diff --git a/lite/fluid/eigen.h b/lite/fluid/eigen.h
index 4314a6c492be19a7213a7f438bcbc86ae2f85492..eac5332b53c857b05aacbfa95ee2e4b9fcd98a93 100644
--- a/lite/fluid/eigen.h
+++ b/lite/fluid/eigen.h
@@ -118,8 +118,8 @@ struct EigenScalar {
   using ConstType = Eigen::TensorMap<
       Eigen::TensorFixedSize<const T, Eigen::Sizes<>, MajorType, IndexType>>;
 
-  static Type From(const Tensor& tensor) {
-    return Type(const_cast<T*>(tensor.data<T>()));
+  static Type From(Tensor* tensor) {
+    return Type(const_cast<T*>(tensor->data<T>()));
   }  // NOLINT
 
   static ConstType From(const Tensor& tensor) {
diff --git a/lite/kernels/x86/reduce_compute.h b/lite/kernels/x86/reduce_compute.h
index faace5e24ecc58ca0a518c64a598a105dc00f93e..655f104ce65906f1904a7cf02d703069b0a7a2bf 100644
--- a/lite/kernels/x86/reduce_compute.h
+++ b/lite/kernels/x86/reduce_compute.h
@@ -56,7 +56,7 @@ class ReduceSumCompute : public KernelLite<TARGET(kX86), PRECISION(kFloat)> {
     if (reduce_all) {
       // Flatten and reduce 1-D tensor
       auto x = lite::fluid::EigenVector<T>::Flatten(*input);
-      auto out = lite::fluid::EigenScalar<T>::From(*output);
+      auto out = lite::fluid::EigenScalar<T>::From(output);
       // auto& place = *platform::CPUDeviceContext().eigen_device();
       auto reduce_dim = Eigen::array<int, 1>({{0}});
       SumFunctor functor;
diff --git a/lite/kernels/x86/reduce_op_function.h b/lite/kernels/x86/reduce_op_function.h
index be3ef6ed0d79b7aac7c0ff2f307dec58c3368ca6..b3ddab64e4bf8dc72cec3b86398f42269c5a947c 100644
--- a/lite/kernels/x86/reduce_op_function.h
+++ b/lite/kernels/x86/reduce_op_function.h
@@ -70,7 +70,7 @@ void ReduceFunctor(const lite::Tensor& input,
   Functor functor;
 
   if (D == 1) {
-    auto out = EigenScalar<T>::From(*output);
+    auto out = EigenScalar<T>::From(output);
     functor(&x, &out, reduce_dim);
   } else {
     auto out = EigenTensor<T, (D - R_D)>::From(*output, out_dims);