fix upsample bf16 issue for channels last path by using high pricsion to compute index (#83847)
Given the following case:
```
import torch
a = torch.ones(1, 3, 320, 480).bfloat16().to(memory_format=torch.channels_last)
out_bf16 = torch.nn.functional.interpolate(a, size = (640, 960), scale_factor = None, mode = 'bilinear', align_corners = False, recompute_scale_factor= None, antialias = False)
out_fp32= torch.nn.functional.interpolate(a.float(), size = (640, 960), scale_factor = None, mode = 'bilinear', align_corners = False, recompute_scale_factor= None, antialias = False)
print(out_bf16[0, 2, :, :])
print(out_fp32[0, 2, :, :])
```
the boundary of bfloat16 output gets a wrong value:
```
tensor([[1.0000e+00, 1.0000e+00, 1.0000e+00, ..., 1.0000e+00, 1.0000e+00,
1.0000e+00],
[1.0000e+00, 1.0000e+00, 1.0000e+00, ..., 1.0000e+00, 1.0000e+00,
1.0000e+00],
[1.0000e+00, 1.0000e+00, 1.0000e+00, ..., 1.0000e+00, 1.0000e+00,
1.0000e+00],
...,
[1.0000e+00, 1.0000e+00, 1.0000e+00, ..., 1.0000e+00, 1.0000e+00,
1.0000e+00],
[1.0000e+00, 1.0000e+00, 1.0000e+00, ..., 1.0000e+00, 1.0000e+00,
1.0000e+00],
[0.0000e+00, 0.0000e+00, 1.8367e-40, ..., 0.0000e+00, 0.0000e+00,
0.0000e+00]], dtype=torch.bfloat16)
tensor([[1., 1., 1., ..., 1., 1., 1.],
[1., 1., 1., ..., 1., 1., 1.],
[1., 1., 1., ..., 1., 1., 1.],
...,
[1., 1., 1., ..., 1., 1., 1.],
[1., 1., 1., ..., 1., 1., 1.],
[1., 1., 1., ..., 1., 1., 1.]])
```
the expected behavior is that the bfloat16 output value should also be one. The main reason is that we use low precision to compute the index, see
https://github.com/pytorch/pytorch/blob/fcb124406bdf86bc2d15e999d5a3e09b86238bba/aten/src/ATen/native/UpSample.h#L448, we should use a high precison to do the computation as GPU path:
https://github.com/pytorch/pytorch/blob/fcb124406bdf86bc2d15e999d5a3e09b86238bba/aten/src/ATen/native/cuda/UpSample.cuh#L123
Pull Request resolved: https://github.com/pytorch/pytorch/pull/83847
Approved by: https://github.com/frank-wei
