15.12.2020 · Are you seeing the illegal memory access using the “bad” GPU or another one? Which GPU are you using at the moment? I assume you haven’t changed anything in the tutorial and are just running the script as it is?
08.01.2018 · Starting with PyTorch 0.4.1 there is the detect_anomaly context manager, which automatically inserts assertions equivalent to assert not torch.isnan(grad).any() between all steps of backward propagation.
Context-manager that enable anomaly detection for the autograd engine. ... Any backward computation that generate “nan” value will raise an error. Warning.
01.12.2020 · I meet with Nan loss issue in my training, so now I’m trying to use anomaly detection in autograd for debugging. I found 2 classes, torch.autograd.detect_anomaly and torch.autograd.set_detect_anomaly. But I’m getting dif…
23.07.2020 · After Further debugging, I find that add a gradient hook to vs and modify the gradient to replace the nan with 0 does solve the problem mentioned above. That is to say, the nan gradient from torch.std() is replaced with 0.. However, I then found there is another nan bug in this code. And since I’m using torch.autograd.detect_anomaly() to find out which line is the culprit, …
23.04.2020 · I have noticed that there are NaNs in the gradients of my model. This is confirmed by torch.autograd.detect_anomaly(): RuntimeError: Function 'DivBackward0' returned nan values in its 1th output. I do not know which division causes the problem since DivBackward0 does not seem to be a unique name. However, I have added asserts to all divisions (like assert …
17.12.2021 · Hello. I am training a CNN network with cross_entropy loss. When I train the network with debugging tool wrapped up “with torch.autograd.set_detect_anomaly(True):”