diff --git a/python/paddle/fluid/tests/unittests/test_pad3d_op.py b/python/paddle/fluid/tests/unittests/test_pad3d_op.py
index 88d3d80a14c78a94aeb0d50d04ea26578b4e2b24..8dc825e60bc4dee05ebf903e608981a4dd4366e2 100644
--- a/python/paddle/fluid/tests/unittests/test_pad3d_op.py
+++ b/python/paddle/fluid/tests/unittests/test_pad3d_op.py
@@ -467,12 +467,15 @@ class TestPad1dAPI(unittest.TestCase):
         for place in self.places:
             input_shape = (3, 4, 5)
             pad = [1, 2]
+            pad_int = 1
             value = 100
             input_data = np.random.rand(*input_shape).astype(np.float32)
 
             pad_reflection = nn.Pad1D(padding=pad, mode="reflect")
             pad_replication = nn.Pad1D(padding=pad, mode="replicate")
             pad_constant = nn.Pad1D(padding=pad, mode="constant", value=value)
+            pad_constant_int = nn.Pad1D(
+                padding=pad_int, mode="constant", value=value)
             pad_circular = nn.Pad1D(padding=pad, mode="circular")
 
             data = paddle.to_tensor(input_data)
@@ -492,6 +495,14 @@ class TestPad1dAPI(unittest.TestCase):
                 input_data, pad, "constant", value=value, data_format="NCL")
             self.assertTrue(np.allclose(output.numpy(), np_out))
 
+            output = pad_constant_int(data)
+            np_out = self._get_numpy_out(
+                input_data, [pad_int] * 2,
+                "constant",
+                value=value,
+                data_format="NCL")
+            self.assertTrue(np.allclose(output.numpy(), np_out))
+
             output = pad_circular(data)
             np_out = self._get_numpy_out(
                 input_data, pad, "circular", value=value, data_format="NCL")
@@ -541,12 +552,15 @@ class TestPad2dAPI(unittest.TestCase):
         for place in self.places:
             input_shape = (3, 4, 5, 6)
             pad = [1, 2, 2, 1]
+            pad_int = 1
             value = 100
             input_data = np.random.rand(*input_shape).astype(np.float32)
 
             pad_reflection = nn.Pad2D(padding=pad, mode="reflect")
             pad_replication = nn.Pad2D(padding=pad, mode="replicate")
             pad_constant = nn.Pad2D(padding=pad, mode="constant", value=value)
+            pad_constant_int = nn.Pad2D(
+                padding=pad_int, mode="constant", value=value)
             pad_circular = nn.Pad2D(padding=pad, mode="circular")
 
             data = paddle.to_tensor(input_data)
@@ -566,6 +580,14 @@ class TestPad2dAPI(unittest.TestCase):
                 input_data, pad, "constant", value=value, data_format="NCHW")
             self.assertTrue(np.allclose(output.numpy(), np_out))
 
+            output = pad_constant_int(data)
+            np_out = self._get_numpy_out(
+                input_data, [pad_int] * 4,
+                "constant",
+                value=value,
+                data_format="NCHW")
+            self.assertTrue(np.allclose(output.numpy(), np_out))
+
             output = pad_circular(data)
             np_out = self._get_numpy_out(
                 input_data, pad, "circular", data_format="NCHW")
@@ -617,12 +639,15 @@ class TestPad3dAPI(unittest.TestCase):
         for place in self.places:
             input_shape = (3, 4, 5, 6, 7)
             pad = [1, 2, 2, 1, 1, 0]
+            pad_int = 1
             value = 100
             input_data = np.random.rand(*input_shape).astype(np.float32)
 
             pad_reflection = nn.Pad3D(padding=pad, mode="reflect")
             pad_replication = nn.Pad3D(padding=pad, mode="replicate")
             pad_constant = nn.Pad3D(padding=pad, mode="constant", value=value)
+            pad_constant_int = nn.Pad3D(
+                padding=pad_int, mode="constant", value=value)
             pad_circular = nn.Pad3D(padding=pad, mode="circular")
 
             data = paddle.to_tensor(input_data)
@@ -642,6 +667,14 @@ class TestPad3dAPI(unittest.TestCase):
                 input_data, pad, "constant", value=value, data_format="NCDHW")
             self.assertTrue(np.allclose(output.numpy(), np_out))
 
+            output = pad_constant_int(data)
+            np_out = self._get_numpy_out(
+                input_data, [pad_int] * 6,
+                "constant",
+                value=value,
+                data_format="NCDHW")
+            self.assertTrue(np.allclose(output.numpy(), np_out))
+
             output = pad_circular(data)
             np_out = self._get_numpy_out(
                 input_data, pad, "circular", data_format="NCDHW")
diff --git a/python/paddle/nn/layer/common.py b/python/paddle/nn/layer/common.py
index 05d619bd729d8754b36ecf8aa8e386ca839860cc..d0f97625bcba757ad80cf897a1aa9dffeb88641f 100644
--- a/python/paddle/nn/layer/common.py
+++ b/python/paddle/nn/layer/common.py
@@ -38,6 +38,13 @@ __all__ = [
 ]
 
 
+def _npairs(x, n):
+    if isinstance(x, (paddle.Tensor, list)):
+        return x
+    x = [x] * (n * 2)
+    return x
+
+
 class Linear(layers.Layer):
     r"""
 
@@ -915,7 +922,8 @@ class Pad1D(layers.Layer):
     If mode is 'reflect', pad[0] and pad[1] must be no greater than width-1.
 
     Parameters:
-        padding (Tensor | List[int32]): The padding size with data type int32. [len(padding)/2] dimensions
+        padding (Tensor | List[int] | int): The padding size with data type int. If is int, use the
+            same padding in both dimensions. Else [len(padding)/2] dimensions
             of input will be padded. The pad has the form (pad_left, pad_right).
         mode (str): Four modes: 'constant' (default), 'reflect', 'replicate', 'circular'.
             When in 'constant' mode, this op uses a constant value to pad the input tensor.
@@ -968,7 +976,7 @@ class Pad1D(layers.Layer):
                  data_format="NCL",
                  name=None):
         super(Pad1D, self).__init__()
-        self._pad = padding
+        self._pad = _npairs(padding, 1)
         self._mode = mode
         self._value = value
         self._data_format = data_format
@@ -996,8 +1004,9 @@ class Pad2D(layers.Layer):
     than width-1. The height dimension has the same condition.
 
     Parameters:
-        padding (Tensor | List[int32]): The padding size with data type int32. [len(padding)/2] dimensions
-            of input will be padded. The pad has the form (pad_left, pad_right, pad_top, pad_bottom).
+        padding (Tensor | List[int] | int): The padding size with data type int. If is int, use the
+            same padding in all dimensions. Else [len(padding)/2] dimensions of input will be padded. 
+            The pad has the form (pad_left, pad_right, pad_top, pad_bottom). 
         mode (str): Four modes: 'constant' (default), 'reflect', 'replicate', 'circular'.
             When in 'constant' mode, this op uses a constant value to pad the input tensor.
             When in 'reflect' mode, uses reflection of the input boundaries to pad the input tensor.
@@ -1051,7 +1060,7 @@ class Pad2D(layers.Layer):
                  data_format="NCHW",
                  name=None):
         super(Pad2D, self).__init__()
-        self._pad = padding
+        self._pad = _npairs(padding, 2)
         self._mode = mode
         self._value = value
         self._data_format = data_format
@@ -1079,7 +1088,8 @@ class Pad3D(layers.Layer):
     than width-1. The height and depth dimension has the same condition.
 
     Parameters:
-        padding (Tensor | List[int32]): The padding size with data type int32. [len(padding)/2] dimensions
+        padding (Tensor | List[int] | int): The padding size with data type int. If is int, use the
+            same padding in all dimensions. Else [len(padding)/2] dimensions
             of input will be padded. The pad has the form (pad_left, pad_right, pad_top, pad_bottom, pad_front, pad_back).
         mode (str): Four modes: 'constant' (default), 'reflect', 'replicate', 'circular'.
             When in 'constant' mode, this op uses a constant value to pad the input tensor.
@@ -1134,7 +1144,7 @@ class Pad3D(layers.Layer):
                  data_format="NCDHW",
                  name=None):
         super(Pad3D, self).__init__()
-        self._pad = padding
+        self._pad = _npairs(padding, 3)
         self._mode = mode
         self._value = value
         self._data_format = data_format