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- Hacker News
- Neat. XLA predates MLIR. Interesting stories there. You'd have to stop by the Bay Area LLVM monthly meetup to hear them. :-X
> 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.)
by ndesaulniers - XLA uses MLIR very much, the interface to it is StableHLO, which is a MLIR dialect.by steeve
- > Uses MLIR, XLA does not.
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.
by mathisfun123 - I can't tell what you mean by this comment. Is XLA good? is XLA bad, cuz it doesn't use MLIR? Speak loud, for we are hard of hearing.
- > Neat. XLA predates MLIR. Interesting stories there. You'd have to stop by the Bay Area LLVM monthly meetup to hear them. :-X
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.
by infinitewalk - 1. Why not Julia? IIRC, some/all of this already exists in that ecosystem.
> Something to note is that quantum algorithms are never purely quantum. There is a tonne of classical processing needed to get inputs ready, post-process outputs, and even construct and represent the quantum circuits themselves.
2. Did you face any problems for the 'surrounding' areas by JAX imposing the limitation of pure functions?
by Archit3ch - Main reason is that the quantum computing/scientific research user base is still very Python entrenchedby infinitewalk
- > Plus, no need to build XLA using Bazel either! You’re welcome.
I don't get it, Bazel is incredible.
by steeve - Many people find it complicated.by melodyogonna
- This is actually really neat, I had an agent create some experimental probes and check out whats going on here and it seems like a pretty cool project. I'm gonna need to digest how this might be useful but I think this could be cool for some sort of FPGA uses perhaps.
> 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.
I'm not so convinced about this.
It's actually really easy to write two mathematically equivalent formulations in Python of something basic that have over a 2x performance difference in them after jax jit.
XLA is not that smart. And I'm not talking some niche nonsense I mean simple matrix multiplication graphs and residual connections on the CUDA backend.
by kingstnap - Just for receipts I span up a basic test for XLA:
Can you figure out that 16 matrix vector multiplications into a concatenate is the same as concatenating first into a matrix matrix operation which can go on the GEMM. This is like the most basic thing you can imagine doing.
Turns out no it doesn't and there is a 23% performance difference by moving the concatenate up in the python code in my specific test.
Not to say that it didn't recognize it. I had an agent look at the XLA and the graph actually does a partial fusion into a sum and stack. But does not realize the whole thing is just a GEMM.
In more complicated examples the differences you can get can be much larger.
by kingstnap