diff --git a/paddle/fluid/operators/slice_op.cc b/paddle/fluid/operators/slice_op.cc
index 9d96f9670c633f1937f4fff4065bd219cea930df..f25b08bbbabf6ae93b8f6b94edfeb6fff466c370 100644
--- a/paddle/fluid/operators/slice_op.cc
+++ b/paddle/fluid/operators/slice_op.cc
@@ -291,6 +291,34 @@ class SliceOpGradMaker : public framework::SingleGradOpMaker<T> {
   }
 };
 
+template <typename T>
+class SliceDoubleOpGradMaker : public framework::SingleGradOpMaker<T> {
+ public:
+  using framework::SingleGradOpMaker<T>::SingleGradOpMaker;
+
+ protected:
+  std::unique_ptr<T> Apply() const override {
+    auto *bind = new T();
+    if (this->HasInput("StartsTensor")) {
+      bind->SetInput("StartsTensor", this->Input("StartsTensor"));
+    }
+    if (this->HasInput("EndsTensor")) {
+      bind->SetInput("EndsTensor", this->Input("EndsTensor"));
+    }
+    if (this->HasInput("StartsTensorList")) {
+      bind->SetInput("StartsTensorList", this->Input("StartsTensorList"));
+    }
+    if (this->HasInput("EndsTensorList")) {
+      bind->SetInput("EndsTensorList", this->Input("EndsTensorList"));
+    }
+    bind->SetInput("Input", this->OutputGrad(framework::GradVarName("Input")));
+    bind->SetOutput("Out", this->InputGrad(framework::GradVarName("Out")));
+    bind->SetAttrMap(this->Attrs());
+    bind->SetType("slice");
+    return std::unique_ptr<T>(bind);
+  }
+};
+
 DECLARE_NO_NEED_BUFFER_VARS_INFERENCE(SliceOpGradNoNeedBufferVarsInference,
                                       "Input");
 
@@ -302,6 +330,8 @@ REGISTER_OPERATOR(slice, ops::SliceOp, ops::SliceOpMaker,
                   ops::SliceOpGradMaker<paddle::framework::OpDesc>,
                   ops::SliceOpGradMaker<paddle::imperative::OpBase>);
 REGISTER_OPERATOR(slice_grad, ops::SliceOpGrad,
+                  ops::SliceDoubleOpGradMaker<paddle::framework::OpDesc>,
+                  ops::SliceDoubleOpGradMaker<paddle::imperative::OpBase>,
                   ops::SliceOpGradNoNeedBufferVarsInference);
 
 REGISTER_OP_CPU_KERNEL(
diff --git a/python/paddle/fluid/tests/unittests/test_nn_grad.py b/python/paddle/fluid/tests/unittests/test_nn_grad.py
index 4e9703fa9b5e90617007bd45c2f66526ff6af01d..c6cfe01dce40458684c7464ca5ebddd389c62cbe 100644
--- a/python/paddle/fluid/tests/unittests/test_nn_grad.py
+++ b/python/paddle/fluid/tests/unittests/test_nn_grad.py
@@ -43,6 +43,41 @@ class TestMulGradCheck(unittest.TestCase):
             self.func(p)
 
 
+class TestSliceOpDoubleGradCheck(unittest.TestCase):
+    def func(self, place):
+        self.config()
+
+        out = fluid.layers.slice(
+            self.inputs, axes=self.axes, starts=self.starts, ends=self.ends)
+        gradient_checker.double_grad_check(
+            [self.inputs], out, x_init=self.x_arr, place=place)
+
+    def config(self):
+        self.starts = [1, 0, -1]
+        self.ends = [3, 3, 6]
+        self.axes = [0, 1, 2]
+        self.x_arr = np.random.random([3, 4, 5, 2]).astype("float64")
+        self.inputs = layers.create_parameter(
+            dtype="float64", shape=[3, 4, 5, 2], name='x')
+
+    def test_grad(self):
+        places = [fluid.CPUPlace()]
+        if core.is_compiled_with_cuda():
+            places.append(fluid.CUDAPlace(0))
+        for place in places:
+            self.func(place)
+
+
+class TestSliceOpDoubleGradCheckCase3(TestSliceOpDoubleGradCheck):
+    def config(self):
+        self.starts = [1, -1, 1]
+        self.ends = [3, 3, 3]
+        self.axes = [0, 1, 2]
+        self.x_arr = np.random.random([3, 3, 3]).astype("float64")
+        self.inputs = layers.create_parameter(
+            dtype="float64", shape=[3, 3, 3], name='x3')
+
+
 class TestReduceMeanWithDimDoubleGradCheck(unittest.TestCase):
     @prog_scope()
     def func(self, place):