Improving Automatic Parallel Training via Balanced Memory Workload Optimization

• URL: http://arxiv.org/abs/2307.02031v2
• Date: Sat, 24 Feb 2024 08:12:53 GMT
• Title: Improving Automatic Parallel Training via Balanced Memory Workload Optimization
• Authors: Yujie Wang, Youhe Jiang, Xupeng Miao, Fangcheng Fu, Shenhan Zhu, Xiaonan Nie, Yaofeng Tu, Bin Cui
• Abstract summary: Transformer models have emerged as the leading approach for achieving state-of-the-art performance across various application domains. We present Galvatron-BMW, a novel system framework that integrates multiple parallelism prevalent dimensions and automatically identifies the most efficient hybrid parallelism strategy. Our evaluations on different Transformer models demonstrate the capabilities of Galvatron-BMW in automating distributed training under varying GPU memory constraints.
• Score: 36.87527680184956
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Transformer models have emerged as the leading approach for achieving state-of-the-art performance across various application domains, serving as the foundation for advanced large-scale deep learning (DL) models. However, efficiently training these models across multiple GPUs remains a complex challenge due to the abundance of parallelism options. Existing DL systems either require manual efforts to design distributed training plans or limit parallelism combinations to a constrained search space. In this paper, we present Galvatron-BMW, a novel system framework that integrates multiple prevalent parallelism dimensions and automatically identifies the most efficient hybrid parallelism strategy. To effectively navigate this vast search space, we employ a decision tree approach for decomposition and pruning based on intuitive insights. We further utilize a dynamic programming search algorithm to derive the optimal plan. Moreover, to improve resource utilization and enhance system efficiency, we propose a bi-objective optimization workflow that focuses on workload balance. Our evaluations on different Transformer models demonstrate the capabilities of Galvatron-BMW in automating distributed training under varying GPU memory constraints. Across all tested scenarios, Galvatron-BMW consistently achieves superior system throughput, surpassing previous approaches that rely on limited parallelism strategies.

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