diff --git a/python/paddle/__init__.py b/python/paddle/__init__.py
index fafe08a81f24af21f3e22bb2d080cbe71d4dfeeb..6f8229e6f18f52058bf957d55d31ddabe73ebfdd 100755
--- a/python/paddle/__init__.py
+++ b/python/paddle/__init__.py
@@ -257,6 +257,7 @@ from .framework import CUDAPinnedPlace  #DEFINE_ALIAS
 
 from .framework import grad  #DEFINE_ALIAS
 from .framework import no_grad  #DEFINE_ALIAS
+from .framework import set_grad_enabled  #DEFINE_ALIAS
 from .framework import save  #DEFINE_ALIAS
 from .framework import load  #DEFINE_ALIAS
 from .framework import DataParallel  #DEFINE_ALIAS
diff --git a/python/paddle/fluid/tests/unittests/test_imperative_basic.py b/python/paddle/fluid/tests/unittests/test_imperative_basic.py
index e6e7b8222a4b30c066cb9dc07a98c5570e6a0a8a..9dae36c3c223f89617fdad0fe8c4e42daa0a2613 100644
--- a/python/paddle/fluid/tests/unittests/test_imperative_basic.py
+++ b/python/paddle/fluid/tests/unittests/test_imperative_basic.py
@@ -296,6 +296,28 @@ class TestImperative(unittest.TestCase):
             self.assertTrue(tmp._grad_ivar() is None)
             self.assertTrue(l0.weight._grad_ivar() is not None)
 
+    def test_paddle_imperative_set_grad_enabled(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 paddle.set_grad_enabled(False):
+                self.assertTrue(l1.weight.stop_gradient is False)
+                tmp = l1.weight * 2
+                with paddle.set_grad_enabled(True):
+                    tmp2 = l1.weight * 2
+                self.assertTrue(tmp.stop_gradient)
+                self.assertTrue(tmp2.stop_gradient is False)
+            x = fluid.dygraph.to_variable(data)
+            y = l0(x) + tmp2
+            o = l1(y)
+            o.backward()
+
+            self.assertTrue(tmp._grad_ivar() is None)
+            self.assertTrue(tmp2._grad_ivar() is not 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():
diff --git a/python/paddle/framework/__init__.py b/python/paddle/framework/__init__.py
index 5a616d81659b2361266542ca9e6f5ce40503506d..b8684874085a9c44048d1473ddec956dcff3cf3e 100644
--- a/python/paddle/framework/__init__.py
+++ b/python/paddle/framework/__init__.py
@@ -18,12 +18,16 @@ __all__ = [
     'NPUPlace', 'get_default_dtype', 'set_default_dtype'
 ]
 
-__all__ += ['grad', 'LayerList', 'load', 'save', 'no_grad', 'DataParallel']
+__all__ += [
+    'grad', 'set_grad_enabled', 'LayerList', 'load', 'save', 'no_grad',
+    'DataParallel'
+]
 
 from . import random
 from .random import seed
 from .framework import get_default_dtype
 from .framework import set_default_dtype
+from .framework import set_grad_enabled
 
 from ..fluid.param_attr import ParamAttr  #DEFINE_ALIAS
 # from ..fluid.layers.tensor import create_global_var  #DEFINE_ALIAS
diff --git a/python/paddle/framework/framework.py b/python/paddle/framework/framework.py
index 41ec18ce32d3036c3db86aaa98053f59ff61f717..77be85a3195fd6dbac6f07c57e867f01343dfc2a 100644
--- a/python/paddle/framework/framework.py
+++ b/python/paddle/framework/framework.py
@@ -15,7 +15,9 @@
 # TODO: define framework api 
 from paddle.fluid.layer_helper_base import LayerHelperBase
 from paddle.fluid.data_feeder import convert_dtype
+from paddle.fluid.framework import _dygraph_tracer
 import numpy as np
+from contextlib import contextmanager
 
 __all__ = ['set_default_dtype', 'get_default_dtype']
 
@@ -80,3 +82,37 @@ def get_default_dtype():
             paddle.get_default_dtype()
     """
     return LayerHelperBase.get_default_dtype()
+
+
+@contextmanager
+def set_grad_enabled(mode):
+    """
+    :api_attr: imperative
+
+    Create a context which enables or disables dygraph gradient calculation.
+
+    Args:
+        mode(bool): whether to enable (`True`), or disable (`False`) grad.
+
+    Examples:
+        .. code-block:: python
+            x = paddle.ones([3, 2])
+            x.stop_gradient = False
+            with torch.set_grad_enabled(False):
+                y = x * 2
+                with torch.set_grad_enabled(True):
+                    z = x * 2
+            print(y.stop_gradient)   # True
+            print(z.stop_gradient)   # False
+    """
+
+    tracer = _dygraph_tracer()
+    if tracer:
+        prev_mode = tracer._has_grad
+        tracer._has_grad = mode
+        try:
+            yield
+        finally:
+            tracer._has_grad = prev_mode
+    else:
+        yield