Summary of Efficiently Dispatching Flash Attention For Partially Filled Attention Masks, by Agniv Sharma and Jonas Geiping
Efficiently Dispatching Flash Attention For Partially Filled Attention Masks
by Agniv Sharma, Jonas Geiping
First submitted to arxiv on: 23 Sep 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 paper introduces Binary Block Masking, an efficient modification to the state-of-the-art algorithm Flash Attention, making it mask-aware for processing sparse attention matrices. This is particularly relevant in applications where attention masks are used to reduce complexity, such as sequence packing techniques or tree masking in MEDUSA. The proposed method enhances Flash Attention’s performance by exploiting sparsity patterns in attention matrices, leading to up to 9x runtime improvements on real-world scenarios. Furthermore, the authors propose optimizations for contiguous non-zero patterns and extremely sparse masks, demonstrating the potential for significant speedups. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper makes it faster to use transformers with sparse or partially filled attention matrices. Transformers are really good at paying attention to important parts of text or speech, but they can get slowed down when dealing with large amounts of data that has some empty spaces. The authors came up with a new way to make the transformer work more efficiently by taking advantage of these empty spaces. They tested their idea on real-world scenarios and found that it made things run up to 9 times faster! This could be really useful in lots of applications, like making language translation or speech recognition faster. |
Keywords
» Artificial intelligence » Attention » Mask » Transformer » Translation
