Summary of Linear Attention Sequence Parallelism, by Weigao Sun et al.
Linear Attention Sequence Parallelism
by Weigao Sun, Zhen Qin, Dong Li, Xuyang Shen, Yu Qiao, Yiran Zhong
First submitted to arxiv on: 3 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: 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 This paper introduces Linear Attention Sequence Parallelism (LASP), a novel approach for parallelizing linear attention-based transformer models. LASP leverages the right-product kernel trick of linear attention to reduce communication overhead, making it more efficient and usable on GPUs. The authors also enhance computation efficiency through kernel fusion and intermediate state caching. LASP is compatible with batch-level data parallel methods, allowing distributed training on large clusters with long sequences. The paper conducts extensive experiments on varying sequence lengths from 2K to 4096K, demonstrating that LASP can scale up to 4096K on 128 GPUs, outperforming existing SP methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary LASP is a new way to process really long sentences using special transformers. It makes sure the computers don’t get too slow by sharing work efficiently. This helps with large groups of computers working together. The paper tests LASP on different sentence lengths and shows it can handle sentences up to 4 million words long on a big group of computers. |
Keywords
* Artificial intelligence * Attention * Kernel trick * Transformer
