Fixing interpolate on uint8 unsqueezed 3D CL tensor (#100258)
Description:
- Fixed a bug with memory format issue:
When input is channels last 4d tensor that was produced as following
```
t = torch.ones(1, 3, 32, 32).contiguous(memory_format=torch.channels_last)
t = t[0]
t = t[None, ...]
```
upsampling will produce output with channels first memory format but our avx code does not take that into account.
Here is a repro code to show that nightly is broken for this particular case:
```python
import torch
torch.manual_seed(0)
input = torch.randint(0, 256, size=(1, 3, 256, 256), dtype=torch.uint8).contiguous(memory_format=torch.channels_last)
input = input[0]
input = input[None, ...]
assert input.is_contiguous(memory_format=torch.channels_last)
output = torch.nn.functional.interpolate(input, (224, 224), mode="bilinear", antialias=True)
expected = torch.nn.functional.interpolate(input.float(), (224, 224), mode="bilinear", antialias=True)
assert output.is_contiguous()
assert expected.is_contiguous()
torch.testing.assert_close(expected, output.float(), atol=1, rtol=1)
# >
# Traceback (most recent call last):
# File "<stdin>", line 1, in <module>
# File "/pytorch/torch/testing/_comparison.py", line 1511, in assert_close
# raise error_metas[0].to_error(msg)
# AssertionError: Tensor-likes are not close!
#
# Mismatched elements: 14120 / 150528 (9.4%)
# Greatest absolute difference: 214.6112518310547 at index (0, 1, 152, 13) (up to 1 allowed)
# Greatest relative difference: 17.005144119262695 at index (0, 2, 26, 2) (up to 1 allowed)
```
- Also renamed needs_unpacking by skip_unpacking
Pull Request resolved: https://github.com/pytorch/pytorch/pull/100258
Approved by: https://github.com/NicolasHug
