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107 lines
3.5 KiB
107 lines
3.5 KiB
"""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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"CheckerError",
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"SymbolicValueError",
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"UnsupportedOperatorError",
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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 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__(self, name: str, version: int, supported_version: Optional[int]):
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if supported_version is not None:
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diagnostic_rule: diagnostics.infra.Rule = (
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diagnostics.rules.operator_supported_in_newer_opset_version
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)
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msg = diagnostic_rule.format_message(name, version, supported_version)
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diagnostics.diagnose(diagnostic_rule, diagnostics.levels.ERROR, msg)
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else:
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if name.startswith(("aten::", "prim::", "quantized::")):
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diagnostic_rule = diagnostics.rules.missing_standard_symbolic_function
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msg = diagnostic_rule.format_message(
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name, version, _constants.PYTORCH_GITHUB_ISSUES_URL
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)
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diagnostics.diagnose(diagnostic_rule, diagnostics.levels.ERROR, msg)
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else:
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diagnostic_rule = diagnostics.rules.missing_custom_symbolic_function
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msg = diagnostic_rule.format_message(name)
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diagnostics.diagnose(diagnostic_rule, diagnostics.levels.ERROR, msg)
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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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# Add its input and output to the message.
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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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