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Native Sparse Attention: Hardware-Aligned and Natively Trainable Sparse Attention

Yuan et al., 2025, Native Sparse Attention (Proceedings of the 63rd Annual Meeting of ACL (Volume 1: Long Papers))

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2.1 Background

  1. Why does sparse attention need different optimizations for training and for decoding?
    Training and prefilling process many queries at once, so they are compute-bound and the goal is fewer FLOPs. Decoding produces one token per step but must load the whole KV cache, so it is memory-bound and the goal is to read less memory.
    • Yuan et al., 2025, Native Sparse Attention (Proceedings of the 63rd Annual Meeting of ACL (Volume 1: Long Papers)), §2.1, p. 23079

Source

  • Jingyang Yuan, Huazuo Gao, Damai Dai, Junyu Luo, Liang Zhao, Zhengyan Zhang, Zhenda Xie, Yuxing Wei, Lean Wang, Zhiping Xiao, Yuqing Wang, Chong Ruan, Ming Zhang, Wenfeng Liang and Wangding Zeng. 2025. Native Sparse Attention: Hardware-Aligned and Natively Trainable Sparse Attention. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 23078–23097. License: CC BY 4.0. Flashcards adapted from this work. doi:10.18653/v1/2025.acl-long.1126 https://aclanthology.org/2025.acl-long.1126/