diff --git a/paddle/operators/layer_norm_op.cc b/paddle/operators/layer_norm_op.cc
index 1c6d2ae4d05becaeed34d66cad398cc90f9d3ece..8fcac00e08fc27a92a20e829013e0f66af73f613 100644
--- a/paddle/operators/layer_norm_op.cc
+++ b/paddle/operators/layer_norm_op.cc
@@ -291,32 +291,28 @@ class LayerNormGradKernel<platform::CPUDeviceContext, T>
       auto d_x_map = EigenMatrixMapRowMajor<T>(d_x->data<T>(), left, right);
       auto triple_product_func = [](T ele) { return ele * ele * ele; };
       auto inv_std_func = [](T ele) { return std::sqrt(1 / ele); };
+
+      auto inv_std_map = var_map.unaryExpr(inv_std_func).eval();
       // TODO(zcd): these code can be refined
       if (d_scale) {
         auto scale_map =
             ConstEigenMatrixMapRowMajor<T>(scale->data<T>(), 1, right);
         // dy_dx
-        auto dx_end = var_map.unaryExpr(inv_std_func)
-                          .replicate(1, right)
-                          .cwiseProduct(d_y_map)
-                          .cwiseProduct(scale_map.replicate(left, 1));
+        auto dx_end =
+            inv_std_map.replicate(1, right).cwiseProduct(d_y_map).cwiseProduct(
+                scale_map.replicate(left, 1));
+
         // dy_dmean_dx
-        auto dx_mean = (T(-1.0) / right) *
-                       var_map.unaryExpr(inv_std_func)
-                           .replicate(1, right)
-                           .cwiseProduct(d_y_map)
-                           .cwiseProduct(scale_map.replicate(left, 1))
-                           .rowwise()
-                           .sum()
-                           .replicate(1, right);
+        auto dx_mean =
+            (T(-1.0) / right) * dx_end.rowwise().sum().replicate(1, right);
+
         // dy_var_dx
         auto dvar_end_part = (x_map - mean_map.replicate(1, right))
                                  .cwiseProduct(scale_map.replicate(left, 1))
                                  .cwiseProduct(d_y_map)
                                  .rowwise()
                                  .sum();
-        auto dvar_end = var_map.unaryExpr(inv_std_func)
-                            .unaryExpr(triple_product_func)
+        auto dvar_end = inv_std_map.unaryExpr(triple_product_func)
                             .cwiseProduct(dvar_end_part)
                             .replicate(1, right);
         auto dx_var =
@@ -326,24 +322,18 @@ class LayerNormGradKernel<platform::CPUDeviceContext, T>
         d_x_map = dx_end + dx_mean + dx_var;
       } else {
         // dy_dx
-        auto dx_end = var_map.unaryExpr(inv_std_func)
-                          .replicate(1, right)
-                          .cwiseProduct(d_y_map);
+        auto dx_end = inv_std_map.replicate(1, right).cwiseProduct(d_y_map);
+
         // dy_dmean_dx
-        auto dx_mean = (T(-1.0) / right) *
-                       var_map.unaryExpr(inv_std_func)
-                           .replicate(1, right)
-                           .cwiseProduct(d_y_map)
-                           .rowwise()
-                           .sum()
-                           .replicate(1, right);
+        auto dx_mean =
+            (T(-1.0) / right) * dx_end.rowwise().sum().replicate(1, right);
+
         // dy_var_dx
         auto dvar_end_part = (x_map - mean_map.replicate(1, right))
                                  .cwiseProduct(d_y_map)
                                  .rowwise()
                                  .sum();
-        auto dvar_end = var_map.unaryExpr(inv_std_func)
-                            .unaryExpr(triple_product_func)
+        auto dvar_end = inv_std_map.unaryExpr(triple_product_func)
                             .cwiseProduct(dvar_end_part)
                             .replicate(1, right);
         auto dx_var =