add static mode backward test

test/legacy_test/test_set_value_op.py

@@ -1978,5 +1978,59 @@ def test_check_grad(self):
         self.check_grad_with_place(place, ['Input'], 'Out', check_dygraph=False)
 
 
+class TestSetValueWithScalarInStatic(unittest.TestCase):
+    def setUp(self):
+        paddle.enable_static()
+        self.shape = (10, 2)
+        self.exe = paddle.static.Executor()
+        self.train_program = paddle.static.Program()
+        self.startup_program = paddle.static.Program()
+
+    def test_value_input_is_scalar(self):
+        with paddle.static.program_guard(
+            self.train_program, self.startup_program
+        ):
+            x = paddle.ones(self.shape)
+            x.stop_gradient = False
+            y = x * 1
+
+            # mock test case x[0, 0] = 10 with no ValueTensor input
+            inputs = {
+                'Input': y,
+            }
+            attrs = {
+                'axes': [0, 1],
+                'starts': [0, 0],
+                'ends': [1, 1],
+                'steps': [1, 1],
+                'values': [10],
+                'shape': [1],
+            }
+
+            helper = LayerHelper("set_value")
+            out = helper.create_variable_for_type_inference(dtype=y.dtype)
+
+            helper.append_op(
+                type="set_value",
+                inputs=inputs,
+                outputs={'Out': out},
+                attrs=attrs,
+            )
+
+            np_data = np.ones(self.shape).astype('float32')
+
+            paddle.static.append_backward(out.sum())
+            res = self.exe.run(
+                self.train_program, fetch_list=[out, x.grad_name]
+            )
+
+            np_data[0, 0] = 10
+            expected_x_grad = np.ones(self.shape)
+            expected_x_grad[0, 0] = 0
+
+        np.testing.assert_array_equal(res[0], np_data)
+        np.testing.assert_array_equal(res[1], expected_x_grad)
+
+
 if __name__ == '__main__':
     unittest.main()