![]() For a smaller activation cost, our method outperforms the T5 dense model in 7 out of the 11 tasks. For the same computational cost, our method demonstrates higher performance in fine-tuning 11 selected tasks in the GLUE and SuperGLUE benchmarks. ![]() We systematically study pre-training speedups using the same computational resources of the Switch Transformer top-1 and GShard top-2 gating of prior work and find that our method improves training convergence time by more than 2x. As a result, each token can be routed to a variable number of experts and each expert can have a fixed bucket size. Instead of letting tokens select the top-k experts, we have experts selecting the top-k tokens. To address this, we propose a heterogeneous mixture-of-experts employing an expert choice method. Prior work allocates a fixed number of experts to each token using a top-k function regardless of the relative importance of different tokens. one resulting in load imbalance) can cause certain experts to be under-trained, leading to an expert being under or over-specialized. However, a poor expert routing strategy (e.g. Sparsely-activated Mixture-of-experts (MoE) models allow the number of parameters to greatly increase while keeping the amount of computation for a given token or a given sample unchanged. Expert Choice support is here to help with questions about how to build your decision model and collect judgments.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |