Eager Updates For Overlapped Communication and Computation in DiLoCo
Authors: name: Satyen Kale, name: Arthur Douillard, name: Yanislav Donchev

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.