Dynamic Mixture of Experts: An Auto-Tuning Approach for Efficient Transformer Models

ICLR 2025 Conference Submission4766 Authors

Keywords: Mixture of Experts, Transformer Model

TL;DR: We propose DynMoE, a method designed to automatically determine the hyper-parameter values for MoE models without compromising performance.

Abstract: The Sparse Mixture of Experts (SMoE) has been widely employed to enhance the efficiency of training and inference for Transformer-based foundational models, yielding promising results. However, the performance of SMoE heavily depends on the choice of hyper-parameters, such as the number of experts and the number of experts to be activated (referred to as top-$k$), resulting in significant computational overhead due to the extensive model training by searching over various hyper-parameter configurations. As a remedy, we introduce the Dynamic Mixture of Experts (DynMoE) technique. DynMoE incorporates (1) a novel gating method that enables each token to automatically determine the number of experts to activate. (2) An adaptive process automatically adjusts the number of experts during training. Extensive numerical results across Vision, Language, and Vision-Language tasks demonstrate the effectiveness of our approach to achieve competitive performance compared to GMoE for vision and language tasks, and MoE-LLaVA for vision-language tasks, while maintaining efficiency by activating fewer parameters. Our code will be made publicly available.

Primary Area: other topics in machine learning (i.e., none of the above)

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Submission Number: 4766