diff --git a/paddle/fluid/operators/activation_op_npu.cc b/paddle/fluid/operators/activation_op_npu.cc
index 24ebc00c4841c9364959b112ab3490884efe88e4..02ebdefc38cec2802d083d254c3f179f13a3ae03 100644
--- a/paddle/fluid/operators/activation_op_npu.cc
+++ b/paddle/fluid/operators/activation_op_npu.cc
@@ -73,24 +73,19 @@ class PowGradNPUKernel : public framework::OpKernel<T> {
     runner_pow.Run(stream);
 
     // Step 2: Construct a broadcast factor, which has the same shape with x.
-    // 2.1 Get the shape of x
-    Tensor x_shape(framework::proto::VarType::INT32);
-    x_shape.mutable_data<int32_t>({x_dims.size()}, place);
-    TensorFromVector(framework::vectorize<int32_t>(x_dims),
-                     ctx.device_context(), &x_shape);
 
-    // 2.2 Get a factor tensor with shape [1].
+    // 2.1 Get a factor tensor with shape [1].
     Tensor factor_tensor(framework::proto::VarType::FP32);
     factor_tensor.mutable_data<float>({1}, place);
     TensorFromVector(std::vector<float>{factor}, ctx.device_context(),
                      &factor_tensor);
 
-    // 2.3 Get the factor which has the shape with x and the same value with
+    // 2.2 Get the factor which has the shape with x and the same value with
     // factor.
     Tensor factor_bc_tensor(framework::proto::VarType::FP32);
     factor_bc_tensor.mutable_data<float>(x_dims, place);
     auto runner_bc = NpuOpRunner("FillD", {factor_tensor}, {factor_bc_tensor},
-                                 {{"dims", x_dims}});
+                                 {{"dims", framework::vectorize(x_dims)}});
     runner_bc.Run(stream);
 
     // Step 3: Compute x_power_mul_factor = factor * x.pow(factor-1)