diff --git a/mindspore/nn/layer/normalization.py b/mindspore/nn/layer/normalization.py
index 5119e9168b..f90b8d28ed 100644
--- a/mindspore/nn/layer/normalization.py
+++ b/mindspore/nn/layer/normalization.py
@@ -156,19 +156,23 @@ class _BatchNorm(Cell):
                 axes, re_shape = _shape_infer(F.shape(x), self.num_features)
                 y = self._global_sync(x, axes, re_shape)
             elif self.is_graph_mode and (self.is_ge_backend or self.is_ascend):
-                y, batch_mean, batch_var, _, _ = \
-                    self.bn_train(x,
-                                  self.gamma,
-                                  self.beta,
-                                  None,
-                                  None)
-
-                mean_sub = self.sub_mean(self.moving_mean, batch_mean)
-                temp_mean = self.mul_mean(mean_sub, self.momentum)
-                mean_sub2 = self.sub_var(self.moving_variance, batch_var)
-                temp_variance = self.mul_var(mean_sub2, self.momentum)
-                y = F.depend(y, self.assign_sub_mean(self.moving_mean, temp_mean))
-                y = F.depend(y, self.assign_sub_var(self.moving_variance, temp_variance))
+                if self.is_global:
+                    axes, re_shape = _shape_infer(F.shape(x), self.num_features)
+                    y = self._global_sync(x, axes, re_shape)
+                else:
+                    y, batch_mean, batch_var, _, _ = \
+                        self.bn_train(x,
+                                      self.gamma,
+                                      self.beta,
+                                      None,
+                                      None)
+
+                    mean_sub = self.sub_mean(self.moving_mean, batch_mean)
+                    temp_mean = self.mul_mean(mean_sub, self.momentum)
+                    mean_sub2 = self.sub_var(self.moving_variance, batch_var)
+                    temp_variance = self.mul_var(mean_sub2, self.momentum)
+                    y = F.depend(y, self.assign_sub_mean(self.moving_mean, temp_mean))
+                    y = F.depend(y, self.assign_sub_var(self.moving_variance, temp_variance))
             else:
                 y = self.bn_train(x,
                                   self.gamma,
diff --git a/mindspore/nn/layer/pooling.py b/mindspore/nn/layer/pooling.py
index 927af43225..89bc65bb09 100644
--- a/mindspore/nn/layer/pooling.py
+++ b/mindspore/nn/layer/pooling.py
@@ -184,7 +184,7 @@ class AvgPool2d(_PoolNd):
         Tensor of shape :math:`(N, C_{out}, H_{out}, W_{out})`.
 
     Examples:
-        >>> pool = nn.AvgPool2d(kernel_size=3, strides=1)
+        >>> pool = nn.AvgPool2d(kernel_size=3, stride=1)
         >>> x = Tensor(np.random.randint(0, 10, [1, 2, 4, 4]), mindspore.float32)
         [[[[5. 5. 9. 9.]
             [8. 4. 3. 0.]