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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