OneFlow: Redesign the Distributed Deep Learning Framework from Scratch

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.

References in corpus (7)

• Scaling Laws for Neural Language Models
• Revisiting Distributed Synchronous SGD
• Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsity
• Wide & Deep Learning for Recommender Systems
• ZeRO-Infinity: Breaking the GPU Memory Wall for Extreme Scale Deep Learning
• Exploring the limits of Concurrency in ML Training on Google TPUs
• Stanza: Layer Separation for Distributed Training in Deep Learning

Cited by in corpus (4)

• Merak: An Efficient Distributed DNN Training Framework with Automated 3D Parallelism for Giant Foundation Models
• Fuzzing Automatic Differentiation in Deep-Learning Libraries
• AutoDDL: Automatic Distributed Deep Learning with Near-Optimal Bandwidth Cost
• SWIFT: Expedited Failure Recovery for Large-scale DNN Training