diff --git a/python/paddle/fluid/layers/nn.py b/python/paddle/fluid/layers/nn.py
index 32d411b8309d9c8a76b7ca92ec6bc3c59526500e..27f83a60bd55ebaf131d82edf9f4ce83ea3a13d6 100644
--- a/python/paddle/fluid/layers/nn.py
+++ b/python/paddle/fluid/layers/nn.py
@@ -2139,12 +2139,16 @@ def pool2d(input,
                           input tensor is NCHW, where N is batch size, C is
                           the number of channels, H is the height of the
                           feature, and W is the width of the feature.
-        pool_size (int|list|tuple): The pool kernel size. If pool kernel size is a tuple,
+        pool_size (int|list|tuple): The pool kernel size. If pool kernel size is a tuple or list,
             it must contain two integers, (pool_size_Height, pool_size_Width).
             Otherwise, the pool kernel size will be a square of an int.
         pool_type: ${pooling_type_comment}
-        pool_stride (int): stride of the pooling layer.
-        pool_padding (int): padding size.
+        pool_stride (int|list|tuple): The pool stride size. If pool stride size is a tuple or list,
+            it must contain two integers, (pool_stride_Height, pool_stride_Width).
+            Otherwise, the pool stride size will be a square of an int.
+        pool_padding (int|list|tuple): The pool padding size. If pool padding size is a tuple,
+            it must contain two integers, (pool_padding_on_Height, pool_padding_on_Width).
+            Otherwise, the pool padding size will be a square of an int.
         global_pooling (bool): ${global_pooling_comment}
         use_cudnn (bool): ${use_cudnn_comment}
         ceil_mode (bool): ${ceil_mode_comment}
diff --git a/python/paddle/fluid/tests/unittests/test_layers.py b/python/paddle/fluid/tests/unittests/test_layers.py
index c4310fe0067664c1393ba2c25c6826f3dd08686f..559c9cda4812e2c099f25b31dffd823a2fa7620d 100644
--- a/python/paddle/fluid/tests/unittests/test_layers.py
+++ b/python/paddle/fluid/tests/unittests/test_layers.py
@@ -206,7 +206,12 @@ class TestBook(unittest.TestCase):
         program = Program()
         with program_guard(program):
             x = layers.data(name='x', shape=[3, 224, 224], dtype='float32')
-            self.assertIsNotNone(layers.pool2d(x, pool_size=[5, 3]))
+            self.assertIsNotNone(
+                layers.pool2d(
+                    x,
+                    pool_size=[5, 3],
+                    pool_stride=[1, 2],
+                    pool_padding=(2, 1)))
 
     def test_lstm_unit(self):
         program = Program()