训练MoE足足提速70%！华为只用了3招

Core Viewpoint - The article discusses how the MoE (Mixture of Experts) model has become a key tool for model vendors to scale model capabilities under the Scaling Law, while also highlighting the training challenges associated with MoE, particularly inefficiencies leading to over half of the training time being wasted on waiting [1][2]. Group 1: MoE Training Challenges - The efficiency of MoE model training clusters faces two main challenges: communication and computation waiting due to expert parallelism, and load imbalance leading to additional waiting [4][7]. - When the model size increases, experts need to be split across different devices for parallel processing, which introduces extra All-to-All communication. This results in many computing units being idle while waiting for communication [5]. - The core of the MoE algorithm is "to the victor belong the spoils," leading to some hot experts being frequently called upon while cold experts are underutilized, causing further waiting due to varying computation loads across different model layers [8]. Group 2: Huawei's Solutions - Huawei has developed an optimization solution named Adaptive Pipe & EDPB to address the training bottlenecks of MoE, enabling smooth operation without waiting [3]. - The solution includes a "communication cover" technology that separates computation from communication, allowing calculations to proceed without waiting for data transfer [9]. - The "dynamic expert routing" feature adjusts the load dynamically based on real-time data distribution, achieving load balancing and eliminating communication bottlenecks [9]. Group 3: DeployMind Simulation Platform - Huawei has created the DeployMind simulation platform, which can simulate millions of training scenarios in just one hour, allowing for rapid analysis of diverse training loads and optimal strategy selection [10]. - This modeling framework has achieved a 90% accuracy rate, enabling efficient parallel selection that balances computation, communication, and memory for the Pangu Ultra MoE 718B model [11]. Group 4: Communication Optimization - The Adaptive Pipe communication cover framework achieves over 98% communication cover, allowing computations to proceed without waiting for communication [12][19]. - Huawei's innovative two-step communication process enhances communication speed by reducing inter-machine communication, effectively doubling the speed compared to traditional methods [15][16]. Group 5: Load Balancing and Throughput Improvement - The EDPB (Expert Dynamic Prediction Balancing) technology addresses load imbalance in MoE training, achieving a 25.5% increase in throughput [21][22]. - EDPB features include predictive load trends, dual-layer optimization for computation and communication, and intelligent triggering for expert migration based on pre-evaluated benefits [23][24][25]. - The overall system has achieved a 72.6% increase in end-to-end training throughput for the Pangu Ultra MoE 718B model, demonstrating the effectiveness of Huawei's optimization strategies [29][30].