Show warning if Tensor.random_()'s from and to are not in [-(2^digits), 2^digits] bounds for floating-point types (#37537)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/37537
The documentation states that `random_()` samples "from the discrete uniform distribution". Floating-point types can support _discrete_ _uniform_ distribution only within range [-(2^digits), 2^digits], where `digits = std::numeric_limits<fp_type>::digits`, or
- [-(2^53), 2^53] for double
- [-(2^24), 2^24] for double
- [-(2^11), 2^11] for half
- [-(2^8), 2^8] for bfloat16
The worst scenario is when the floating-point type can not represent numbers between `from` and `to`. E.g.
```
torch.empty(10, dtype=torch.float).random_(16777217, 16777218)
tensor([16777216., 16777216., 16777216., 16777216., 16777216., 16777216.,
16777216., 16777216., 16777216., 16777216.])
```
Because 16777217 can not be represented in float
Test Plan: Imported from OSS
Differential Revision: D21380387
Pulled By: pbelevich
fbshipit-source-id: 80d77a5b592fff9ab35155a63045b71dcc8db2fd
