diff --git a/python/paddle/nn/functional/norm.py b/python/paddle/nn/functional/norm.py
index 8aca31921808552b6ca7d905911dae06ce323037..1a5fc109805e05ba71aca967b599b680fa302c9c 100644
--- a/python/paddle/nn/functional/norm.py
+++ b/python/paddle/nn/functional/norm.py
@@ -186,24 +186,25 @@ def batch_norm(x,
     else:
         trainable_statistics = not use_global_stats
 
-    if _non_static_mode():
-        if in_dygraph_mode():
-            batch_norm_out, _, _, _, _, _ = _C_ops.final_state_batch_norm(
-                x, weight, bias, running_mean, running_var, momentum, epsilon,
-                data_format, not training, use_global_stats,
-                trainable_statistics, False)
-
-        elif _in_legacy_dygraph():
-            # for dygraph need tuple
-            attrs = ("momentum", momentum, "epsilon", epsilon, "is_test",
-                     not training, "data_layout", data_format, "use_mkldnn",
-                     False, "fuse_with_relu", False, "use_global_stats",
-                     use_global_stats, "trainable_statistics",
-                     trainable_statistics)
-
-            batch_norm_out, _, _, _, _, _ = _C_ops.batch_norm(
-                x, weight, bias, running_mean, running_var, None, mean_out,
-                variance_out, *attrs)
+    if in_dygraph_mode():
+        batch_norm_out, _, _, _, _, _ = _C_ops.final_state_batch_norm(
+            x, weight, bias, running_mean, running_var, momentum, epsilon,
+            data_format, not training, use_global_stats, trainable_statistics,
+            False)
+
+        return dygraph_utils._append_activation_in_dygraph(
+            batch_norm_out, act=None)
+
+    elif _in_legacy_dygraph():
+        # for dygraph need tuple
+        attrs = ("momentum", momentum, "epsilon", epsilon, "is_test",
+                 not training, "data_layout", data_format, "use_mkldnn", False,
+                 "fuse_with_relu", False, "use_global_stats", use_global_stats,
+                 "trainable_statistics", trainable_statistics)
+
+        batch_norm_out, _, _, _, _, _ = _C_ops.batch_norm(
+            x, weight, bias, running_mean, running_var, None, mean_out,
+            variance_out, *attrs)
 
         return dygraph_utils._append_activation_in_dygraph(
             batch_norm_out, act=None)