> So if XLA already uses LLVM, why is our approach different?
Uses MLIR, XLA does not.
> So… what is the point?
> Honestly? We aren’t entirely sure yet.
> Let me be perfectly clear: this is not going to beat XLA for standard deep learning workloads. XLA has years of hyper-specific optimizations for linear algebra on GPUs and TPUs. If you are training a massive transformer, stick to standard JAX.
> But what we do think is cool is what happens when you connect JAX directly to the broader LLVM ecosystem and drop the heavy XLA runtime. (Plus, no need to build XLA using Bazel either! You’re welcome.)
I would love to hear those stories! Sadly I'm based in Toronto, so dropping by the Bay Area meetups isn't in the cards anytime soon.
If any of that history ever makes it into a blog post, I'd be first in line to read it.
https://github.com/search?q=repo%3Aopenxla%2Fxla+mlir&type=c...
> 1.5k files
you're behind the times. XLA moved over probably ~2 years ago - "Captain Awesome" eventually relented.
I am an active contributer to MLIR, if you knew my name and you weren't some weirdo, you could know me. This is a pseudonym account of which I have several, since I grew up in an age where you hid your identity. Now how XLA interacts with that I have no clue dude. Maybe help with that and don't be a low effort dick.
relax you'll live longer.