[GitHub] piiswrong commented on a change in pull request #11482: make gluon rnn layers hybrid blocks

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

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_r199273377
 
 

 ##########
 File path: python/mxnet/gluon/rnn/rnn_layer.py
 ##########
 @@ -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)):
             states = [states]
-        for state, info in zip(states, self.state_info(batch_size)):
-            if state.shape != info['shape']:
-                raise ValueError(
-                    "Invalid recurrent state shape. Expecting %s, got %s."%(
-                        str(info['shape']), str(state.shape)))
-        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()
-        if inputs.context.device_type == 'gpu' or \
-           self._mode in ['lstm', 'gru'] and not self._dropout:
-            out = self._forward_kernel(inputs, states)
-        else:
-            out = self._forward(inputs, states)
+        if F is ndarray:
+            for state, info in zip(states, self.state_info(batch_size)):
+                if state.shape != info['shape']:
+                    raise ValueError(
+                        "Invalid recurrent state shape. Expecting %s, got %s."%(
+                            str(info['shape']), str(state.shape)))
+        out = self._forward_kernel(F, inputs, states, **kwargs)
 
         # out is (output, state)
         return out[0] if skip_states else out
 
-    def _forward(self, inputs, states):
-        """forward using gluon cell"""
-        ns = len(states)
-        axis = self._layout.find('T')
-        states = sum(zip(*((j for j in i) for i in states)), ())
-        outputs, states = self._unfused.unroll(
-            inputs.shape[axis], inputs, states,
-            layout=self._layout, merge_outputs=True)
-        new_states = []
-        for i in range(ns):
-            state = ndarray.concat(*(j.reshape((1,)+j.shape) for j in states[i::ns]), dim=0)
-            new_states.append(state)
-
-        return outputs, new_states
-
-    def _forward_kernel(self, inputs, states):
+    def __call__(self, inputs, *states):
 
 Review comment:
   implement infer_shape instead?

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