Efficient Transformers: Sparsely-Gated Mixture of Experts (MoE)

Feed-forward layers in Transformer models are often massive, creating an efficiency bottleneck. Sparsely-Gated Mixture of Experts (MoE) offers an elegant solution. MoE decomposes the large feed-forward layer into multiple smaller 'expert' networks and uses a router to select the optimal subset of experts for each token's computation, significantly reducing computational cost and improving efficiency. This post details the workings of MoE, provides a NumPy implementation, and discusses key issues like expert load balancing.