Capacity-Aware Inference: Mitigating the Straggler Effect in Mixture of Experts
Researchers from the University of Maryland have introduced a novel approach to optimize inference efficiency in Mixture of Experts (MoE) large language models. The study addresses the "Straggler Effect," a phenomenon where imbalanced token-to-expert assignments cause global delays as underloaded experts wait for overloaded ones. To mitigate this, the team proposes two methods: Capacity-Aware Token Drop, which discards excess tokens from overloaded experts to reduce latency with minimal performance loss, and Capacity-Aware Expanded Drop, which allows tokens to access additional local experts to improve load balancing and utilization. Extensive experiments on models like OLMoE and Mixtral-8x7B-Instruct demonstrate significant improvements. For instance, the Expanded Drop method achieved a 1.85x inference speedup and a 0.2% performance improvement on Mixtral. The research highlights substantial gains in expert utilization and overall efficiency for both language and multimodal MoE architectures. The accompanying code has been released publicly to facilitate further adoption and development in scalable AI infrastructure.
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Capacity-Aware Inference: Mitigating the Straggler Effect in Mixture of Experts
Researchers from the University of Maryland have introduced a novel approach to optimize inference efficiency in Mixture of Experts (MoE) large language models. The study addresses the "Straggler Effect," a phenomenon where imbalanced token-to-expert assignments cause global delays as underloaded experts wait for overloaded ones. To mitigate this, the team proposes two methods: Capacity-Aware Token Drop, which discards excess tokens from overloaded experts to reduce latency with minimal performance loss, and Capacity-Aware Expanded Drop, which allows tokens to access additional local experts to improve load balancing and utilization. Extensive experiments on models like OLMoE and Mixtral-8x7B-Instruct demonstrate significant improvements. For instance, the Expanded Drop method achieved a 1.85x inference speedup and a 0.2% performance improvement on Mixtral. The research highlights substantial gains in expert utilization and overall efficiency for both language and multimodal MoE architectures. The accompanying code has been released publicly to facilitate further adoption and development in scalable AI infrastructure.
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