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Source code for torch.autograd.anomaly_mode

import torch
import warnings

from typing import Any

[docs]class detect_anomaly(object): 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. - Any backward computation that generate "nan" value will raise an error. .. warning:: This mode should be enabled only for debugging as the different tests will slow down your program execution. Example: >>> 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) -> None: self.prev = torch.is_anomaly_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: torch.set_anomaly_enabled(True) def __exit__(self, *args: Any) -> None: torch.set_anomaly_enabled(self.prev)
[docs]class set_detect_anomaly(object): 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. Arguments: mode (bool): Flag whether to enable anomaly detection (``True``), or disable (``False``). """ def __init__(self, mode: bool) -> None: self.prev = torch.is_anomaly_enabled() torch.set_anomaly_enabled(mode) def __enter__(self) -> None: pass def __exit__(self, *args: Any) -> None: torch.set_anomaly_enabled(self.prev)

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