Summary of Fine-grained Attention I/o Complexity: Comprehensive Analysis For Backward Passes, by Xiaoyu Li et al.
Fine-grained Attention I/O Complexity: Comprehensive Analysis for Backward Passes
by Xiaoyu Li, Yingyu Liang, Zhenmei Shi, Zhao Song, Yufa Zhou
First submitted to arxiv on: 12 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computational Complexity (cs.CC); 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 paper presents a comprehensive analysis of the Input/Output (I/O) complexity for attention mechanisms in Large Language Models (LLMs). The authors focus on backward passes, categorizing into small and large cache scenarios. They establish tight bounds on I/O complexity across all cache sizes using the red-blue pebble game framework. The findings confirm that FlashAttention is optimal for both forward and backward passes for large caches, while providing an algorithm that improves existing methods for small caches. The paper also extends its analysis to sparse attention, deriving fine-grained lower bounds for both forward and backward passes and both small and large caches. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at how computers process long strings of text in Large Language Models (LLMs). It’s hard for computers to do this because the process gets slower as the string gets longer. The authors found ways to make it faster by thinking about where information is stored inside the computer. They also looked at a way to speed things up called sparse attention and showed that certain methods are better than others. |
Keywords
* Artificial intelligence * Attention
