diff --git a/python/paddle/fluid/tests/unittests/test_pad3d_op.py b/python/paddle/fluid/tests/unittests/test_pad3d_op.py
index c29352bb51af687063245d1cfc978f3550d39e53..88d3d80a14c78a94aeb0d50d04ea26578b4e2b24 100644
--- a/python/paddle/fluid/tests/unittests/test_pad3d_op.py
+++ b/python/paddle/fluid/tests/unittests/test_pad3d_op.py
@@ -251,7 +251,9 @@ class TestPadAPI(unittest.TestCase):
                        mode,
                        value=0,
                        data_format="NCDHW"):
-        if data_format == "NCDHW":
+        if mode == "constant" and len(pad) == len(input_data.shape) * 2:
+            pad = np.reshape(pad, (-1, 2)).tolist()
+        elif data_format == "NCDHW":
             pad = [
                 (0, 0),
                 (0, 0),
@@ -316,6 +318,7 @@ class TestPadAPI(unittest.TestCase):
         paddle.disable_static()
         input_shape = (1, 2, 3, 4, 5)
         pad = [1, 2, 1, 1, 3, 4]
+        pad_3 = [1, 2, 1, 1, 3, 4, 5, 6, 7, 8]
         mode = "constant"
         value = 100
         input_data = np.random.rand(*input_shape).astype(np.float32)
@@ -323,6 +326,8 @@ class TestPadAPI(unittest.TestCase):
             input_data, pad, mode, value, data_format="NCDHW")
         np_out2 = self._get_numpy_out(
             input_data, pad, mode, value, data_format="NDHWC")
+        np_out3 = self._get_numpy_out(
+            input_data, pad_3, mode, value, data_format="NCDHW")
         tensor_data = paddle.to_tensor(input_data)
 
         y1 = F.pad(tensor_data,
@@ -335,14 +340,21 @@ class TestPadAPI(unittest.TestCase):
                    mode=mode,
                    value=value,
                    data_format="NDHWC")
+        y3 = F.pad(tensor_data,
+                   pad=pad_3,
+                   mode=mode,
+                   value=value,
+                   data_format="NCDHW")
 
         self.assertTrue(np.allclose(y1.numpy(), np_out1))
         self.assertTrue(np.allclose(y2.numpy(), np_out2))
+        self.assertTrue(np.allclose(y3.numpy(), np_out3))
 
     def test_dygraph_2(self):
         paddle.disable_static()
         input_shape = (2, 3, 4, 5)
         pad = [1, 1, 3, 4]
+        pad_3 = [1, 2, 1, 1, 3, 4, 5, 6]
         mode = "constant"
         value = 100
         input_data = np.random.rand(*input_shape).astype(np.float32)
@@ -350,6 +362,8 @@ class TestPadAPI(unittest.TestCase):
             input_data, pad, mode, value, data_format="NCHW")
         np_out2 = self._get_numpy_out(
             input_data, pad, mode, value, data_format="NHWC")
+        np_out3 = self._get_numpy_out(
+            input_data, pad_3, mode, value, data_format="NCHW")
 
         tensor_data = paddle.to_tensor(input_data)
         tensor_pad = paddle.to_tensor(pad, dtype="int32")
@@ -364,14 +378,21 @@ class TestPadAPI(unittest.TestCase):
                    mode=mode,
                    value=value,
                    data_format="NHWC")
+        y3 = F.pad(tensor_data,
+                   pad=pad_3,
+                   mode=mode,
+                   value=value,
+                   data_format="NCHW")
 
         self.assertTrue(np.allclose(y1.numpy(), np_out1))
         self.assertTrue(np.allclose(y2.numpy(), np_out2))
+        self.assertTrue(np.allclose(y3.numpy(), np_out3))
 
     def test_dygraph_3(self):
         paddle.disable_static()
         input_shape = (3, 4, 5)
         pad = [3, 4]
+        pad_3 = [3, 4, 5, 6, 7, 8]
         mode = "constant"
         value = 100
         input_data = np.random.rand(*input_shape).astype(np.float32)
@@ -379,6 +400,8 @@ class TestPadAPI(unittest.TestCase):
             input_data, pad, mode, value, data_format="NCL")
         np_out2 = self._get_numpy_out(
             input_data, pad, mode, value, data_format="NLC")
+        np_out3 = self._get_numpy_out(
+            input_data, pad_3, mode, value, data_format="NCL")
         tensor_data = paddle.to_tensor(input_data)
         tensor_pad = paddle.to_tensor(pad, dtype="int32")
 
@@ -392,9 +415,15 @@ class TestPadAPI(unittest.TestCase):
                    mode=mode,
                    value=value,
                    data_format="NLC")
+        y3 = F.pad(tensor_data,
+                   pad=pad_3,
+                   mode=mode,
+                   value=value,
+                   data_format="NCL")
 
         self.assertTrue(np.allclose(y1.numpy(), np_out1))
         self.assertTrue(np.allclose(y2.numpy(), np_out2))
+        self.assertTrue(np.allclose(y3.numpy(), np_out3))
 
 
 class TestPad1dAPI(unittest.TestCase):
diff --git a/python/paddle/nn/functional/common.py b/python/paddle/nn/functional/common.py
index 1cf3599e846b9550d8b99512cd476c05095320fc..5c5e3f37916da14fb4c3457c4583e4416efbbe41 100644
--- a/python/paddle/nn/functional/common.py
+++ b/python/paddle/nn/functional/common.py
@@ -1158,6 +1158,9 @@ def alpha_dropout(x, p=0.5, training=True, name=None):
 def pad(x, pad, mode='constant', value=0, data_format="NCHW", name=None):
     """
     Pad tensor according to 'pad' and 'mode'.
+    If mode is 'constant' and length of pad is twice as length of x dimension,
+    then the padding will be started from the first dimension and moved back onto x
+    according to 'pad' and 'value'.
     If mode is 'reflect', pad[0] and pad[1] must be no greater
     than width-1. The height and depth dimension has the same condition.
 
@@ -1273,6 +1276,9 @@ def pad(x, pad, mode='constant', value=0, data_format="NCHW", name=None):
 
     unsqueezed_dim = []
 
+    if mode == "constant" and isinstance(pad, list) and len(pad) == x_dim * 2:
+        return layers.pad(x, pad, pad_value=value)
+
     if isinstance(pad, Variable):
         if data_format in ["NCL", "NCHW", "NCDHW"]:
             data_format = "NCDHW"