Repository: pytorch
Issue: torch.nn.functional import grid_sample
Labels: needs reproduction,triaged

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## Issue description

torch.nn.functional.grid_sample() behavior is differ from pytorch 1.2.0

## Code example

pytorch 1.2.0:
grid_sample(input=inp, grid=grid, mode='bilinear', padding_mode='border')

pytorch 1.4.0:
the correct way to reproduce behavior of 1.2.0 is:
grid_sample(input=inp, grid=grid, mode='bilinear', padding_mode='border', align_corners=True)
but the default value of align_corners is None, so that produces some errors when update to pytorch 1.4 from 1.2


## System Info

PyTorch 1.2.0:

Collecting environment information...
PyTorch version: 1.2.0
Is debug build: No
CUDA used to build PyTorch: 10.0

OS: Microsoft Windows 10 Enterprise
GCC version: (x86_64-posix-seh-rev0, Built by MinGW-W64 project) 8.1.0
CMake version: version 3.15.5

Python version: 3.6
Is CUDA available: Yes
CUDA runtime version: 10.1.105
GPU models and configuration: GPU 0: GeForce GTX 1080 Ti
Nvidia driver version: 418.96
cuDNN version: Could not collect

Versions of relevant libraries:
[pip] numpy==1.18.1
[pip] pytorch-memlab==0.0.2
[pip] torch==1.2.0
[pip] torchvision==0.4.0
[conda] blas                      1.0                         mkl
[conda] libmklml                  2019.0.3                      0
[conda] mkl                       2019.5                      281    anaconda
[conda] mkl-service               2.3.0            py36hb782905_0    anaconda
[conda] mkl_fft                   1.0.12           py36h14836fe_0
[conda] mkl_random                1.0.2            py36h343c172_0
[conda] pytorch                   1.2.0           py3.6_cuda100_cudnn7_1    pytorch
[conda] pytorch-memlab            0.0.2                    pypi_0    pypi
[conda] torchvision               0.4.0                py36_cu100    pytorch


PyTorch 1.4.0:
Collecting environment information...
PyTorch version: 1.4.0
Is debug build: No
CUDA used to build PyTorch: 10.1

OS: Microsoft Windows 10 Enterprise
GCC version: (x86_64-posix-seh-rev0, Built by MinGW-W64 project) 8.1.0
CMake version: version 3.15.5

Python version: 3.6
Is CUDA available: Yes
CUDA runtime version: 10.1.105
GPU models and configuration: GPU 0: GeForce GTX 1080 Ti
Nvidia driver version: 418.96
cuDNN version: Could not collect

Versions of relevant libraries:
[pip] numpy==1.18.1
[pip] pytorch-memlab==0.0.2
[pip] torch==1.4.0
[pip] torchvision==0.5.0
[conda] blas                      1.0                         mkl
[conda] libmklml                  2019.0.3                      0
[conda] mkl                       2019.5                      281    anaconda
[conda] mkl-service               2.3.0            py36hb782905_0    anaconda
[conda] mkl_fft                   1.0.12           py36h14836fe_0
[conda] mkl_random                1.0.2            py36h343c172_0
[conda] pytorch                   1.4.0           py3.6_cuda101_cudnn7_0    pytorch
[conda] pytorch-memlab            0.0.2                    pypi_0    pypi
[conda] torchvision               0.5.0                py36_cu101    pytorch