DiLoCo: Distributed Low-Communication Training of Language Models

This is really cool, and I expect groups like Eleuther will want to integrate it into their workflows.

That said, there is a fundamental rule here -- gradients must, at some point, be shared. This paper says they don't have to be shared every step. Which is really great. But, gradient sizes are on the order of magnitude of the network being trained.

To get this useful for indie LLM training, I'd guess that you'll want it to work sharing gradients not more often than once every few hours to day -- more than that, and bandwidth costs sending 50GB weights files around during training are going to kill you.

The paper seems to indicate they can share every few thousand? (8 workers x 500 times bandwidth reduction?) steps, but sharing every few millions would help that distributed large LLM use case, and my guess is that this is going to be a bit harder to get to converge. But hopefully not!

Related: there's an n^2 problem for getting the gradients out to lots of distributed compute resources.

So, if anyone wants to work on step 2, I'd suggest there's an engineering task: 1-N gradient upload and distribution, and then a science task: revalidating this with a few orders of magnitude less common checkins.

There are a lot of distributed data parallel and federated learning (FL) algorithms that could be applied to training LLMs, and there have been several papers that tackle applying these. I don't think I've seen 500 steps in the FL literature (little eye opening to be honest), and I don't think I've tested more than 50 steps between communication rounds personally. I'd be interested in testing other algorithms, partial client participation, hierarchical federation approaches, error-feedback, and so on.

So we can SETI@Home or Folding@Home for large language models, now? Not sure how small the minimum size of the compute cluster can be. If it's still out of consumer reach then this would either be just an intermediate research step, or a way for small-er (but still professional/well-resourced) labs to collaborate together. I'm not sure the latter would be helpful, as if they wanted to collaborate together they could probably already do that by pooling resources for a large cloud compute run.

A few years ago, someone (Apple?) was working on a way to distribute the training of ML models across personal devices. The idea was that the master model in the Cloud could be trained on your personal data without the data ever leaving your device. Not sure if that was ever put into production, but this feels like a scaled-up version of that with distributed data centers instead of iPhones

It's unfortunate that the larger model trained to evaluate the technique is "only" 400M parameters, I'd love to see it applied to models with billions of parameters.