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About this paper
Chang Chen, Min Li, Zhihua Wu, Dianhai Yu, Chao Yang
TA-MoE introduces a topology-aware framework for large-scale Mixture-of-Expert (MoE) training that dynamically adapts data dispatch patterns to the underlying network infrastructure. This approach achieves up to 4.77x training throughput speedup by alleviating communication bottlenecks across diverse hardware environments, all while preserving model accuracy.
Key insights
- Aligning MoE data dispatch with the underlying network topology, specifically by sending more data over higher-bandwidth links, is crucial for optimizing distributed training efficiency.
- A principled communication cost model can be simplified to derive near-optimal, bandwidth-proportional data routing targets for MoE layers.
- Guiding MoE routing via an auxiliary loss allows for adaptive, topology-aware dispatch that enhances performance without sacrificing model accuracy or convergence characteristics.
Summary by alphaXiv.
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