detect_anomaly

class tensorplay.autograd.anomaly_mode.detect_anomaly(check_nan=True)[source]

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

>>> import tensorplay
>>> from tensorplay 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 = tensorplay.rand(10, 10, requires_grad=True)
>>> out = run_fn(inp)
>>> out.backward()
    Traceback (most recent call last):
      File "<stdin>", line 1, in <module>
        out.backward()
    RuntimeError: Some error in backward
>>> with autograd.detect_anomaly():
...     inp = tensorplay.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>
    RuntimeError: Some error in backward