gloo VS xla

These are two separate libraries that do very different things but share the same name. They are also written in two separate languages. That is a sizable gap between them, and reusing names happens often with libraries. Gloo (rust-wasm, this post) is also not new. Though, relative to Gloo (Go, solo-io), it is newer. But, there is also a Github repo even older than Gloo (solo-io): https://github.com/facebookincubator/gloo. As well, even if these were for some odd reason all about wasm, none of them are actually that popular. solo-io Gloo has the most stars (though that isn't the best metric of popularity, since it is relative to the community that actually uses it), but 3k simply isn't that much. There is certainly a good argument to look down on libraries that reuse popular library names, but this isn't really the case here. Both started not too long after each other (solo-io would not have most of the stars it currently has when Gloo-Rust started), are in separate languages (thus separate communities), and do very separate things.

backends from native torch distributed configuration: nccl, gloo, mpi.

> The PyTorch/XLA Team at Google

Meanwhile you have an issue from 5 years ago with 0 support

https://github.com/pytorch/xla/issues/202

PyTorch has had an XLA backend for years. I don't know how performant it is though. https://pytorch.org/xla

It's curtains for XLA, to be precise. And PyTorch officially supports XLA backend nowadays too ([1]), which kind of makes JAX and PyTorch standing on the same foundation.

1. https://github.com/pytorch/xla

Pytorch supports other kinds of accelerators (e.g. FPGA, and https://github.com/pytorch/glow), but unless you want to become a ML systems engineer and have money and time to throw away, or a business case to fund it, it is not worth it. In general, both pytorch and tensorflow have hardware abstractions that will compile down to device code. (XLA, https://github.com/pytorch/xla, https://github.com/pytorch/glow). TPUs and GPUs have very different strengths; so getting top performance requires a lot of manual optimizations. Considering the the cost of training LLM, it is time well spent.

While apparently TPUs can theoretically achieve great speedups, getting to the point where they beat a single GPU requires a lot of fiddling around and debugging. A specific setup is required to make it work properly. E.g., here it says that to exploit TPUs you might need a better CPU to keep the TPU busy, than the one in colab. The tutorials I looked at oversimplified the whole matter, the same goes for pytorch-lightning which implies switching to TPU is as easy as changing a single parameter. Furthermore, none of the tutorials I saw (even after specifically searching for that) went into detail about why and how to set up a GCS bucket for data loading.

XLA on TPUs via pytorch/xla.

I don't know of any such trick except for using TensorFlow. In fact, I benchmarked PyTorch XLA vs TensorFlow and found that the former's performance was quite abysmal: PyTorch XLA is very slow on Google Colab. The developers' explanation, as I understood it, was that TF was using features not available to the PyTorch XLA developers and that they therefore could not compete on performance. The situation may be different today, I don't know really.