Eager Updates For Overlapped Communication and Computation in DiLoCo (2502.12996v1)

Abstract: Distributed optimization methods such as DiLoCo have been shown to be effective in training very large models across multiple distributed workers, such as datacenters. These methods split updates into two parts: an inner optimization phase, where the workers independently execute multiple optimization steps on their own local data, and an outer optimization step, where the inner updates are synchronized. While such approaches require orders of magnitude less communication than standard data-parallel training, in settings where the workers are datacenters, even the limited communication requirements of these approaches can still cause significant slow downs due to the blocking necessary at each outer optimization step. In this paper, we investigate techniques to mitigate this issue by overlapping communication with computation in a manner that allows the outer optimization step to fully overlap with the inner optimization phase. We show that a particular variant, dubbed eager updates, provides competitive performance with standard DiLoCo in settings with low bandwidth between workers.

Eager Updates for Overlapped Communication and Computation in DiLoCo

In this paper, the authors investigate the integration of eager updates into the DiLoCo framework for distributed optimization, which is particularly relevant for the training of large-scale models across distributed data centers. The paper identifies a key inefficiency in the conventional DiLoCo framework, which involves latency from communication overheads during the outer optimization stage. This limitation becomes pronounced in cross-data center configurations with low bandwidth, where workers must halt computations until full synchronization of updates, thus leading to idle computational resources.

The paper explores a method to overlap communication and computation, effectively allowing the outer optimization step to coincide with the subsequent inner optimization phase. This is achieved through a mechanism referred to as eager updates. Eager updates are premised on the anticipation of communication delays; they use the local gradient, available immediately, as a proxy for the outer gradient while the full all-reduce operation transpires concurrently over multiple iterations. This reduces the dependence on synchronized updates and facilitates continuous progression in computations.

The proposed eager updates have been benchmarked against the standard DiLoCo approach across various communication bandwidth scenarios. Under conditions of low bandwidth, eager updates match the performance of DiLoCo while significantly decreasing the communication burden associated with model training. This demonstrates the efficacy of the approach in managing communication constraints without compromising model convergence, particularly for large-scale models where the operation bandwidth is typically a limiting factor.

Quantitatively, the computational efficiency improvement is notable; training tasks can proceed with near-full computational utilization due to lessened synchronization bottlenecks. The paper further explores algorithmic modifications allowing the delayed outer gradients to be effectively leveraged without impacting convergence reliability significantly. Such strategic enhancements in overlap methods promise substantial performance benefits in practical implementations, bridging the gap between model training efficiency and infrastructure limitations.

The outcomes of eager updates extend beyond immediate performance enhancements. This work points towards future developments in AI training methodologies focused on optimizing communication intricacies within distributed frameworks. It raises compelling questions about the role of asynchronous communication schemes in scaling neural training systems and contributing to the broader debate on federated optimization methods' applicability within contemporary AI challenges.

Overall, this research comprises a valuable discourse on distributed model training, emphasizing the importance of adaptive frameworks for scalable AI solutions. Future work may further delve into theoretical analyses of convergence properties with delayed gradients and explore the fine-grained impact on specific model architectures within even more restricted communication settings.