Fix torch.nn.functional.interpolate microbenchmark for non-4D inputs
Summary: This diff fixes the `interpolate` microbenchmark for non-4D inputs, which are not supported by the `bilinear` mode
Test Plan:
5D and 3D:
```
# Benchmarking PyTorch: interpolate
# Mode: Eager
# Name: interpolate_input_size(1,3,16,320,320)_output_size(8,256,256)
# Input: input_size: (1, 3, 16, 320, 320), output_size: (8, 256, 256)
Forward Execution Time (us) : 221008.660
# Benchmarking PyTorch: interpolate
# Mode: Eager
# Name: interpolate_input_size(4,512,320)_output_size(256,)
# Input: input_size: (4, 512, 320), output_size: (256,)
Forward Execution Time (us) : 9727.900
```
4D
```
# Benchmarking PyTorch: interpolate
# Mode: Eager
# Name: interpolate_input_size(1,3,60,40)_output_size(24,24)_channels_lastTrue
# Input: input_size: (1, 3, 60, 40), output_size: (24, 24), channels_last: True
Forward Execution Time (us) : 375.181
```
Reviewed By: fmassa
Differential Revision: D26486678
fbshipit-source-id: 5d476afba3f35da9f8b86db16e21505bdb00888b
