add prefetch for trainer.test

python/paddle/v2/trainer.py

@@ -79,6 +79,10 @@ class SGD(object):
         self.__gradient_machine__ = gm
         self.__gradient_machine__.randParameters()
         parameters.append_gradient_machine(gm)
+        self.__parameter_updater__ = None
+
+    def use_remote_sparse_updater(self):
+        return self.__use_sparse_updater__ and not self.__is_local__
 
     def train(self, reader, num_passes=1, event_handler=None, feeding=None):
         """
@@ -103,6 +107,7 @@ class SGD(object):
         else:
             parameter_updater = self.__optimizer__.create_remote_updater(
                 num_passes, self.__use_sparse_updater__)
+        self.__parameter_updater__ = parameter_updater
         parameter_updater.init(self.__gradient_machine__)
 
         self.__gradient_machine__.start()
@@ -122,11 +127,12 @@ class SGD(object):
                     v2_event.BeginIteration(
                         pass_id=pass_id, batch_id=batch_id))
                 pass_type = parameter_updater.startBatch(len(data_batch))
-                if self.__use_sparse_updater__ and not self.__is_local__:
-                    self.__gradient_machine__.prefetch(feeder(data_batch))
+                in_args = feeder(data_batch)
+                if self.use_remote_sparse_updater():
+                    self.__gradient_machine__.prefetch(in_args)
                     parameter_updater.getParametersRemote()
-                self.__gradient_machine__.forwardBackward(
-                    feeder(data_batch), out_args, pass_type)
+                self.__gradient_machine__.forwardBackward(in_args, out_args,
+                                                          pass_type)
                 self.__gradient_machine__.eval(pass_evaluator)
                 self.__gradient_machine__.eval(batch_evaluator)
                 for each_param in self.__gradient_machine__.getNonStaticParameters(
@@ -157,8 +163,11 @@ class SGD(object):
         num_samples = 0.0
         for data_batch in reader():
             num_samples += len(data_batch)
-            self.__gradient_machine__.forward(
-                feeder(data_batch), out_args, api.PASS_TEST)
+            in_args = feeder(data_batch)
+            if self.use_remote_sparse_updater():
+                self.__gradient_machine__.prefetch(in_args)
+                self.__parameter_updater__.getParametersRemote()
+            self.__gradient_machine__.forward(in_args, out_args, api.PASS_TEST)
             total_cost += out_args.sum()
             self.__gradient_machine__.eval(evaluator)