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https://hazyresearch.stanford.edu/blog/2023-12-11-truly-subq... is an approachable overview of the Monarch approach, for those interested!

There's a lot of opportunity here. Just because matrix multiplication makes for a beautiful mathematical building block, and a very reasonable one to build high-level ML logic on, doesn't mean it needs to be computed the same way, and in the same order, that we learned in linear algebra courses.

I'm quite curious if this is being used in practice at scale, or whether it's still in the lab at the moment!



> doesn't mean it needs to be computed the same way, and in the same order, that we learned in linear algebra courses.

I think this touches on something fundamental. As a stand-alone operation matmul is ugly because it's arbitrary. In other words.. if the goal was just to entangle values, there's a bunch of ways to do it, so why this particular way landing on ae+bg etc? You kind of need algebra/geometry to justify matmul this way, which makes it obviously useful, but now it's still ugly, exactly because you had to invoke this other stuff.

Compare that situation to algebra and geometry themselves, which in a real sense don't need each other. Or to things like logic, sets, categories, processes, numbers, knots, games, etc where you can build up piles of stuff based on it in a whole rich universe before you need to appeal to much that is "outside". And in those universes operations would be defined mostly in ways that were more like "natural" or "necessary" without anything feeling arbitrary.

Traditional matmul is beautiful in the sense of "connections across and between", where all the particulars do become necessary. For those that prefer a certain amount of abstract perfection / platonism / etc or those with a taste for foundations though, it's understandable if it's not that appealing. This is related to, but not the same as the pure vs applied split.




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