专家一半时间在摸鱼？Adaptive Pipe & EDPB让昇腾MoE训练效率提升70%

Core Viewpoint - The article discusses the challenges and solutions related to the training efficiency of the Mixture of Experts (MoE) models, highlighting that over half of the training time is wasted on waiting due to communication and load imbalance issues [2][3][4]. Group 1: MoE Model Training Challenges - The efficiency of MoE model training clusters faces two main challenges: communication waiting due to expert parallelism and load imbalance leading to computation waiting [4]. - The communication waiting arises from the need for All-to-All communication when splitting experts across devices, causing idle computation units [4]. - Load imbalance occurs as some experts are frequently called while others remain underutilized, exacerbated by varying lengths of training data and differences in computational loads across model layers [4]. Group 2: Solutions Implemented - Huawei developed the Adaptive Pipe and EDPB optimization solutions to enhance MoE training efficiency, likening the system to a smart traffic hub that eliminates waiting [5][22]. - The AutoDeploy simulation platform allows for rapid analysis and optimization of training loads, achieving 90% accuracy in finding optimal strategies for hardware specifications [8][22]. - The Adaptive Pipe communication framework achieves over 98% communication masking, allowing computations to proceed without waiting for communication [10][11]. Group 3: Performance Improvements - The EDPB global load balancing technique improves throughput by 25.5% by ensuring balanced expert scheduling during training [14]. - The system's end-to-end training throughput increased by 72.6% in the Pangu Ultra MoE 718B model training, demonstrating significant performance gains [22][23].