Repository: pytorch
Issue: how to workaround the error "don't have an op for vulkan_prepack::create_linear_context" ?
Labels: module: build,triaged,module: vulkan,ciflow/periodic

### 🐛 Describe the bug

I have a modified resnet-50 network, which I want to run on android using vulkan backend.

The custom build of pytorch with USE_VULKAN=1 works fine, but I got the error message "We don't have an op for vulkan_prepack::create_linear_context but it isn't a special case." during "optimize_for_mobile" API invocation.

What's the problem here, and how to deal with it?
(I tried on both release 1.13 and release v2.0.1 tags, but got the same error message above).


```
git clone -b release/1.13 --recursive https://github.com/pytorch/pytorch
cd pytorch
git submodule sync
git submodule update --init --recursive

export CMAKE_PREFIX_PATH=${CONDA_PREFIX:-"$(dirname $(which conda))/../"}
python setup.py build --cmake-only
ccmake build  # or cmake-gui build

BUILD_LITE_INTERPRETER=0 USE_VULKAN=1 USE_VULKAN_SHADERC_RUNTIME=1 USE_VULKAN_WRAPPER=0 python setup.py develop

BUILD_LITE_INTERPRETER=0 ANDROID_ABI=arm64-v8a USE_VULKAN=1 USE_VULKAN_SHADERC_RUNTIME=1 USE_VULKAN_WRAPPER=0 bash ./scripts/build_android.sh

BUILD_LITE_INTERPRETER=0 ANDROID_ABI=arm64-v8a USE_VULKAN=1 USE_VULKAN_SHADERC_RUNTIME=1 USE_VULKAN_WRAPPER=0 bash ./scripts/build_pytorch_android.sh
```

```

>>> import torch
>>> import os
>>> 
>>> from torch.utils.mobile_optimizer import optimize_for_mobile
>>> 
>>> #file_dir = '.'
>>> file_dir = '../pytorch-script/'
>>> model = torch.jit.load(file_dir + '/modified-resnet50-image.pt')
>>> model.eval()
RecursiveScriptModule(original_name=ImageModel)
>>> script_model = torch.jit.script(model)
>>> script_model_vulkan = optimize_for_mobile(script_model, backend='vulkan')
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/mnt/DataExt/devroot/src/pytorch/torch/utils/mobile_optimizer.py", line 67, in optimize_for_mobile
    optimized_cpp_module = torch._C._jit_pass_vulkan_optimize_for_mobile(
                           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
RuntimeError: 0 INTERNAL ASSERT FAILED at "/mnt/DataExt/devroot/src/pytorch/torch/csrc/jit/ir/alias_analysis.cpp":615, please report a bug to PyTorch. We don't have an op for vulkan_prepack::create_linear_context but it isn't a special case.  Argument types: Tensor, Tensor, 

Candidates:
>>> exit()

```

### Versions

Collecting environment information...
PyTorch version: 2.0.0a0+gite9ebda2
Is debug build: False
CUDA used to build PyTorch: 12.1
ROCM used to build PyTorch: N/A

OS: Ubuntu 20.04.6 LTS (x86_64)
GCC version: (Ubuntu 10.3.0-1ubuntu1~20.04) 10.3.0
Clang version: Could not collect
CMake version: version 3.22.1
Libc version: glibc-2.31

Python version: 3.11.3 (main, Apr 19 2023, 23:54:32) [GCC 11.2.0] (64-bit runtime)
Python platform: Linux-5.15.0-73-generic-x86_64-with-glibc2.31
Is CUDA available: True
CUDA runtime version: 12.1.105
CUDA_MODULE_LOADING set to: LAZY
GPU models and configuration: GPU 0: NVIDIA RTX A4000
Nvidia driver version: 530.30.02
cuDNN version: Probably one of the following:
/usr/local/cuda-12.1/targets/x86_64-linux/lib/libcudnn.so.8
/usr/local/cuda-12.1/targets/x86_64-linux/lib/libcudnn_adv_infer.so.8
/usr/local/cuda-12.1/targets/x86_64-linux/lib/libcudnn_adv_train.so.8
/usr/local/cuda-12.1/targets/x86_64-linux/lib/libcudnn_cnn_infer.so.8
/usr/local/cuda-12.1/targets/x86_64-linux/lib/libcudnn_cnn_train.so.8
/usr/local/cuda-12.1/targets/x86_64-linux/lib/libcudnn_ops_infer.so.8
/usr/local/cuda-12.1/targets/x86_64-linux/lib/libcudnn_ops_train.so.8
HIP runtime version: N/A
MIOpen runtime version: N/A
Is XNNPACK available: True

CPU:
Architecture:                    x86_64
CPU op-mode(s):                  32-bit, 64-bit
Byte Order:                      Little Endian
Address sizes:                   45 bits physical, 48 bits virtual
CPU(s):                          16
On-line CPU(s) list:             0-15
Thread(s) per core:              1
Core(s) per socket:              16
Socket(s):                       1
NUMA node(s):                    1
Vendor ID:                       GenuineIntel
CPU family:                      6
Model:                           85
Model name:                      Intel(R) Xeon(R) Gold 5218N CPU @ 2.30GHz
Stepping:                        7
CPU MHz:                         2294.609
BogoMIPS:                        4589.21
Hypervisor vendor:               VMware
Virtualization type:             full
L1d cache:                       512 KiB
L1i cache:                       512 KiB
L2 cache:                        16 MiB
L3 cache:                        22 MiB
NUMA node0 CPU(s):               0-15
Vulnerability Itlb multihit:     KVM: Mitigation: VMX unsupported
Vulnerability L1tf:              Not affected
Vulnerability Mds:               Not affected
Vulnerability Meltdown:          Not affected
Vulnerability Mmio stale data:   Vulnerable: Clear CPU buffers attempted, no microcode; SMT Host state unknown
Vulnerability Retbleed:          Mitigation; Enhanced IBRS
Vulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl and seccomp
Vulnerability Spectre v1:        Mitigation; usercopy/swapgs barriers and __user pointer sanitization
Vulnerability Spectre v2:        Mitigation; Enhanced IBRS, IBPB conditional, RSB filling, PBRSB-eIBRS SW sequence
Vulnerability Srbds:             Not affected
Vulnerability Tsx async abort:   Not affected
Flags:                           fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ss ht syscall nx pdpe1gb rdtscp lm constant_tsc arch_perfmon nopl xtopology tsc_reliable nonstop_tsc cpuid tsc_known_freq pni pclmulqdq ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand hypervisor lahf_lm abm 3dnowprefetch cpuid_fault invpcid_single ssbd ibrs ibpb stibp ibrs_enhanced fsgsbase tsc_adjust bmi1 avx2 smep bmi2 invpcid avx512f avx512dq rdseed adx smap clflushopt clwb avx512cd avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves arat pku ospke avx512_vnni md_clear flush_l1d arch_capabilities

Versions of relevant libraries:
[pip3] numpy==1.24.3
[pip3] torch==1.13.0a0+gitd922c29
[conda] blas                      1.0                         mkl
[conda] magma-cuda110             2.5.2                         1    pytorch
[conda] mkl                       2023.1.0         h6d00ec8_46342
[conda] mkl-include               2023.1.0         h06a4308_46342
[conda] mkl-service               2.4.0           py311h5eee18b_1
[conda] mkl_fft                   1.3.6           py311ha02d727_1
[conda] mkl_random                1.2.2           py311ha02d727_1
[conda] numpy                     1.24.3          py311h08b1b3b_1
[conda] numpy-base                1.24.3          py311hf175353_1
[conda] torch                     1.13.0a0+gitd922c29          pypi_0    pypi


cc @malfet @seemethere