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| """ONNX exporter exceptions.""" | |
| from __future__ import annotations | |
| import textwrap | |
| from typing import Optional | |
| from torch import _C | |
| from torch.onnx import _constants | |
| from torch.onnx._internal import diagnostics | |
| __all__ = [ | |
| "OnnxExporterError", | |
| "OnnxExporterWarning", | |
| "CheckerError", | |
| "SymbolicValueError", | |
| "UnsupportedOperatorError", | |
| ] | |
| class OnnxExporterWarning(UserWarning): | |
| """Base class for all warnings in the ONNX exporter.""" | |
| pass | |
| class OnnxExporterError(RuntimeError): | |
| """Errors raised by the ONNX exporter.""" | |
| pass | |
| class CheckerError(OnnxExporterError): | |
| """Raised when ONNX checker detects an invalid model.""" | |
| pass | |
| class UnsupportedOperatorError(OnnxExporterError): | |
| """Raised when an operator is unsupported by the exporter.""" | |
| def __init__(self, name: str, version: int, supported_version: Optional[int]): | |
| if supported_version is not None: | |
| diagnostic_rule: diagnostics.infra.Rule = ( | |
| diagnostics.rules.operator_supported_in_newer_opset_version | |
| ) | |
| msg = diagnostic_rule.format_message(name, version, supported_version) | |
| diagnostics.diagnose(diagnostic_rule, diagnostics.levels.ERROR, msg) | |
| else: | |
| if name.startswith(("aten::", "prim::", "quantized::")): | |
| diagnostic_rule = diagnostics.rules.missing_standard_symbolic_function | |
| msg = diagnostic_rule.format_message( | |
| name, version, _constants.PYTORCH_GITHUB_ISSUES_URL | |
| ) | |
| diagnostics.diagnose(diagnostic_rule, diagnostics.levels.ERROR, msg) | |
| else: | |
| diagnostic_rule = diagnostics.rules.missing_custom_symbolic_function | |
| msg = diagnostic_rule.format_message(name) | |
| diagnostics.diagnose(diagnostic_rule, diagnostics.levels.ERROR, msg) | |
| super().__init__(msg) | |
| class SymbolicValueError(OnnxExporterError): | |
| """Errors around TorchScript values and nodes.""" | |
| def __init__(self, msg: str, value: _C.Value): | |
| message = ( | |
| f"{msg} [Caused by the value '{value}' (type '{value.type()}') in the " | |
| f"TorchScript graph. The containing node has kind '{value.node().kind()}'.] " | |
| ) | |
| code_location = value.node().sourceRange() | |
| if code_location: | |
| message += f"\n (node defined in {code_location})" | |
| try: | |
| # Add its input and output to the message. | |
| message += "\n\n" | |
| message += textwrap.indent( | |
| ( | |
| "Inputs:\n" | |
| + ( | |
| "\n".join( | |
| f" #{i}: {input_} (type '{input_.type()}')" | |
| for i, input_ in enumerate(value.node().inputs()) | |
| ) | |
| or " Empty" | |
| ) | |
| + "\n" | |
| + "Outputs:\n" | |
| + ( | |
| "\n".join( | |
| f" #{i}: {output} (type '{output.type()}')" | |
| for i, output in enumerate(value.node().outputs()) | |
| ) | |
| or " Empty" | |
| ) | |
| ), | |
| " ", | |
| ) | |
| except AttributeError: | |
| message += ( | |
| " Failed to obtain its input and output for debugging. " | |
| "Please refer to the TorchScript graph for debugging information." | |
| ) | |
| super().__init__(message) | |