Grad strides do not match bucket view strides
Labels: oncall: distributed, triaged, module: memory format, module: ddp

### 🐛 Describe the bug

@mcarilli 
Hello,
I am using torch.nn.parallel.DistributedDataParallel and get the following warning:

> Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass
> /\*\*\*\*\*\*\/miniconda3/envs/pym/lib/python3.10/site-packages/torch/autograd/__init__.py:173: UserWarning: Grad strides do not match bucket view strides. This may indicate grad was not created according to the gradient layout contract, or that the param's strides changed since DDP was constructed.  This is not an error, but may impair performance.
> grad.sizes() = [300, 100, 1, 1], strides() = [100, 1, 1, 1]
> bucket_view.sizes() = [300, 100, 1, 1], strides() = [100, 1, 100, 100] (Triggered internally at  /opt/conda/conda-bld/pytorch_1659484803030/work/torch/csrc/distributed/c10d/reducer.cpp:312.)

I could not write the minimal reproducing code because I can't find what part of the code create this warning. 
However, I know that it is due to an operation in my model as the warning does not appear with other models.
But I do not get any warning when running the same code with Apex, so could it be an issue with the native DDP?

### Versions

Collecting environment information...
PyTorch version: 1.12.1
Is debug build: False
CUDA used to build PyTorch: 11.6
ROCM used to build PyTorch: N/A

OS: Ubuntu 18.04.3 LTS (x86_64)
GCC version: (Ubuntu 7.5.0-3ubuntu1~18.04) 7.5.0
Clang version: Could not collect
CMake version: Could not collect
Libc version: glibc-2.27

Python version: 3.10.6 | packaged by conda-forge | (main, Aug 22 2022, 20:35:26) [GCC 10.4.0] (64-bit runtime)
Python platform: Linux-5.4.0-120-generic-x86_64-with-glibc2.27
Is CUDA available: True
CUDA runtime version: Could not collect
GPU models and configuration: 
GPU 0: NVIDIA GeForce RTX 3090
GPU 1: NVIDIA GeForce RTX 3090
GPU 2: NVIDIA GeForce RTX 3090
GPU 3: NVIDIA GeForce RTX 3090
GPU 4: NVIDIA GeForce RTX 3090
GPU 5: NVIDIA GeForce RTX 3090
GPU 6: NVIDIA GeForce RTX 3090
GPU 7: NVIDIA GeForce RTX 3090

Nvidia driver version: 515.48.07
cuDNN version: Could not collect
HIP runtime version: N/A
MIOpen runtime version: N/A  
Is XNNPACK available: True   

Versions of relevant libraries:
[pip3] numpy==1.23.2
[pip3] torch==1.12.1
[pip3] torchaudio==0.12.1
[pip3] torchvision==0.13.1   
[conda] blas                      2.116                       mkl    conda-forge
[conda] blas-devel                3.9.0            16_linux64_mkl    conda-forge
[conda] cudatoolkit               11.6.0              hecad31d_10    conda-forge
[conda] libblas                   3.9.0            16_linux64_mkl    conda-forge
[conda] libcblas                  3.9.0            16_linux64_mkl    conda-forge
[conda] liblapack                 3.9.0            16_linux64_mkl    conda-forge
[conda] liblapacke                3.9.0            16_linux64_mkl    conda-forge
[conda] mkl                       2022.1.0           h84fe81f_915    conda-forge
[conda] mkl-devel                 2022.1.0           ha770c72_916    conda-forge
[conda] mkl-include               2022.1.0           h84fe81f_915    conda-forge
[conda] numpy                     1.23.2          py310h53a5b5f_0    conda-forge
[conda] pytorch                   1.12.1          py3.10_cuda11.6_cudnn8.3.2_0    pytorch
[conda] pytorch-mutex             1.0                        cuda    pytorch
[conda] torchaudio                0.12.1              py310_cu116    pytorch
[conda] torchvision               0.13.1              py310_cu116    pytorch


cc @pietern @mrshenli @pritamdamania87 @zhaojuanmao @satgera @rohan-varma @gqchen @aazzolini @osalpekar @jiayisuse @SciPioneer @H-Huang @kwen2501 @VitalyFedyunin @jamesr66a @ezyang