[GitHub] MoritzMaxeiner commented on issue #8836: Backward shape inconsistent with custom HybridBlock and gluon.loss

[GitBox]

MoritzMaxeiner commented on issue #8836: Backward shape inconsistent with 
custom HybridBlock and gluon.loss
URL: 
https://github.com/apache/incubator-mxnet/issues/8836#issuecomment-347384351
 
 
   @reminisce I've removed the time unrolling, but if I remove either of the 
two cells, or the reshape operation, the issue won't arise, so I don't think I 
can reduce it any further.
   
   ```python
   import mxnet as mx
   
   class Test(mx.gluon.HybridBlock):
       def __init__(self, input_size, output_size, **kwargs):
           super(Test, self).__init__(**kwargs)
           self.input_size = input_size
           self.output_size = output_size
           self.hidden_unit_size = output_size*input_size
   
           self.num_cells = 2
           with self.name_scope():
               self.cell_a = mx.gluon.rnn.GRUCell(self.hidden_unit_size, 
input_size=input_size)
               self.cell_b = mx.gluon.rnn.GRUCell(self.hidden_unit_size, 
input_size=input_size)
   
       def hybrid_forward(self, F, inputs, states):
           prev_h = states[0]
           if F is mx.symbol:
               prev_h = F.split(prev_h, axis=0, num_outputs=self.num_cells, 
squeeze_axis=1)
   
           cell_a_next_h, _ = self.cell_a(inputs, [prev_h[0]])
   
           cell_b_next_h, _ = self.cell_b(prev_h[1], [prev_h[1]])
   
           b_output = cell_b_next_h.reshape(shape=(0, self.input_size, 
self.output_size))
   
           return cell_a_next_h, b_output, []
   
       def state_info(self, batch_size=0):
           return [{'shape': (self.num_cells, batch_size, 
self.hidden_unit_size), '__layout__': 'LNC'}]
   
       def begin_state(self, batch_size=0, func=mx.ndarray.zeros, **kwargs):
           states = []
           for i, info in enumerate(self.state_info(batch_size)):
               if info is not None:
                   info.update(kwargs)
               else:
                   info = kwargs
               states.append(func(name='%sh0_%d'%(self.prefix, i), **info))
           return states
   
   args_nof_examples = 1
   args_nof_batches = 1
   args_batch_size = 1
   
   args_input_size = 1
   args_output_size = 1
   
   data = mx.ndarray.zeros(shape=(args_nof_examples, args_input_size))
   labels = mx.ndarray.ones((args_nof_examples, args_input_size))
   gen = mx.io.NDArrayIter(data, labels, args_batch_size, 
last_batch_handle='discard')
   
   with mx.cpu(0) as context:
       model = Test(args_input_size, args_output_size)
       model.initialize(mx.init.Xavier(), ctx = context)
       model.hybridize()
   
       loss = mx.gluon.loss.SoftmaxCrossEntropyLoss()
   
       states = model.begin_state(args_batch_size)
       for batch in gen:
           with mx.autograd.record():
               a, b, _ = model(batch.data[0], states)
               L = loss(b, batch.label[0])
           L.backward()
   ```

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