+ jJRt^RIt^RItRR.t!RR4t!RR4tR#)zAutograd anomaly mode.Ndetect_anomalyset_detect_anomalyc^a]tRt^ toRtR V3RlRlltV3RlRltV3RlRltRtVt R #) ra Context-manager that enables 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 generates "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 c<V^8dQhRR/#returnN)format __classdict__s"\/home/luca/screen-checking/.venv/lib/python3.14/site-packages/torch/autograd/anomaly_mode.py __annotate__detect_anomaly.__annotate__Os   $  c\P!4VnWn\P!4Vn\ P!R^R7R#)zqAnomaly Detection has been enabled. This mode will increase the runtime and should only be enabled for debugging.) stacklevelN)torchis_anomaly_enabledprev check_nanis_anomaly_check_nan_enabledprev_check_nanwarningswarn)selfrs&&r __init__detect_anomaly.__init__Os>,,. "#@@B  8  rc<V^8dQhRR/#rr )r r s"r r rZs8848rcH\P!RVP4R#)TN)rset_anomaly_enabledrrs&r __enter__detect_anomaly.__enter__Zs !!$7rc$<V^8dQhRS[RR/#rargsrNobject)r r s"r r r]BBfBBrc\\P!VPVP4R#Nrrrrrr%s&*r __exit__detect_anomaly.__exit__] !!$))T-@-@Ar)rrrNT __name__ __module__ __qualname____firstlineno____doc__rr!r-__static_attributes____classdictcell__r s@r rr s,@D   88BBrc^a]tRt^atoRtR V3RlRlltV3RlRltV3RlRltRtVt R #) ra,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" c*<V^8dQhRS[RS[RR/#)rmoderrN)bool)r r s"r r set_detect_anomaly.__annotate__rs"33T3d3d3rc\P!4Vn\P!4Vn\P !W4R#r*)rrrrrr)rr<rs&&&r rset_detect_anomaly.__init__rs1,,. #@@B !!$2rc<V^8dQhRR/#rr )r r s"r r r>ws  4 rcR#r*r r s&r r!set_detect_anomaly.__enter__ws rc$<V^8dQhRS[RR/#r$r&)r r s"r r r>zr(rc\\P!VPVP4R#r*r+r,s&*r r-set_detect_anomaly.__exit__zr/r)rrNr0r1r9s@r rras* 33   BBr)r6rr__all__rrr rr rHs6  1 2RBRBjBBr