diff --git a/python/paddle/nn/layer/norm.py b/python/paddle/nn/layer/norm.py
index aed7e455d63122a2a0cbe3da07f8443b90b99678..9e1486d1d2a334f6daaec98b64de1eb011d3b8cb 100644
--- a/python/paddle/nn/layer/norm.py
+++ b/python/paddle/nn/layer/norm.py
@@ -999,6 +999,8 @@ class BatchNorm(Layer):
                 self._use_global_stats,
                 self._trainable_statistics,
             )
+            if self._act is None:
+                return batch_norm_out
             return dygraph_utils._append_activation_in_dygraph(
                 batch_norm_out, act=self._act, use_mkldnn=self._use_mkldnn
             )
diff --git a/python/paddle/tensor/manipulation.py b/python/paddle/tensor/manipulation.py
index 3c3355d7bb80a8f05da7c045f1506423d2f42de0..0c74a900e1b72af82096e800666c892a15a7f513 100644
--- a/python/paddle/tensor/manipulation.py
+++ b/python/paddle/tensor/manipulation.py
@@ -1916,31 +1916,20 @@ def split(x, num_or_sections, axis=0, name=None):
     input = x
     dim = axis
     if in_dygraph_mode():
-        num = None
-        attrs = ()
-
         if isinstance(dim, Variable):
             dim = dim.numpy()
             dim = dim.item(0)
         assert len(input.shape) + dim >= 0, "(rank(x) + axis) must >= 0"
         dim = (len(input.shape) + dim) if dim < 0 else dim
-        attrs += ('axis', dim)
 
-        if isinstance(num_or_sections, int):
-            num = num_or_sections
-            attrs += ('num', num_or_sections)
-        elif isinstance(num_or_sections, (list, tuple)):
-            num = len(num_or_sections)
+        if isinstance(num_or_sections, (list, tuple)):
             if utils._contain_var(num_or_sections):
                 for index, item in enumerate(num_or_sections):
                     if isinstance(item, Variable):
                         num_or_sections[index] = num_or_sections[index].numpy()[
                             0
                         ]
-                attrs += ('sections', list(num_or_sections))
-            else:
-                attrs += ('sections', list(num_or_sections))
-        else:
+        elif not isinstance(num_or_sections, int):
             raise TypeError(
                 "The type of 'num_or_sections' in split must be int, list or tuple in imperative mode, but "
                 "received %s." % (type(num_or_sections))
diff --git a/python/paddle/tensor/search.py b/python/paddle/tensor/search.py
index 8c0e912ac8e3100cd2efdc2685303eaefc2ec85a..7fe0850ac5cc2e9a820c6a023569966077f43f80 100755
--- a/python/paddle/tensor/search.py
+++ b/python/paddle/tensor/search.py
@@ -612,15 +612,6 @@ def where(condition, x=None, y=None, name=None):
     if x is None or y is None:
         raise ValueError("either both or neither of x and y should be given")
 
-    if not paddle.in_dynamic_mode():
-        check_variable_and_dtype(condition, 'condition', ['bool'], 'where')
-        check_variable_and_dtype(
-            x, 'x', ['float32', 'float64', 'int32', 'int64'], 'where'
-        )
-        check_variable_and_dtype(
-            y, 'y', ['float32', 'float64', 'int32', 'int64'], 'where'
-        )
-
     condition_shape = list(condition.shape)
     x_shape = list(x.shape)
     y_shape = list(y.shape)
@@ -646,6 +637,14 @@ def where(condition, x=None, y=None, name=None):
     if in_dygraph_mode():
         return _C_ops.where(broadcast_condition, broadcast_x, broadcast_y)
     else:
+        check_variable_and_dtype(condition, 'condition', ['bool'], 'where')
+        check_variable_and_dtype(
+            x, 'x', ['float32', 'float64', 'int32', 'int64'], 'where'
+        )
+        check_variable_and_dtype(
+            y, 'y', ['float32', 'float64', 'int32', 'int64'], 'where'
+        )
+
         helper = LayerHelper("where", **locals())
         out = helper.create_variable_for_type_inference(dtype=x.dtype)