ml-compiler-opt

I did a bit of work on this last summer on (much) smaller models [1] and it was briefly discussed towards the end of last year's MLGO panel [2]. For heuristic replacements specifically, you might be able to glean some things (or just use interpretable models like decision trees), but something like a neural network works fundamentally differently than the existing heuristics, so you probably wouldn't see most of the performance gains. For just tuning heuristics, the usual practice is to make most of the parameters configurable and then use something like bayesian optimization to try and find an optimal set, and this is sometimes done as a baseline in pieces of ML-in-compiler research. 1. https://github.com/google/ml-compiler-opt/pull/109 2. https://youtu.be/0uUKDQyn1Z4?si=PHrx9RICJIiA3E6C