Summary of Hsr-enhanced Sparse Attention Acceleration, by Bo Chen et al.
HSR-Enhanced Sparse Attention Acceleration
by Bo Chen, Yingyu Liang, Zhizhou Sha, Zhenmei Shi, Zhao Song
First submitted to arxiv on: 14 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
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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 The proposed approach accelerates attention computation in Large Language Models (LLMs) by leveraging the inherent sparsity within attention mechanisms. The novel method, which employs a Half-Space Reporting (HSR) data structure, significantly reduces running time complexity for both generation decoding and prompt prefilling tasks. Specifically, it achieves a running time of O(mn^4/5) for generation decoding, outperforming the naive approach O(mn). This innovation enables efficient long-context processing in LLMs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large Language Models (LLMs) are powerful tools that can understand and generate human-like text. However, they often struggle with tasks that require them to process long pieces of information. The problem is that these models use attention mechanisms, which can be slow when working with large amounts of data. To solve this issue, researchers have developed a new approach that speeds up the processing of attention mechanisms. This method uses special data structures to quickly identify important parts of the attention matrix and focus on those areas. As a result, it can process long pieces of information much faster than before. This breakthrough has the potential to enable LLMs to be used in many more applications. |
Keywords
» Artificial intelligence » Attention » Prompt
