structured Sparse Attention, by Qianchao Zhu et al.


SampleAttention: Near-Lossless Acceleration of Long Context LLM Inference with Adaptive Structured Sparse Attention

by Qianchao Zhu, Jiangfei Duan, Chang Chen, Siran Liu, Xiuhong Li, Guanyu Feng, Xin Lv, Huanqi Cao, Xiao Chuanfu, Xingcheng Zhang, Dahua Lin, Chao Yang

First submitted to arxiv on: 17 Jun 2024

Categories

  • Main: Computation and Language (cs.CL)
  • Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)

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GrooveSquid.com Paper Summaries

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Summary difficulty Written by Summary
High Paper authors High Difficulty Summary
Read the original abstract here
Medium GrooveSquid.com (original content) Medium Difficulty Summary
This paper proposes a novel approach to efficient attention mechanisms for large language models (LLMs). The authors address the issue of quadratic complexity in vanilla attention, which leads to significant Time-to-First-Token (TTFT) latency. By providing both theoretical and empirical foundations, they introduce SampleAttention, an adaptive structured and near-lossless sparse attention mechanism that leverages observed sparse patterns to reduce TTFT by up to 2.42 times compared to FlashAttention while maintaining model accuracy.
Low GrooveSquid.com (original content) Low Difficulty Summary
This paper helps make language models faster and more efficient. It solves a problem where the computer takes too long to process lots of information. The solution is called SampleAttention, which is like a filter that quickly finds important things in the text. This makes the computer work faster without losing any of its abilities. The results show that this new method can make language models work up to 2.42 times faster.

Keywords

» Artificial intelligence  » Attention  » Token