Summary of The I/o Complexity Of Attention, or How Optimal Is Flash Attention?, by Barna Saha et al.
The I/O Complexity of Attention, or How Optimal is Flash Attention?
by Barna Saha, Christopher Ye
First submitted to arxiv on: 12 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computational Complexity (cs.CC); Data Structures and Algorithms (cs.DS); Information Theory (cs.IT)
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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 abstract discusses the limitations of self-attention in Transformer architectures, which are typically addressed using the FlashAttention algorithm. However, this approach is still hindered by its I/O complexity, which becomes a bottleneck when scaling Transformers. The paper investigates whether FlashAttention’s I/O complexity is optimal for all values of M, the cache size. By analyzing the known lower bounds and output sizes, the authors aim to determine if there are more efficient approaches to computing attention. The paper contributes to the development of scalable Transformer architectures, which are crucial for many natural language processing tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary FlashAttention is a way to make Transformers work better with lots of information. It uses a special kind of memory that helps it look at things quickly. But this memory can only hold so much information before it gets slow. The question the paper answers is whether FlashAttention’s way of using memory is the best way. By studying how well it does and comparing it to other ideas, the authors want to help make Transformers even more powerful. |
Keywords
* Artificial intelligence * Attention * Natural language processing * Self attention * Transformer
