CO2: Efficient Distributed Training with Full Communication-Computation Overlap (2401.16265v1)

Abstract: The fundamental success of LLMs 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.

Overview of the CO2 Framework for Distributed Training

The paper "CO2: Efficient Distributed Training with Full Communication-Computation Overlap" presents a framework aimed at enhancing the efficiency of training large-scale neural networks in distributed settings, specifically where communication bandwidth is limited. This work identifies a crucial challenge in distributed deep learning: communication overhead, which can severely limit scalability and computational efficiency. The CO2 approach seeks to address these limitations by maximizing the overlap between communication and computation in the training process.

Key Contributions

CO2 introduces a novel approach to distributed data-parallel training, focusing on the use of local-updating and asynchronous communication. This dual methodology is designed to facilitate a full overlap between communication and computation phases during training. Here are the core contributions:

1. Local-Updating with Asynchronous Communication: CO2 employs local updates on each worker node without immediate synchronization after each update, which is a departure from traditional synchronous model updates seen in standard distributed data-parallel training paradigms like Distributed Data Parallel (DDP).
2. Staleness Gap Penalty and Outer Momentum Clipping: To ensure convergence and maintain training stability in the face of asynchronous updates, CO2 proposes two techniques: a staleness gap penalty, which quantifies and penalizes the discrepancies caused by asynchronous updates, and outer momentum clipping, which prevents abnormal values in momentum updates.
3. Integration with ZeRO Optimizers: CO2 seamlessly integrates with ZeRO-series optimizers, which are designed to reduce memory consumption during large-model training by partitioning the optimizer state, forward, and backward paths.
4. Empirical and Theoretical Validation: The paper presents a rigorous mathematical proof of convergence under the CO2 framework, establishing an upper bound on convergence rates. Empirically, CO2 is shown to achieve high scalability across a range of tasks in computer vision and natural language processing, effectively utilizing up to 128 GPUs in a multi-node cluster environment.
5. Scalability Improvements: CO2 demonstrates remarkable scalability improvements, making it feasible to train large models efficiently even in environments with limited inter-node communication bandwidth.

Empirical Results

The empirical evaluation section of the paper highlights CO2's performance across a variety of tasks, using models such as ResNet-50, ViT, GPT-2, and RoBERTa on datasets like ImageNet, OpenWebText, and others. The outcomes exhibit CO2's ability to maintain competitive convergence rates and generalization performance when compared to other state-of-the-art optimization strategies, such as Adamw and SlowMo, while significantly improving throughput.

Implications and Future Directions

The practical implications of the CO2 framework are substantial for the fields of machine learning and AI, particularly as model sizes and data volumes continue to grow. By addressing communication bottlenecks, CO2 democratizes access to large-scale training, making it a viable option for organizations with limited resources. Theoretical advancements like CO2's staleness-aware mechanisms offer pathways for further refinement in optimizing asynchronous communication models.

Looking forward, the adaptability of the CO2 framework suggests potential applicability in federated learning and other decentralized systems where communication efficiency is critical. Moreover, future work can explore refining the staleness gap penalty to dynamically adjust to more complex training landscapes or leveraging CO2's principles in hardware accelerations.

In summary, the CO2 framework represents a substantial step forward in making distributed training of large-scale models more accessible, efficient, and stable, with both significant theoretical backing and practical applications demonstrated.