## Description In MxNet 2.0, we would like to provide a distribution module, analogous to Pytorch distribution. The main difference from theirs is that we use numpy op and it allows hybridization. The current project code can be seen from https://github.com/xidulu/incubator-mxnet/tree/distribution_dev/python/mxnet/gluon/probability.
The basic skeleton divides into following parts: 1. Stochastic `HybridBlock` and `HybridSequential`: they build upon gluon `HybridBlock` and `HybridSequential` and allows adding extra loss to each layer. 2. Distribution class: it implements a variety of functionalities including `prob`, `log_prob`, `sample`, `broadcast_to`, `mean`, `variance`, etc. 3. KL divergence: `kl_divergence(p, q)` function searches over registered KL divergence functions and performs computation. 4. Transform: transform one distribution to another invertible distribution. 5. Independent: reinterprets some of the batch dims of a distribution as event dims. Two features that is currently either not supported or kind of broken in MxNet will be very useful to this projects: symbolic shape and control flow. At the moment, we will implement most of distribution in frontend. We will move the computation to backend when new numpy probability ops such as `chisquare`, `dirichlet` and `multivariate_normal` are introduced into MxNet. ## References - https://pytorch.org/docs/stable/distributions.html - https://docs.scipy.org/doc/numpy-1.14.1/reference/routines.random.html @xidulu @szha @leezu @haojin2 -- You are receiving this because you were mentioned. Reply to this email directly or view it on GitHub: https://github.com/apache/incubator-mxnet/issues/17240
