piiswrong commented on a change in pull request #11482: make gluon rnn layers
hybrid blocks
URL: https://github.com/apache/incubator-mxnet/pull/11482#discussion_r199273220
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File path: python/mxnet/gluon/rnn/rnn_layer.py
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@@ -173,67 +177,63 @@ def begin_state(self, batch_size=0, func=ndarray.zeros,
**kwargs):
states.append(func(name='%sh0_%d'%(self.prefix, i), **info))
return states
- def forward(self, inputs, states=None):
- batch_size = inputs.shape[self._layout.find('N')]
+ def hybrid_forward(self, F, inputs, states=None, **kwargs):
+ if F is ndarray:
+ batch_size = inputs.shape[self._layout.find('N')]
+ if self._input_size == 0:
+ for i in range(self._dir):
+ self.i2h_weight[i].shape = (self._gates*self._hidden_size,
inputs.shape[2])
+ self.i2h_weight[i]._finish_deferred_init()
skip_states = states is None
if skip_states:
- states = self.begin_state(batch_size, ctx=inputs.context)
- if isinstance(states, ndarray.NDArray):
+ if F is ndarray:
+ states = self.begin_state(batch_size, ctx=inputs.context)
+ else:
+ states = self.begin_state(0, func=symbol.zeros)
+ if isinstance(states, (ndarray.NDArray, symbol.Symbol)):
Review comment:
(ndarray.NDArray, symbol.Symbol) -> tensor_types
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