Tutel: Adaptive Mixture-of-Experts at Scale

By Changho Hwang, Wei Cui, Yifan Xiong, Ziyue Yang, Ze Liu, Han Hu, Zilong Wang, Rafael Salas, Jithin Jose, Prabhat Ram and 5 more. 2022

273 citations recorded by the source corpus.

Sparsely-gated mixture-of-experts (MoE) has been widely adopted to scale deep learning models to trillion-plus parameters with fixed computational cost. The algorithmic performance of MoE relies on its token routing mechanism that forwards each input token to the right sub-models or experts. While token routing 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 dynamic workload. We present Flex, a highly scalable stack design and implementation for MoE with dynamically adaptive parallelism and pipelining. Flex designs an identical layout for distributing MoE model parameters and input data, which can be leveraged by all possible parallelism or pipelining methods without any mathematical inequivalence or tensor migration overhead. This enables adaptive parallelism/pipelining optimization at zero cost during runtime. Based on this key 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 that Flex efficiently and effectively runs a real-world MoE-based model named SwinV2-MoE, built upon Swin Transformer V2, a state-of-the-art computer vision architecture. On efficiency, Flex accelerates SwinV2-MoE, achieving up to 1.55x and 2.11x speedup in training and inference over Fairseq, respectively. On effectiveness, the SwinV2-MoE model achieves superior accuracy in both pre-training and down-stream computer vision tasks such as COCO object detection than the counterpart dense model, indicating the readiness of Flex for end-to-end real-world model training and inference.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Radiology, Nuclear Medicine and Imaging, Model Architectures

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Citations in the corpus

MegaBlocks: Efficient Sparse Training with Mixture-of-Experts (2022, methodology): “Second, block sparsity maps efﬁciently to hardware accelerators built around systolic array matrix multipliers like GPUs and TPUs.”
A Survey on Mixture of Experts in Large Language Models (2024, unknown): “Therefore, recent studies like DeepSpeed-MoE [64], FastMoE [104], HetuMoE [109] and Tutel [105] have focused on the development of tailored GPU kernels to enhance the operation efﬁciency.”
QMoE: Practical Sub-1-Bit Compression of Trillion-Parameter Models (2023, background): “Since MoEs can feature rather different computational profiles from standard dense models, there is also significant research on optimizing inference and training systems (Barham et al., 2022; Gale et al., 2023; Hwang et al., 2023).”
Mixture-of-Experts in Remote Sensing: A Survey (2026, unknown): “Works such as FastMoE [45], DeepSpeed-MoE [118], Tutel [55], HetuMoE [108], FasterMoE [46] and MegaBlocks [32] focus on efficient distributed training and inference, including all-to-all communication scheduling, CUDA kernel optimizations, expert placement, and parallelism strategies.”