diff --git a/python/paddle/fluid/dygraph/base.py b/python/paddle/fluid/dygraph/base.py
index bf484b35c7bf9a2b17126789ff247bd73095fe7b..c19a2f926e068c930a2ab3dc6d610da6d6ca145b 100644
--- a/python/paddle/fluid/dygraph/base.py
+++ b/python/paddle/fluid/dygraph/base.py
@@ -11,20 +11,49 @@
 # 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 ..wrapped_decorator import signature_safe_contextmanager
+from ..wrapped_decorator import signature_safe_contextmanager, wrap_decorator
+import contextlib
 import numpy as np
 
 from paddle.fluid import core
 from paddle.fluid import framework
 from .tracer import Tracer
 
-__all__ = ['enabled', 'guard', 'to_variable']
+__all__ = [
+    'enabled',
+    'no_grad',
+    'guard',
+    'to_variable',
+]
 
 
 def enabled():
     return framework.in_dygraph_mode()
 
 
+@contextlib.contextmanager
+def _switch_tracer_mode_guard_(is_train=True):
+    tracer = framework._dygraph_tracer()
+    if tracer:
+        mode = tracer._train_mode
+        tracer._train_mode = is_train
+        yield
+        tracer._train_mode = mode
+    else:
+        yield
+
+
+def _no_grad_(func):
+    def __impl__(*args, **kwargs):
+        with _switch_tracer_mode_guard_(is_train=False):
+            return func(*args, **kwargs)
+
+    return __impl__
+
+
+no_grad = wrap_decorator(_no_grad_)
+
+
 @signature_safe_contextmanager
 def guard(place=None):
     train = framework.Program()
diff --git a/python/paddle/fluid/dygraph_grad_clip.py b/python/paddle/fluid/dygraph_grad_clip.py
index bcc307511ecc6bc1625044fdb190c44335f07712..826f918f36ece2eab5ddf17c1c0b3c86ca4e6438 100644
--- a/python/paddle/fluid/dygraph_grad_clip.py
+++ b/python/paddle/fluid/dygraph_grad_clip.py
@@ -22,6 +22,7 @@ import functools
 from . import layers
 from . import framework
 from . import core
+from .dygraph import base as imperative_base
 
 __all__ = [
     'GradClipByValue',
@@ -37,6 +38,7 @@ class GradClipBase(object):
     def _clip(self, para_and_grad):
         raise NotImplementedError
 
+    @imperative_base.no_grad
     def __call__(self, para_and_grad):
         return self._clip(para_and_grad)
 
@@ -86,6 +88,7 @@ class GradClipByValue(GradClipBase):
             
     """
 
+    @imperative_base.no_grad
     def __init__(self, min_value, max_value=None):
 
         if min_value is None:
@@ -164,6 +167,7 @@ class GradClipByNorm(GradClipBase):
 
     """
 
+    @imperative_base.no_grad
     def __init__(self, clip_norm):
         self.clip_norm = clip_norm
 
@@ -243,6 +247,7 @@ class GradClipByGlobalNorm(GradClipBase):
 
     """
 
+    @imperative_base.no_grad
     def __init__(self, max_global_norm):
         self.max_global_norm = layers.fill_constant(
             shape=[1], dtype='float32', value=max_global_norm)
diff --git a/python/paddle/fluid/optimizer.py b/python/paddle/fluid/optimizer.py
index c3d136c29a30187b35da6e172821d425d8d44f1c..50705bba2e93ece695991212f635ab7ab8010b8c 100644
--- a/python/paddle/fluid/optimizer.py
+++ b/python/paddle/fluid/optimizer.py
@@ -55,6 +55,7 @@ class Optimizer(object):
     but need to use one of it's implementation.
     """
 
+    @imperative_base.no_grad
     def __init__(self, learning_rate, regularization=None, name=None):
         if framework.in_dygraph_mode():
             if not isinstance(learning_rate, float) and \
@@ -472,6 +473,7 @@ class Optimizer(object):
                 optimize_ops = self.apply_gradients(params_grads)
         return optimize_ops
 
+    @imperative_base.no_grad
     def minimize(self,
                  loss,
                  startup_program=None,
diff --git a/python/paddle/fluid/tests/unittests/test_imperative_decorator.py b/python/paddle/fluid/tests/unittests/test_imperative_decorator.py
new file mode 100644
index 0000000000000000000000000000000000000000..2b92bcb38befcd7d1d2932e2681dc167fa6777ee
--- /dev/null
+++ b/python/paddle/fluid/tests/unittests/test_imperative_decorator.py
@@ -0,0 +1,48 @@
+# Copyright (c) 2019 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.
+
+import paddle.fluid as fluid
+import paddle.fluid.framework as framework
+import unittest
+
+
+class TestTracerMode(unittest.TestCase):
+    def setUp(self):
+        self.init_mode = True
+
+    def get_tracer_mode(self):
+        assert fluid.dygraph.enabled(), "Dygraph mode must be enabled"
+
+    @fluid.dygraph.no_grad
+    def no_grad_func(self, a):
+        self.assertEqual(self.tracer._train_mode, False)
+        return a
+
+    def test_main(self):
+        with fluid.dygraph.guard():
+            self.tracer = framework._dygraph_tracer()
+            self.tracer._train_mode = self.init_mode
+
+            self.assertEqual(self.no_grad_func(1), 1)
+
+            self.assertEqual(self.tracer._train_mode, self.init_mode)
+
+
+class TestTracerMode2(TestTracerMode):
+    def setUp(self):
+        self.init_mode = False
+
+
+if __name__ == '__main__':
+    unittest.main()