9/1/2023 0 Comments Expert choice 11 serial key![]() Method improves training convergence time by more than 2x. Transformer top-1 and GShard top-2 gating of prior work and find that our ![]() Pre-training speedups using the same computational resources of the Switch As a result, each token can be routed to a variable number ofĮxperts and each expert can have a fixed bucket size. Instead of letting tokens select the top-k experts, we have experts selecting Propose a heterogeneous mixture-of-experts employing an expert choice method. Regardless of the relative importance of different tokens. Prior workĪllocates a fixed number of experts to each token using a top-k function Under-trained, leading to an expert being under or over-specialized. one resulting in load imbalance) can cause certain experts to be Parameters to greatly increase while keeping the amount of computation for a Authors: Yanqi Zhou, Tao Lei, Hanxiao Liu, Nan Du, Yanping Huang, Vincent Zhao, Andrew Dai, Zhifeng Chen, Quoc Le, James Laudon Download PDF Abstract: Sparsely-activated Mixture-of-experts (MoE) models allow the number of
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