Exporting the operator 'prim::is_cuda' to ONNX opset version 14 is not supported
Labels: module: onnx, triaged

### 🐛 Describe the bug

Error 'Exporting the operator 'prim::is_cuda' to ONNX opset version 14 is not supported' is happening during export to onnx model with TransformerEncoder

```
/home/roman/miniconda3/envs/pt/lib/python3.10/site-packages/torch/onnx/_internal/jit_utils.py:307: UserWarning: Constant folding in symbolic shape inference fails: index out of range in self (Triggered internally at ../torch/csrc/jit/passes/onnx/shape_type_inference.cpp:439.)
  _C._jit_pass_onnx_node_shape_type_inference(node, params_dict, opset_version)
Traceback (most recent call last):
  File "/home/roman/projects/python/tools/test_onnx.py", line 24, in <module>
    torch.onnx.export(
  File "/home/roman/miniconda3/envs/pt/lib/python3.10/site-packages/torch/onnx/utils.py", line 507, in export
    _export(
  File "/home/roman/miniconda3/envs/pt/lib/python3.10/site-packages/torch/onnx/utils.py", line 1567, in _export
    graph, params_dict, torch_out = _model_to_graph(
  File "/home/roman/miniconda3/envs/pt/lib/python3.10/site-packages/torch/onnx/utils.py", line 1128, in _model_to_graph
    graph = _optimize_graph(
  File "/home/roman/miniconda3/envs/pt/lib/python3.10/site-packages/torch/onnx/utils.py", line 666, in _optimize_graph
    graph = _C._jit_pass_onnx(graph, operator_export_type)
  File "/home/roman/miniconda3/envs/pt/lib/python3.10/site-packages/torch/onnx/utils.py", line 1908, in _run_symbolic_function
    return symbolic_fn(graph_context, *inputs, **attrs)
  File "/home/roman/miniconda3/envs/pt/lib/python3.10/site-packages/torch/onnx/symbolic_opset9.py", line 6951, in prim_if
    torch._C._jit_pass_onnx_block(
  File "/home/roman/miniconda3/envs/pt/lib/python3.10/site-packages/torch/onnx/utils.py", line 1918, in _run_symbolic_function
    raise errors.UnsupportedOperatorError(
torch.onnx.errors.UnsupportedOperatorError: Exporting the operator 'prim::is_cuda' to ONNX opset version 14 is not supported. Please feel free to request support or submit a pull request on PyTorch GitHub: https://github.com/pytorch/pytorch/issues.

```


Here is the code to reproduce:
```
import torch
from torch import Tensor
from torch.nn import Module, TransformerEncoderLayer, TransformerEncoder


class TestNet(Module):
    def __init__(self, tf_n_channels=3, device=torch.device('cpu'), *args, **kwargs):
        super().__init__(*args, **kwargs)
        encoder_layer = TransformerEncoderLayer(d_model=tf_n_channels * 5, nhead=tf_n_channels, dim_feedforward=60,
                                                dropout=0.0, device=device, dtype=torch.float32)
        self.transformer = TransformerEncoder(encoder_layer, num_layers=2)

    def forward(self, _input: Tensor) -> Tensor:
        return self.transformer(_input, is_causal=True, mask=torch.ones((_input.size(0), _input.size(0)),
                                                                        dtype=torch.bool,
                                                                        device=_input.device).triu(diagonal=1))


if __name__ == '__main__':
    _device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    example_inputs = torch.randn((100, 1, 15)).to(_device, dtype=torch.float)
    model = TestNet(device=_device)
    model = torch.jit.script(model)
    torch.onnx.export(
        model,
        example_inputs,
        "test_model.onnx",
        export_params=True,
        do_constant_folding=True,
        verbose=True
    )
```

- OS Ubuntu 22.04
- cuda 12.1
- pytorch 2.1.0.dev20230508+cu121
- onnxruntime 1.14.1

### Versions

```
$ python collect_env.py
Collecting environment information...
PyTorch version: 2.1.0.dev20230508+cu121
Is debug build: False
CUDA used to build PyTorch: 12.1
ROCM used to build PyTorch: N/A

OS: Ubuntu 22.04 LTS (x86_64)
GCC version: (Ubuntu 11.3.0-1ubuntu1~22.04) 11.3.0
Clang version: 14.0.0-1ubuntu1
CMake version: version 3.25.0
Libc version: glibc-2.35

Python version: 3.10.10 | packaged by conda-forge | (main, Mar 24 2023, 20:08:06) [GCC 11.3.0] (64-bit runtime)
Python platform: Linux-5.15.0-71-generic-x86_64-with-glibc2.35
Is CUDA available: True
CUDA runtime version: 12.1.105
CUDA_MODULE_LOADING set to: LAZY
GPU models and configuration: GPU 0: NVIDIA GeForce RTX 3090
Nvidia driver version: 530.30.02
cuDNN version: Probably one of the following:
/usr/lib/libcudnn.so.8
/usr/lib/libcudnn_adv_infer.so.8
/usr/lib/libcudnn_adv_train.so.8
/usr/lib/libcudnn_cnn_infer.so.8
/usr/lib/libcudnn_cnn_train.so.8
/usr/lib/libcudnn_ops_infer.so.8
/usr/lib/libcudnn_ops_train.so.8
/usr/lib/x86_64-linux-gnu/libcudnn.so.8.9.0
/usr/lib/x86_64-linux-gnu/libcudnn_adv_infer.so.8.9.0
/usr/lib/x86_64-linux-gnu/libcudnn_adv_train.so.8.9.0
/usr/lib/x86_64-linux-gnu/libcudnn_cnn_infer.so.8.9.0
/usr/lib/x86_64-linux-gnu/libcudnn_cnn_train.so.8.9.0
/usr/lib/x86_64-linux-gnu/libcudnn_ops_infer.so.8.9.0
/usr/lib/x86_64-linux-gnu/libcudnn_ops_train.so.8.9.0
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
Address sizes:                   46 bits physical, 48 bits virtual
Byte Order:                      Little Endian
CPU(s):                          24
On-line CPU(s) list:             0-23
Vendor ID:                       GenuineIntel
Model name:                      12th Gen Intel(R) Core(TM) i9-12900K
CPU family:                      6
Model:                           151
Thread(s) per core:              2
Core(s) per socket:              16
Socket(s):                       1
Stepping:                        2
CPU max MHz:                     5200.0000
CPU min MHz:                     800.0000
BogoMIPS:                        6374.40
Flags:                           fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush dts acpi mmx fxsr sse sse2 ss ht tm pbe syscall nx pdpe1gb rdtscp lm constant_tsc art arch_perfmon pebs bts rep_good nopl xtopology nonstop_tsc cpuid aperfmperf tsc_known_freq pni pclmulqdq dtes64 monitor ds_cpl vmx smx est tm2 ssse3 sdbg fma cx16 xtpr pdcm pcid sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand lahf_lm abm 3dnowprefetch cpuid_fault cat_l2 invpcid_single cdp_l2 ssbd ibrs ibpb stibp ibrs_enhanced tpr_shadow vnmi flexpriority ept vpid ept_ad fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid rdt_a rdseed adx smap clflushopt clwb intel_pt sha_ni xsaveopt xsavec xgetbv1 xsaves split_lock_detect avx_vnni dtherm ida arat pln pts hwp hwp_notify hwp_act_window hwp_epp hwp_pkg_req umip pku ospke waitpkg gfni vaes vpclmulqdq tme rdpid movdiri movdir64b fsrm md_clear serialize pconfig arch_lbr flush_l1d arch_capabilities
Virtualization:                  VT-x
L1d cache:                       640 KiB (16 instances)
L1i cache:                       768 KiB (16 instances)
L2 cache:                        14 MiB (10 instances)
L3 cache:                        30 MiB (1 instance)
NUMA node(s):                    1
NUMA node0 CPU(s):               0-23
Vulnerability Itlb multihit:     Not affected
Vulnerability L1tf:              Not affected
Vulnerability Mds:               Not affected
Vulnerability Meltdown:          Not affected
Vulnerability Mmio stale data:   Not affected
Vulnerability Retbleed:          Not affected
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

Versions of relevant libraries:
[pip3] numpy==1.24.1
[pip3] pytorch-lightning==2.0.2
[pip3] pytorch-triton==2.1.0+7d1a95b046
[pip3] torch==2.1.0.dev20230508+cu121
[pip3] torch-tensorrt==1.3.0
[pip3] torchaudio==2.1.0.dev20230508+cu121
[pip3] torchmetrics==0.11.4
[pip3] torchvision==0.16.0.dev20230508+cu121
[pip3] triton==2.0.0
[conda] cudatoolkit               11.3.1               h2bc3f7f_2    anaconda
[conda] cudatoolkit-dev           11.7.0               h1de0b5d_6    conda-forge
[conda] numpy                     1.24.1                   pypi_0    pypi
[conda] pytorch-lightning         2.0.2                    pypi_0    pypi
[conda] pytorch-triton            2.1.0+7d1a95b046          pypi_0    pypi
[conda] torch                     2.1.0.dev20230508+cu121          pypi_0    pypi
[conda] torch-tensorrt            1.3.0                    pypi_0    pypi
[conda] torchaudio                2.1.0.dev20230508+cu121          pypi_0    pypi
[conda] torchmetrics              0.11.4                   pypi_0    pypi
[conda] torchvision               0.16.0.dev20230508+cu121          pypi_0    pypi
[conda] triton                    2.0.0                    pypi_0    pypi

```