diff --git a/python/paddle/v2/fluid/clip.py b/python/paddle/v2/fluid/clip.py
index 386df9823de9119287abf87569eab0b283ecc802..3028029e60fde2f481b4348ab1b0a4980ebb2b60 100644
--- a/python/paddle/v2/fluid/clip.py
+++ b/python/paddle/v2/fluid/clip.py
@@ -12,14 +12,18 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+import copy
+
 import functools
 import layers
 import framework
 from . import core
 
 __all__ = [
-    'GradientClipByValue',
     'ErrorClipByValue',
+    'GradientClipByValue',
+    'GradientClipByNorm',
+    'GradientClipByGlobalNorm',
     'append_gradient_clip_ops',
     'error_clip_callback',
 ]
@@ -155,10 +159,11 @@ class GradientClipByGlobalNorm(BaseGradientClipAttr):
         return param, new_grad
 
 
-def gradient_clip_by_global_norm(clip_norm,
-                                 param_list=None,
-                                 group_name="default_group",
-                                 program=None):
+def set_gradient_clip(clip, param_list=None, program=None):
+    if not isinstance(clip, BaseGradientClipAttr):
+        raise TypeError(
+            "'clip' should be an instance of BaseGradientClipAttr's derived class"
+        )
     if program is None:
         program = framework.default_main_program()
     if param_list is None:
@@ -171,8 +176,7 @@ def gradient_clip_by_global_norm(clip_norm,
         )
 
     for param in param_list:
-        param.gradient_clip_attr = GradientClipByGlobalNorm(clip_norm,
-                                                            group_name)
+        param.gradient_clip_attr = copy.deepcopy(clip)
 
 
 def append_gradient_clip_ops(param_grad):
diff --git a/python/paddle/v2/fluid/tests/test_gradient_clip.py b/python/paddle/v2/fluid/tests/test_gradient_clip.py
index 4e6e6a1ef6961d8f087dfc1ac5a4c4a8ad90032e..9337791c21183fd7c2e5d6b9d47c99d762c93d46 100644
--- a/python/paddle/v2/fluid/tests/test_gradient_clip.py
+++ b/python/paddle/v2/fluid/tests/test_gradient_clip.py
@@ -40,7 +40,8 @@ p_g = fluid.backward.append_backward(loss=avg_cost)
 p_g_clip = fluid.backward.append_backward(loss=avg_cost_clip)
 
 with fluid.program_guard(main_program=prog_clip):
-    fluid.clip.gradient_clip_by_global_norm(clip_norm=CLIP)
+    fluid.clip.set_gradient_clip(
+        fluid.clip.GradientClipByGlobalNorm(clip_norm=CLIP))
     p_g_clip = fluid.clip.append_gradient_clip_ops(p_g_clip)
 
 grad_list = [elem[1] for elem in p_g]