diff --git a/mindspore/ops/_grad/grad_array_ops.py b/mindspore/ops/_grad/grad_array_ops.py
index 2bd3dc2dc24..e4a4e9c9ddb 100644
--- a/mindspore/ops/_grad/grad_array_ops.py
+++ b/mindspore/ops/_grad/grad_array_ops.py
@@ -1065,8 +1065,8 @@ def get_bprop_masked_select(self):
     """Generate bprop for MaskedSelect"""
     op = G.MaskedSelectGrad()
 
-    def bprop(x, mask, dout):
+    def bprop(x, mask, out, dout):
         dx = op(x, mask, dout)
-        return (dx,)
+        return (dx, zeros_like(mask))
 
     return bprop
diff --git a/tests/st/ops/cpu/test_masked_select_op.py b/tests/st/ops/cpu/test_masked_select_op.py
index dc909f067fa..7cf7e1851b4 100644
--- a/tests/st/ops/cpu/test_masked_select_op.py
+++ b/tests/st/ops/cpu/test_masked_select_op.py
@@ -21,7 +21,6 @@ import mindspore.nn as nn
 from mindspore import Tensor
 from mindspore.ops import operations as P
 from mindspore.ops import composite as C
-from mindspore.ops.operations import _grad_ops as G
 
 def maskedselect():
     x = np.array([1, 2, 3, 4]).astype(np.int32)
@@ -49,13 +48,20 @@ class Grad(nn.Cell):
         gout = self.grad(self.network)(x, mask, grad)
         return gout
 
+class Net(nn.Cell):
+    def __init__(self):
+        super(Net, self).__init__()
+        self.op = P.MaskedSelect()
+
+    def construct(self, x, mask):
+        return self.op(x, mask)
 
 def masked_select_grad():
     x = np.array([1, 2, 3, 4]).astype(np.int32)
     mask = np.array([[0], [1], [0], [1]]).astype(np.bool)
     dy = np.array([i for i in range(8)]).astype(np.int32)
-    grad = G.MaskedSelectGrad()
-    return grad(Tensor(x), Tensor(mask), Tensor(dy))
+    grad = Grad(Net())
+    return grad(Tensor(x), Tensor(mask), Tensor(dy))[0]
 
 
 @pytest.mark.level0