[bug] Device dispatcher can choose CPU path for CUDA tensors.
Labels: module: build, triaged, module: dispatch, module: codegen

### 🐛 Describe the bug

It is possible to confuse device dispatcher to choose wrong path when device dispatches are specified in a single line in `native_functions.yaml`.

To reproduce checkout the code from https://github.com/pytorch/pytorch/pull/79201 at https://github.com/pytorch/pytorch/pull/79201/commits/d03cfcfa406a75a5bcd11e0f6adeaa461b338e07.
Then
```python
In [1]: import torch

In [2]: ig = torch.rand(3, 3, device='cuda')

In [3]: ic = ig.to('cpu')

In [4]: res = torch.foo(ic, ic, ic)
CPU kernel

In [5]: res = torch.foo(ig, ig, ig)
CPU kernel
Segmentation fault (core dumped)

```

When dispatch keys are on separate lines, line in https://github.com/pytorch/pytorch/pull/79201/commits/9caf9267ecc5e56444cd1ad380a783b30159ee22, everything is fine.

### Versions
```
PyTorch version: 1.13.0a0+git75a01c2
Is debug build: False
CUDA used to build PyTorch: 11.2
ROCM used to build PyTorch: N/A

OS: Ubuntu 20.04.4 LTS (x86_64)
GCC version: (GCC) 10.3.0
Clang version: Could not collect
CMake version: version 3.22.3
Libc version: glibc-2.31

Python version: 3.10.4 | packaged by conda-forge | (main, Mar 24 2022, 17:39:04) [GCC 10.3.0] (64-bit runtime)
Python platform: Linux-5.4.0-104-generic-x86_64-with-glibc2.31
Is CUDA available: True
CUDA runtime version: 11.4.120
GPU models and configuration: 
GPU 0: NVIDIA GeForce RTX 2060
GPU 1: NVIDIA GeForce RTX 2060

Nvidia driver version: 465.19.01
cuDNN version: Probably one of the following:
/usr/local/cuda-10.1.243/targets/x86_64-linux/lib/libcudnn.so.8
/usr/local/cuda-10.1.243/targets/x86_64-linux/lib/libcudnn_adv_infer.so.8
/usr/local/cuda-10.1.243/targets/x86_64-linux/lib/libcudnn_adv_train.so.8
/usr/local/cuda-10.1.243/targets/x86_64-linux/lib/libcudnn_cnn_infer.so.8
/usr/local/cuda-10.1.243/targets/x86_64-linux/lib/libcudnn_cnn_train.so.8
/usr/local/cuda-10.1.243/targets/x86_64-linux/lib/libcudnn_ops_infer.so.8
/usr/local/cuda-10.1.243/targets/x86_64-linux/lib/libcudnn_ops_train.so.8
/usr/local/cuda-10.2.89/targets/x86_64-linux/lib/libcudnn.so.8
/usr/local/cuda-10.2.89/targets/x86_64-linux/lib/libcudnn_adv_infer.so.8
/usr/local/cuda-10.2.89/targets/x86_64-linux/lib/libcudnn_adv_train.so.8
/usr/local/cuda-10.2.89/targets/x86_64-linux/lib/libcudnn_cnn_infer.so.8
/usr/local/cuda-10.2.89/targets/x86_64-linux/lib/libcudnn_cnn_train.so.8
/usr/local/cuda-10.2.89/targets/x86_64-linux/lib/libcudnn_ops_infer.so.8
/usr/local/cuda-10.2.89/targets/x86_64-linux/lib/libcudnn_ops_train.so.8
/usr/local/cuda-11.4.2/targets/x86_64-linux/lib/libcudnn.so.8
/usr/local/cuda-11.4.2/targets/x86_64-linux/lib/libcudnn_adv_infer.so.8
/usr/local/cuda-11.4.2/targets/x86_64-linux/lib/libcudnn_adv_train.so.8
/usr/local/cuda-11.4.2/targets/x86_64-linux/lib/libcudnn_cnn_infer.so.8
/usr/local/cuda-11.4.2/targets/x86_64-linux/lib/libcudnn_cnn_train.so.8
/usr/local/cuda-11.4.2/targets/x86_64-linux/lib/libcudnn_ops_infer.so.8
/usr/local/cuda-11.4.2/targets/x86_64-linux/lib/libcudnn_ops_train.so.8
HIP runtime version: N/A
MIOpen runtime version: N/A
Is XNNPACK available: False

Versions of relevant libraries:
[pip3] mypy==0.812
[pip3] mypy-extensions==0.4.3
[pip3] numpy==1.22.3
[pip3] torch==1.13.0a0+gitd03cfcf
[conda] magma-cuda112             2.5.2                         1    pytorch
[conda] mkl                       2022.0.1           h8d4b97c_803    conda-forge
[conda] mkl-include               2022.0.1           h8d4b97c_803    conda-forge
[conda] numpy                     1.22.3                   pypi_0    pypi
[conda] torch                     1.13.0a0+gitd03cfcf           dev_0    <develop>

```
cc @malfet @seemethere @ezyang @bhosmer @bdhirsh