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"""ONNX exporter exceptions.""" |
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from __future__ import annotations |
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import textwrap |
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from typing import Optional |
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from torch import _C |
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from torch.onnx import _constants |
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from torch.onnx._internal import diagnostics |
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__all__ = [ |
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"OnnxExporterError", |
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"OnnxExporterWarning", |
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"CallHintViolationWarning", |
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"CheckerError", |
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"UnsupportedOperatorError", |
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"SymbolicValueError", |
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] |
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class OnnxExporterWarning(UserWarning): |
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"""Base class for all warnings in the ONNX exporter.""" |
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pass |
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class CallHintViolationWarning(OnnxExporterWarning): |
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"""Warning raised when a type hint is violated during a function call.""" |
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pass |
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class OnnxExporterError(RuntimeError): |
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"""Errors raised by the ONNX exporter.""" |
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pass |
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class CheckerError(OnnxExporterError): |
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"""Raised when ONNX checker detects an invalid model.""" |
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pass |
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class UnsupportedOperatorError(OnnxExporterError): |
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"""Raised when an operator is unsupported by the exporter.""" |
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def __init__( |
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self, |
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domain: str, |
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op_name: str, |
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version: int, |
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supported_version: Optional[int], |
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): |
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if domain in {"", "aten", "prim", "quantized"}: |
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msg = f"Exporting the operator '{domain}::{op_name}' to ONNX opset version {version} is not supported. " |
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if supported_version is not None: |
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msg += ( |
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f"Support for this operator was added in version {supported_version}, " |
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"try exporting with this version." |
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) |
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diagnostics.context.diagnose( |
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diagnostics.rules.operator_supported_in_newer_opset_version, |
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diagnostics.levels.ERROR, |
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message_args=( |
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f"{domain}::{op_name}", |
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version, |
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supported_version, |
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), |
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) |
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else: |
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msg += "Please feel free to request support or submit a pull request on PyTorch GitHub: " |
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msg += _constants.PYTORCH_GITHUB_ISSUES_URL |
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diagnostics.context.diagnose( |
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diagnostics.rules.missing_standard_symbolic_function, |
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diagnostics.levels.ERROR, |
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message_args=( |
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f"{domain}::{op_name}", |
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version, |
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_constants.PYTORCH_GITHUB_ISSUES_URL, |
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), |
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) |
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else: |
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msg = ( |
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f"ONNX export failed on an operator with unrecognized namespace '{domain}::{op_name}'. " |
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"If you are trying to export a custom operator, make sure you registered " |
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"it with the right domain and version." |
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) |
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diagnostics.context.diagnose( |
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diagnostics.rules.missing_custom_symbolic_function, |
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diagnostics.levels.ERROR, |
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message_args=(f"{domain}::{op_name}",), |
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) |
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super().__init__(msg) |
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class SymbolicValueError(OnnxExporterError): |
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"""Errors around TorchScript values and nodes.""" |
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def __init__(self, msg: str, value: _C.Value): |
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message = ( |
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f"{msg} [Caused by the value '{value}' (type '{value.type()}') in the " |
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f"TorchScript graph. The containing node has kind '{value.node().kind()}'.] " |
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) |
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code_location = value.node().sourceRange() |
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if code_location: |
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message += f"\n (node defined in {code_location})" |
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try: |
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message += "\n\n" |
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message += textwrap.indent( |
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( |
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"Inputs:\n" |
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+ ( |
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"\n".join( |
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f" #{i}: {input_} (type '{input_.type()}')" |
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for i, input_ in enumerate(value.node().inputs()) |
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) |
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or " Empty" |
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) |
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+ "\n" |
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+ "Outputs:\n" |
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+ ( |
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"\n".join( |
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f" #{i}: {output} (type '{output.type()}')" |
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for i, output in enumerate(value.node().outputs()) |
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) |
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or " Empty" |
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) |
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), |
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" ", |
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) |
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except AttributeError: |
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message += ( |
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" Failed to obtain its input and output for debugging. " |
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"Please refer to the TorchScript graph for debugging information." |
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) |
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super().__init__(message) |
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