Your MoE Model Does Not Have to Select Fixed Number of Experts

Standard Mixture-of-Experts (MoE) models typically employ a fixed top-k routing methodology, which can lead to inefficiencies by treating all tokens uniformly regardless of their complexity. Dynamic routing offers a solution by adaptively selecting the optimal number of experts for each token, thereby enhancing both performance and efficiency. Techniques such as thresholding, dynamic proposers, and zero-computation experts exemplify this approach by allowing flexibility in expert allocation. For instance, thresholding can activate experts based on probability, while dynamic proposers predict the number of required experts, and zero-computation experts reduce computational cost without affecting model capacity. Despite the potential benefits, challenges remain in balancing performance and efficiency, implementing specialized kernels, controlling sparsity, and ensuring load balancing among experts. Dynamic routing is increasingly significant for improving the performance and efficiency of MoE models, especially as they evolve into large-scale language models.

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