diff --git a/mindspore/model_zoo/Bert_NEZHA/bert_for_pre_training.py b/mindspore/model_zoo/Bert_NEZHA/bert_for_pre_training.py
index bc51ba5d483..046b2adbe2c 100644
--- a/mindspore/model_zoo/Bert_NEZHA/bert_for_pre_training.py
+++ b/mindspore/model_zoo/Bert_NEZHA/bert_for_pre_training.py
@@ -370,7 +370,7 @@ class BertTrainOneStepWithLossScaleCell(nn.Cell):
         self.parallel_mode = context.get_auto_parallel_context("parallel_mode")
         if self.parallel_mode in [ParallelMode.DATA_PARALLEL, ParallelMode.HYBRID_PARALLEL]:
             self.reducer_flag = True
-        self.grad_reducer = None
+        self.grad_reducer = F.identity
         if self.reducer_flag:
             mean = context.get_auto_parallel_context("mirror_mean")
             degree = get_group_size()
@@ -428,9 +428,8 @@ class BertTrainOneStepWithLossScaleCell(nn.Cell):
                                                            mstype.float32))
         grads = self.hyper_map(F.partial(grad_scale, scaling_sens), grads)
         grads = self.clip_gradients(grads, GRADIENT_CLIP_TYPE, GRADIENT_CLIP_VALUE)
-        if self.reducer_flag:
-            # apply grad reducer on grads
-            grads = self.grad_reducer(grads)
+        # apply grad reducer on grads
+        grads = self.grad_reducer(grads)
         self.get_status(init)
         flag_sum = self.reduce_sum(init, (0,))
         if self.is_distributed:
diff --git a/mindspore/nn/wrap/loss_scale.py b/mindspore/nn/wrap/loss_scale.py
index c6d61e6983a..ba8e6cbb7c8 100644
--- a/mindspore/nn/wrap/loss_scale.py
+++ b/mindspore/nn/wrap/loss_scale.py
@@ -220,7 +220,7 @@ class TrainOneStepWithLossScaleCell(Cell):
         self.depend_parameter_use = ControlDepend(depend_mode=1)
         self.allreduce = P.AllReduce()
         self.parallel_mode = _get_parallel_mode()
-        self.grad_reducer = None
+        self.grad_reducer = F.identity
         self.reducer_flag = self.parallel_mode in [ParallelMode.DATA_PARALLEL, ParallelMode.HYBRID_PARALLEL]
         if self.reducer_flag:
             mean = _get_mirror_mean()
@@ -250,9 +250,8 @@ class TrainOneStepWithLossScaleCell(Cell):
             scaling_sens = sens
         grads = self.grad(self.network, weights)(data, label, F.cast(scaling_sens, F.dtype(loss)))
         grads = self.hyper_map(F.partial(_grad_scale, scaling_sens), grads)
-        if self.reducer_flag:
-            # apply grad reducer on grads
-            grads = self.grad_reducer(grads)
+        # apply grad reducer on grads
+        grads = self.grad_reducer(grads)
         # get the overflow buffer
         if not self.gpu_target:
             self.get_status(init)