diff --git a/example/bert_clue/dataset.py b/example/bert_clue/dataset.py
index 9dbe7b8ce41e583b5e654b85163208c80ad43191..f930b67330d4ab6123a10a7c0ebe2c415d215737 100644
--- a/example/bert_clue/dataset.py
+++ b/example/bert_clue/dataset.py
@@ -52,7 +52,7 @@ def create_bert_dataset(epoch_size=1, device_num=1, rank=0, do_shuffle="true", e
     ds = ds.map(input_columns="input_ids", operations=type_cast_op)
     # apply batch operations
     ds = ds.batch(bert_net_cfg.batch_size, drop_remainder=True)
-    ds = ds.repeat(repeat_count)
+    ds = ds.repeat(new_repeat_count)
     logger.info("data size: {}".format(ds.get_dataset_size()))
     logger.info("repeatcount: {}".format(ds.get_repeat_count()))
     return ds, new_repeat_count
diff --git a/example/bert_clue/run_pretrain.py b/example/bert_clue/run_pretrain.py
index c587d41bc321decca5dd35245fbbb9c057c0b4bc..e46806c315a5fcd8c0cf1d3c62da55797ecd498d 100644
--- a/example/bert_clue/run_pretrain.py
+++ b/example/bert_clue/run_pretrain.py
@@ -81,6 +81,11 @@ def run_pretrain():
         context.reset_auto_parallel_context()
         context.set_auto_parallel_context(parallel_mode=ParallelMode.DATA_PARALLEL, mirror_mean=True,
                                           device_num=device_num)
+        from mindspore.parallel._auto_parallel_context import auto_parallel_context
+        if bert_net_cfg.num_hidden_layers == 12:
+            auto_parallel_context().set_all_reduce_fusion_split_indices([28, 55, 82, 109, 136, 163, 190, 205])
+        elif bert_net_cfg.num_hidden_layers == 24:
+            auto_parallel_context().set_all_reduce_fusion_split_indices([38, 93, 148, 203, 258, 313, 368, 397])
         D.init()
         rank = args_opt.device_id % device_num
     else: