diff --git a/python/paddle/v2/fluid/clip.py b/python/paddle/v2/fluid/clip.py
index 776c0f3f0276cd228db9846e473c65d44e10bbb7..be0c2735f24c40f7c90b29a29fe47880acfedee1 100644
--- a/python/paddle/v2/fluid/clip.py
+++ b/python/paddle/v2/fluid/clip.py
@@ -4,6 +4,7 @@ from . import core
 
 __all__ = [
     'GradientClipByValue',
+    'ErrorClipByValue',
     'append_gradient_clip_ops',
     'error_clip_callback',
 ]
@@ -25,12 +26,12 @@ class ErrorClipByValue(BaseErrorClipAttr):
         self.min = min
 
     def append_clip_op(self, block, grad_name):
-        block.append_op(
-            type="clip",
-            inputs={"X": grad_name},
-            outputs={"Out": grad_name},
-            attrs={"min": self.min,
-                   "max": self.max})
+        clip_op_desc = block.desc.append_op()
+        clip_op_desc.set_type("clip")
+        clip_op_desc.set_input("X", [grad_name])
+        clip_op_desc.set_output("Out", [grad_name])
+        clip_op_desc.set_attr("min", self.min)
+        clip_op_desc.set_attr("max", self.max)
 
 
 def error_clip_callback(block, context):
@@ -41,6 +42,11 @@ def error_clip_callback(block, context):
                          op_desc.output_arg_names()):
         fwd_var = block.var_recursive(grad_to_var[grad_n])
         error_clip = getattr(fwd_var, "error_clip", None)
+        if not (error_clip is None or isinstance(error_clip,
+                                                 BaseErrorClipAttr)):
+            raise TypeError(
+                "Variable's error_clip should be an instance of BaseErrorClipAttr or None."
+            )
         if error_clip is not None:
             error_clip.append_clip_op(block, grad_n)
 
diff --git a/python/paddle/v2/fluid/framework.py b/python/paddle/v2/fluid/framework.py
index bdbfe9da0772fdbd00dfc8ed00413ece56f48407..f78f2a331a6ea882df3368c54013b08c1a1debc5 100644
--- a/python/paddle/v2/fluid/framework.py
+++ b/python/paddle/v2/fluid/framework.py
@@ -280,6 +280,9 @@ class Variable(object):
         uid = core.unique_integer(prefix)  # unique during whole process.
         return "_".join([prefix, str(uid)])
 
+    def set_error_clip(self, error_clip):
+        self.error_clip = error_clip
+
 
 def get_all_op_protos():
     """
diff --git a/python/paddle/v2/fluid/tests/test_clip.py b/python/paddle/v2/fluid/tests/test_clip.py
new file mode 100644
index 0000000000000000000000000000000000000000..a71823f7e82d2f3007de3d17afa1770c688792d4
--- /dev/null
+++ b/python/paddle/v2/fluid/tests/test_clip.py
@@ -0,0 +1,67 @@
+from __future__ import print_function
+import numpy as np
+import paddle.v2 as paddle
+import paddle.v2.fluid as fluid
+
+BATCH_SIZE = 128
+CLIP_MAX = 2e-6
+CLIP_MIN = -1e-6
+
+prog = fluid.framework.Program()
+
+with fluid.program_guard(main_program=prog):
+    image = fluid.layers.data(name='x', shape=[784], dtype='float32')
+
+    hidden1 = fluid.layers.fc(input=image, size=128, act='relu')
+    hidden2 = fluid.layers.fc(input=hidden1, size=64, act='relu')
+    predict = fluid.layers.fc(input=hidden2, size=10, act='softmax')
+
+    label = fluid.layers.data(name='y', shape=[1], dtype='int64')
+
+    cost = fluid.layers.cross_entropy(input=predict, label=label)
+    avg_cost = fluid.layers.mean(x=cost)
+
+prog_clip = prog.clone()
+prog_clip.block(0).var(hidden1.name).set_error_clip(
+    fluid.clip.ErrorClipByValue(
+        max=CLIP_MAX, min=CLIP_MIN))
+
+avg_cost_clip = prog_clip.block(0).var(avg_cost.name)
+fluid.backward.append_backward(loss=avg_cost)
+fluid.backward.append_backward(
+    loss=avg_cost_clip, callback=fluid.clip.error_clip_callback)
+
+hidden1_grad = prog.block(0).var(hidden1.name + "@GRAD")
+hidden1_grad_clip = prog_clip.block(0).var(hidden1.name + "@GRAD")
+
+hidden2_grad = prog.block(0).var(hidden2.name + "@GRAD")
+hidden2_grad_clip = prog_clip.block(0).var(hidden2.name + "@GRAD")
+
+train_reader = paddle.batch(
+    paddle.reader.shuffle(
+        paddle.dataset.mnist.train(), buf_size=8192),
+    batch_size=BATCH_SIZE)
+
+place = fluid.CPUPlace()
+exe = fluid.Executor(place)
+feeder = fluid.DataFeeder(feed_list=[image, label], place=place)
+exe.run(fluid.default_startup_program())
+
+count = 0
+for data in train_reader():
+    count += 1
+    if count > 5:
+        break
+    out1, out2 = exe.run(prog,
+                         feed=feeder.feed(data),
+                         fetch_list=[hidden1_grad, hidden2_grad])
+    out1_clip, out2_clip = exe.run(
+        prog_clip,
+        feed=feeder.feed(data),
+        fetch_list=[hidden1_grad_clip, hidden2_grad_clip])
+    if not ((out1.clip(
+            min=CLIP_MIN, max=CLIP_MAX) == out1_clip).all() and
+            (out2 == out2_clip).all()):
+        exit(1)
+
+exit(0)