CO2: Efficient Distributed Training with Full Communication-Computation Overlap

TL;DR

CO2 is a novel distributed training method that enables efficient large-scale training on low-bandwidth clusters by overlapping communication with computation, improving scalability and stability across diverse tasks.

Contribution

The paper introduces CO2, a new asynchronous communication approach with local updates, staleness gap penalty, and momentum clipping, enhancing training efficiency on limited bandwidth clusters.

Findings

Achieves high scalability on clusters with limited bandwidth.

Demonstrates effective convergence and generalization in vision and NLP tasks.

Supports deployment on up to 128 GPUs with significant speedup.

Abstract

The fundamental success of large language models hinges upon the efficacious implementation of large-scale distributed training techniques. Nevertheless, building a vast, high-performance cluster featuring high-speed communication interconnectivity is prohibitively costly, and accessible only to prominent entities. In this work, we aim to lower this barrier and democratize large-scale training with limited bandwidth clusters. We propose a new approach called CO2 that introduces local-updating and asynchronous communication to the distributed data-parallel training, thereby facilitating the full overlap of COmunication with COmputation. CO2 is able to attain a high scalability even on extensive multi-node clusters constrained by very limited communication bandwidth. We further propose the staleness gap penalty and outer momentum clipping techniques together with CO2 to bolster its convergence and training stability. Besides, CO2 exhibits seamless integration with well-established ZeRO-series optimizers which mitigate memory consumption of model states with large model training. We also provide a mathematical proof of convergence, accompanied by the establishment of a stringent upper bound. Furthermore, we validate our findings through an extensive set of practical experiments encompassing a wide range of tasks in the fields of computer vision and natural language processing. These experiments serve to demonstrate the capabilities of CO2 in terms of convergence, generalization, and scalability when deployed across configurations comprising up to 128 A100 GPUs. The outcomes emphasize the outstanding capacity of CO2 to hugely improve scalability, no matter on clusters with 800Gbps RDMA or 80Gbps TCP/IP inter-node connections.