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Source code for torch.autograd.anomaly_mode

# mypy: allow-untyped-defs
r"""Autograd anomaly mode."""
import warnings

import torch


__all__ = ["detect_anomaly", "set_detect_anomaly"]


[docs]class detect_anomaly: r"""Context-manager that enable anomaly detection for the autograd engine. This does two things: - Running the forward pass with detection enabled will allow the backward pass to print the traceback of the forward operation that created the failing backward function. - If ``check_nan`` is ``True``, any backward computation that generate "nan" value will raise an error. Default ``True``. .. warning:: This mode should be enabled only for debugging as the different tests will slow down your program execution. Example: >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_ANOMALY) >>> import torch >>> from torch import autograd >>> class MyFunc(autograd.Function): ... @staticmethod ... def forward(ctx, inp): ... return inp.clone() ... @staticmethod ... def backward(ctx, gO): ... # Error during the backward pass ... raise RuntimeError("Some error in backward") ... return gO.clone() >>> def run_fn(a): ... out = MyFunc.apply(a) ... return out.sum() >>> inp = torch.rand(10, 10, requires_grad=True) >>> out = run_fn(inp) >>> out.backward() Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/your/pytorch/install/torch/_tensor.py", line 93, in backward torch.autograd.backward(self, gradient, retain_graph, create_graph) File "/your/pytorch/install/torch/autograd/__init__.py", line 90, in backward allow_unreachable=True) # allow_unreachable flag File "/your/pytorch/install/torch/autograd/function.py", line 76, in apply return self._forward_cls.backward(self, *args) File "<stdin>", line 8, in backward RuntimeError: Some error in backward >>> with autograd.detect_anomaly(): ... inp = torch.rand(10, 10, requires_grad=True) ... out = run_fn(inp) ... out.backward() Traceback of forward call that caused the error: File "tmp.py", line 53, in <module> out = run_fn(inp) File "tmp.py", line 44, in run_fn out = MyFunc.apply(a) Traceback (most recent call last): File "<stdin>", line 4, in <module> File "/your/pytorch/install/torch/_tensor.py", line 93, in backward torch.autograd.backward(self, gradient, retain_graph, create_graph) File "/your/pytorch/install/torch/autograd/__init__.py", line 90, in backward allow_unreachable=True) # allow_unreachable flag File "/your/pytorch/install/torch/autograd/function.py", line 76, in apply return self._forward_cls.backward(self, *args) File "<stdin>", line 8, in backward RuntimeError: Some error in backward """ def __init__(self, check_nan=True) -> None: # noqa: D107 self.prev = torch.is_anomaly_enabled() self.check_nan = check_nan self.prev_check_nan = torch.is_anomaly_check_nan_enabled() warnings.warn( "Anomaly Detection has been enabled. " "This mode will increase the runtime " "and should only be enabled for debugging.", stacklevel=2, ) def __enter__(self) -> None: # noqa: D105 torch.set_anomaly_enabled(True, self.check_nan) def __exit__(self, *args: object) -> None: # noqa: D105 torch.set_anomaly_enabled(self.prev, self.prev_check_nan)
[docs]class set_detect_anomaly: r"""Context-manager that sets the anomaly detection for the autograd engine on or off. ``set_detect_anomaly`` will enable or disable the autograd anomaly detection based on its argument :attr:`mode`. It can be used as a context-manager or as a function. See ``detect_anomaly`` above for details of the anomaly detection behaviour. Args: mode (bool): Flag whether to enable anomaly detection (``True``), or disable (``False``). check_nan (bool): Flag whether to raise an error when the backward generate "nan" """ def __init__(self, mode: bool, check_nan: bool = True) -> None: # noqa: D107 self.prev = torch.is_anomaly_enabled() self.prev_check_nan = torch.is_anomaly_check_nan_enabled() torch.set_anomaly_enabled(mode, check_nan) def __enter__(self) -> None: # noqa: D105 pass def __exit__(self, *args: object) -> None: # noqa: D105 torch.set_anomaly_enabled(self.prev, self.prev_check_nan)

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