Summary of Flashmask: Efficient and Rich Mask Extension Of Flashattention, by Guoxia Wang et al.
FlashMask: Efficient and Rich Mask Extension of FlashAttention
by Guoxia Wang, Jinle Zeng, Xiyuan Xiao, Siming Wu, Jiabin Yang, Lujing Zheng, Zeyu Chen, Jiang Bian, Dianhai Yu, Haifeng Wang
First submitted to arxiv on: 2 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 proposes FlashMask, an extension of FlashAttention that efficiently represents attention masks using a column-wise sparse representation. This approach reduces memory complexity from O(N^2) to O(N) and enables kernel optimizations that eliminate unnecessary computations by leveraging sparsity in the attention mask. The authors evaluate FlashMask’s performance in fine-tuning and alignment training of large language models, achieving significant throughput improvements and surpassing existing approaches like FlexAttention. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary FlashMask is a new way to handle attention masks in Transformer models. It makes it possible to process really long sequences quickly and efficiently. This helps with tasks that require understanding long pieces of text, like language translation or question answering. The authors tested FlashMask on some big language models and found that it worked faster than other approaches. |
Keywords
» Artificial intelligence » Alignment » Attention » Fine tuning » Mask » Question answering » Transformer » Translation
