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Sparsely-gated mixture-of-experts (MoE) has been widely adopted to scale deeplearning models to trillion-plus parameters with fixed computational cost. Thealgorithmic performance of MoE relies on its token routing mechanism thatforwards each input token to the right sub-models or experts. While tokenrouting dynamically determines the amount of expert workload at runtime,existing systems suffer inefficient computation due to their static execution,namely static parallelism and pipelining, which does not adapt to the dynamicworkload. We present Flex, a highly scalable stack design and implementationfor MoE with dynamically adaptive parallelism and pipelining. Flex designs anidentical layout for distributing MoE model parameters and input data, whichcan be leveraged by all possible parallelism or pipelining methods without anymathematical inequivalence or tensor migration overhead. This enables adaptiveparallelism/pipelining optimization at zero cost during runtime. Based on thiskey design, Flex also implements various MoE acceleration techniques.Aggregating all techniques, Flex finally delivers huge speedup at any scale --4.96x and 5.75x speedup of a single MoE layer over 16 and 2,048 A100 GPUs,respectively, over the previous state-of-the-art. Our evaluation shows thatFlex efficiently and effectively runs a real-world MoE-based model namedSwinV2-MoE, built upon Swin Transformer V2, a state-of-the-art computer visionarchitecture. On efficiency, Flex accelerates SwinV2-MoE, achieving up to 1.55xand 2.11x speedup in training and inference over Fairseq, respectively. Oneffectiveness, the SwinV2-MoE model achieves superior accuracy in bothpre-training and down-stream computer vision tasks such as COCO objectdetection than the counterpart dense model, indicating the readiness of Flexfor end-to-end real-world model training and inference.

Tutel: Adaptive Mixture-of-Experts at Scale