[ONNX] Use topk to export max(dim,keepdim) to onnx
Labels: module: onnx, triaged, onnx-triaged, bug

### 🐛 Describe the bug

### Model 1:

```python
class MinTest(nn.Module):
    def __init__(self):
        super().__init__()

    def forward(self, x):
        top_probs, top = x.max(1, keepdim=True)
        return top_probs, top

```

![image](https://user-images.githubusercontent.com/873905/165182186-9cd3b5f8-aea8-4ae0-80b2-42aff27674f5.png)

### Model 2:

```python
class MinTest(nn.Module):
    def __init__(self):
        super().__init__()

    def forward(self, x):
        top_probs, top = x.topk(1, dim=1)
        return top_probs, top
```

![image](https://user-images.githubusercontent.com/873905/165182316-95dbc0df-52c5-4666-aad9-4c4348ef1ffd.png)

### Export:

``` python
m = MinTest().eval()

x = torch.rand(1,146,768)

torch.onnx.export(m, x, "/trt/output/min.onnx", verbose=True, input_names=['x'], output_names=["top_probs", "top"], opset_version=14)

```

There are two issues with exporting "Max(1,keep_dim=True)"

1. It is not efficient (to call both max and argmax when they could be combined)
2. onnxruntime CUDA EP does an extra copy to memory for argmax (another issue for onnxruntime [here](https://github.com/microsoft/onnxruntime/issues/11348))



### Versions

Collecting environment information...
PyTorch version: 1.10.2
Is debug build: False
CUDA used to build PyTorch: 11.3
ROCM used to build PyTorch: N/A

OS: Ubuntu 20.04.2 LTS (x86_64)
GCC version: (Ubuntu 9.4.0-1ubuntu1~20.04.1) 9.4.0
Clang version: Could not collect
CMake version: version 3.21.0
Libc version: glibc-2.10

Python version: 3.7.12 | packaged by conda-forge | (default, Oct 26 2021, 06:08:21)  [GCC 9.4.0] (64-bit runtime)
Python platform: Linux-5.4.0-1074-azure-x86_64-with-debian-bullseye-sid
Is CUDA available: True
CUDA runtime version: 11.4.48
GPU models and configuration: GPU 0: Tesla V100-PCIE-16GB
Nvidia driver version: 470.103.01
cuDNN version: Probably one of the following:
/usr/lib/x86_64-linux-gnu/libcudnn.so.8.2.2
/usr/lib/x86_64-linux-gnu/libcudnn_adv_infer.so.8.2.2
/usr/lib/x86_64-linux-gnu/libcudnn_adv_train.so.8.2.2
/usr/lib/x86_64-linux-gnu/libcudnn_cnn_infer.so.8.2.2
/usr/lib/x86_64-linux-gnu/libcudnn_cnn_train.so.8.2.2
/usr/lib/x86_64-linux-gnu/libcudnn_ops_infer.so.8.2.2
/usr/lib/x86_64-linux-gnu/libcudnn_ops_train.so.8.2.2
HIP runtime version: N/A
MIOpen runtime version: N/A
Is XNNPACK available: True

Versions of relevant libraries:
[pip3] numpy==1.21.6
[pip3] torch==1.10.2
[pip3] torchlars==0.1.2
[pip3] torchvision==0.11.3
[conda] blas                      1.0                         mkl
[conda] cudatoolkit               11.3.1               h2bc3f7f_2
[conda] ffmpeg                    4.3                  hf484d3e_0    pytorch
[conda] mkl                       2021.4.0           h8d4b97c_729    conda-forge
[conda] mkl-service               2.4.0            py37h402132d_0    conda-forge
[conda] mkl_fft                   1.3.1            py37h3e078e5_1    conda-forge
[conda] mkl_random                1.2.2            py37h219a48f_0    conda-forge
[conda] numpy                     1.21.6                   pypi_0    pypi
[conda] pytorch                   1.10.2          py3.7_cuda11.3_cudnn8.2.0_0    pytorch
[conda] pytorch-mutex             1.0                        cuda    pytorch
[conda] torchlars                 0.1.2                    pypi_0    pypi
[conda] torchvision               0.11.3               py37_cu113    pytorch