`pt.max` is not differentiable in PyMC models

[jessegrabowski]

Description

The following model.dlogp() raises a NotImplemented error:

with pm.Model() as model:
    x = pm.Normal('x', shape=(2,))
    mu = x.max()
    pm.Normal('obs', mu, observed=np.random.uniform())

This should be differentiable (pt.max has gradients implemented), so it seems like something is going wrong in rewrites, either with respect to logp rewrites or with respect to MaxAndArgmax

[bburan]

min also raises a NotImplementedError.

[ricardoV94]

min is just implemented as negative of max of negative, so that's expected

[tanish1729]

hi! i can take up this issue. i can see that this has to sort out dealing with gradients in the rewrites for some specific functions. could you provide some more details for me to start working

[ricardoV94]

@tanish1729 this one is not really a beginner friendly issue. I'll try and fix it now myself. Let us know if you need help finding more suitable issues

[tanish1729]

oh great i see you did this yourself. i'll go through the code and see if i can understand it.
what are some other good beginner friendly issues open rn?

[ricardoV94]

oh great i see you did this yourself. i'll go through the code and see if i can understand it. what are some other good beginner friendly issues open rn?

You can filter issues on Github by labels: https://github.com/pymc-devs/pymc/issues?q=is%3Aissue+is%3Aopen+label%3A%22beginner+friendly%22

[bburan]

@ricardoV94 Thanks so much for fixing this so quickly. I can verify this solved the issue in my model and I am now able to run my model using NUTS only. Brings runtime down from ~2 hours to 10 minutes (3 minutes if I use an experimental NUTS sampler such as numpyro).

[ricardoV94]

You're welcome. By the way we don't consider the numpyro integration experimental anymore