diff --git a/paddle/fluid/operators/clip_op.h b/paddle/fluid/operators/clip_op.h
index 03abfe7eb703b021dac2261dcd9c87d440b04001..68f5d5460efd16a79d6e1553c2fb78da31fc704a 100644
--- a/paddle/fluid/operators/clip_op.h
+++ b/paddle/fluid/operators/clip_op.h
@@ -66,7 +66,7 @@ template <typename DeviceContext, typename T>
 class ClipKernel : public framework::OpKernel<T> {
  public:
   void Compute(const framework::ExecutionContext& context) const override {
-    auto max = context.Attr<T>("max");
+    auto max = static_cast<T>(context.Attr<float>("max"));
     Tensor max_cpu;
     if (context.HasInput("Max")) {
       auto* max_t = context.Input<Tensor>("Max");
@@ -77,8 +77,9 @@ class ClipKernel : public framework::OpKernel<T> {
       }
       max = max_data[0];
     }
+    max = static_cast<T>(max);
 
-    auto min = context.Attr<T>("min");
+    auto min = context.Attr<float>("min");
     Tensor min_cpu;
     if (context.HasInput("Min")) {
       auto* min_t = context.Input<Tensor>("Min");
@@ -141,7 +142,7 @@ template <typename DeviceContext, typename T>
 class ClipGradKernel : public framework::OpKernel<T> {
  public:
   void Compute(const framework::ExecutionContext& context) const override {
-    auto max = context.Attr<T>("max");
+    auto max = static_cast<T>(context.Attr<float>("max"));
     Tensor max_cpu;
     if (context.HasInput("Max")) {
       auto* max_t = context.Input<Tensor>("Max");
@@ -152,8 +153,9 @@ class ClipGradKernel : public framework::OpKernel<T> {
       }
       max = max_data[0];
     }
+    max = static_cast<T>(max);
 
-    auto min = context.Attr<T>("min");
+    auto min = context.Attr<float>("min");
     Tensor min_cpu;
     if (context.HasInput("Min")) {
       auto* min_t = context.Input<Tensor>("Min");
@@ -164,6 +166,7 @@ class ClipGradKernel : public framework::OpKernel<T> {
       }
       min = min_data[0];
     }
+    min = static_cast<T>(min);
 
     auto* d_out =
         context.Input<framework::LoDTensor>(framework::GradVarName("Out"));
diff --git a/python/paddle/fluid/tests/unittests/test_clip_op.py b/python/paddle/fluid/tests/unittests/test_clip_op.py
index 2e1f9d41747e3a99b4b4a0650a52973459b85c7b..b56d9f6668e8bcbd37443fb88b1f5f4dd40a2511 100644
--- a/python/paddle/fluid/tests/unittests/test_clip_op.py
+++ b/python/paddle/fluid/tests/unittests/test_clip_op.py
@@ -138,8 +138,9 @@ class TestClipAPI(unittest.TestCase):
         out_6 = paddle.clip(images, max=max)
         out_7 = paddle.clip(images, max=-1.)
         out_8 = paddle.clip(images)
+        out_9 = paddle.clip(paddle.cast(images, 'float64'), min=0.2, max=0.9)
 
-        res1, res2, res3, res4, res5, res6, res7, res8 = exe.run(
+        res1, res2, res3, res4, res5, res6, res7, res8, res9 = exe.run(
             fluid.default_main_program(),
             feed={
                 "image": data,
@@ -147,7 +148,7 @@ class TestClipAPI(unittest.TestCase):
                 "max": np.array([0.8]).astype('float32')
             },
             fetch_list=[
-                out_1, out_2, out_3, out_4, out_5, out_6, out_7, out_8
+                out_1, out_2, out_3, out_4, out_5, out_6, out_7, out_8, out_9
             ])
 
         self.assertTrue(np.allclose(res1, data.clip(0.2, 0.8)))
@@ -158,6 +159,8 @@ class TestClipAPI(unittest.TestCase):
         self.assertTrue(np.allclose(res6, data.clip(max=0.8)))
         self.assertTrue(np.allclose(res7, data.clip(max=-1)))
         self.assertTrue(np.allclose(res8, data))
+        self.assertTrue(
+            np.allclose(res9, data.astype(np.float64).clip(0.2, 0.9)))
 
     def test_clip_dygraph(self):
         place = fluid.CUDAPlace(0) if fluid.core.is_compiled_with_cuda(
diff --git a/python/paddle/tensor/math.py b/python/paddle/tensor/math.py
index d2db2a7cb71945e137e46d6793f8cba1f7adf12f..b6314ef1ba37937a39073ec68cf1cf540b27bf64 100755
--- a/python/paddle/tensor/math.py
+++ b/python/paddle/tensor/math.py
@@ -1611,11 +1611,8 @@ def clip(x, min=None, max=None, name=None):
             # [[4.5, 6.4]
     """
 
-    np_dtype = np.float32
-    if x.dtype == VarDesc.VarType.FP64:
-        np_dtype = np.float64
-    fmin = float(np.finfo(np_dtype).min)
-    fmax = float(np.finfo(np_dtype).max)
+    fmin = float(np.finfo(np.float32).min)
+    fmax = float(np.finfo(np.float32).max)
 
     if in_dygraph_mode():
         if isinstance(min, Variable):