A Hybrid Tensor-Expert-Data Parallelism Approach to Optimize Mixture-of-Experts Training
This work addresses the bottleneck of distributed training for high-quality MoE models, which is incremental as it builds on existing parallelism methods.
The paper tackles the problem of training large Mixture-of-Experts (MoE) models by introducing DeepSpeed-TED, a hybrid parallel algorithm that enables training with 4 to 8x larger base models than the state-of-the-art, achieving a 26% speedup on a 40 billion parameter model.
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.