SE-MoE: A Scalable and Efficient Mixture-of-Experts Distributed Training and Inference System

• URL: http://arxiv.org/abs/2205.10034v2
• Date: Mon, 12 Jun 2023 12:07:22 GMT
• Title: SE-MoE: A Scalable and Efficient Mixture-of-Experts Distributed Training and Inference System
• Authors: Liang Shen, Zhihua Wu, WeiBao Gong, Hongxiang Hao, Yangfan Bai, HuaChao Wu, Xinxuan Wu, Jiang Bian, Haoyi Xiong, Dianhai Yu, Yanjun Ma
• Abstract summary: Mixture-of-Experts (MoE) models have been proposed to lower the cost of training subject to the overall size of models/data. We present SE-MoE that proposes Elastic MoE training with 2D prefetch and Fusion communication over Hierarchical storage. For scalable inference in a single node, especially when the model size is larger than GPU memory, SE-MoE forms the CPU-GPU memory jointly into a ring of sections to load the model, and executes the computation tasks across the memory sections in a round-robin manner for efficient inference.
• Score: 24.335267149209848
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: With the increasing diversity of ML infrastructures nowadays, distributed training over heterogeneous computing systems is desired to facilitate the production of big models. Mixture-of-Experts (MoE) models have been proposed to lower the cost of training subject to the overall size of models/data through gating and parallelism in a divide-and-conquer fashion. While DeepSpeed has made efforts in carrying out large-scale MoE training over heterogeneous infrastructures, the efficiency of training and inference could be further improved from several system aspects, including load balancing, communication/computation efficiency, and memory footprint limits. In this work, we present SE-MoE that proposes Elastic MoE training with 2D prefetch and Fusion communication over Hierarchical storage, so as to enjoy efficient parallelisms in various types. For scalable inference in a single node, especially when the model size is larger than GPU memory, SE-MoE forms the CPU-GPU memory jointly into a ring of sections to load the model, and executes the computation tasks across the memory sections in a round-robin manner for efficient inference. We carried out extensive experiments to evaluate SE-MoE, where SE-MoE successfully trains a Unified Feature Optimization (UFO) model with a Sparsely-Gated Mixture-of-Experts model of 12B parameters in 8 days on 48 A100 GPU cards. The comparison against the state-of-the-art shows that SE-MoE outperformed DeepSpeed with 33% higher throughput (tokens per second) in training and 13% higher throughput in inference in general. Particularly, under unbalanced MoE Tasks, e.g., UFO, SE-MoE achieved 64% higher throughput with 18% lower memory footprints. The code of the framework will be released on: https://github.com/PaddlePaddle/Paddle.

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