diff --git a/fluid/neural_machine_translation/transformer/train.py b/fluid/neural_machine_translation/transformer/train.py
index 08a0488f43429df783320db613f399c6080094ab..c4b6d6d95450d2557b218d649254fc1c9e3c56cc 100644
--- a/fluid/neural_machine_translation/transformer/train.py
+++ b/fluid/neural_machine_translation/transformer/train.py
@@ -14,6 +14,7 @@ from model import transformer, position_encoding_init
 
 import logging
 import sys
+import copy
 
 def parse_args():
     parser = argparse.ArgumentParser("Training for Transformer.")
@@ -623,7 +624,7 @@ def train(args):
                 is_test=False)
 
             optimizer=None
-            if args.local:
+            if args.sync:
                 lr_decay = fluid.layers.learning_rate_scheduler.noam_decay(
                     ModelHyperParams.d_model, TrainTaskConfig.warmup_steps)
                 optimizer = fluid.optimizer.Adam(
@@ -631,7 +632,7 @@ def train(args):
                     beta1=TrainTaskConfig.beta1,
                     beta2=TrainTaskConfig.beta2,
                     epsilon=TrainTaskConfig.eps)
-            elif args.sync == False:
+            else:
                 optimizer = fluid.optimizer.SGD(0.003)
             optimizer.minimize(avg_cost)
 
@@ -681,7 +682,7 @@ def train(args):
             startup_program=startup_prog)
 
         if training_role == "PSERVER":
-            loggin.info("distributed: pserver started")
+            logging.info("distributed: pserver started")
             current_endpoint = os.getenv("POD_IP") + ":" + os.getenv(
                 "PADDLE_PORT")
             if not current_endpoint:
@@ -694,7 +695,7 @@ def train(args):
             exe.run(pserver_startup)
             exe.run(pserver_prog)
         elif training_role == "TRAINER":
-            loggin.info("distributed: trainer started")
+            logging.info("distributed: trainer started")
             trainer_prog = t.get_trainer_program()
             train_loop(exe, train_prog, startup_prog, dev_count, sum_cost,
                        avg_cost, token_num, predict, pyreader)