Address precision matrix instability of MVN distribution (#21366)
Summary:
Currently, when the input of MVN is precision matrix, we take inverse to convert the result to covariance matrix. This, however, will easily make the covariance matrix not positive definite, hence will trigger a cholesky error.
For example,
```
import torch
torch.manual_seed(0)
x = torch.randn(10)
P = torch.exp(-(x - x.unsqueeze(-1)) ** 2)
torch.distributions.MultivariateNormal(loc=torch.ones(10), precision_matrix=P)
```
will trigger `RuntimeError: cholesky_cpu: U(8,8) is zero, singular U.`
This PR uses some math tricks ([ref](https://nbviewer.jupyter.org/gist/fehiepsi/5ef8e09e61604f10607380467eb82006#Precision-to-scale_tril)) to only take inverse of a triangular matrix, hence increase the stability.
cc fritzo, neerajprad , SsnL
Pull Request resolved: https://github.com/pytorch/pytorch/pull/21366
Differential Revision: D15696972
Pulled By: ezyang
fbshipit-source-id: cec13f7dfdbd06dee94b8bed8ff0b3e720c7a188
