3 ms·
I'm probably being very naive with this but, could mechanistic interpretability play a role here? Specifically, to experimentally short-list the LLM's most effe
by BruceEel 3y ago
I'm probably being very naive with this but, could mechanistic interpretability play a role here? Specifically, to experimentally short-list the LLM's most effective optimizations, then try to peek inside the LLM and perhaps fish out some novel optimization algorithm that could be efficiently implemented without the LLM?
- boomanaiden154 3y agoI 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 https://github.com/google/ml-compiler-opt/pull/109 2. https://youtu.be/0uUKDQyn1Z4?si=PHrx9RICJIiA3E6C https://youtu.be/0uUKDQyn1Z4?si=PHrx9RICJIiA3E6C
- BruceEel 3y agoAh interesting. Hadn't seen that presentation, thanks for sharing!