OneFlow: Redesign the Distributed Deep Learning Framework from Scratch

TL;DR

OneFlow is a new distributed deep learning framework that simplifies programming for various parallelism paradigms using SBP abstraction and actor model, demonstrating improved efficiency and flexibility over existing frameworks.

Contribution

It introduces SBP and actor model-based design for a flexible, efficient distributed training framework that surpasses existing solutions in large model training.

Findings

Outperforms well-known customized libraries.

Efficient training of large DNN models.

Simplifies programming of parallelism paradigms.

Abstract

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://github.com/Oneflow-Inc/oneflow.