diff --git a/mindspore/nn/layer/normalization.py b/mindspore/nn/layer/normalization.py
index c85b945a0d98e2a001819ebd760a5e72341a8b40..04de71f71c1f368e3f6dcadc248572fae89f6e7d 100644
--- a/mindspore/nn/layer/normalization.py
+++ b/mindspore/nn/layer/normalization.py
@@ -79,7 +79,7 @@ class _BatchNorm(Cell):
                 if self.rank_id in self.rank_list[i] and self.group != 1:
                     self.is_global = True
                     management.create_group('group' + str(i), self.rank_list[i])
-                    self.all_reduce = _GlobalBNHelper('group' + str(i))
+                    self.all_reduce = P.AllReduce(P.ReduceOp.SUM, 'group' + str(i)).add_prim_attr('fusion', 1)
         self.shape = P.Shape()
         self.reduce_mean = P.ReduceMean()
         self.square = P.Square()
diff --git a/tests/ut/python/nn/test_batchnorm.py b/tests/ut/python/nn/test_batchnorm.py
index 24f0de85f7d11ae825814636f7217bc2eb54bdf6..b6e27e69502541dfae282c36b4ffa5d04a22a773 100644
--- a/tests/ut/python/nn/test_batchnorm.py
+++ b/tests/ut/python/nn/test_batchnorm.py
@@ -90,5 +90,4 @@ def test_global_bn():
                                       device_num=size, parameter_broadcast=True)
     net = GlobalBNNet()
     input_data = Tensor(np.array([[2.4, 2.1], [3.2, 5.4]], dtype=np.float32))
-    net.set_train()
-    out = net(input_data)
+    _executor.compile(net,input_data)