init support remote updater

paddle/api/PaddleAPI.h

@@ -469,6 +469,7 @@ private:
 
 enum GradientMatchineCreateMode {
   CREATE_MODE_NORMAL = 0,
+  CREATE_MODE_SGD_SPARSE_CPU_TRAINING = 3,
   CREATE_MODE_TESTING = 4
 };
 

python/paddle/v2/topology.py

@@ -73,6 +73,16 @@ class Topology(object):
 
         assert isinstance(self.__model_config__, ModelConfig)
 
+    def use_sparse_updater(self):
+        """
+        check if any parameter require to use sparse_update
+        :return:
+        """
+        for parameter in self.__model_config__.parameters:
+            if parameter.sparse_update or parameter.sparse_remote_update:
+                return True
+        return False
+
     def proto(self):
         return self.__model_config__
 

python/paddle/v2/trainer.py

@@ -42,7 +42,7 @@ class SGD(object):
     :type extra_layers: paddle.v2.config_base.Layer
     """
 
-    def __init__(self, cost, parameters, update_equation, extra_layers=None):
+    def __init__(self, cost, parameters, update_equation, extra_layers=None, is_local=True):
 
         if not isinstance(parameters, v2_parameters.Parameters):
             raise TypeError('parameters should be parameters')
@@ -55,15 +55,21 @@ class SGD(object):
         self.__topology__ = topology
         self.__parameters__ = parameters
         self.__topology_in_proto__ = topology.proto()
-
-        # In local mode, disable sparse_remote_update.
-        for param in self.__topology_in_proto__.parameters:
-            if param.sparse_remote_update:
-                param.sparse_remote_update = False
-
+        self.__is_local__ = is_local
+
+        self.__use_sparse_updater__ = self.__topology__.use_sparse_updater()
+        # # In local mode, disable sparse_remote_update.
+        if is_local:
+            self.__use_sparse_updater__ = False
+            for param in self.__topology_in_proto__.parameters:
+                if param.sparse_remote_update:
+                    param.sparse_remote_update = False
+
+        self.__gm_create_mode__ = api.CREATE_MODE_NORMAL if not \
+            self.__use_sparse_updater__ else api.CREATE_MODE_SGD_SPARSE_CPU_TRAINING
         self.__data_types__ = topology.data_type()
         gm = api.GradientMachine.createFromConfigProto(
-            self.__topology_in_proto__, api.CREATE_MODE_NORMAL,
+            self.__topology_in_proto__, self.__gm_create_mode__,
             self.__optimizer__.enable_types())
         assert isinstance(gm, api.GradientMachine)
         self.__gradient_machine__ = gm
@@ -88,7 +94,10 @@ class SGD(object):
             event_handler = default_event_handler
         __check_train_args__(**locals())
 
-        updater = self.__optimizer__.create_local_updater()
+        if self.__is_local__:
+            updater = self.__optimizer__.create_local_updater()
+        else:
+            updater = self.__optimizer__.create_remote_updater(num_passes)
         updater.init(self.__gradient_machine__)
 
         self.__gradient_machine__.start()
@@ -108,6 +117,9 @@ class SGD(object):
                     v2_event.BeginIteration(
                         pass_id=pass_id, batch_id=batch_id))
                 pass_type = updater.startBatch(len(data_batch))
+                if self.__use_sparse_updater__:
+                    self.__gradient_machine__.prefetch(feeder(data_batch))
+                    updater.getParametersRemote()
                 self.__gradient_machine__.forwardBackward(
                     feeder(data_batch), out_args, pass_type)
                 self.__gradient_machine__.eval(pass_evaluator)