336 lines
10 KiB
Python
336 lines
10 KiB
Python
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import os
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import textwrap
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from enum import auto, Enum
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from traceback import extract_stack, format_exc, format_list, StackSummary
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from typing import cast, NoReturn, Optional
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import torch._guards
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from . import config
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from .utils import counters
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def exportdb_error_message(case_name):
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return (
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"For more information about this error, see: "
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+ "https://pytorch.org/docs/main/generated/exportdb/index.html#"
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+ case_name.replace("_", "-")
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)
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import logging
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log = logging.getLogger(__name__)
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graph_breaks_log = torch._logging.getArtifactLogger(__name__, "graph_breaks")
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class TorchDynamoException(RuntimeError):
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pass
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class InternalTorchDynamoError(TorchDynamoException):
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pass
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class RestartAnalysis(TorchDynamoException):
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pass
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class SpeculationRestartAnalysis(RestartAnalysis):
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pass
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class UnspecializeRestartAnalysis(RestartAnalysis):
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pass
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class SkipFrame(TorchDynamoException):
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pass
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class TorchRuntimeError(TorchDynamoException):
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pass
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class InvalidBackend(TorchDynamoException):
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def __init__(self, name):
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super().__init__(
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f"Invalid backend: {name!r}, see `torch._dynamo.list_backends()` for available backends."
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)
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class ResetRequired(TorchDynamoException):
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def __init__(self):
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super().__init__(
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textwrap.dedent(
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"""
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Must call `torch._dynamo.reset()` before changing backends. Detected two calls to
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`torch.compile()` with a different backend compiler arguments.
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"""
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)
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)
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class BackendCompilerFailed(TorchDynamoException):
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def __init__(self, backend_fn, inner_exception):
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self.backend_name = getattr(backend_fn, "__name__", "?")
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self.inner_exception = inner_exception
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msg = f"backend={self.backend_name!r} raised:\n{type(inner_exception).__name__}: {inner_exception}"
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super().__init__(msg)
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class Unsupported(TorchDynamoException):
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def __init__(self, msg):
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super().__init__(msg)
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self.real_stack = torch._guards.TracingContext.extract_stack()
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self.msg = msg
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self.category: Optional[str] = None
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self.add_to_stats()
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def remove_from_stats(self):
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assert self.category is not None
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counters[self.category][self.msg] -= 1
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if counters[self.category][self.msg] <= 0:
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del counters[self.category][self.msg]
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def add_to_stats(self, category="unimplemented"):
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self.category = category
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counters[category][self.msg] += 1
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class RecompileError(TorchDynamoException):
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pass
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class ArgsMismatchError(Unsupported):
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def __init__(self, msg):
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super().__init__(msg)
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class AttributeMutationError(Unsupported):
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def __init__(self, msg):
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super().__init__(msg)
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class CondOpArgsMismatchError(ArgsMismatchError):
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"""
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Internal error from cond() due to arguments mismatch.
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"""
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def __init__(self, msg):
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super().__init__(msg)
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class UserErrorType(Enum):
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DYNAMIC_CONTROL_FLOW = auto()
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ANTI_PATTERN = auto()
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STANDARD_LIBRARY = auto()
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CONSTRAINT_VIOLATION = auto()
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DYNAMIC_DIM = auto()
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INVALID_INPUT = auto()
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INVALID_OUTPUT = auto()
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class UserError(Unsupported):
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def __init__(self, error_type: UserErrorType, msg, case_name=None):
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"""
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Type of errors that would be valid in Eager, but not supported in TorchDynamo.
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The error message should tell user about next actions.
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error_type: Type of user error
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msg: Actionable error message
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case_name: (Optional) Unique name (snake case) for the usage example in exportdb.
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"""
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if case_name is not None:
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assert isinstance(case_name, str)
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if msg.endswith("."):
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msg += " "
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else:
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msg += "\n"
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msg += exportdb_error_message(case_name)
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super().__init__(msg)
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self.error_type = error_type
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self.message = msg
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class UncapturedHigherOrderOpError(TorchDynamoException):
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pass
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class IncorrectUsage(Exception):
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pass
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# These exceptions are ok to fallback to eager/graph_break.
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exceptions_allowed_to_be_fallback = (
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torch._subclasses.fake_tensor.DataDependentOutputException,
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torch._subclasses.fake_tensor.DynamicOutputShapeException,
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torch._subclasses.fake_tensor.UnsupportedOperatorException,
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torch._subclasses.fake_tensor.UnsupportedFakeTensorException,
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)
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def unimplemented_with_warning(e: Exception, code, msg: str) -> NoReturn:
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# This function calls unimplemented internally and eventually graph breaks
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# or falls to eager. unimplemented itself does not print any user warnings,
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# i.e., its very silent. This helper function is intended when an error is
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# encountered in the torch.compile stack which is worth showing as warning
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# to the user. For example, if AOT Autograd backend fails with a fake tensor
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# exception, its ok to fallback to eager but not silently. Here, we can use
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# this function to log the message and the stack trace.
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graph_break_msg = format_error_msg_verbose(e, code)
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graph_breaks_log.debug("%s", graph_break_msg)
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log.warning(msg)
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raise unimplemented(msg) from e
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def unimplemented(msg: str) -> NoReturn:
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assert msg != os.environ.get("BREAK", False)
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raise Unsupported(msg)
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def warning(msg: str) -> None:
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counters["warnings"][msg] += 1
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assert msg != os.environ.get("BREAK", False)
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# KeyError has special handling for its args
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# see https://github.com/python/cpython/blob/3.11/Objects/exceptions.c#L2534 for details
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class KeyErrorMsg:
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def __init__(self, value):
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self.value = value
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def __str__(self):
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return str(self.value)
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def __repr__(self) -> str:
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return self.__str__()
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def augment_exc_message(exc: Exception, msg: str = "\n", export: bool = False) -> None:
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import traceback
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exc.innermost_user_frame_summary = None # type: ignore[attr-defined]
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real_stack = get_real_stack(exc)
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if real_stack is not None and len(real_stack) > 0:
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exc.innermost_user_frame_summary = real_stack[-1] # type: ignore[attr-defined]
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msg += f"\nfrom user code:\n {''.join(traceback.format_list(real_stack))}"
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if config.replay_record_enabled and hasattr(exc, "record_filename"):
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msg += f"\nLast frame execution written to {exc.record_filename}. To run only this frame while debugging, run\
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torch._dynamo.replay('{exc.record_filename}').\n"
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if not config.verbose and hasattr(exc, "real_stack"):
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msg += '\nSet TORCH_LOGS="+dynamo" and TORCHDYNAMO_VERBOSE=1 for more information\n'
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if hasattr(exc, "inner_exception") and hasattr(
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exc.inner_exception, "minifier_path"
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):
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if hasattr(exc.inner_exception, "buck_command"):
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msg += (
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f"\nMinifier script written to {exc.inner_exception.minifier_path}. Run "
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f"this buck command to find the smallest traced graph "
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f"which reproduces this error: {exc.inner_exception.buck_command}\n"
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)
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else:
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msg += (
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f"\nMinifier script written to {exc.inner_exception.minifier_path}. Run "
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"this script to find the smallest traced graph which reproduces this error.\n"
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)
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if not config.suppress_errors and not export:
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msg += (
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"\n\n"
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"You can suppress this exception and fall back to eager by setting:\n"
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" import torch._dynamo\n"
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" torch._dynamo.config.suppress_errors = True\n"
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)
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old_msg = "" if len(exc.args) == 0 else str(exc.args[0])
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if isinstance(exc, KeyError):
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exc.args = (KeyErrorMsg(old_msg + msg),) + exc.args[1:]
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else:
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new_msg = old_msg + msg
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exc.args = (new_msg,) + exc.args[1:]
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def get_real_stack(exc: Exception, frame=None) -> Optional[StackSummary]:
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real_stack = getattr(exc, "real_stack", None)
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if real_stack is None:
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return None
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# NB: it's possible for real_stack to be []; we still attempt to
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# report a stack anyway because the stack_above_dynamo may still
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# be useful for debugging
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stack_above_dynamo = []
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if frame is not None:
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# NB: frame is PyInterpreterFrame on Python 3.11 and later,
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# not a TRUE frame object. You can't actually feed it
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# to traceback because it doesn't have enough information.
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# To solve this problem, we technically should just materialize
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# the frame, the same way _PyFrame_GetFrameObject would do
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# (but we cannot actually do this, because this populates
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# frame_obj field, which default eval frame doesn't like).
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#
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# Fortunately, in this case, we can hack it: there's no need
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# to actually use the truly top frame, we can just extract
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# from where we are right now and rely on filter_stack to
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# get rid of all the dynamo frames. For ease of testing
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# we apply this behavior to ALL Python versions
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stack_above_dynamo = filter_stack(extract_stack())
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return cast(StackSummary, stack_above_dynamo + real_stack)
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# filter out all frames after entering dynamo
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def filter_stack(stack):
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user_stack = []
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for frame in stack:
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if "convert_frame" in frame.filename:
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break
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if "eval_frame" in frame.filename or "torch._dynamo.optimize(" in frame.line:
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continue
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user_stack.append(frame)
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return user_stack
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def format_error_msg_verbose(
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exc: Exception, code, record_filename=None, frame=None
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) -> str:
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msg = (
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f"WON'T CONVERT {code.co_name} {code.co_filename} line {code.co_firstlineno}\n"
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)
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msg += "=" * 10 + " TorchDynamo Stack Trace " + "=" * 10 + "\n"
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msg += format_exc()
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real_stack = get_real_stack(exc, frame)
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if real_stack is not None:
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msg += (
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"\n"
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+ "=" * 10
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+ " The above exception occurred while processing the following code "
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+ "=" * 10
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+ "\n\n"
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)
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msg += "".join(format_list(real_stack))
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msg += "\n"
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msg += "=" * 10
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return msg
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def format_error_msg(exc: Exception, code, record_filename=None, frame=None) -> str:
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msg = os.linesep * 2
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if config.verbose:
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msg = format_error_msg_verbose(exc, code, record_filename, frame)
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else:
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msg = f"WON'T CONVERT {code.co_name} {code.co_filename}\
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line {code.co_firstlineno} \ndue to: \n{format_exc()}"
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return msg
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