> Hi @dibgerge > The reason "num_events" is required is that we cannot call `logit.shape` or > `prob.shape` in the symbolic mode, as you can see, TF would use a very > complicated subroutine to get num_events in the symbol mode: > https://github.com/tensorflow/probability/blob/v0.11.1/tensorflow_probability/python/distributions/categorical.py#L352 > while torch does not have such issue : > https://github.com/pytorch/pytorch/blob/master/torch/distributions/categorical.py#L57 > > You are also right about the `logit` issue, that could be a **bug**. But > MXNet currently does not have a logsumexp Op, a relative Op would be > logaddexp, which is still not merged yet: #15857 > A possible solution could be applying npx.logsoftmax on the logit.
Thank you for the clarification. I am not very familiar with the symbolic code. I wonder if it is worth implementing this subroutine to calculate shape if at all possible, or it may add significant overhead. Also wondering, would there be performance differences between `npx.logsoftmax` and the `logaddexp` PR? -- You are receiving this because you are subscribed to this thread. Reply to this email directly or view it on GitHub: https://github.com/apache/incubator-mxnet/issues/19722#issuecomment-754336962
