Capacity-Aware Inference: Mitigating the Straggler Effect in Mixture of Experts

• URL: http://arxiv.org/abs/2503.05066v1
• Date: Fri, 07 Mar 2025 01:11:39 GMT
• Title: Capacity-Aware Inference: Mitigating the Straggler Effect in Mixture of Experts
• Authors: Shwai He, Weilin Cai, Jiayi Huang, Ang Li,
• Abstract summary: Mixture of Experts (MoE) is an effective architecture for scaling large language models by leveraging sparse expert activation.<n>MoE suffers from inference inefficiencies due to imbalanced token-to-expert assignment, where some experts are overloaded while others remain underutilized.<n>We propose Capacity-Aware Inference, including two key techniques: (1) textbftextitCapacity-Aware Token Drop, which discards overloaded tokens to regulate the maximum latency of MoE, and (2) textbftextitCapacity-Aware Token Reroute, which reallocates overflowed tokens to underutilized experts.
• Score: 9.393481672669564
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: The Mixture of Experts (MoE) is an effective architecture for scaling large language models by leveraging sparse expert activation, optimizing the trade-off between performance and efficiency. However, under expert parallelism, MoE suffers from inference inefficiencies due to imbalanced token-to-expert assignment, where some experts are overloaded while others remain underutilized. This imbalance leads to poor resource utilization and increased latency, as the most burdened expert dictates the overall delay, a phenomenon we define as the \textbf{\textit{Straggler Effect}}. To mitigate this, we propose Capacity-Aware Inference, including two key techniques: (1) \textbf{\textit{Capacity-Aware Token Drop}}, which discards overloaded tokens to regulate the maximum latency of MoE, and (2) \textbf{\textit{Capacity-Aware Token Reroute}}, which reallocates overflowed tokens to underutilized experts, balancing the token distribution. These techniques collectively optimize both high-load and low-load expert utilization, leading to a more efficient MoE inference pipeline. Extensive experiments demonstrate the effectiveness of our methods, showing significant improvements in inference efficiency, e.g., 0.2\% average performance increase and a 1.94$\times$ inference speedup on Mixtral-8$\times$7B-Instruct.

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