`torch.nn.LayerNorm` Abort with "invalid device ordinal" Error
Labels: module: nn, module: error checking, triaged, module: norms and normalization

### 🐛 Describe the bug

A test case of `torch.nn.LayerNorm` aborted with error message "CUDA error: invalid device ordinal" even if there's no device assignment in the code.
Test:
```python
import torch

def test():
    arg_class = torch.nn.LayerNorm(720,1e-05,[18,1024],24,)
    arg_tensor = torch.rand([64, 128, 720], dtype=torch.float32).clone().cuda()
    arg = [arg_tensor,]
    res = arg_class(*arg)

test()
```
Error log:
```
Traceback (most recent call last):
  File "/home/yuyao/trial/test.py", line 9, in <module>
    test()
  File "/home/yuyao/trial/test.py", line 4, in test
    arg_class = torch.nn.LayerNorm(720,1e-05,[18,1024],24,)
  File "/home/yuyao/.local/lib/python3.10/site-packages/torch/nn/modules/normalization.py", line 176, in __init__
    self.weight = Parameter(torch.empty(self.normalized_shape, **factory_kwargs))
RuntimeError: CUDA error: invalid device ordinal
CUDA kernel errors might be asynchronously reported at some other API call,so the stacktrace below might be incorrect.
For debugging consider passing CUDA_LAUNCH_BLOCKING=1.
```

### Versions

```
PyTorch version: 1.14.0a0+gitbdc9911
Is debug build: False
CUDA used to build PyTorch: 11.6
ROCM used to build PyTorch: N/A

OS: Ubuntu 22.04.1 LTS (x86_64)
GCC version: (Ubuntu 11.3.0-1ubuntu1~22.04) 11.3.0
Clang version: 11.1.0-6
CMake version: version 3.22.1
Libc version: glibc-2.35

Python version: 3.10.6 (main, Nov  2 2022, 18:53:38) [GCC 11.3.0] (64-bit runtime)
Python platform: Linux-5.15.0-52-generic-x86_64-with-glibc2.35
Is CUDA available: True
CUDA runtime version: 11.6.124
CUDA_MODULE_LOADING set to: LAZY
GPU models and configuration: 
GPU 0: NVIDIA GeForce RTX 3090
GPU 1: NVIDIA GeForce RTX 3090
GPU 2: NVIDIA GeForce RTX 3090

Nvidia driver version: 515.65.01
cuDNN version: Probably one of the following:
/usr/lib/x86_64-linux-gnu/libcudnn.so.8.4.1
/usr/lib/x86_64-linux-gnu/libcudnn_adv_infer.so.8.4.1
/usr/lib/x86_64-linux-gnu/libcudnn_adv_train.so.8.4.1
/usr/lib/x86_64-linux-gnu/libcudnn_cnn_infer.so.8.4.1
/usr/lib/x86_64-linux-gnu/libcudnn_cnn_train.so.8.4.1
/usr/lib/x86_64-linux-gnu/libcudnn_ops_infer.so.8.4.1
/usr/lib/x86_64-linux-gnu/libcudnn_ops_train.so.8.4.1
HIP runtime version: N/A
MIOpen runtime version: N/A
Is XNNPACK available: True

Versions of relevant libraries:
[pip3] numpy==1.21.5
[pip3] torch==1.14.0a0+gitbdc9911
[pip3] torchvision==0.13.1
[conda] blas                      1.0                         mkl  
[conda] cudatoolkit               11.3.1               h2bc3f7f_2  
[conda] mkl                       2021.4.0           h06a4308_640  
[conda] mkl-service               2.4.0            py39h7f8727e_0  
[conda] mkl_fft                   1.3.1            py39hd3c417c_0  
[conda] mkl_random                1.2.2            py39h51133e4_0  
[conda] numpy                     1.21.5           py39he7a7128_1  
[conda] numpy-base                1.21.5           py39hf524024_1  
[conda] numpydoc                  1.2                pyhd3eb1b0_0  
[conda] torch                     1.14.0a0+gitce2f870          pypi_0    pypi
```

cc @albanD @mruberry @jbschlosser @walterddr @kshitij12345 @saketh-are