Added a context manager, `torch._library.fake_profile.register_fake_profile(op_profiles)`, where given an operator profile, it will generate and register a fake impl for the operator based on the operator profile.
The input to `register_fake_profile` is a dictionary mapping operator name to a set of profiles which describe the input and outputs of the operator. Here's an example of a profile for `mylib.foo.default`:
```
"mylib.foo.default": {
OpProfile(
args_profile=(
TensorMetadata(rank=2, dtype=torch.float32, device=torch.device("cpu"), layout=torch.strided,),
TensorMetadata(rank=2, dtype=torch.float32, device=torch.device("cpu"), layout=torch.strided,),
),
out_profile=TensorMetadata(rank=2, dtype=torch.float32, device=torch.device("cpu"), layout=torch.strided,),
)
}
```
`foo`'s profile contains only one profile, which says that for 2 input tensors of rank 2, dtype float32, device cpu, we will return one tensor of rank 2, dtype float32, and device cpu.
This will then generate a fake kernel where given 2 input tensors of rank 2 (and the other tensor metadata), we will output one tensor of rank 2 (and the other tensor metadata). If the operator also supports other input ranks, then we can add to the profile for the fake impl to support more input types.
This profile can either be manually written or created by draft-export, and then checked into the codebase.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/150807
Approved by: https://github.com/zou3519
ghstack dependencies: #150806
