diff --git a/python/paddle/fluid/layers/control_flow.py b/python/paddle/fluid/layers/control_flow.py
index edae54294b91e9a8999eaf6d74270df951a764ba..a997afbf4d35f3365605b264ac144a96e16394c7 100755
--- a/python/paddle/fluid/layers/control_flow.py
+++ b/python/paddle/fluid/layers/control_flow.py
@@ -33,7 +33,8 @@ __all__ = [
     'While', 'Switch', 'increment', 'array_write', 'create_array', 'less_than',
     'less_equal', 'greater_than', 'greater_equal', 'equal', 'not_equal',
     'array_read', 'array_length', 'cond', 'IfElse', 'DynamicRNN', 'StaticRNN',
-    'reorder_lod_tensor_by_rank', 'Print', 'is_empty', 'case', 'switch_case'
+    'reorder_lod_tensor_by_rank', 'Print', 'is_empty', 'case', 'switch_case',
+    'while_loop'
 ]
 
 
@@ -918,6 +919,93 @@ class While(object):
                    "is_test": self.is_test})
 
 
+def while_loop(cond, body, loop_vars, name=None):
+    """
+    while_loop is one of the control flows. Repeats while_loop `body` until `cond` returns False.
+
+    Args:
+        cond(Callable): A callable returning a boolean tensor controlling whether to continue looping.
+        body(Callable): A callable returning a tuple or list of tensors of the same arity (length and structure)
+            and types as ``loops_vars`` .
+        loop_vars(list|tuple): A list or tuple of tensors that is passed to both ``cond`` and ``body`` .
+        name(str, optional): Normally there is no need for users to set this property. For more information, please
+            refer to :ref:`api_guide_Name`. Default is None.
+    
+    Returns:
+        A list or tuple of tensors which returned by ``body`` .
+    
+    Returen type:
+        list(Variable)|tuple(Variable).
+
+    Raises:
+        TypeError: If the type of ``cond`` is not callable.
+        TypeError: If the type of ``body`` is not callable.
+        TypeError: If the type of ``loop_vars`` is not list or tuple.
+        TypeError: If the type of ``cond`` returns is not Variable.
+        TypeError: If the type of ``cond`` returns is not a boolean variable.
+        TypeError: If the shape of ``cond`` returns is not equals 1.
+        ValueError: If the ``var_loops`` is empty.
+
+    Examples:
+        .. code-block:: python
+
+            import paddle.fluid as fluid
+            import paddle.fluid.layers as layers
+
+            def cond(i):
+                return layers.less_than(i, ten)
+
+            def body(i):
+                return layers.increment(x=i, value=1, in_place=True)
+
+            main_program = fluid.default_main_program()
+            startup_program = fluid.default_startup_program()
+
+            with fluid.program_guard(main_program, startup_program):
+                i = layers.fill_constant(shape=[1], dtype='int64', value=0)     # loop counter
+                ten = layers.fill_constant(shape=[1], dtype='int64', value=10)  # loop length
+                out = layers.while_loop(cond, body, [i])
+                
+                exe = fluid.Executor(fluid.CPUPlace())
+                res = exe.run(main_program, feed={}, fetch_list=out)
+                print(res) # [array([10])]
+    """
+    helper = LayerHelper('while_loop', **locals())
+
+    if not callable(cond):
+        raise TypeError("cond in while_loop should be callable")
+    if not callable(body):
+        raise TypeError("body in while_loop should be callable")
+    if not isinstance(loop_vars, (list, tuple)):
+        raise TypeError("loop_vars in while_loop should be a list or tuple")
+    if len(loop_vars) == 0:
+        raise ValueError("loop_vars in while_loop should not be empty")
+
+    pre_cond = cond(*loop_vars)
+    if not isinstance(pre_cond, Variable):
+        raise TypeError("cond in while_loop should return a variable")
+    if pre_cond.dtype != core.VarDesc.VarType.BOOL:
+        raise TypeError("cond in while_loop should return a boolean variable")
+    if reduce(lambda a, b: a * b, pre_cond.shape, 1) != 1:
+        raise TypeError(
+            "the shape of the variable returned by cond should be [],"
+            "but given shape as {0}.".format(list(pre_cond.shape)))
+
+    while_loop_block = While(pre_cond)
+    with while_loop_block.block():
+        output_vars = body(*loop_vars)
+        if len(loop_vars) == 1:
+            assign(output_vars, loop_vars[0])
+            now_cond = cond(output_vars)
+        else:
+            for i in range(len(output_vars)):
+                assign(output_vars[i], loop_vars[i])
+            now_cond = cond(*output_vars)
+        assign(now_cond, pre_cond)
+
+    return loop_vars
+
+
 def lod_rank_table(x, level=0):
     """
     LoD Rank Table Operator. Given an input variable **x** and a level number
diff --git a/python/paddle/fluid/tests/unittests/test_while_loop_op.py b/python/paddle/fluid/tests/unittests/test_while_loop_op.py
new file mode 100644
index 0000000000000000000000000000000000000000..78ceb5250cf658431b09bfc34fe820747c26c81d
--- /dev/null
+++ b/python/paddle/fluid/tests/unittests/test_while_loop_op.py
@@ -0,0 +1,205 @@
+# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from __future__ import print_function
+
+import numpy as np
+import unittest
+
+import paddle.fluid as fluid
+import paddle.fluid.core as core
+import paddle.fluid.layers as layers
+import paddle.fluid.framework as framework
+from paddle.fluid.executor import Executor
+from paddle.fluid.framework import Program, program_guard
+
+
+class TestApiWhileLoop(unittest.TestCase):
+    def test_var_tuple(self):
+        def cond(i):
+            return layers.less_than(i, ten)
+
+        def body(i):
+            return layers.elementwise_add(x=i, y=one)
+
+        main_program = Program()
+        startup_program = Program()
+        with program_guard(main_program, startup_program):
+            i = layers.fill_constant(shape=[1], dtype='int64', value=0)
+            one = layers.fill_constant(shape=[1], dtype='int64', value=1)
+            ten = layers.fill_constant(shape=[1], dtype='int64', value=10)
+            out = layers.while_loop(cond, body, (i, ))
+
+        place = fluid.CPUPlace()
+        exe = fluid.Executor(place)
+        res = exe.run(main_program, fetch_list=out)
+        self.assertTrue(
+            np.allclose(np.asarray(res[0]), np.full((1), 10, np.int64)))
+
+    def test_var_list(self):
+        def cond(i, mem):
+            return layers.less_than(i, ten)
+
+        def body(i, mem):
+            mem = layers.elementwise_add(x=mem, y=one)
+            i = layers.increment(i)
+            return [i, mem]
+
+        main_program = Program()
+        startup_program = Program()
+        with program_guard(main_program, startup_program):
+            i = layers.zeros(shape=[1], dtype='int64')
+            ten = layers.fill_constant(shape=[1], dtype='int64', value=10)
+            mem = layers.data(name="mem", shape=[10], dtype='float32')
+            one = layers.fill_constant(shape=[10], dtype='float32', value=1)
+            out = layers.while_loop(cond, body, [i, mem])
+
+            data = np.random.rand(10).astype('float32')
+            data_one = np.ones(10).astype('float32')
+
+        place = fluid.CPUPlace()
+        exe = fluid.Executor(place)
+        res = exe.run(main_program, feed={'mem': data}, fetch_list=out)
+        for i in range(10):
+            data = np.add(data, data_one)
+        self.assertTrue(np.allclose(np.asarray(res[1]), data))
+
+
+class TestApiWhileLoop_Nested(unittest.TestCase):
+    def test_nested_net(self):
+        def external_cond(i, j, init, sums):
+            return layers.less_than(i, loop_len1)
+
+        def external_body(i, j, init, sums):
+            def internal_cond(j, init, sums):
+                return layers.less_than(j, loop_len2)
+
+            def internal_body(j, init, sums):
+                init = layers.elementwise_add(x=init, y=ones)
+                sums = layers.elementwise_add(x=init, y=sums)
+                j = layers.increment(j)
+                return [j, init, sums]
+
+            result = layers.while_loop(internal_cond, internal_body,
+                                       [j, init, sums])
+            j = result[0]
+            init = result[1]
+            sums = result[2]
+            sums = layers.elementwise_add(x=init, y=sums)
+            i = layers.increment(i)
+            return [i, j, init, sums]
+
+        main_program = Program()
+        startup_program = Program()
+        with program_guard(main_program, startup_program):
+            i = layers.zeros(shape=[1], dtype='int64')
+            j = layers.zeros(shape=[1], dtype='int64')
+            init = layers.data(name="init", shape=[3, 3], dtype='float32')
+            sums = layers.data(name="sums", shape=[3, 3], dtype='float32')
+            loop_len1 = layers.fill_constant(shape=[1], dtype='int64', value=2)
+            loop_len2 = layers.fill_constant(shape=[1], dtype='int64', value=3)
+            ones = layers.fill_constant(shape=[3, 3], dtype='float32', value=1)
+
+            res = layers.while_loop(external_cond, external_body,
+                                    [i, j, init, sums])
+
+            data = np.random.rand(3, 3).astype('float32')
+            data_sums = np.zeros([3, 3]).astype('float32')
+
+        place = fluid.CPUPlace()
+        exe = fluid.Executor(place)
+        ret = exe.run(main_program,
+                      feed={'init': data,
+                            'sums': data_sums},
+                      fetch_list=res)
+        for i in range(3):
+            data = np.add(data, 1)
+            data_sums = np.add(data, data_sums)
+        for j in range(2):
+            data_sums = np.add(data, data_sums)
+        self.assertTrue(np.allclose(np.asarray(ret[3]), data_sums))
+
+
+class TestApiWhileLoop_Error(unittest.TestCase):
+    def test_error(self):
+        def cond_returns_constant(i):
+            return 1
+
+        def cond_returns_not_bool_tensor(i):
+            return layers.increment(i)
+
+        def cond_returns_bool_tensor(i):
+            return layers.less_than(i, ten)
+
+        def cond_returns_2d_tensor(i):
+            return layers.less_than(i, ten_2d)
+
+        def body(i):
+            return layers.increment(i)
+
+        main_program = Program()
+        startup_program = Program()
+        with program_guard(main_program, startup_program):
+            data = layers.fill_constant(shape=[1], dtype='int64', value=1)
+            data_1d = layers.fill_constant(shape=[1], dtype='int64', value=1)
+            data_2d = layers.fill_constant(shape=[2, 2], dtype='int64', value=1)
+            ten = layers.fill_constant(shape=[1], dtype='int64', value=10)
+            ten_2d = layers.fill_constant(shape=[2, 2], dtype='int64', value=10)
+
+            # The type of `cond` in Op(while_loop) must be callable 
+            def type_error_cond():
+                out = layers.while_loop(data, body, [data_1d])
+
+            self.assertRaises(TypeError, type_error_cond)
+
+            # The type of `body` in Op(while_loop) must be callable
+            def type_error_body():
+                out = layers.while_loop(cond_returns_bool_tensor, data,
+                                        [data_1d])
+
+            self.assertRaises(TypeError, type_error_body)
+
+            # The type of `loop_vars` in Op(while_loop) must be list or tuple
+            def type_error_loop_vars():
+                out = layers.while_loop(cond_returns_bool_tensor, body, data_1d)
+
+            self.assertRaises(TypeError, type_error_loop_vars)
+
+            # The value of `loop_vars` is empty
+            def value_error_loop_vars():
+                out = layers.while_loop(cond_returns_bool_tensor, body, [])
+
+            self.assertRaises(ValueError, value_error_loop_vars)
+
+            # The type of `cond` returns in Op(while_loop) must be Variable
+            def type_error_cond_returns_not_variable():
+                out = layers.while_loop(cond_returns_constant, body, [data_1d])
+
+            self.assertRaises(TypeError, type_error_cond_returns_not_variable)
+
+            # The type of `cond` returns in Op(while_loop) must be a bollean variable
+            def type_error_cond_returns_not_boolean():
+                out = layers.while_loop(cond_returns_not_bool_tensor, body,
+                                        [data_1d])
+
+            self.assertRaises(TypeError, type_error_cond_returns_not_boolean)
+
+            # The shape of `cond` returns in Op(while_loop) must be 1
+            def type_error_shape_cond_returns_2d():
+                out = layers.while_loop(cond_returns_2d_tensor, body, [data_2d])
+
+            self.assertRaises(TypeError, type_error_shape_cond_returns_2d)
+
+
+if __name__ == '__main__':
+    unittest.main()