Merge branch 'develop' of upstream into agg_nlp_models

fluid/PaddleNLP/neural_machine_translation/transformer/train.py

@@ -145,14 +145,15 @@ def parse_args():
     return args
 
 
-def append_nccl2_prepare(trainer_id, worker_endpoints, current_endpoint):
+def append_nccl2_prepare(startup_prog, trainer_id, worker_endpoints,
+                         current_endpoint):
     assert (trainer_id >= 0 and len(worker_endpoints) > 1 and
             current_endpoint in worker_endpoints)
     eps = copy.deepcopy(worker_endpoints)
     eps.remove(current_endpoint)
-    nccl_id_var = fluid.default_startup_program().global_block().create_var(
+    nccl_id_var = startup_prog.global_block().create_var(
         name="NCCLID", persistable=True, type=fluid.core.VarDesc.VarType.RAW)
-    fluid.default_startup_program().global_block().append_op(
+    startup_prog.global_block().append_op(
         type="gen_nccl_id",
         inputs={},
         outputs={"NCCLID": nccl_id_var},
@@ -403,7 +404,7 @@ def test_context(exe, train_exe, dev_count):
                 TrainTaskConfig.label_smooth_eps,
                 use_py_reader=args.use_py_reader,
                 is_test=True)
-
+    test_prog = test_prog.clone(for_test=True)
     test_data = prepare_data_generator(
         args, is_test=True, count=dev_count, pyreader=pyreader)
 
@@ -680,10 +681,11 @@ def train(args):
             logging.info("trainers_num:{}".format(trainers_num))
             logging.info("worker_endpoints:{}".format(worker_endpoints))
             logging.info("current_endpoint:{}".format(current_endpoint))
-            append_nccl2_prepare(trainer_id, worker_endpoints, current_endpoint)
-            train_loop(exe,
-                       fluid.default_main_program(), dev_count, sum_cost,
-                       avg_cost, token_num, predict, trainers_num, trainer_id)
+            append_nccl2_prepare(startup_prog, trainer_id, worker_endpoints,
+                                 current_endpoint)
+            train_loop(exe, train_prog, startup_prog, dev_count, sum_cost,
+                       avg_cost, token_num, predict, pyreader, trainers_num,
+                       trainer_id)
             return
 
         port = os.getenv("PADDLE_PORT", "6174")