Update interpolate to use new upsample overloads (#43025)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/43025
- Use new overloads that better reflect the arguments to interpolate.
- More uniform interface for upsample ops allows simplifying the Python code.
- Also reorder overloads in native_functions.yaml to give them priority.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/37177
ghstack-source-id: 106938111
Test Plan:
test_nn has pretty good coverage.
Relying on CI for ONNX, etc.
Didn't test FC because this change is *not* forward compatible.
To ensure backwards compatibility, I ran this code before this change
```python
def test_func(arg):
interp = torch.nn.functional.interpolate
with_size = interp(arg, size=(16,16))
with_scale = interp(arg, scale_factor=[2.1, 2.2], recompute_scale_factor=False)
with_compute = interp(arg, scale_factor=[2.1, 2.2])
return (with_size, with_scale, with_compute)
traced_func = torch.jit.trace(test_func, torch.randn(1,1,1,1))
sample = torch.randn(1, 3, 7, 7)
output = traced_func(sample)
assert not torch.allclose(output[1], output[2])
torch.jit.save(traced_func, "model.pt")
torch.save((sample, output), "data.pt")
```
then this code after this change
```python
model = torch.jit.load("model.pt")
sample, golden = torch.load("data.pt")
result = model(sample)
for r, g in zip(result, golden):
assert torch.allclose(r, g)
```
Reviewed By: AshkanAliabadi
Differential Revision: D21209991
fbshipit-source-id: 5b2ebb7c3ed76947361fe532d1dbdd6faa3544c8
