Stride-1 tf.nn.conv2d with XLA is 1.5x slower then without XLA, as far as stride-2 tf.nn.depthwise_conv2d
Hi @shkarupa-alex ,

I am not sure whether this is better comparison of XLA performance or not. I have increased the `repeats` to 1000 and also found similar results. Please refer to attached [gist](https://colab.research.google.com/gist/SuryanarayanaY/ddd84ed3c829b1b5b15206fc4efb59c6/60312-conv-vs-depthwise-speed.ipynb#scrollTo=vtxDK4y79_7B).

```
stride-1 conv 44.51
stride-1 conv_jit 64.96
stride-1 sepconv 19.95
stride-1 sepconv_jit 17.47
stride-1 dwconv 9.0
stride-1 dwconv_jit 5.89

stride-2 conv 32.42
stride-2 conv_jit 28.98
stride-2 sepconv 5.18
stride-2 sepconv_jit 6.97
stride-2 dwconv 2.58
stride-2 dwconv_jit 4.29
```


 @shkarupa-alex @SuryanarayanaY Thanks for the bug! Would you be interested in pursuing this further? E.g. when looked under nsys systems profiler, are the kernel names different? A next step would be looking into `conv_algorithm_picker`, enabling logs and checking why in XLA:GPU a different kernel from cuDNN is chosen from TF.
<details><summary>Click to expand!</summary> 
 
 ### Issue Type

Bug

### Have you reproduced the bug with TF nightly?

Yes

### Source

binary

### Tensorflow Version

2.12.0, 2.13.0-dev20230412

### Custom Code

Yes

### OS Platform and Distribution

Google Colab

### Mobile device

_No response_

### Python version

Google Colab

### Bazel version

_No response_

### GCC/Compiler version

_No response_

### CUDA/cuDNN version

11.8

### GPU model and memory

_No response_

### Current Behaviour?

See example below to reproduce. Here is speed test results:

stride-1 conv 40.66
stride-1 conv_jit 64.11  // conv2d is slower with JIT but only if stride=1
stride-2 conv 40.18
stride-2 conv_jit 28.05  // when stride=2 it is FASTER with JIT

stride-1 dwconv 9.82
stride-1 dwconv_jit 5.72  // dwconv is faster with JIT but only if stride=1
stride-2 dwconv 2.59
stride-2 dwconv_jit 4.2  // when stride=2 it is SLOWER with JIT

### Standalone code to reproduce the issue

```shell
https://colab.research.google.com/drive/1zqqPVVKt4ILRA1rCoWjB1uOtB3D0hDc-?usp=sharing
```


### Relevant log output

_No response_</details>