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Sparse mixture of expert architectures (MoEs) scale model capacity withoutlarge increases in training or inference costs. Despite their success, MoEssuffer from a number of issues: training instability, token dropping, inabilityto scale the number of experts, or ineffective finetuning. In this work, weproposeSoft MoE, a fully-differentiable sparse Transformer that addresses thesechallenges, while maintaining the benefits of MoEs. Soft MoE performs animplicit soft assignment by passing different weighted combinations of allinput tokens to each expert. As in other MoE works, experts in Soft MoE onlyprocess a subset of the (combined) tokens, enabling larger model capacity atlower inference cost. In the context of visual recognition, Soft MoE greatlyoutperforms standard Transformers (ViTs) and popular MoE variants (TokensChoice and Experts Choice). For example, Soft MoE-Base/16 requires 10.5x lowerinference cost (5.7x lower wall-clock time) than ViT-Huge/14 while matching itsperformance after similar training. Soft MoE also scales well: Soft MoE Huge/14with 128 experts in 16 MoE layers has over 40x more parameters than ViTHuge/14, while inference time cost grows by only 2%, and it performssubstantially better.

From Sparse to Soft Mixtures of Experts