Repository: pytorch
Issue: op for aten::bitwise_and during torch.jit.trace
Labels: oncall: jit

### 🐛 Describe the bug

Hi, I'm trying to run jit.trace on the openfold model found [here](https://github.com/aqlaboratory/openfold). However, I keep running into this error as shown here:
```
---------------------------------------------------------------------------
RuntimeError                              Traceback (most recent call last)
/tmp/ipykernel_18250/2760271908.py in <module>
      1 #flattened_inputs, _ = flatten_to_tuple(batch)
      2 #parallelModel  = nn.DataParallel(model)
----> 3 trace = torch.jit.trace(model, batch, strict=False)  # Succeeds
      4 
      5 #adapter = TracingAdapter(model, batch)

/data/openfold/lib/conda/envs/openfold_venv/lib/python3.7/site-packages/torch/jit/_trace.py in trace(func, example_inputs, optimize, check_trace, check_inputs, check_tolerance, strict, _force_outplace, _module_class, _compilation_unit)
    748             strict,
    749             _force_outplace,
--> 750             _module_class,
    751         )
    752 

/data/openfold/lib/conda/envs/openfold_venv/lib/python3.7/site-packages/torch/jit/_trace.py in trace_module(mod, inputs, optimize, check_trace, check_inputs, check_tolerance, strict, _force_outplace, _module_class, _compilation_unit)
    963                 strict,
    964                 _force_outplace,
--> 965                 argument_names,
    966             )
    967             check_trace_method = module._c._get_method(method_name)

RuntimeError: 0INTERNAL ASSERT FAILED at "../torch/csrc/jit/ir/alias_analysis.cpp":607, please report a bug to PyTorch. We don't have an op for aten::bitwise_and but it isn't a special case.  Argument types: Tensor, bool, 

Candidates:
	aten::bitwise_and.Tensor(Tensor self, Tensor other) -> (Tensor)
	aten::bitwise_and.Tensor_out(Tensor self, Tensor other, *, Tensor(a!) out) -> (Tensor(a!))
	aten::bitwise_and.Scalar(Tensor self, Scalar other) -> (Tensor)
	aten::bitwise_and.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> (Tensor(a!))
```

Just based on some quick searches it looks like it could be some issues with the OpenFold model using `bool` datatype in its tensors... Having a hard time to pinpoint where in the model this is failing on, and not sure if there were ways to get around the aten::bitwise_and operation issue here. Any help would be appreciated.


### Versions

```Collecting environment information...
PyTorch version: 1.11.0+cu102
Is debug build: False
CUDA used to build PyTorch: 10.2
ROCM used to build PyTorch: N/A

OS: Ubuntu 18.04.6 LTS (x86_64)
GCC version: (Ubuntu 7.5.0-3ubuntu1~18.04) 7.5.0
Clang version: Could not collect
CMake version: version 3.22.5
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-5.4.0-1083-aws-x86_64-with-debian-buster-sid
Is CUDA available: True
CUDA runtime version: 10.1.243
GPU models and configuration: GPU 0: Tesla V100-SXM2-16GB
Nvidia driver version: 510.47.03
cuDNN version: Probably one of the following:
/usr/local/cuda-11.1/targets/x86_64-linux/lib/libcudnn.so.8.0.5
/usr/local/cuda-11.1/targets/x86_64-linux/lib/libcudnn_adv_infer.so.8.0.5
/usr/local/cuda-11.1/targets/x86_64-linux/lib/libcudnn_adv_train.so.8.0.5
/usr/local/cuda-11.1/targets/x86_64-linux/lib/libcudnn_cnn_infer.so.8.0.5
/usr/local/cuda-11.1/targets/x86_64-linux/lib/libcudnn_cnn_train.so.8.0.5
/usr/local/cuda-11.1/targets/x86_64-linux/lib/libcudnn_ops_infer.so.8.0.5
/usr/local/cuda-11.1/targets/x86_64-linux/lib/libcudnn_ops_train.so.8.0.5
/usr/local/cuda-11.2/targets/x86_64-linux/lib/libcudnn.so.8.1.1
/usr/local/cuda-11.2/targets/x86_64-linux/lib/libcudnn_adv_infer.so.8.1.1
/usr/local/cuda-11.2/targets/x86_64-linux/lib/libcudnn_adv_train.so.8.1.1
/usr/local/cuda-11.2/targets/x86_64-linux/lib/libcudnn_cnn_infer.so.8.1.1
/usr/local/cuda-11.2/targets/x86_64-linux/lib/libcudnn_cnn_train.so.8.1.1
/usr/local/cuda-11.2/targets/x86_64-linux/lib/libcudnn_ops_infer.so.8.1.1
/usr/local/cuda-11.2/targets/x86_64-linux/lib/libcudnn_ops_train.so.8.1.1
HIP runtime version: N/A
MIOpen runtime version: N/A
Is XNNPACK available: True

Versions of relevant libraries:
[pip3] numpy==1.20.0
[pip3] pytorch-lightning==1.5.10
[pip3] torch==1.11.0
[pip3] torch-neuron==1.11.0.2.3.0.0
[pip3] torchmetrics==0.9.1
[pip3] torchvision==0.12.0
[conda] blas                      1.0                         mkl    intel
[conda] cudatoolkit               10.2.89             h713d32c_10    conda-forge
[conda] cudatoolkit-dev           10.1.243             h516909a_3    conda-forge
[conda] mkl                       2022.1.0           h84fe81f_915    conda-forge
[conda] mkl-service               2.4.0            py37h94185c7_0    conda-forge
[conda] mkl_fft                   1.3.1            py37h90e98c2_3    conda-forge
[conda] mkl_random                1.2.2            py37h693438c_1    conda-forge
[conda] mkl_umath                 0.1.1           py37h3242e30_26    intel
[conda] numpy                     1.20.0                   pypi_0    pypi
[conda] pytorch-lightning         1.5.10                   pypi_0    pypi
[conda] pytorch-mutex             1.0                        cuda    pytorch
[conda] torch                     1.11.0                   pypi_0    pypi
[conda] torch-neuron              1.11.0.2.3.0.0           pypi_0    pypi
[conda] torchmetrics              0.9.1                    pypi_0    pypi
[conda] torchvision               0.12.0                   pypi_0    pypi```