I don’t know what the issue would be, but I wanted to point out you don’t have to use real data for problems like this. You can do something like
zeros = torch.zeros((your_input_shape))
print(model.forward(zeros).sum())
print(model.forward(zeros).sum())
print(model.forward(zeros).sum())

If there is nondeterminacy, it should show up.
You could probably even go into your model’s forward definition, and do print(out.sum()) at multiple points, to see where the nondeterminacy is showing up. But I guess you already found out it has something to do with the dropout.