set fleet_send_batch_num a default value according to trainer num

(1) set fleet_send_batch_num a default value according to trainer num， the previous 80000 is fixed，if trainer num is much less or larger than 100，global shuffle may have timeout error.

(2) fix load one table bug, add barrier

python/paddle/fluid/dataset.py

@@ -235,7 +235,7 @@ class InMemoryDataset(DatasetBase):
         """ Init. """
         super(InMemoryDataset, self).__init__()
         self.proto_desc.name = "MultiSlotInMemoryDataFeed"
-        self.fleet_send_batch_size = 80000
+        self.fleet_send_batch_size = None
         self.queue_num = None
         self.merge_by_lineid = False
 
@@ -413,6 +413,8 @@ class InMemoryDataset(DatasetBase):
         if fleet is not None:
             fleet._role_maker._barrier_worker()
             trainer_num = fleet.worker_num()
+        if self.fleet_send_batch_size is None:
+            self.fleet_send_batch_size = 800 * trainer_num
         self.dataset.register_client2client_msg_handler()
         self.dataset.set_trainer_num(trainer_num)
         self.dataset.set_fleet_send_batch_size(self.fleet_send_batch_size)

python/paddle/fluid/incubate/fleet/parameter_server/pslib/__init__.py

@@ -320,11 +320,13 @@ class PSLib(Fleet):
         scope = kwargs.get("scope", None)
         model_proto_file = kwargs.get("model_proto_file", None)
         load_combine = kwargs.get("load_combine", False)
+        self._role_maker._barrier_worker()
         if scope is not None and model_proto_file is not None:
             self._load_one_table_from_paddle_model(
                 scope, table_id, model_path, model_proto_file, load_combine)
-        else:
+        elif self._role_maker.is_first_worker():
             self._fleet_ptr.load_model_one_table(table_id, model_path, mode)
+        self._role_maker._barrier_worker()
 
     def _load_one_table_from_paddle_model(self,
                                           scope,