title: Sparser is Faster and Less is More: Efficient Sparse Attention for
    Long-Range Transformers
summary: Accommodating long sequences efficiently in autoregressive Transformers,
  especially within an extended context window, poses significant challenges due
  to the quadratic computational complexity and substantial KV memory
  requirements inherent in self-attention mechanisms. In this work, we introduce
  SPARSEK Attention, a novel sparse attention mechanism designed to overcome
  these computational and memory obstacles while maintaining performance. Our
  approach integrates a scoring network and a differentiable top-k mask operator,
  SPARSEK, to select a constant number of KV pairs for each query, thereby
  enabling gradient-based optimization. As a result, SPARSEK Attention offers
  linear time complexity and constant memory footprint during generation.
  Experimental results reveal that SPARSEK Attention outperforms previous sparse
  attention methods and provides significant speed improvements during both
  training and inference, particularly in language modeling and downstream tasks.
  Furthermore, our method can be seamlessly integrated into pre-trained Large
  Language Models (LLMs) with minimal fine-tuning, offering a practical solution
  for effectively managing long-range dependencies in diverse applications.
paper_authors._id: 667a851b67be4d7b18f3ff57
paper_authors.name: Chao Lou
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-06-25T13:34:33.454Z
paper_authors._id: 64b74fc475b23e68c539e806
paper_authors.avatarUrl: /avatars/2476676fe910a01d96b9f42e068353ed.svg
paper_authors.fullname: Chao Lou
paper_authors.type: user
paper_authors.user: chaochaoL
paper_authors._id: 667a851b67be4d7b18f3ff58
paper_authors.name: Zixia Jia
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-06-25T13:34:39.717Z
paper_authors._id: 64b7ae6cf53ae848e72b997d
paper_authors.avatarUrl: /avatars/b55dd3d6fcb3ccac2e3880d01a9bdc63.svg
paper_authors.fullname: Zixia Jia
paper_authors.type: user
paper_authors.user: vickyandkekey
paper_authors._id: 667a851b67be4d7b18f3ff59
paper_authors.name: Zilong Zheng
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-06-25T13:35:01.138Z
paper_authors._id: 63a95a6a7930fa8c7dd63d4e
paper_authors.avatarUrl: /avatars/d9d0420f7ddfe2f3a7e029fb05f1c89f.svg
paper_authors.fullname: Zilong Zheng
paper_authors.type: user
paper_authors.user: zlzheng
paper_authors._id: 667a851b67be4d7b18f3ff5a
paper_authors.name: Kewei Tu
paper_title: Sparser is Faster and Less is More: Efficient Sparse Attention for
    Long-Range Transformers
paper_summary: Accommodating long sequences efficiently in autoregressive Transformers,
  especially within an extended context window, poses significant challenges due
  to the quadratic computational complexity and substantial KV memory
  requirements inherent in self-attention mechanisms. In this work, we introduce
  SPARSEK Attention, a novel sparse attention mechanism designed to overcome
  these computational and memory obstacles while maintaining performance. Our
  approach integrates a scoring network and a differentiable top-k mask operator,
  SPARSEK, to select a constant number of KV pairs for each query, thereby
  enabling gradient-based optimization. As a result, SPARSEK Attention offers
  linear time complexity and constant memory footprint during generation.
  Experimental results reveal that SPARSEK Attention outperforms previous sparse
  attention methods and provides significant speed improvements during both
  training and inference, particularly in language modeling and downstream tasks.
  Furthermore, our method can be seamlessly integrated into pre-trained Large
  Language Models (LLMs) with minimal fine-tuning, offering a practical solution
  for effectively managing long-range dependencies in diverse applications.
paper_ai_summary: SPARSEK Attention addresses inefficiencies in autoregressive Transformers by providing linear time complexity and constant memory footprint, enabling efficient processing of long sequences and integration into large pre-trained models.
paper_ai_keywords: autoregressive Transformers extended context window computational complexity KV memory self-attention mechanisms SPARSEK Attention scoring network differentiable top-k mask operator gradient-based optimization language modeling downstream tasks Large Language Models (LLMs)