`INTERNAL ASSERT FAILED` -When using the PyTorch docker environment released by pytorch, a Vulcan support issue occurs
Labels: module: build, triaged, module: docker

### 🐛 Describe the bug

When I run `torch.nn.Linear` or `torch.nn.ConvTranspose2d`  with `optimize_for_mobile` in the packaged docker environment [released by pytorch](https://hub.docker.com/u/pytorch), a Vulcan support issue occurs, and then crashes.
It is similar to [#86791](https://github.com/pytorch/pytorch/issues/86791).
It seems that the currently released docker environments may have some flags not enabled during building, resulting in some functions not working properly (regardless of `runtime` environment or `devel` environment).
I wonder if this is expected behavior and if I always need to manually build pytorch from the source to enable some special support.



### To Reproduce
```python
from torch import nn
import torch
from torch.utils.mobile_optimizer import optimize_for_mobile

with torch.no_grad():
    x = torch.ones(1, 3, 32, 32)

    def test():
        tmp_result= torch.nn.ConvTranspose2d(3, 3,  kernel_size=1)
        # tmp_result= torch.nn.Linear(32, 32)
        return tmp_result
    
    model = test()
    optimized_traced = optimize_for_mobile(torch.jit.trace(model, x), backend='vulkan')
```

The error result is
```
Traceback (most recent call last):
  File "test.py", line 15, in <module>
    optimized_traced = optimize_for_mobile(torch.jit.trace(model, x), backend='vulkan')
  File "/opt/conda/lib/python3.7/site-packages/torch/utils/mobile_optimizer.py", line 67, in optimize_for_mobile
    optimized_cpp_module = torch._C._jit_pass_vulkan_optimize_for_mobile(script_module._c, preserved_methods_str)
RuntimeError: 0 INTERNAL ASSERT FAILED at "/opt/conda/conda-bld/pytorch_1656352464346/work/torch/csrc/jit/ir/alias_analysis.cpp":608, please report a bug to PyTorch. We don't have an op for vulkan_prepack::linear_prepack but it isn't a special case.  Argument types: Tensor, Tensor, 
```

### Expected behavior
It is hoped that the docker environments released by pytorch can be built with these flags to enable corresponding support and ensure that all torch functions are implemented as expected. 

### Versions


docker pull command:
```
docker pull pytorch/pytorch:1.10.0-cuda11.3-cudnn8-devel
docker pull pytorch/pytorch:1.12.0-cuda11.3-cudnn8-runtime
docker pull pytorch/pytorch:1.13.0-cuda11.6-cudnn8-runtime
```

<details>
  <summary>pytorch 1.10.0</summary>
  <pre><code>
  [pip3] numpy==1.21.2
  [pip3] torch==1.10.0
  [pip3] torch-tb-profiler==0.4.0
  [pip3] torchelastic==0.2.0
  [pip3] torchtext==0.11.0
  [pip3] torchvision==0.11.0
  [conda] blas                      1.0                         mkl  
  [conda] cudatoolkit               11.3.1               ha36c431_9    nvidia
  [conda] ffmpeg                    4.3                  hf484d3e_0    pytorch
  [conda] mkl                       2021.3.0           h06a4308_520  
  [conda] mkl-service               2.4.0            py37h7f8727e_0  
  [conda] mkl_fft                   1.3.1            py37hd3c417c_0  
  [conda] mkl_random                1.2.2            py37h51133e4_0  
  [conda] numpy                     1.21.2           py37h20f2e39_0  
  [conda] numpy-base                1.21.2           py37h79a1101_0  
  [conda] pytorch                   1.10.0          py3.7_cuda11.3_cudnn8.2.0_0    pytorch
  [conda] pytorch-mutex             1.0                        cuda    pytorch
  [conda] torch-tb-profiler         0.4.0                    pypi_0    pypi
  [conda] torchelastic              0.2.0                    pypi_0    pypi
  [conda] torchtext                 0.11.0                     py37    pytorch
  [conda] torchvision               0.11.0               py37_cu113    pytorch</code></pre>
</details>


<details>
  <summary>pytorch 1.12.0</summary>
  <pre><code>
  [pip3] numpy==1.21.5
  [pip3] torch==1.12.0
  [pip3] torchelastic==0.2.0
  [pip3] torchtext==0.13.0
  [pip3] torchvision==0.13.0
  [conda] blas                      1.0                         mkl  
  [conda] cudatoolkit               11.3.1               ha36c431_9    nvidia
  [conda] ffmpeg                    4.3                  hf484d3e_0    pytorch
  [conda] mkl                       2021.4.0           h06a4308_640  
  [conda] mkl-service               2.4.0            py37h7f8727e_0  
  [conda] mkl_fft                   1.3.1            py37hd3c417c_0  
  [conda] mkl_random                1.2.2            py37h51133e4_0  
  [conda] numpy                     1.21.5           py37he7a7128_2  
  [conda] numpy-base                1.21.5           py37hf524024_2  
  [conda] pytorch                   1.12.0          py3.7_cuda11.3_cudnn8.3.2_0    pytorch
  [conda] pytorch-mutex             1.0                        cuda    pytorch
  [conda] torchelastic              0.2.0                    pypi_0    pypi
  [conda] torchtext                 0.13.0                     py37    pytorch
  [conda] torchvision               0.13.0               py37_cu113    pytorch</code></pre>
</details>

<details>
  <summary>pytorch 1.13.0</summary>
  <pre><code>
  [pip3] numpy==1.22.3
  [pip3] torch==1.13.0
  [pip3] torchtext==0.14.0
  [pip3] torchvision==0.14.0
  [conda] blas                      1.0                         mkl  
  [conda] ffmpeg                    4.3                  hf484d3e_0    pytorch
  [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                     1.22.3           py39he7a7128_0  
  [conda] numpy-base                1.22.3           py39hf524024_0  
  [conda] pytorch                   1.13.0          py3.9_cuda11.6_cudnn8.3.2_0    pytorch
  [conda] pytorch-cuda              11.6                 h867d48c_0    pytorch
  [conda] pytorch-mutex             1.0                        cuda    pytorch
  [conda] torchtext                 0.14.0                     py39    pytorch
  [conda] torchvision               0.14.0               py39_cu116    pytorch</code></pre>
</details>

cc @malfet @seemethere