[ThWHSrSSKrSSKrSS/r"SS5r"SS5rg)zAutograd anomaly mode.Ndetect_anomalyset_detect_anomalycB\rSrSrSrS S SjjrS SjrS\SS4SjrS r g) r a 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 "", line 1, in 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 "", 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 out = run_fn(inp) File "tmp.py", line 44, in run_fn out = MyFunc.apply(a) Traceback (most recent call last): File "", line 4, in 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 "", line 8, in backward RuntimeError: Some error in backward returnNc[R"5UlXl[R"5Ul[ R"SSS9 g)NzqAnomaly Detection has been enabled. This mode will increase the runtime and should only be enabled for debugging.) stacklevel)torchis_anomaly_enabledprev check_nanis_anomaly_check_nan_enabledprev_check_nanwarningswarn)selfrs S/var/www/auris/envauris/lib/python3.13/site-packages/torch/autograd/anomaly_mode.py__init__detect_anomaly.__init__Ms>,,. "#@@B  8  cF[R"SUR5 g)NT)r set_anomaly_enabledrrs r __enter__detect_anomaly.__enter__Xs !!$7rargscZ[R"URUR5 gNr rr rrrs r__exit__detect_anomaly.__exit__[ !!$))T-@-@Ar)rr rTrN) __name__ __module__ __qualname____firstlineno____doc__rrobjectr"__static_attributes__rrrr s(?B  8BfBBrcN\rSrSrSrS S\S\SS4SjjrS SjrS \SS4S jr S r g)r_a,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" moderrNc[R"5Ul[R"5Ul[R "X5 gr)r r r rrr)rr1rs rrset_detect_anomaly.__init__ps1,,. #@@B !!$2rcgrr.rs rrset_detect_anomaly.__enter__us rrcZ[R"URUR5 grr r!s rr"set_detect_anomaly.__exit__xr$r)r rr%r&) r'r(r)r*r+boolrrr,r"r-r.rrrr_s< 3T3d3d3  BfBBr)r+rr __all__rrr.rrr:s6  1 2QBQBhBBr