Eager Updates For Overlapped Communication and Computation in DiLoCo
This work addresses slow-downs in distributed training for large-scale AI models, though it is incremental as it builds on existing DiLoCo methods.
The paper tackles the communication bottleneck in distributed optimization methods like DiLoCo for training large models across datacenters, by introducing eager updates to overlap communication with computation, achieving competitive performance in low-bandwidth settings.
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.