The Kumaraswamy distribution is defined over the open interval (0,1) but torch.distributions.kumaraswamy.Kumaraswamy can generate samples that equal 1. This causes “nan” outputs when computing the log_prob of such samples.

import torch as ch
import torch.distributions as dist
kumaraswamy = dist.kumaraswamy.Kumaraswamy(ch.Tensor([0.2]), ch.Tensor([0.2]))
samples = kumaraswamy.sample(ch.Size([1000000]))
assert len(samples[samples==1.0]) > 0

Since torch.distributions.uniform.Uniform can generate samples that equal 0, these are perhaps transformed to samples that equal 1 for the Kumaraswamy distribution, outside its support.

This doesn’t seem likely because your test with “only” 1,000,000 samples is
quite unlikely to sample an exact 0.0 from Uniform.

Note that floating-point numbers can get closer to 0.0 than to 1.0. I expect
that samples from Uniform that are close to 0.0 (but not equal) get transformed
to 1.0 (and Uniform samples that are close to 1.0 get transformed to 0.0).

Thanks for pinging on this issue!
I assume this Uniform usage might create these invalid values? @kiranchari As @KFrank mentioned, would you mind creating an issue on GitHub so that we could track and fix it, please?