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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",
"CallHintViolationWarning",
"CheckerError",
"UnsupportedOperatorError",
"SymbolicValueError",
]
class OnnxExporterWarning(UserWarning):
"""Base class for all warnings in the ONNX exporter."""
pass
class CallHintViolationWarning(OnnxExporterWarning):
"""Warning raised when a type hint is violated during a function call."""
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,
domain: str,
op_name: str,
version: int,
supported_version: Optional[int],
):
if domain in {"", "aten", "prim", "quantized"}:
msg = f"Exporting the operator '{domain}::{op_name}' to ONNX opset version {version} is not supported. "
if supported_version is not None:
msg += (
f"Support for this operator was added in version {supported_version}, "
"try exporting with this version."
)
diagnostics.context.diagnose(
diagnostics.rules.operator_supported_in_newer_opset_version,
diagnostics.levels.ERROR,
message_args=(
f"{domain}::{op_name}",
version,
supported_version,
),
)
else:
msg += "Please feel free to request support or submit a pull request on PyTorch GitHub: "
msg += _constants.PYTORCH_GITHUB_ISSUES_URL
diagnostics.context.diagnose(
diagnostics.rules.missing_standard_symbolic_function,
diagnostics.levels.ERROR,
message_args=(
f"{domain}::{op_name}",
version,
_constants.PYTORCH_GITHUB_ISSUES_URL,
),
)
else:
msg = (
f"ONNX export failed on an operator with unrecognized namespace '{domain}::{op_name}'. "
"If you are trying to export a custom operator, make sure you registered "
"it with the right domain and version."
)
diagnostics.context.diagnose(
diagnostics.rules.missing_custom_symbolic_function,
diagnostics.levels.ERROR,
message_args=(f"{domain}::{op_name}",),
)
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)
|
