https://arxiv.org/abs/2502.11089 Skip to main content Cornell University We gratefully acknowledge support from the Simons Foundation, member institutions, and all contributors. Donate arxiv logo > cs > arXiv:2502.11089 [ ] Help | Advanced Search [All fields ] Search arXiv logo Cornell University Logo [ ] GO quick links * Login * Help Pages * About Computer Science > Computation and Language arXiv:2502.11089 (cs) [Submitted on 16 Feb 2025 (v1), last revised 27 Feb 2025 (this version, v2)] Title:Native Sparse Attention: Hardware-Aligned and Natively Trainable Sparse Attention Authors:Jingyang Yuan, Huazuo Gao, Damai Dai, Junyu Luo, Liang Zhao, Zhengyan Zhang, Zhenda Xie, Y. X. Wei, Lean Wang, Zhiping Xiao, Yuqing Wang, Chong Ruan, Ming Zhang, Wenfeng Liang, Wangding Zeng View a PDF of the paper titled Native Sparse Attention: Hardware-Aligned and Natively Trainable Sparse Attention, by Jingyang Yuan and 14 other authors View PDF HTML (experimental) Abstract:Long-context modeling is crucial for next-generation language models, yet the high computational cost of standard attention mechanisms poses significant computational challenges. Sparse attention offers a promising direction for improving efficiency while maintaining model capabilities. We present NSA, a Natively trainable Sparse Attention mechanism that integrates algorithmic innovations with hardware-aligned optimizations to achieve efficient long-context modeling. NSA employs a dynamic hierarchical sparse strategy, combining coarse-grained token compression with fine-grained token selection to preserve both global context awareness and local precision. Our approach advances sparse attention design with two key innovations: (1) We achieve substantial speedups through arithmetic intensity-balanced algorithm design, with implementation optimizations for modern hardware. (2) We enable end-to-end training, reducing pretraining computation without sacrificing model performance. As shown in Figure 1, experiments show the model pretrained with NSA maintains or exceeds Full Attention models across general benchmarks, long-context tasks, and instruction-based reasoning. Meanwhile, NSA achieves substantial speedups over Full Attention on 64k-length sequences across decoding, forward propagation, and backward propagation, validating its efficiency throughout the model lifecycle. Subjects: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Machine Learning (cs.LG) Cite as: arXiv:2502.11089 [cs.CL] (or arXiv:2502.11089v2 [cs.CL] for this version) https://doi.org/10.48550/arXiv.2502.11089 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Wenfeng Liang [view email] [v1] Sun, 16 Feb 2025 11:53:44 UTC (915 KB) [v2] Thu, 27 Feb 2025 09:01:21 UTC (916 KB) Full-text links: Access Paper: View a PDF of the paper titled Native Sparse Attention: Hardware-Aligned and Natively Trainable Sparse Attention, by Jingyang Yuan and 14 other authors * View PDF * HTML (experimental) * TeX Source * Other Formats view license Current browse context: cs.CL < prev | next > new | recent | 2025-02 Change to browse by: cs cs.AI cs.LG References & Citations * NASA ADS * Google Scholar * Semantic Scholar a export BibTeX citation Loading... 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