this is so far from accurate it should be considered libelous; from the link
> PyTorch/XLA is set to migrate to the open source OpenXLA
so PyTorch on the XLA backend is set to migrate to use OpenXLA instead of XLA. but basically everyone moved from XLA to OpenXLA because there is no more OSS XLA. so that's it. in general, PyTorch has several backends, including plenty of homegrown CUDA and CPU kernels. in fact the majority of your PyTorch code runs through PyTorch's own kernels.
You can use OpenXLA, but it's not the default. The main use-case for OpenXLA is running PyTorch on Google TPUs. OpenXLA also supports GPUs, but I am not sure how many people use that. Afaik JAX uses OpenXLA as backend to run on GPUs.
If you use model.compile() in PyTorch, you use TorchInductor and OpenAIs Triton by default.
Thank you for saying something useful here. I was vaguely under the impression that pytorch 2.0 had fully flipped to defaulting to openxla. That seems to not be the case.
Good to hear more than a cheap snub. OpenAI Triton as the reason other GPUs work is a real non-shit answer, it seems. And interesting to hear JAX too. Thank you for being robustly useful & informative.
