diff --git a/paddle/operators/cos_sim_op.h b/paddle/operators/cos_sim_op.h
index fecb5a79b2397dd73d991a1a87efcf84d60ef882..3a7e67506d29f72ca3f73f6b2e5278324e2ce3c2 100644
--- a/paddle/operators/cos_sim_op.h
+++ b/paddle/operators/cos_sim_op.h
@@ -15,6 +15,7 @@
 #pragma once
 #include "paddle/framework/eigen.h"
 #include "paddle/framework/op_registry.h"
+#include "paddle/operators/elementwise_add_op.h"
 
 namespace paddle {
 namespace operators {
@@ -27,6 +28,28 @@ template <typename T, int MajorType = Eigen::RowMajor,
           typename IndexType = Eigen::DenseIndex>
 using EigenVector = framework::EigenVector<T, MajorType, IndexType>;
 
+template <typename T, typename DeviceContext>
+void Function_forward(T* out, T* x_norm, T* y_norm,
+                      ElementIterator<T, DeviceContext>& x,
+                      ElementIterator<T, DeviceContext>& y, int row, int col) {
+  for (int i = 0; i < row; ++i) {
+    T xx = 0;
+    T yy = 0;
+    T xy = 0;
+    for (int j = 0; j < col; ++j) {
+      xy += (*x) * (*y);
+      xx += (*x) * (*x);
+      yy += (*y) * (*y);
+      ++y;
+      ++x;
+    }
+    x_norm[i] = sqrt(xx);
+    y_norm[i] = sqrt(yy);
+
+    out[i] = xy / (x_norm[i] * y_norm[i]);
+  }
+}
+
 template <typename DeviceContext, typename T>
 class CosSimKernel : public framework::OpKernel<T> {
  public:
@@ -41,32 +64,63 @@ class CosSimKernel : public framework::OpKernel<T> {
     out_x_norm->mutable_data<T>(context.GetPlace());
     out_y_norm->mutable_data<T>(context.GetPlace());
 
-    // convert Tensor to Eigen Tensor
     int rows_x = in_x->dims()[0];
     int rows_y = in_y->dims()[0];
-    auto x = EigenMatrix<T>::Reshape(*in_x, 1);
-    auto y = EigenMatrix<T>::Reshape(*in_y, 1);
-    auto z = EigenVector<T>::Flatten(*out_z);
-    auto x_norm = EigenVector<T>::Flatten(*out_x_norm);
-    auto y_norm = EigenVector<T>::Flatten(*out_y_norm);
-
-    // compute
-    auto& place =
-        *context.template device_context<DeviceContext>().eigen_device();
-    auto row_along = Eigen::array<int, 1>({{1}});
-    x_norm.device(place) = x.square().sum(row_along).sqrt();
-    y_norm.device(place) = y.square().sum(row_along).sqrt();
-    if (rows_x == rows_y) {
-      auto xy = (x * y).sum(Eigen::array<int, 1>({{1}}));
-      z.device(place) = xy / x_norm / y_norm;
-    } else {
-      Eigen::DSizes<int, 2> bcast(rows_x, 1);
-      auto xy = (x * y.broadcast(bcast)).sum(row_along);
-      z.device(place) = xy / x_norm / y_norm.broadcast(bcast);
-    }
+
+    int cols = framework::product(in_x->dims()) / rows_x;
+    auto x_iter = ElementIterator<T, DeviceContext>(in_x->data<T>(), rows_x,
+                                                    cols, rows_x, cols);
+    auto y_iter = ElementIterator<T, DeviceContext>(in_y->data<T>(), rows_y,
+                                                    cols, rows_x, cols);
+
+    Function_forward(out_z->data<T>(), out_x_norm->data<T>(),
+                     out_y_norm->data<T>(), x_iter, y_iter, rows_x, cols);
+    //
+    //    // convert Tensor to Eigen Tensor
+    ////    int rows_x = in_x->dims()[0];
+    ////    int rows_y = in_y->dims()[0];
+    //    auto x = EigenMatrix<T>::Reshape(*in_x, 1);
+    //    auto y = EigenMatrix<T>::Reshape(*in_y, 1);
+    //    auto z = EigenVector<T>::Flatten(*out_z);
+    //    auto x_norm = EigenVector<T>::Flatten(*out_x_norm);
+    //    auto y_norm = EigenVector<T>::Flatten(*out_y_norm);
+    //
+    //    // compute
+    //    auto& place =
+    //        *context.template device_context<DeviceContext>().eigen_device();
+    //    auto row_along = Eigen::array<int, 1>({{1}});
+    //    x_norm.device(place) = x.square().sum(row_along).sqrt();
+    //    y_norm.device(place) = y.square().sum(row_along).sqrt();
+    //    if (rows_x == rows_y) {
+    //      auto xy = (x * y).sum(Eigen::array<int, 1>({{1}}));
+    //      z.device(place) = xy / x_norm / y_norm;
+    //    } else {
+    //      Eigen::DSizes<int, 2> bcast(rows_x, 1);
+    //      auto xy = (x * y.broadcast(bcast)).sum(row_along);
+    //      z.device(place) = xy / x_norm / y_norm.broadcast(bcast);
+    //    }
   }
 };
 
+template <typename T, typename DeviceContext>
+void Function_element(T* result, ElementIterator<T, DeviceContext> dz,
+                      ElementIterator<T, DeviceContext> y,
+                      ElementIterator<T, DeviceContext> x_norm,
+                      ElementIterator<T, DeviceContext> y_norm,
+                      ElementIterator<T, DeviceContext> z,
+                      ElementIterator<T, DeviceContext> x, int num, int block) {
+  for (int i = 0; i < num; ++i) {
+    result[i % block] += (*dz) * ((*y) / ((*x_norm) * (*y_norm)) -
+                                  (*z) * (*x) / ((*x_norm) * (*x_norm)));
+    ++dz;
+    ++y;
+    ++x_norm;
+    ++y_norm;
+    ++z;
+    ++x;
+  }
+}
+
 template <typename DeviceContext, typename T>
 class CosSimGradKernel : public framework::OpKernel<T> {
  public:
@@ -81,63 +135,50 @@ class CosSimGradKernel : public framework::OpKernel<T> {
     auto* out_grad_y = context.Output<Tensor>(framework::GradVarName("Y"));
     auto* in_grad_z = context.Input<Tensor>(framework::GradVarName("Out"));
 
-    // convert Tensor to Eigen Tensor
-    auto x = EigenMatrix<T>::Reshape(*in_x, 1);
-    auto y = EigenMatrix<T>::Reshape(*in_y, 1);
-    auto z = EigenMatrix<T>::Reshape(*in_z, 1);
-    auto x_norm = EigenMatrix<T>::Reshape(*in_x_norm, 1);
-    auto y_norm = EigenMatrix<T>::Reshape(*in_y_norm, 1);
-    auto dz = EigenMatrix<T>::Reshape(*in_grad_z, 1);
-
     // compute gradident
     int rows_x = in_x->dims()[0];
     int rows_y = in_y->dims()[0];
     int cols = framework::product(in_x->dims()) / rows_x;
-    Eigen::DSizes<int, 2> bcast_cols(1, cols);
-    auto z_bcast = z.broadcast(bcast_cols);
-    auto dz_bcast = dz.broadcast(bcast_cols);
-    auto x_snorm_bcast = x_norm.square().eval().broadcast(bcast_cols);
-    auto& place =
-        *context.template device_context<DeviceContext>().eigen_device();
-    if (rows_x == rows_y) {
-      auto y_snorm_bcast = y_norm.square().eval().broadcast(bcast_cols);
-      auto norm_prod_bcast = (x_norm * y_norm).eval().broadcast(bcast_cols);
-      // compute dx
-      if (out_grad_x) {
-        out_grad_x->mutable_data<T>(context.GetPlace());
-        auto dx = EigenMatrix<T>::Reshape(*out_grad_x, 1);
-        auto grad = y / norm_prod_bcast - z_bcast * x / x_snorm_bcast;
-        dx.device(place) = dz_bcast * grad;
-      }
-      // compute dy
-      if (out_grad_y) {
-        out_grad_y->mutable_data<T>(context.GetPlace());
-        auto dy = EigenMatrix<T>::Reshape(*out_grad_y, 1);
-        auto grad = x / norm_prod_bcast - z_bcast * y / y_snorm_bcast;
-        dy.device(place) = dz_bcast * grad;
-      }
-    } else {
-      Eigen::DSizes<int, 2> bcast_rows(rows_x, 1);
-      Eigen::DSizes<int, 2> bcast_rows_cols(rows_x, cols);
-      auto y_bcast = y.broadcast(bcast_rows);
-      auto y_snorm_bcast = y_norm.square().eval().broadcast(bcast_rows_cols);
-      auto norm_prod_bcast = (x_norm * y_norm.eval().broadcast(bcast_rows))
-                                 .eval()
-                                 .broadcast(bcast_cols);
-      // compute dx
-      if (out_grad_x) {
-        out_grad_x->mutable_data<T>(context.GetPlace());
-        auto dx = EigenMatrix<T>::Reshape(*out_grad_x, 1);
-        auto grad = y_bcast / norm_prod_bcast - z_bcast * x / x_snorm_bcast;
-        dx.device(place) = dz_bcast * grad;
-      }
-      // compute dy
-      if (out_grad_y) {
-        out_grad_y->mutable_data<T>(context.GetPlace());
-        auto dy = EigenVector<T>::Flatten(*out_grad_y);
-        auto grad = x / norm_prod_bcast - z_bcast * y_bcast / y_snorm_bcast;
-        dy.device(place) = (dz_bcast * grad).sum(Eigen::array<int, 1>({{0}}));
-      }
+
+    //////////////////////////////
+    // ##
+    auto x_iter = ElementIterator<T, DeviceContext>(in_x->data<T>(), rows_x,
+                                                    cols, rows_x, cols);
+    auto y_iter = ElementIterator<T, DeviceContext>(in_y->data<T>(), rows_y,
+                                                    cols, rows_x, cols);
+    auto z_iter = ElementIterator<T, DeviceContext>(in_z->data<T>(), rows_x, 1,
+                                                    rows_x, cols);
+    auto dz_iter = ElementIterator<T, DeviceContext>(in_grad_z->data<T>(),
+                                                     rows_x, 1, rows_x, cols);
+    auto x_norm_iter = ElementIterator<T, DeviceContext>(
+        in_x_norm->data<T>(), rows_x, 1, rows_x, cols);
+    auto y_norm_iter = ElementIterator<T, DeviceContext>(
+        in_y_norm->data<T>(), rows_y, 1, rows_x, cols);
+    // ##
+    //////////////////////////////
+    // compute dx
+    if (out_grad_x) {
+      out_grad_x->mutable_data<T>(context.GetPlace());
+
+      //////////////////////////////
+      // ##
+      Function_element(out_grad_x->data<T>(), dz_iter, y_iter, x_norm_iter,
+                       y_norm_iter, z_iter, x_iter, rows_x * cols,
+                       rows_x * cols);
+      // ##
+      //////////////////////////////
+    }
+    // compute dy
+    if (out_grad_y) {
+      out_grad_y->mutable_data<T>(context.GetPlace());
+
+      //////////////////////////////
+      // ##
+      Function_element(out_grad_y->data<T>(), dz_iter, x_iter, y_norm_iter,
+                       x_norm_iter, z_iter, y_iter, rows_x * cols,
+                       rows_y * cols);
+      // ##
+      //////////////////////////////
     }
   }
 };
diff --git a/paddle/operators/elementwise_op_function.h b/paddle/operators/elementwise_op_function.h
index 7ebfc7df8c117edd7bcf14cc5ae6ba3dc1302c03..33b7d06467e84056fe0dcb25f0f52bdb2e4519f9 100644
--- a/paddle/operators/elementwise_op_function.h
+++ b/paddle/operators/elementwise_op_function.h
@@ -131,6 +131,61 @@ class MidWiseTransformIterator<T, platform::CPUDeviceContext> {
   int post_;
 };
 
+template <typename T, typename Place>
+class ElementIterator;
+
+// Fixed(zcd) : Only support 2D
+template <typename T>
+class ElementIterator<T, platform::CPUDeviceContext> {
+ public:
+  ElementIterator(const T* ptr, int t_m, int t_n, int m, int n)
+      : ptr_(ptr),
+        index_(0),
+        i_(0),
+        j_(0),
+        t_m_(t_m),
+        t_n_(t_n),
+        m_(m),
+        n_(n) {}
+
+  ElementIterator<T, platform::CPUDeviceContext>& operator++() {
+    ++j_;
+
+    if ((j_ == n_)) {
+      j_ = 0;
+      ++i_;
+    }
+    int t_i = (t_m_ == 1) ? 0 : i_;
+    int t_j = (t_n_ == 1) ? 0 : j_;
+    index_ = t_i * t_n_ + t_j;
+
+    return *this;
+  }
+
+  bool operator==(
+      const ElementIterator<T, platform::CPUDeviceContext>& rhs) const {
+    return (ptr_ + index_) == &(*rhs);
+  }
+
+  bool operator!=(
+      const ElementIterator<T, platform::CPUDeviceContext>& rhs) const {
+    return (ptr_ + index_) != &(*rhs);
+  }
+
+  const T& operator*() { return ptr_[index_]; }
+
+ private:
+  // t_m_ == m_ || t_n_ == n_ || (t_m_ == 1 && t_m_ == 1)
+  const T* ptr_;
+  int index_;
+  int i_;
+  int j_;
+  int64_t t_m_;
+  int64_t t_n_;
+  int64_t m_;
+  int64_t n_;
+};
+
 #ifdef __NVCC__
 template <typename T>
 class RowwiseTransformIterator<T, platform::CUDADeviceContext>