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Star Attention: Efficient LLM Inference over Long Sequences
🤗 Paper Upvotes: 32 | cs.CL, cs.AI, cs.LG
Authors:
Shantanu Acharya, Fei Jia, Boris Ginsburg
Title:
Star Attention: Efficient LLM Inference over Long Sequences
Arxiv:
http://arxiv.org/abs/2411.17116v1
Abstract:
Inference with Transformer-based Large Language Models (LLMs) on long sequences is both costly and slow due to the quadratic complexity of the self-attention mechanism. We introduce Star Attention, a two-phase block-sparse approximation that improves computational efficiency by sharding attention across multiple hosts while minimizing communication overhead. In the first phase, the context is processed using blockwise-local attention across hosts, in parallel. In the second phase, query and response tokens attend to all prior cached tokens through sequence-global attention. Star Attention integrates seamlessly with most Transformer-based LLMs trained with global attention, reducing memory requirements and inference time by up to 11x while preserving 95-100% of accuracy.
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By Jingwen Liang, Gengyu Wang🤗 Paper Upvotes: 32 | cs.CL, cs.AI, cs.LG
Authors:
Shantanu Acharya, Fei Jia, Boris Ginsburg
Title:
Star Attention: Efficient LLM Inference over Long Sequences
Arxiv:
http://arxiv.org/abs/2411.17116v1
Abstract:
Inference with Transformer-based Large Language Models (LLMs) on long sequences is both costly and slow due to the quadratic complexity of the self-attention mechanism. We introduce Star Attention, a two-phase block-sparse approximation that improves computational efficiency by sharding attention across multiple hosts while minimizing communication overhead. In the first phase, the context is processed using blockwise-local attention across hosts, in parallel. In the second phase, query and response tokens attend to all prior cached tokens through sequence-global attention. Star Attention integrates seamlessly with most Transformer-based LLMs trained with global attention, reducing memory requirements and inference time by up to 11x while preserving 95-100% of accuracy.