diff --git a/python/paddle/__init__.py b/python/paddle/__init__.py
index 0f95fccab1705..4e87dd21b16c2 100755
--- a/python/paddle/__init__.py
+++ b/python/paddle/__init__.py
@@ -103,8 +103,6 @@
 from .tensor.logic import logical_or  #DEFINE_ALIAS
 from .tensor.logic import logical_xor  #DEFINE_ALIAS
 from .tensor.logic import not_equal  #DEFINE_ALIAS
-# from .tensor.logic import reduce_all  #DEFINE_ALIAS
-# from .tensor.logic import reduce_any  #DEFINE_ALIAS
 from .tensor.logic import allclose  #DEFINE_ALIAS
 from .tensor.logic import equal_all  #DEFINE_ALIAS
 # from .tensor.logic import isnan        #DEFINE_ALIAS
@@ -161,6 +159,8 @@
 # from .tensor.math import reduce_min  #DEFINE_ALIAS
 # from .tensor.math import reduce_prod  #DEFINE_ALIAS
 # from .tensor.math import reduce_sum  #DEFINE_ALIAS
+from .tensor.math import all  #DEFINE_ALIAS
+from .tensor.math import any  #DEFINE_ALIAS
 from .tensor.math import round  #DEFINE_ALIAS
 from .tensor.math import rsqrt  #DEFINE_ALIAS
 from .tensor.math import scale  #DEFINE_ALIAS
diff --git a/python/paddle/fluid/layers/nn.py b/python/paddle/fluid/layers/nn.py
index c2bb96ead2bf9..0842576413fbd 100755
--- a/python/paddle/fluid/layers/nn.py
+++ b/python/paddle/fluid/layers/nn.py
@@ -315,6 +315,8 @@ def fc(input,
         .. code-block:: python
 
           import paddle.fluid as fluid
+          import paddle
+          paddle.enable_static()
           # when input is single tensor
           data = fluid.data(name="data", shape=[-1, 32], dtype="float32")
           fc = fluid.layers.fc(input=data, size=1000, act="tanh")
@@ -468,6 +470,9 @@ def embedding(input,
 
           import paddle.fluid as fluid
           import numpy as np
+          import paddle
+          paddle.enable_static()
+          
           data = fluid.data(name='x', shape=[None, 1], dtype='int64')
 
           # example 1
@@ -731,6 +736,8 @@ def linear_chain_crf(input, label, param_attr=None, length=None):
 
             import paddle.fluid as fluid
             import numpy as np
+            import paddle
+            paddle.enable_static()
 
             #define net structure, using LodTensor
             train_program = fluid.Program()
@@ -855,6 +862,8 @@ def crf_decoding(input, param_attr, label=None, length=None):
         .. code-block:: python
 
            import paddle.fluid as fluid
+           import paddle
+           paddle.enable_static()
 
            # LoDTensor-based example
            num_labels = 10
@@ -1458,6 +1467,9 @@ def conv2d(input,
         .. code-block:: python
 
           import paddle.fluid as fluid
+          import paddle
+          paddle.enable_static()
+          
           data = fluid.data(name='data', shape=[None, 3, 32, 32], dtype='float32')
           conv2d = fluid.layers.conv2d(input=data, num_filters=2, filter_size=3, act="relu")
     """
@@ -1728,6 +1740,8 @@ def conv3d(input,
         .. code-block:: python
 
           import paddle.fluid as fluid
+          import paddle
+          paddle.enable_static()
           data = fluid.data(name='data', shape=[None, 3, 12, 32, 32], dtype='float32')
           conv3d = fluid.layers.conv3d(input=data, num_filters=2, filter_size=3, act="relu")
     """
@@ -2377,6 +2391,7 @@ def adaptive_pool2d(input,
           #             output[:, :, i, j] = avg(input[:, :, hstart: hend, wstart: wend])
           #
           import paddle
+          paddle.enable_static()
           data = paddle.rand(shape=[1,3,32,32])
           pool_out = paddle.fluid.layers.adaptive_pool2d(
                             input=data,
@@ -2531,6 +2546,7 @@ def adaptive_pool3d(input,
           #
 
           import paddle
+          paddle.enable_static()
           data = paddle.rand(shape=[1,3,32,32,32])
           pool_out = paddle.fluid.layers.adaptive_pool3d(
                             input=data,
@@ -2726,6 +2742,8 @@ def batch_norm(input,
         .. code-block:: python
 
             import paddle.fluid as fluid
+            import paddle
+            paddle.enable_static()
             x = fluid.data(name='x', shape=[3, 7, 3, 7], dtype='float32')
             hidden1 = fluid.layers.fc(input=x, size=200, param_attr='fc1.w')
             hidden2 = fluid.layers.batch_norm(input=hidden1)
@@ -2735,6 +2753,8 @@ def batch_norm(input,
             # batch_norm with momentum as Variable
             import paddle.fluid as fluid
             import paddle.fluid.layers.learning_rate_scheduler as lr_scheduler
+            import paddle
+            paddle.enable_static()
 
             def get_decay_momentum(momentum_init, decay_steps, decay_rate):
                 global_step = lr_scheduler._decay_step_counter()
@@ -3134,6 +3154,8 @@ def instance_norm(input,
         .. code-block:: python
 
             import paddle.fluid as fluid
+            import paddle
+            paddle.enable_static()
             x = fluid.data(name='x', shape=[3, 7, 3, 7], dtype='float32')
             hidden1 = fluid.layers.fc(input=x, size=200, param_attr='fc1.w')
             hidden2 = fluid.layers.instance_norm(input=hidden1)
@@ -3269,6 +3291,7 @@ def data_norm(input,
         .. code-block:: python
 
             import paddle
+            paddle.enable_static()
 
             x = paddle.randn(shape=[32,100])
             hidden2 = paddle.static.nn.data_norm(input=x)
@@ -3451,6 +3474,8 @@ def layer_norm(input,
 
             import paddle.fluid as fluid
             import numpy as np
+            import paddle
+            paddle.enable_static()
             x = fluid.data(name='x', shape=[-1, 32, 32], dtype='float32')
             hidden1 = fluid.layers.layer_norm(input=x, begin_norm_axis=1)
             place = fluid.CPUPlace()
@@ -3566,6 +3591,9 @@ def group_norm(input,
        .. code-block:: python
 
             import paddle.fluid as fluid
+            import paddle
+            paddle.enable_static()
+            
             data = fluid.data(name='data', shape=[None, 8, 32, 32], dtype='float32')
             x = fluid.layers.group_norm(input=data, groups=4)
     """
@@ -3887,6 +3915,8 @@ def conv2d_transpose(input,
        .. code-block:: python
 
           import paddle.fluid as fluid
+          import paddle
+          paddle.enable_static()
           data = fluid.data(name='data', shape=[None, 3, 32, 32], dtype='float32')
           conv2d_transpose = fluid.layers.conv2d_transpose(input=data, num_filters=2, filter_size=3)
     """
@@ -4177,6 +4207,8 @@ def conv3d_transpose(input,
        .. code-block:: python
 
           import paddle.fluid as fluid
+          import paddle
+          paddle.enable_static()
           data = fluid.data(name='data', shape=[None, 3, 12, 32, 32], dtype='float32')
           conv3d_transpose = fluid.layers.conv3d_transpose(input=data, num_filters=2, filter_size=3)
     """
@@ -4659,7 +4691,7 @@ def reduce_all(input, dim=None, keep_dim=False, name=None):
     This OP computes the ``logical and`` of tensor elements over the given dimension, and output the result.
 
     Args:
-        input (Variable): The input variable which is a Tensor or LoDTensor, the input data type should be `bool`.
+        input (Tensor): the input tensor, it's data type should be `bool`.
         dim (list|int|optional): The dimension along which the logical and is computed.
             If :attr:`None`, compute the logical and over all elements of
             :attr:`input` and return a Tensor variable with a single element,
@@ -4672,11 +4704,12 @@ def reduce_all(input, dim=None, keep_dim=False, name=None):
                        will be named automatically. The default value is None.
 
     Returns:
-        Variable, the output data type is bool. : The reduced tensor variable with ``logical and`` in given dims.
+        Tensor, the output data type is bool. : The reduced tensor variable with ``logical and`` in given dims.
 
     Examples:
         .. code-block:: python
 
+            import paddle
             import paddle.fluid as fluid
             import paddle.fluid.layers as layers
             import numpy as np
@@ -4684,15 +4717,15 @@ def reduce_all(input, dim=None, keep_dim=False, name=None):
             # x is a bool Tensor variable with following elements:
             #    [[True, False]
             #     [True, True]]
-            x = layers.assign(np.array([[1, 0], [1, 1]], dtype='int32'))
-            x = layers.cast(x, 'bool')
+            x = paddle.assign(np.array([[1, 0], [1, 1]], dtype='int32'))
+            x = paddle.cast(x, 'bool')
 
-            out = layers.reduce_all(x)  # False
-            out = layers.reduce_all(x, dim=0)  # [True, False]
-            out = layers.reduce_all(x, dim=-1)  # [False, True]
+            out = paddle.reduce_all(x)  # False
+            out = paddle.reduce_all(x, dim=0)  # [True, False]
+            out = paddle.reduce_all(x, dim=-1)  # [False, True]
             # keep_dim=False, x.shape=(2,2), out.shape=(2,)
 
-            out = layers.reduce_all(x, dim=1, keep_dim=True)  # [[False], [True]]
+            out = paddle.reduce_all(x, dim=1, keep_dim=True)  # [[False], [True]]
             # keep_dim=True, x.shape=(2,2), out.shape=(2,1)
 
     """
@@ -4719,7 +4752,7 @@ def reduce_any(input, dim=None, keep_dim=False, name=None):
     This OP computes the ``logical or`` of tensor elements over the given dimension, and output the result.
 
     Args:
-        input (Variable): The input variable which is a Tensor or LoDTensor, the input data type should be `bool`.
+        input (Tensor): the input tensor, it's data type should be `bool`.
         dim (list|int|optional): The dimension along which the logical and is computed.
             If :attr:`None`, compute the logical and over all elements of
             :attr:`input` and return a Tensor variable with a single element,
@@ -4728,14 +4761,15 @@ def reduce_any(input, dim=None, keep_dim=False, name=None):
         keep_dim (bool): Whether to reserve the reduced dimension in the
             output Tensor. The result tensor will have one fewer dimension
             than the :attr:`input` unless :attr:`keep_dim` is true. The default value is False.
-        name(str|None): A name for this layer(optional). If set None, the layer
+        name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`.
 
     Returns:
-        Variable, the output data type is bool. : The reduced tensor variable with ``logical or`` in given dims.
+        Tensor, the output data type is bool. : The reduced tensor variable with ``logical or`` in given dims.
 
     Examples:
         .. code-block:: python
 
+            import paddle
             import paddle.fluid as fluid
             import paddle.fluid.layers as layers
             import numpy as np
@@ -4743,15 +4777,15 @@ def reduce_any(input, dim=None, keep_dim=False, name=None):
             # x is a bool Tensor variable with following elements:
             #    [[True, False]
             #     [False, False]]
-            x = layers.assign(np.array([[1, 0], [0, 0]], dtype='int32'))
-            x = layers.cast(x, 'bool')
+            x = paddle.assign(np.array([[1, 0], [0, 0]], dtype='int32'))
+            x = paddle.cast(x, 'bool')
 
-            out = layers.reduce_any(x)  # True
-            out = layers.reduce_any(x, dim=0)  # [True, False]
-            out = layers.reduce_any(x, dim=-1)  # [True, False]
+            out = paddle.reduce_any(x)  # True
+            out = paddle.reduce_any(x, dim=0)  # [True, False]
+            out = paddle.reduce_any(x, dim=-1)  # [True, False]
             # keep_dim=False, x.shape=(2,2), out.shape=(2,)
 
-            out = layers.reduce_any(x, dim=1,
+            out = paddle.reduce_any(x, dim=1,
                                      keep_dim=True)  # [[True], [False]]
             # keep_dim=True, x.shape=(2,2), out.shape=(2,1)
 
@@ -5613,6 +5647,8 @@ def im2sequence(input,
         .. code-block:: python
 
             import paddle.fluid as fluid
+            import paddle
+            paddle.enable_static()
             data = fluid.data(name='data', shape=[None, 3, 32, 32],
                                      dtype='float32')
             output = fluid.layers.im2sequence(
@@ -5669,6 +5705,8 @@ def row_conv(input, future_context_size, param_attr=None, act=None):
     Examples:
         >>>  # for LodTensor inputs
         >>> import paddle.fluid as fluid
+        >>> import paddle
+        >>> paddle.enable_static()
         >>> x = fluid.data(name='x', shape=[9, 16],
         >>>                        dtype='float32', lod_level=1)
         >>> out = fluid.layers.row_conv(input=x, future_context_size=2)
@@ -5982,6 +6020,8 @@ def autoincreased_step_counter(counter_name=None, begin=1, step=1):
         .. code-block:: python
 
            import paddle.fluid as fluid
+           import paddle
+           paddle.enable_static()
            global_step = fluid.layers.autoincreased_step_counter(
                counter_name='@LR_DECAY_COUNTER@', begin=0, step=1)
     """
@@ -9730,6 +9770,8 @@ def prelu(x, mode, param_attr=None, name=None):
         .. code-block:: python
 
             import paddle.fluid as fluid
+            import paddle
+            paddle.enable_static()
             from paddle.fluid.param_attr import ParamAttr
             x = fluid.data(name="x", shape=[None,5,10,10], dtype="float32")
             mode = 'channel'
@@ -14307,6 +14349,9 @@ def deformable_conv(input,
           #deformable conv v2:
 
           import paddle.fluid as fluid
+          import paddle
+          paddle.enable_static()
+          
           C_in, H_in, W_in = 3, 32, 32
           filter_size, deformable_groups = 3, 1
           data = fluid.data(name='data', shape=[None, C_in, H_in, W_in], dtype='float32')
diff --git a/python/paddle/fluid/tests/unittests/test_reduce_op.py b/python/paddle/fluid/tests/unittests/test_reduce_op.py
index 80b201d084218..e549a2eca2d7d 100644
--- a/python/paddle/fluid/tests/unittests/test_reduce_op.py
+++ b/python/paddle/fluid/tests/unittests/test_reduce_op.py
@@ -767,5 +767,117 @@ def test_dygraph(self):
         self.assertTrue((out3 == np.sum(np_x, axis=(0, 1, 2))).all())
 
 
+class TestAllAPI(unittest.TestCase):
+    def setUp(self):
+        np.random.seed(123)
+        paddle.enable_static()
+        self.places = [fluid.CPUPlace()]
+        if core.is_compiled_with_cuda():
+            self.places.append(fluid.CUDAPlace(0))
+
+    def check_static_result(self, place):
+        with fluid.program_guard(fluid.Program(), fluid.Program()):
+            input = fluid.data(name="input", shape=[4, 4], dtype="bool")
+            result = paddle.all(x=input)
+            input_np = np.random.randint(0, 2, [4, 4]).astype("bool")
+
+            exe = fluid.Executor(place)
+            fetches = exe.run(fluid.default_main_program(),
+                              feed={"input": input_np},
+                              fetch_list=[result])
+            self.assertTrue(np.allclose(fetches[0], np.all(input_np)))
+
+    def test_static(self):
+        for place in self.places:
+            self.check_static_result(place=place)
+
+    def test_dygraph(self):
+        paddle.disable_static()
+        for place in self.places:
+            with fluid.dygraph.guard(place):
+                np_x = np.random.randint(0, 2, (12, 10)).astype(np.bool)
+                x = fluid.layers.assign(np_x)
+                x = fluid.layers.cast(x, 'bool')
+
+                out1 = paddle.all(x)
+                np_out1 = out1.numpy()
+                expect_res1 = np.all(np_x)
+                self.assertTrue((np_out1 == expect_res1).all())
+
+                out2 = paddle.all(x, axis=0)
+                np_out2 = out2.numpy()
+                expect_res2 = np.all(np_x, axis=0)
+                self.assertTrue((np_out2 == expect_res2).all())
+
+                out3 = paddle.all(x, axis=-1)
+                np_out3 = out3.numpy()
+                expect_res3 = np.all(np_x, axis=-1)
+                self.assertTrue((np_out3 == expect_res3).all())
+
+                out4 = paddle.all(x, axis=1, keepdim=True)
+                np_out4 = out4.numpy()
+                expect_res4 = np.all(np_x, axis=1, keepdims=True)
+                self.assertTrue((np_out4 == expect_res4).all())
+
+        paddle.enable_static()
+
+
+class TestAnyAPI(unittest.TestCase):
+    def setUp(self):
+        np.random.seed(123)
+        paddle.enable_static()
+        self.places = [fluid.CPUPlace()]
+        if core.is_compiled_with_cuda():
+            self.places.append(fluid.CUDAPlace(0))
+
+    def check_static_result(self, place):
+        with fluid.program_guard(fluid.Program(), fluid.Program()):
+            input = fluid.data(name="input", shape=[4, 4], dtype="bool")
+            result = paddle.any(x=input)
+            input_np = np.random.randint(0, 2, [4, 4]).astype("bool")
+
+            exe = fluid.Executor(place)
+            fetches = exe.run(fluid.default_main_program(),
+                              feed={"input": input_np},
+                              fetch_list=[result])
+            self.assertTrue(np.allclose(fetches[0], np.any(input_np)))
+
+    def test_static(self):
+        for place in self.places:
+            self.check_static_result(place=place)
+
+    def test_dygraph(self):
+        paddle.disable_static()
+        for place in self.places:
+            with fluid.dygraph.guard(place):
+                np_x = np.random.randint(0, 2, (12, 10)).astype(np.bool)
+                x = fluid.layers.assign(np_x)
+                x = fluid.layers.cast(x, 'bool')
+
+                out1 = paddle.any(x)
+                np_out1 = out1.numpy()
+                expect_res1 = np.any(np_x)
+                self.assertTrue((np_out1 == expect_res1).all())
+
+                out2 = paddle.any(x, axis=0)
+                np_out2 = out2.numpy()
+                expect_res2 = np.any(np_x, axis=0)
+                self.assertTrue((np_out2 == expect_res2).all())
+
+                out3 = paddle.any(x, axis=-1)
+                np_out3 = out3.numpy()
+                expect_res3 = np.any(np_x, axis=-1)
+                self.assertTrue((np_out3 == expect_res3).all())
+
+                out4 = paddle.any(x, axis=1, keepdim=True)
+                np_out4 = out4.numpy()
+                expect_res4 = np.any(np_x, axis=1, keepdims=True)
+                self.assertTrue((np_out4 == expect_res4).all())
+
+        paddle.enable_static()
+
+
 if __name__ == '__main__':
+    import paddle
+    paddle.enable_static()
     unittest.main()
diff --git a/python/paddle/tensor/__init__.py b/python/paddle/tensor/__init__.py
index 773e6ebc7af2e..41a78767586cc 100755
--- a/python/paddle/tensor/__init__.py
+++ b/python/paddle/tensor/__init__.py
@@ -66,8 +66,6 @@
 from .logic import logical_or  #DEFINE_ALIAS
 from .logic import logical_xor  #DEFINE_ALIAS
 from .logic import not_equal  #DEFINE_ALIAS
-# from .logic import reduce_all  #DEFINE_ALIAS
-# from .logic import reduce_any  #DEFINE_ALIAS
 from .logic import allclose  #DEFINE_ALIAS
 from .logic import equal_all  #DEFINE_ALIAS
 # from .logic import isnan        #DEFINE_ALIAS
@@ -163,6 +161,8 @@
 from .math import isinf  #DEFINE_ALIAS
 from .math import isnan  #DEFINE_ALIAS
 from .math import prod  #DEFINE_ALIAS
+from .math import all  #DEFINE_ALIAS
+from .math import any  #DEFINE_ALIAS
 from .random import multinomial  #DEFINE_ALIAS
 from .random import standard_normal
 from .random import normal
diff --git a/python/paddle/tensor/logic.py b/python/paddle/tensor/logic.py
index 1fc1c17d2edb2..44ccaffe9b9c6 100644
--- a/python/paddle/tensor/logic.py
+++ b/python/paddle/tensor/logic.py
@@ -27,6 +27,8 @@
 from ..fluid.layers import logical_not  #DEFINE_ALIAS
 from ..fluid.layers import logical_or  #DEFINE_ALIAS
 from ..fluid.layers import logical_xor  #DEFINE_ALIAS
+from ..fluid.layers import reduce_all  #DEFINE_ALIAS
+from ..fluid.layers import reduce_any  #DEFINE_ALIAS
 
 __all__ = [
     'equal',
diff --git a/python/paddle/tensor/math.py b/python/paddle/tensor/math.py
index 895d0c175905c..36793e0769672 100755
--- a/python/paddle/tensor/math.py
+++ b/python/paddle/tensor/math.py
@@ -21,7 +21,7 @@
 from paddle.tensor import cast
 import paddle
 from ..fluid import layers
-from ..fluid.framework import core, _varbase_creator, in_dygraph_mode, Variable
+from ..fluid.framework import core, _varbase_creator, in_dygraph_mode, Variable, convert_np_dtype_to_dtype_
 from ..fluid.layer_helper import LayerHelper
 from ..fluid.data_feeder import check_variable_and_dtype, check_type, check_dtype, convert_dtype
 from ..fluid.layers.layer_function_generator import _generate_doc_string_, generate_activation_fn, generate_layer_fn
@@ -46,6 +46,8 @@
 from ..fluid.layers import floor    #DEFINE_ALIAS
 from ..fluid.layers import log    #DEFINE_ALIAS
 from ..fluid.layers import reciprocal    #DEFINE_ALIAS
+from ..fluid.layers import reduce_all    #DEFINE_ALIAS
+from ..fluid.layers import reduce_any    #DEFINE_ALIAS
 # from ..fluid.layers import reduce_max    #DEFINE_ALIAS
 # from ..fluid.layers import reduce_min    #DEFINE_ALIAS
 # from ..fluid.layers import reduce_prod    #DEFINE_ALIAS
@@ -1933,3 +1935,201 @@ def increment(x, value=1.0, name=None):
         outputs={'Out': [x]},
         attrs={'step': float(value)})
     return x
+
+
+def all(x, axis=None, keepdim=False, name=None):
+    """
+    Computes the the ``logical and`` of tensor elements over the given dimension.
+
+    Args:
+        x (Tensor): An N-D Tensor, the input data type should be `bool`.
+        axis (int|list|tuple, optional): The dimensions along which the ``logical and`` is compute. If
+            :attr:`None`, and all elements of :attr:`x` and return a
+            Tensor variable with a single element, otherwise must be in the
+            range :math:`[-rank(x), rank(x))`. If :math:`axis[i] < 0`,
+            the dimension to reduce is :math:`rank + axis[i]`.
+        keepdim (bool, optional): Whether to reserve the reduced dimension in the
+            output Tensor. The result Tensor will have one fewer dimension
+            than the :attr:`x` unless :attr:`keepdim` is true, default
+            value is False.
+        name (str, optional): The default value is None. Normally there is no need for
+            user to set this property.  For more information, please refer to :ref:`api_guide_Name`
+
+    Returns:
+        Tensor: Results the ``logical and`` on the specified axis of input Tensor `x`,  it's data type is bool.
+
+    Raises:
+        ValueError: If the data type of `x` is not bool.
+        TypeError: The type of :attr:`axis` must be int, list or tuple.
+
+    Examples:
+        .. code-block:: python
+
+            import paddle
+            import paddle.fluid as fluid
+            import paddle.fluid.layers as layers
+            import numpy as np
+            
+            # set as static mode
+            paddle.disable_static()
+            
+            # x is a bool Tensor variable with following elements:
+            #    [[True, False]
+            #     [True, True]]
+            x = layers.assign(np.array([[1, 0], [1, 1]], dtype='int32'))
+            print(x)
+            x = layers.cast(x, 'bool')
+            
+            # out1 should be [False]
+            out1 = paddle.all(x)  # [False]
+            print(out1)
+            
+            # out2 should be [True, False]
+            out2 = paddle.all(x, axis=0)  # [True, False]
+            print(out2)
+            
+            # keep_dim=False, out3 should be [False, True], out.shape should be (2,)
+            out3 = paddle.all(x, axis=-1)  # [False, True]
+            print(out3)
+            
+            # keep_dim=True, out4 should be [[False], [True]], out.shape should be (2,1)
+            out4 = paddle.all(x, axis=1, keep_dim=True)
+            out4 = layers.cast(out4, 'int32')  # [[False], [True]]
+            print(out4)
+            
+    """
+    if axis is not None and not isinstance(axis, (list, tuple)):
+        axis = [axis]
+
+    if not axis:
+        reduce_all_flag = True
+    else:
+        if len(axis) == len(x.shape):
+            reduce_all_flag = True
+        else:
+            reduce_all_flag = False
+
+    attrs = {
+        'dim': axis if axis != None and axis != [] and axis != () else [0],
+        'keep_dim': keepdim,
+        'reduce_all': reduce_all_flag
+    }
+    dtype_flag = False
+
+
+    if in_dygraph_mode():
+        axis = axis if axis != None and axis != [] else [0]
+        return core.ops.reduce_all(x, 'dim', axis, 'keep_dim', keepdim,
+                                       'reduce_all', reduce_all_flag)
+    check_variable_and_dtype(x, 'x', ['bool'], 'all')
+
+
+    check_type(axis, 'axis', (int, list, tuple, type(None)), 'all')
+
+    helper = LayerHelper('all', **locals())
+    out = helper.create_variable_for_type_inference(dtype=x.dtype)
+    helper.append_op(
+        type='reduce_all',
+        inputs={'X': x},
+        outputs={'Out': out},
+        attrs=attrs)
+    return out
+
+
+def any(x, axis=None, keepdim=False, name=None):
+    """
+    Computes the the ``logical or`` of tensor elements over the given dimension.
+
+    Args:
+        x (Tensor): An N-D Tensor, the input data type should be `bool`.
+        axis (int|list|tuple, optional): The dimensions along which the ``logical or`` is compute. If
+            :attr:`None`, and all elements of :attr:`x` and return a
+            Tensor variable with a single element, otherwise must be in the
+            range :math:`[-rank(x), rank(x))`. If :math:`axis[i] < 0`,
+            the dimension to reduce is :math:`rank + axis[i]`.
+        keepdim (bool, optional): Whether to reserve the reduced dimension in the
+            output Tensor. The result Tensor will have one fewer dimension
+            than the :attr:`x` unless :attr:`keepdim` is true, default
+            value is False.
+        name (str, optional): The default value is None. Normally there is no need for
+            user to set this property.  For more information, please refer to :ref:`api_guide_Name`
+
+    Returns:
+        Tensor: Results the ``logical or`` on the specified axis of input Tensor `x`,  it's data type is bool.
+
+    Raises:
+        ValueError: If the data type of `x` is not bool.
+        TypeError: The type of :attr:`axis` must be int, list or tuple.
+
+    Examples:
+        .. code-block:: python
+
+            import paddle
+            import paddle.fluid as fluid
+            import paddle.fluid.layers as layers
+            import numpy as np
+            
+            # set as static mode
+            paddle.disable_static()
+            
+            # x is a bool Tensor variable with following elements:
+            #    [[True, False]
+            #     [False, False]]
+            x = layers.assign(np.array([[1, 0], [1, 1]], dtype='int32'))
+            print(x)
+            x = layers.cast(x, 'bool')
+            
+            # out1 should be [True]
+            out1 = paddle.any(x)  # [True]
+            print(out1)
+            
+            # out2 should be [True, False]
+            out2 = paddle.any(x, axis=0)  # [True, False]
+            print(out2)
+            
+            # keep_dim=False, out3 should be [True, False], out.shape should be (2,)
+            out3 = paddle.any(x, axis=-1)  # [True, False]
+            print(out3)
+            
+            # keep_dim=True, result should be [[True], [False]], out.shape should be (2,1)
+            out4 = paddle.any(x, axis=1, keep_dim=True)
+            out4 = layers.cast(out4, 'int32')  # [[True], [False]]
+            print(out4)
+            
+    """
+    if axis is not None and not isinstance(axis, (list, tuple)):
+        axis = [axis]
+
+    if not axis:
+        reduce_all_flag = True
+    else:
+        if len(axis) == len(x.shape):
+            reduce_all_flag = True
+        else:
+            reduce_all_flag = False
+
+    attrs = {
+        'dim': axis if axis != None and axis != [] and axis != () else [0],
+        'keep_dim': keepdim,
+        'reduce_all': reduce_all_flag
+    }
+    dtype_flag = False
+
+
+    if in_dygraph_mode():
+        axis = axis if axis != None and axis != [] else [0]
+        return core.ops.reduce_any(x, 'dim', axis, 'keep_dim', keepdim,
+                                       'reduce_all', reduce_all_flag)
+    check_variable_and_dtype(x, 'x', ['bool'], 'any')
+
+
+    check_type(axis, 'axis', (int, list, tuple, type(None)), 'any')
+
+    helper = LayerHelper('any', **locals())
+    out = helper.create_variable_for_type_inference(dtype=x.dtype)
+    helper.append_op(
+        type='reduce_any',
+        inputs={'X': x},
+        outputs={'Out': out},
+        attrs=attrs)
+    return out