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[2502.13189] MoBA: Mixture of Block Attention for Long-Context LLMs

arxiv.org · 6,312 words · saved by 1 readers

Abstract:Scaling the effective context length is essential for advancing large language models (LLMs) toward artificial general intelligence (AGI). However, the quadratic increase in computational complexity inherent in traditional attention mechanisms presents a prohibitive overhead. Existing approaches either impose strongly biased structures, such as sink or window attention which are task-specific, or radically modify the attention mechanism into linear approximations, whose performance in complex reasoning tasks remains inadequately explored. In this work, we propose a solution that adheres to the ``less structure'' principle, allowing the model to determine where to attend autonomously, rather than introducing predefined biases. We introduce Mixture of Block Attention (MoBA), an innovative approach that applies the principles of Mixture of Experts (MoE) to the attention mechanism. This novel architecture demonstrates superior performance on long-context tasks while offering a key advantage: the ability to seamlessly transition between full and sparse attention, enhancing efficiency without the risk of compromising performance. MoBA has already been deployed to support Kimi's long-context requests and demonstrates significant advancements in efficient attention computation for LLMs. Our code is available at this https URL.

M O BA: M IXTURE OF B LOCK ATTENTION FOR L ONG -C ONTEXT LLM S T ECHNICAL R EPORT Enzhe Lu1 Zhejun Jiang1 Jingyuan Liu1 Yulun Du1 Tao Jiang1 Chao Hong1 Shaowei Liu1 Weiran He1 Enming Yuan1 Yuzhi Wang1 Zhiqi Huang1 Huan Yuan1 arXiv:2502.13189v1 [cs.LG] 18 Feb 2025…

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