Enable distribution validation if __debug__ (#48743)

Summary:
Fixes https://github.com/pytorch/pytorch/issues/47123
Follows https://github.com/pyro-ppl/pyro/pull/2701

This turns on `Distribution` validation by default. The motivation is to favor beginners by providing helpful error messages. Advanced users focused on speed can disable validation by calling
```py
torch.distributions.Distribution.set_default_validate_args(False)
```
or by disabling individual distribution validation via `MyDistribution(..., validate_args=False)`.

In practice I have found many beginners forget or do not know about validation. Therefore I have [enabled it by default](https://github.com/pyro-ppl/pyro/pull/2701) in Pyro. I believe PyTorch could also benefit from this change. Indeed validation caught a number of bugs in `.icdf()` methods, in tests, and in PPL benchmarks, all of which have been fixed in this PR.

## Release concerns
- This may slightly slow down some models. Concerned users may disable validation.
- This may cause new `ValueErrors` in models that rely on unsupported behavior, e.g. `Categorical.log_prob()` applied to continuous-valued tensors (only {0,1}-valued tensors are supported).

We should clearly note this change in release notes.

Pull Request resolved: https://github.com/pytorch/pytorch/pull/48743

Reviewed By: heitorschueroff

Differential Revision: D25304247

Pulled By: neerajprad

fbshipit-source-id: 8d50f28441321ae691f848c55f71aa80cb356b41

torch/distributions/exponential.py

@@ -68,8 +68,6 @@ class Exponential(ExponentialFamily):
         return 1 - torch.exp(-self.rate * value)
 
     def icdf(self, value):
-        if self._validate_args:
-            self._validate_sample(value)
         return -torch.log(1 - value) / self.rate
 
     def entropy(self):