[GitHub] John-Boik opened a new issue #8495: test_cross_device_autograd fails

[GitBox <gi...@apache.org>]

John-Boik opened a new issue #8495: test_cross_device_autograd fails
URL: https://github.com/apache/incubator-mxnet/issues/8495
 
 
   A snippet of code to test auto-differentiation across devices fails on my machine and I don't understand why.  The code is copied from the test_cross_device_autograd function in [test_operator_gpu.py](test_operator_gpu.py).
    
   The following fails:
   ```
   from mxnet import nd, autograd
   x = nd.random_uniform(shape=(10,))
   x.attach_grad()
   with autograd.record():        
       y = nd.tanh(x)
       y = y.copyto(mx.gpu(0))
       #y.attach_grad()
       y.backward()
   ```
   The error message is:
   `src/ndarray/autograd.cc:237: Check failed: !i.entry_.is_none() Cannot differentiate node because it is not in a computational graph. You need to set is_recording to true or use autograd.record() to save computational graphs for backward. If you want to differentiate the same graph twice, you need to pass retain_graph=True to backward.`
   However, if I uncomment the y.attach_grad() line, then there is no error.
   I'm using mxnet v.11.0, python3, and ububtu 16.04.  Can anyone explain why an extra call to attach_grad() is required, and is this expected behavior now (is the test code outdated)? 

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