diff --git a/python/paddle/fluid/dygraph/base.py b/python/paddle/fluid/dygraph/base.py
index 2c55dc8951a3733c9a3ccffbad1cb3ad9a83511a..92ddb3223dfea687bb0778fb23e8e2f95d9e92f4 100644
--- a/python/paddle/fluid/dygraph/base.py
+++ b/python/paddle/fluid/dygraph/base.py
@@ -146,12 +146,12 @@ def _switch_tracer_mode_guard_(is_train=True):
         yield
 
 
-def _no_grad_(func):
+def no_grad(func=None):
     """
-    This Decorator will avoid the func being decorated creating backward network in dygraph mode
+    Create a context which disables dygraph gradient calculation.
+    In this mode, the result of every computation will have `stop_gradient=True`.
 
-    Parameter:
-        - **func** (python func): the func don't need grad
+    Also functions as a decorator. (Make sure to instantiate without parenthesis.)
 
     Examples:
 
@@ -160,6 +160,24 @@ def _no_grad_(func):
         import numpy as np
         import paddle.fluid as fluid
 
+        # use as generator
+
+        data = np.array([[2, 3], [4, 5]]).astype('float32')
+        with fluid.dygraph.guard():
+            l0 = fluid.Linear(2, 2)  # l0.weight.gradient() is None
+            l1 = fluid.Linear(2, 2)
+            with fluid.dygraph.no_grad():
+                # l1.weight.stop_gradient is False
+                tmp = l1.weight * 2  # tmp.stop_gradient is True
+            x = fluid.dygraph.to_variable(data)
+            y = l0(x) + tmp
+            o = l1(y)
+            o.backward()
+            print(tmp.gradient() is None)  # True
+            print(l0.weight.gradient() is None)  # False
+
+        # use as decorator
+
         @fluid.dygraph.no_grad
         def test_layer():
             with fluid.dygraph.guard():
@@ -173,17 +191,15 @@ def _no_grad_(func):
         test_layer()
 
     """
+    if func is None:
+        return _switch_tracer_mode_guard_(is_train=False)
+    else:
 
-    def __impl__(*args, **kwargs):
-        with _switch_tracer_mode_guard_(is_train=False):
-            return func(*args, **kwargs)
-
-    return __impl__
-
+        def __impl__(*args, **kwargs):
+            with _switch_tracer_mode_guard_(is_train=False):
+                return func(*args, **kwargs)
 
-no_grad = wrap_decorator(_no_grad_)
-# for fluidDoc
-no_grad.__doc__ = _no_grad_.__doc__
+        return __impl__
 
 
 @signature_safe_contextmanager
diff --git a/python/paddle/fluid/tests/unittests/test_imperative_basic.py b/python/paddle/fluid/tests/unittests/test_imperative_basic.py
index 32c887a618f3ed5df46c0bfd06fc38c417845487..47831341c489e1741923a6fe0938618fc5a508c3 100644
--- a/python/paddle/fluid/tests/unittests/test_imperative_basic.py
+++ b/python/paddle/fluid/tests/unittests/test_imperative_basic.py
@@ -227,6 +227,24 @@ class TestImperative(unittest.TestCase):
             self.assertTrue(np.array_equal(y, tmp4.numpy()))
             self.assertTrue(np.array_equal(x, tmp5.numpy()))
 
+    def test_no_grad_guard(self):
+        data = np.array([[2, 3], [4, 5]]).astype('float32')
+        with fluid.dygraph.guard():
+            l0 = fluid.Linear(2, 2)
+            self.assertTrue(l0.weight._grad_ivar() is None)
+            l1 = fluid.Linear(2, 2)
+            with fluid.dygraph.no_grad():
+                self.assertTrue(l1.weight.stop_gradient is False)
+                tmp = l1.weight * 2
+                self.assertTrue(tmp.stop_gradient)
+            x = fluid.dygraph.to_variable(data)
+            y = l0(x) + tmp
+            o = l1(y)
+            o.backward()
+
+            self.assertTrue(tmp._grad_ivar() is None)
+            self.assertTrue(l0.weight._grad_ivar() is not None)
+
     def test_sum_op(self):
         x = np.ones([2, 2], np.float32)
         with fluid.dygraph.guard():