diff --git a/BERT/reader/cls.py b/BERT/reader/cls.py
index 767d817ba31fd7df73466919f848d94338e997ff..2c9bad9ed5cd4a27415ba1c6ef3faeffc35d0b74 100644
--- a/BERT/reader/cls.py
+++ b/BERT/reader/cls.py
@@ -18,7 +18,7 @@ import csv
 import numpy as np
 import tokenization
 from batching import prepare_batch_data
-
+import functools
 
 class DataProcessor(object):
     """Base class for data converters for sequence classification data sets."""
@@ -178,17 +178,38 @@ class DataProcessor(object):
                 yield batch, total_token_num
 
         def wrapper():
-            for batch_data, total_token_num in batch_reader(
-                    instance_reader, batch_size, self.in_tokens):
-                batch_data = self.generate_batch_data(
-                    batch_data,
-                    total_token_num,
+            trainers_num = int(os.environ.get('PADDLE_TRAINERS_NUM', 1))
+            trainer_id = int(os.getenv("PADDLE_TRAINER_ID", 0)) + 1
+            if trainers_num > 1:
+                print("start data reader (trainers_num: {}, trainer_id: {})".format(
+                    trainers_num, trainer_id-1))
+            get_prepared_batch_input = functools.partial(
+               self.generate_batch_data,
                     voc_size=-1,
                     mask_id=-1,
                     return_input_mask=True,
                     return_max_len=False,
                     return_num_token=False)
-                yield batch_data
+
+            train_data, train_token_num, idx = None, None, 1
+            for batch_data, total_token_num in batch_reader(
+                    instance_reader, batch_size, self.in_tokens):
+                if trainers_num > 1:
+                    if idx < trainers_num:
+                        if idx == trainer_id:
+                            train_data, train_token_num = batch_data, total_token_num
+                        idx += 1
+                    else:
+                        if idx == trainer_id:
+                            train_data, train_token_num = batch_data, total_token_num
+                        assert train_data is not None, "train data should not be None."
+                        assert train_token_num is not None, "train data should not be None."
+                        batch_data = get_prepared_batch_input(train_data, train_token_num)
+                        yield batch_data
+                        train_data, train_token_num, idx = None, None, 1
+                else:
+                    batch_data = get_prepared_batch_input(batch_data, total_token_num)
+                    yield batch_data
 
         return wrapper