Hi I use tensor.norm somewhere in my code which works fine for all values of p (including fractional ones e.g. p=1.5) but runs into problems with p<1. With anomaly detection on, I get this error:


RuntimeError                              Traceback (most recent call last)
 in 
11             # ===================backward====================
12             optimizer.zero_grad()
—> 13             loss.backward()
14             optimizer.step()
15         # ===================log========================
c:\users\windows.conda\envs\normnet\lib\site-packages\torch\tensor.py in backward(self, gradient, retain_graph, create_graph)
183                 products. Defaults to False.
184         “”"
–> 185         torch.autograd.backward(self, gradient, retain_graph, create_graph)
186
187     def register_hook(self, hook):
c:\users\windows.conda\envs\normnet\lib\site-packages\torch\autograd_init_.py in backward(tensors, grad_tensors, retain_graph, create_graph, grad_variables)
125     Variable._execution_engine.run_backward(
126         tensors, grad_tensors, retain_graph, create_graph,
–> 127         allow_unreachable=True)  # allow_unreachable flag
128
129
RuntimeError: Function ‘NormBackward1’ returned nan values in its 0th output.

any idea how to fix this?