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This PR adds an internal wrapper on the [beartype](https://github.com/beartype/beartype) library to perform runtime type checking in `torch.onnx`. It uses beartype when it is found in the environment and is reduced to a no-op when beartype is not found. Setting the env var `TORCH_ONNX_EXPERIMENTAL_RUNTIME_TYPE_CHECK=ERRORS` will turn on the feature. setting `TORCH_ONNX_EXPERIMENTAL_RUNTIME_TYPE_CHECK=DISABLED` will disable all checks. When not set and `beartype` is installed, a warning message is emitted. Now when users call an api with invalid arguments e.g. ```python torch.onnx.export(conv, y, path, export_params=True, training=False) # traning should take TrainingModel, not bool ``` they get ``` Traceback (most recent call last): File "bisect_m1_error.py", line 63, in <module> main() File "bisect_m1_error.py", line 59, in main reveal_error() File "bisect_m1_error.py", line 32, in reveal_error torch.onnx.export(conv, y, cpu_model_path, export_params=True, training=False) File "<@beartype(torch.onnx.utils.export) at 0x1281f5a60>", line 136, in export File "pytorch/venv/lib/python3.9/site-packages/beartype/_decor/_error/errormain.py", line 301, in raise_pep_call_exception raise exception_cls( # type: ignore[misc] beartype.roar.BeartypeCallHintParamViolation: @beartyped export() parameter training=False violates type hint <class 'torch._C._onnx.TrainingMode'>, as False not instance of <protocol "torch._C._onnx.TrainingMode">. ``` when `TORCH_ONNX_EXPERIMENTAL_RUNTIME_TYPE_CHECK` is not set and `beartype` is installed, a warning message is emitted. ``` >>> torch.onnx.export("foo", "bar", "f") <stdin>:1: CallHintViolationWarning: Traceback (most recent call last): File "/home/justinchu/dev/pytorch/torch/onnx/_internal/_beartype.py", line 54, in _coerce_beartype_exceptions_to_warnings return beartyped(*args, **kwargs) File "<@beartype(torch.onnx.utils.export) at 0x7f1d4ab35280>", line 39, in export File "/home/justinchu/anaconda3/envs/pytorch/lib/python3.9/site-packages/beartype/_decor/_error/errormain.py", line 301, in raise_pep_call_exception raise exception_cls( # type: ignore[misc] beartype.roar.BeartypeCallHintParamViolation: @beartyped export() parameter model='foo' violates type hint typing.Union[torch.nn.modules.module.Module, torch.jit._script.ScriptModule, torch.jit.ScriptFunction], as 'foo' not <protocol "torch.jit.ScriptFunction">, <protocol "torch.nn.modules.module.Module">, or <protocol "torch.jit._script.ScriptModule">. Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/home/justinchu/dev/pytorch/torch/onnx/_internal/_beartype.py", line 63, in _coerce_beartype_exceptions_to_warnings return func(*args, **kwargs) File "/home/justinchu/dev/pytorch/torch/onnx/utils.py", line 482, in export _export( File "/home/justinchu/dev/pytorch/torch/onnx/utils.py", line 1422, in _export with exporter_context(model, training, verbose): File "/home/justinchu/anaconda3/envs/pytorch/lib/python3.9/contextlib.py", line 119, in __enter__ return next(self.gen) File "/home/justinchu/dev/pytorch/torch/onnx/utils.py", line 177, in exporter_context with select_model_mode_for_export( File "/home/justinchu/anaconda3/envs/pytorch/lib/python3.9/contextlib.py", line 119, in __enter__ return next(self.gen) File "/home/justinchu/dev/pytorch/torch/onnx/utils.py", line 95, in select_model_mode_for_export originally_training = model.training AttributeError: 'str' object has no attribute 'training' ``` We see the error is caught right when the type mismatch happens, improving from what otherwise would become `AttributeError: 'str' object has no attribute 'training'` Pull Request resolved: https://github.com/pytorch/pytorch/pull/83673 Approved by: https://github.com/BowenBao