torch.inverse needs to be blacklisted

SUMMARY

The method torch.inverse does not support fp16 arguments so it needs to be blacklisted.

DETAILS

Running the following

from apex import amp
import torch

class Foo(torch.nn.Module):
    def __init__(self, dim):
        super().__init__()
        self.A = torch.nn.Parameter(torch.rand(dim, dim))

    def forward(self, X):
        AX = torch.matmul(self.A, X)
        return torch.inverse(AX)

dim = 3
model = Foo(dim).cuda()
optimizer = torch.optim.AdamW(model.parameters())
model, optimizer = amp.initialize(model, optimizer, opt_level="O1", verbosity=False)

X = torch.rand(dim, dim).cuda()
Y = model(X)

Results in the error

---------------------------------------------------------------------------
RuntimeError                              Traceback (most recent call last)
<ipython-input-4-46f64e772005> in <module>
      1 X = torch.rand(dim, dim).cuda()
----> 2 Y = model(X)
      3 Y

/usr/local/lib/python3.6/dist-packages/torch/nn/modules/module.py in __call__(self, *input, **kwargs)
    530             result = self._slow_forward(*input, **kwargs)
    531         else:
--> 532             result = self.forward(*input, **kwargs)
    533         for hook in self._forward_hooks.values():
    534             hook_result = hook(self, input, result)

<ipython-input-2-d9366f5dfdeb> in forward(self, X)
      6     def forward(self, X):
      7         AX = torch.matmul(self.A, X)
----> 8         return torch.inverse(AX)

RuntimeError: "inverse_cuda" not implemented for 'Half'

No repository file or test is named. Start by reproducing the provided Apex mixed-precision example and locating the operator blacklist handling for torch.inverse. Done means torch.inverse no longer receives fp16 arguments under the affected configuration, with a regression test covering the example.