Easy way to profile model likelihood / gradient evaluation
Easy way to profile model likelihood / gradient evaluation: a task in MiMo-V2.6-RL-harbor-code: MiMo-V2.6-RL Code (Harbor) (Harbor dataset). When sampling is slow, I want to figure out which part of the log-probability (or its gradient) is the bottleneck. Theano has nice profiling support built in…
The task
When sampling is slow, I want to figure out which part of the log-probability (or its gradient) is the bottleneck. Theano has nice profiling support built in via `theano.function(..., profile=True)` plus `ProfileStats.summary()`, but right now there's no convenient way to use it on a pymc3 `Model`.
Part of FineEnvs/MiMo-V2.6-RL-harbor-code.