A Hybrid Tensor-Expert-Data Parallelism Approach to Optimize Mixture-of-Experts Training

• URL: http://arxiv.org/abs/2303.06318v2
• Date: Sun, 14 May 2023 01:52:14 GMT
• Title: A Hybrid Tensor-Expert-Data Parallelism Approach to Optimize Mixture-of-Experts Training
• Authors: Siddharth Singh, Olatunji Ruwase, Ammar Ahmad Awan, Samyam Rajbhandari, Yuxiong He, Abhinav Bhatele
• Abstract summary: Mixture-of-Experts (MoE) is a neural network architecture that adds sparsely activated expert blocks to a base model. Current distributed deep learning frameworks are limited in their ability to train high-quality MoE models with large base models. We present DeepSpeed-TED, a novel, three-dimensional, hybrid parallel algorithm that combines data, tensor, and expert parallelism.
• Score: 13.346719319555943
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Mixture-of-Experts (MoE) is a neural network architecture that adds sparsely activated expert blocks to a base model, increasing the number of parameters without impacting computational costs. However, current distributed deep learning frameworks are limited in their ability to train high-quality MoE models with large base models. In this work, we present DeepSpeed-TED, a novel, three-dimensional, hybrid parallel algorithm that combines data, tensor, and expert parallelism to enable the training of MoE models with 4 to 8x larger base models than the current state-of-the-art. We also describe memory optimizations in the optimizer step, and communication optimizations that eliminate unnecessary data movement. We implement our approach in DeepSpeed and achieve speedups of 26% over a baseline (i.e. without our communication optimizations) when training a 40 billion parameter MoE model (6.7 billion base model with 16 experts) on 128 V100 GPUs.

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