fix

fluid/neural_machine_translation/transformer/train.py

@@ -13,6 +13,8 @@ import reader
 from config import *
 from model import transformer, position_encoding_init
 
+from paddle.fluid.transpiler.details import program_to_code
+
 import logging
 import sys
 import copy
@@ -642,8 +644,13 @@ def train(args):
             if args.sync:
                 lr_decay = fluid.layers.learning_rate_scheduler.noam_decay(
                     ModelHyperParams.d_model, TrainTaskConfig.warmup_steps)
+                print("before adam")
+
+                with fluid.default_main_program()._lr_schedule_guard():
+                    learning_rate = lr_decay * TrainTaskConfig.learning_rate
+
                 optimizer = fluid.optimizer.Adam(
-                    learning_rate=lr_decay * TrainTaskConfig.learning_rate,
+                    learning_rate=learning_rate,
                     beta1=TrainTaskConfig.beta1,
                     beta2=TrainTaskConfig.beta2,
                     epsilon=TrainTaskConfig.eps)
@@ -688,6 +695,13 @@ def train(args):
         trainers = int(os.getenv("PADDLE_TRAINERS_NUM", "0"))
         current_endpoint = os.getenv("POD_IP") + ":" + port
         trainer_id = int(os.getenv("PADDLE_TRAINER_ID"))
+
+        print("pserver_endpoints", pserver_endpoints)
+        print("current_endpoint", current_endpoint)
+        print("trainer_id", trainer_id)
+        print("pserver_ips", pserver_ips)
+        print("port", port)
+
         t = fluid.DistributeTranspiler()
         t.transpile(
             trainer_id,
@@ -707,11 +721,26 @@ def train(args):
             pserver_startup = t.get_startup_program(current_endpoint,
                                                     pserver_prog)
 
+            print("pserver start:")
+            program_to_code(pserver_startup)
+            print("pserver train:")
+            program_to_code(pserver_prog)
+            #sys.exit(0)
+
             exe.run(pserver_startup)
             exe.run(pserver_prog)
         elif training_role == "TRAINER":
             logging.info("distributed: trainer started")
             trainer_prog = t.get_trainer_program()
+
+            '''
+            print("trainer start:")
+            program_to_code(pserver_startup)
+            print("trainer train:")
+            program_to_code(trainer_prog)
+            sys.exit(0)
+            '''
+
             train_loop(exe, train_prog, startup_prog, dev_count, sum_cost,
                        avg_cost, token_num, predict, pyreader)
         else: