diff --git a/python/paddle/v2/config_base.py b/python/paddle/v2/config_base.py
index be3e39a06ef9d33cac6f65f47824da7685859b00..573539a30cc1d9af4737789ade9fee626f0a7358 100644
--- a/python/paddle/v2/config_base.py
+++ b/python/paddle/v2/config_base.py
@@ -22,7 +22,7 @@ class Layer(object):
     def __init__(self, name=None, size=None, parent_layers=None):
         assert isinstance(parent_layers, dict)
         self.name = name
-        self.size = size
+        self.__contex__ = {}
         self.__parent_layers__ = parent_layers
 
     def to_proto(self, context):
@@ -44,7 +44,7 @@ class Layer(object):
             return self.to_proto_impl(**kwargs)
         elif self.context_name() not in context:
             context[self.context_name()] = self.to_proto_impl(**kwargs)
-
+        self.__contex__ = context
         if self.use_context_name():
             return context[self.context_name()]
         else:
@@ -64,6 +64,9 @@ class Layer(object):
     def use_context_name(self):
         return False
 
+    def calcalted_size(self):
+        return self.__contex__[self.context_name()].size
+
 
 def __convert_to_v2__(method_name, parent_names, is_default_name=True):
     if is_default_name:
diff --git a/python/paddle/v2/layer.py b/python/paddle/v2/layer.py
index e24244a48c96dcc4f3a6a484182d243b19e7f57c..a97518ed52562f4856001314accf44ce5e1bc464 100644
--- a/python/paddle/v2/layer.py
+++ b/python/paddle/v2/layer.py
@@ -197,6 +197,10 @@ class MemoryV2(WithExtraParent):
                     val = locs[key]
                     if isinstance(val, RecurrentLayerInput):
                         begin_of_current_rnn.append(val)
+                    elif isinstance(val, collections.Sequence):
+                        for v in val:
+                            if isinstance(v, RecurrentLayerInput):
+                                begin_of_current_rnn.append(v)
 
                 if begin_of_current_rnn:
                     break
@@ -216,7 +220,13 @@ class MemoryV2(WithExtraParent):
 
         if self.__boot_layer_name__ is not None:
             args['boot_layer'] = context[self.__boot_layer_name__]
-        return conf_helps.memory(name=self.name, size=self.size, **args)
+
+        if callable(self.size):
+            real_size = self.size()
+        else:
+            real_size = self.size
+        args['size'] = real_size
+        return conf_helps.memory(name=self.name, **args)
 
     def context_name(self):
         return self.name + "#memory"
@@ -311,6 +321,12 @@ class MixedLayerV2(Layer):
             args[each] = kwargs[each]
         for each in self.__other_kwargs__:
             args[each] = self.__other_kwargs__[each]
+        size = args.get('size', None)
+        if callable(size):
+            real_size = size()
+        else:
+            real_size = size
+        args['size'] = real_size
         return getattr(conf_helps, self.__method_name__)(**args)
 
 
@@ -363,53 +379,15 @@ class RecurrentLayerOutput(Layer):
         RecurrentLayerGroupEnd(name=self.__recurrent_name__)
 
 
-@wrap_name_default()
-def recurrent_group(step, input, name=None):
-    if not isinstance(input, collections.Sequence):
-        input = [input]
-
-    # TODO(qiaolongfei) convert StaticInput to memory according to v2 recurrent_group
-    for i in xrange(len(input)):
-        cur_input = input[i]
-        if isinstance(cur_input, StaticInputV2):
-            input[i] = cur_input.input
-
-    actual_input = [
-        RecurrentLayerInput(
-            recurrent_name=name,
-            index=i,
-            parent_layers={'recurrent_inputs': input})
-        for i in xrange(len(input))
-    ]
-
-    actual_output = step(*actual_input)
-
-    if not isinstance(actual_output, collections.Sequence):
-        actual_output = [actual_output]
-
-    retv = [
-        RecurrentLayerOutput(
-            recurrent_name=name,
-            index=i,
-            parent_layers={'recurrent_outputs': actual_output})
-        for i in xrange(len(actual_output))
-    ]
-    if len(retv) == 1:
-        return retv[0]
-    else:
-        return retv
-
-
 LayerV2 = Layer
 data = DataLayerV2
 AggregateLevel = conf_helps.layers.AggregateLevel
 ExpandLevel = conf_helps.layers.ExpandLevel
-recurrent_group = recurrent_group
 memory = MemoryV2
 
 
 def __layer_name_mapping__(inname):
-    if inname in ['data_layer', 'memory', 'mixed_layer']:
+    if inname in ['data_layer', 'memory', 'mixed_layer', 'recurrent_group']:
         # Do Not handle these layers
         return
     elif inname == 'maxid_layer':
@@ -469,3 +447,55 @@ operator_list = [
 for op in operator_list:
     globals()[op[0]] = __convert_to_v2__(
         op[0], parent_names=op[1], is_default_name=False)
+
+
+@wrap_name_default()
+def recurrent_group(step, input, name=None):
+    if not isinstance(input, collections.Sequence):
+        input = [input]
+
+    non_static_inputs = filter(lambda x: not isinstance(x, StaticInputV2),
+                               input)
+    actual_input = [
+        RecurrentLayerInput(
+            recurrent_name=name,
+            index=i,
+            parent_layers={'recurrent_inputs': non_static_inputs})
+        for i in xrange(len(non_static_inputs))
+    ]
+
+    def __real_step__(*args):
+        rnn_input = list(args)
+        static_inputs = filter(lambda x: isinstance(x, StaticInputV2), input)
+        for static_input in static_inputs:
+            mem_name = "__%s_memory__" % static_input.input.name
+            print memory
+            mem = memory(
+                name=mem_name,
+                is_seq=static_input.is_seq,
+                size=static_input.input.calcalted_size,
+                boot_layer=static_input.input)
+            with mixed(
+                    name=mem_name,
+                    size=static_input.input.calcalted_size,
+                    act=activation.Identity()) as mix:
+                mix += identity_projection(input=mem)
+            rnn_input.insert(input.index(static_input), mix)
+        return step(*rnn_input)
+
+    actual_output = __real_step__(*actual_input)
+
+    if not isinstance(actual_output, collections.Sequence):
+        actual_output = [actual_output]
+
+    retv = [
+        RecurrentLayerOutput(
+            recurrent_name=name,
+            index=i,
+            parent_layers={'recurrent_outputs': actual_output})
+        for i in xrange(len(actual_output))
+    ]
+    if len(retv) == 1:
+        return retv[0]
+    else:
+        return retv