OneFlow: Redesign the Distributed Deep Learning Framework from Scratch

作     者：Yuan, Jinhui Li, Xinqi Cheng, Cheng Liu, Juncheng Guo, Ran Cai, Shenghang Yao, Chi Yang, Fei Yi, Xiaodong Wu, Chuan Zhang, Haoran Zhao, Jie 

作者机构：OneFlow Research Sweden Zhejiang Laboratory China The University of Hong Kong Hong Kong University of Pennsylvania United States State Key Laboratory of Mathematical Engineering and Advanced Computing China 

出 版 物：《arXiv》 (arXiv)

年 卷 期：2021年

核心收录：

主　　题：Deep neural networks 

摘      要：Deep learning frameworks such as TensorFlow and PyTorch provide a productive interface for expressing and training a deep neural network (DNN) model on a single device or using data parallelism. Still, they may not be flexible or efficient enough in training emerging large models on distributed devices, which require more sophisticated parallelism beyond data parallelism. Plugins or wrappers have been developed to strengthen these frameworks for model or pipeline parallelism, but they complicate the usage and implementation of distributed deep learning. Aiming at a simple, neat redesign of distributed deep learning frameworks for various parallelism paradigms, we present OneFlow, a novel distributed training framework based on an SBP (split, broadcast and partial-value) abstraction and the actor model. SBP enables much easier programming of data parallelism and model parallelism than existing frameworks, and the actor model provides a succinct runtime mechanism to manage the complex dependencies imposed by resource constraints, data movement and computation in distributed deep learning. We demonstrate the general applicability and efficiency of OneFlow for training various large DNN models with case studies and extensive experiments. The results show that OneFlow outperforms many well-known customized libraries built on top of the state-of-the-art frameworks. The code of OneFlow is available at: https://***/Oneflow-Inc/oneflow. © 2021, CC BY.