Prophet: Fine-grained Load Balancing for Parallel Training of Large-scale MoE Models
Wei Wang, Zhiquan Lai, Shengwei Li, Weijie Liu, Keshi Ge, Yujie Liu, Ao Shen, Dongsheng Li
Abstract
Mixture of Expert (MoE) has received increasing attention for scaling DNN models to extra-large size with negligible increases in computation. The MoE model has achieved the highest accuracy in several domains. However, a significant load imbalance occurs in the device during the training of a MoE model, resulting in significantly reduced throughput. Previous works on load balancing either harm model convergence or suffer from high execution overhead. To address these issues, we present Prophet: a fine-grained load balancing method for parallel training of large-scale MoE models, which consists of a planner and a scheduler. Prophet planner first employs a fine-grained resource allocation method to determine the possible scenarios for the expert placement in a fine-grained manner, and then efficiently searches for a well-balanced expert placement to balance the load without introducing additional overhead. Prophet scheduler exploits the locality of the token distribution to schedule the resource allocation operations using a layer-wise fine-grained schedule strategy to hide their overhead. We conduct extensive experiments in four clusters and five representative models. The results indicate that Prophet gains up to 2.3x speedup compared to the state-of-the-art MoE frameworks including Deepspeed-MoE and FasterMoE. Additionally, Prophet achieves a load balancing enhancement of up to 12.06x when compared to FasterMoE.