refine code, remove duplicate code between layer and with_extra_parent_layer

demo/seqToseq/api_train_v2.py

 import sys
 import paddle.v2 as paddle
-import paddle.v2.layer.beam_search as beam_search
+import paddle.v2.layers.beam_search as beam_search
 
 
 def seqToseq_net(source_dict_dim, target_dict_dim, is_generating):
@@ -138,7 +138,7 @@ def main():
     source_dict_dim = target_dict_dim = dict_size
 
     # define network topology
-    cost = seqToseq_net(source_dict_dim, target_dict_dim)
+    cost = seqToseq_net(source_dict_dim, target_dict_dim, False)
     parameters = paddle.parameters.create(cost)
 
     # define optimize method and trainer

python/paddle/v2/config_base.py

@@ -98,10 +98,8 @@ class Layer(object):
             kwargs[layer_name] = v1_layer
 
         # parse myself.
-        ret_val = self.to_proto_impl(**kwargs)
-
-        if self.context_name() is not None and \
-                        self.context_name() not in context:
+        ret_val = self.to_proto_impl(context=context, **kwargs)
+        if self.context_name() is not None and self.context_name() not in context:
             context[self.context_name()] = ret_val
 
         # parse children.
@@ -124,7 +122,7 @@ class Layer(object):
         else:
             return context[self.name]
 
-    def to_proto_impl(self, **kwargs):
+    def to_proto_impl(self, context=None, **kwargs):
         raise NotImplementedError()
 
     def context_name(self):
@@ -188,7 +186,7 @@ def __convert_to_v2__(method_name,
         if wrapper is not None:
             __init__ = wrapper(__init__)
 
-        def to_proto_impl(self, **kwargs):
+        def to_proto_impl(self, context=None, **kwargs):
             args = dict()
             for each in kwargs:
                 args[each] = kwargs[each]

python/paddle/v2/layer.py

@@ -98,7 +98,7 @@ class DataLayerV2(Layer):
 
         super(DataLayerV2, self).__init__(name=name, parent_layers=dict())
 
-    def to_proto_impl(self, **kwargs):
+    def to_proto_impl(self, context=None, **kwargs):
         args = dict()
         args['size'] = self.type.dim
         for each in kwargs:
@@ -142,46 +142,11 @@ class WithExtraParent(Layer):
             else:
                 return context[self.name]
 
-        # parse parents
-        kwargs = dict()
+        # parse extra_parent
         for p in self.__extra_parent__:
             p.to_proto(context=context)
 
-        for layer_name in self.__parent_layers__:
-            if not isinstance(self.__parent_layers__[layer_name],
-                              collections.Sequence):
-                v1_layer = self.__parent_layers__[layer_name].to_proto(
-                    context=context)
-            else:
-                v1_layer = map(lambda x: x.to_proto(context=context),
-                               self.__parent_layers__[layer_name])
-            kwargs[layer_name] = v1_layer
-
-        # parse self
-        if self.context_name() is None:
-            return self.to_proto_impl(context=context, **kwargs)
-        elif self.context_name() not in context:
-            context[self.context_name()] = self.to_proto_impl(
-                context=context, **kwargs)
-
-        # parse children.
-        aaa = self.__children_layers__
-        for layer, pnames in self.__children_layers__:
-            drop = False
-
-            # child will only be parsed if all parents are in context.
-            for pname in pnames:
-                if pname not in context:
-                    drop = True
-                    break
-            if drop:
-                continue
-            layer.to_proto(context=context)
-
-        if self.use_context_name():
-            return context[self.context_name()]
-        else:
-            return context[self.name]
+        return super(WithExtraParent, self).to_proto(context=context)
 
 
 class MemoryV2(WithExtraParent):
@@ -307,7 +272,7 @@ class MixedLayerV2(Layer):
     def __exit__(self, *args, **kwargs):
         self.finalized = True
 
-    def to_proto_impl(self, **kwargs):
+    def to_proto_impl(self, context=None, **kwargs):
         args = dict()
         for each in kwargs:
             args[each] = kwargs[each]
@@ -371,7 +336,7 @@ class RecurrentLayerOutput(Layer):
     def context_name(self):
         return self.__recurrent_name__ + ".end"
 
-    def to_proto_impl(self, **kwargs):
+    def to_proto_impl(self, context=None, **kwargs):
         for l in self.__parents__:
             RecurrentLayerGroupSetOutLink(l.name)
         RecurrentLayerGroupEnd(name=self.__recurrent_name__)

python/paddle/v2/layers/beam_search.py

@@ -48,7 +48,7 @@ class RecurrentLayerGroupSetGeneratorV2(Layer):
         super(RecurrentLayerGroupSetGeneratorV2, self).__init__(
             name=eos_name, parent_layers={})
 
-    def to_proto_impl(self, **kwargs):
+    def to_proto_impl(self, context=None, **kwargs):
         RecurrentLayerGroupSetGenerator(
             Generator(
                 eos_layer_name=self.eos_name,