Eager Updates For Overlapped Communication and Computation in DiLoCo
Satyen Kale, Arthur Douillard, Yanislav Donchev
2025-02-19
Summary
This paper talks about improving a method called DiLoCo, which is used to train very large AI models across multiple computers or datacenters. The researchers found a way to make the training process faster by allowing different parts of the process to happen at the same time, instead of waiting for each part to finish before starting the next.
What's the problem?
DiLoCo is good at reducing how much different computers need to talk to each other when training AI models, but it still has moments where everything has to stop and wait for updates to be shared. This waiting time can slow down the whole process, especially when the computers are far apart and can't send information to each other quickly.
What's the solution?
The researchers came up with a technique called 'eager updates' that lets the computers keep working on their individual tasks while they're sharing updates with each other. It's like letting students continue working on their part of a group project while the teacher is collecting and combining everyone's work, instead of making everyone stop and wait.
Why it matters?
This matters because it can make training very large AI models much faster, especially when using computers that are spread out across different locations. Faster training means AI researchers can experiment more quickly and potentially create more advanced AI systems in less time. It also means that organizations with computers in different places can work together more efficiently to build powerful AI models.
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.