Summary of Is Flash Attention Stable?, by Alicia Golden et al.
Is Flash Attention Stable?
by Alicia Golden, Samuel Hsia, Fei Sun, Bilge Acun, Basil Hosmer, Yejin Lee, Zachary DeVito, Jeff Johnson, Gu-Yeon Wei, David Brooks, Carole-Jean Wu
First submitted to arxiv on: 5 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Distributed, Parallel, and Cluster Computing (cs.DC)
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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 A machine learning paper tackles the challenge of training large-scale models by investigating the impact of numeric deviation on Generative AI models. The authors develop a framework to understand and quantify this effect, using a case study with Flash Attention optimization. Their analysis reveals that Flash Attention experiences significantly more numeric deviation than Baseline Attention, but finds that this deviation has a relatively small impact on model weights during training. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Training large-scale Generative AI models can be unstable due to numeric deviation. Researchers have developed a method to understand and quantify this effect, using the Flash Attention optimization as an example. They found that Flash Attention experiences more numeric deviation than Baseline Attention but the impact is relatively small. |
Keywords
» Artificial intelligence » Attention » Machine learning » Optimization
