diff --git a/paddle/fluid/operators/dropout_op_xpu.cc b/paddle/fluid/operators/dropout_op_xpu.cc
index 2f3f7e05f8ccd901e572c2287b7300d0d56dba6b..51aa0b78c9a123ffac0a6ad7a7cdb77b9232df11 100644
--- a/paddle/fluid/operators/dropout_op_xpu.cc
+++ b/paddle/fluid/operators/dropout_op_xpu.cc
@@ -32,20 +32,18 @@ class DropoutXPUKernel : public framework::OpKernel<T> {
         context.Attr<std::string>("dropout_implementation");
     auto& dev_ctx = context.template device_context<DeviceContext>();
 
-    PADDLE_ENFORCE_EQ(!context.HasInput("Seed"), true,
-                      platform::errors::InvalidArgument(
-                          ("Input(Seed) not supported on XPU")));
+    auto* seed =
+        context.HasInput("Seed") ? context.Input<Tensor>("Seed") : nullptr;
+
     int is_upscale = (dropout_implementation == "upscale_in_train");
 
     if (!context.Attr<bool>("is_test")) {
-      std::random_device rnd;
-      // int seed = (context.Attr<bool>("fix_seed")) ?
-      // int(context.Attr<int>("seed")) : (rnd());
-      int seed = 0;
-      if (context.Attr<bool>("fix_seed") == true) {
-        seed = static_cast<int>(context.Attr<int>("seed"));
+      int seed_data = 0;
+      if (seed) {
+        seed_data = *(seed->data<int>());
       } else {
-        seed = rnd();
+        seed_data =
+            context.Attr<bool>("fix_seed") ? context.Attr<int>("seed") : 0;
       }
 
       auto* mask = context.Output<Tensor>("Mask");
@@ -55,26 +53,26 @@ class DropoutXPUKernel : public framework::OpKernel<T> {
         int r = xpu::constant(dev_ctx.x_context(),
                               reinterpret_cast<XPUTyp*>(y_data), y->numel(),
                               XPUTyp(0));
-        PADDLE_ENFORCE_XDNN_SUCCESS(r, "constant ");
+        PADDLE_ENFORCE_XDNN_SUCCESS(r, "constant");
         r = xpu::constant(dev_ctx.x_context(),
                           reinterpret_cast<XPUTyp*>(mask_data), mask->numel(),
                           XPUTyp(0));
-        PADDLE_ENFORCE_XDNN_SUCCESS(r, "constant ");
+        PADDLE_ENFORCE_XDNN_SUCCESS(r, "constant");
         return;
       }
       int r = xpu::dropout(dev_ctx.x_context(),
                            reinterpret_cast<const XPUTyp*>(x->data<T>()),
                            reinterpret_cast<XPUTyp*>(y->data<T>()),
-                           reinterpret_cast<XPUTyp*>(mask_data), seed,
+                           reinterpret_cast<XPUTyp*>(mask_data), seed_data,
                            mask->numel(), is_upscale, dropout_prob);
-      PADDLE_ENFORCE_XDNN_SUCCESS(r, "dropout ");
+      PADDLE_ENFORCE_XDNN_SUCCESS(r, "dropout");
     } else {
       float scale =
           (is_upscale) ? (1.0) : (static_cast<float>(1.0f - dropout_prob));
       int r = xpu::scale(
           dev_ctx.x_context(), reinterpret_cast<const XPUTyp*>(x_data),
           reinterpret_cast<XPUTyp*>(y_data), x->numel(), false, scale, 0.0f);
-      PADDLE_ENFORCE_XDNN_SUCCESS(r, "scale ");
+      PADDLE_ENFORCE_XDNN_SUCCESS(r, "scale");
     }
   }
 };
@@ -103,7 +101,7 @@ class DropoutGradXPUKernel : public framework::OpKernel<T> {
                        reinterpret_cast<const XPUType*>(mask_data),
                        reinterpret_cast<XPUType*>(grad_x->data<T>()),
                        grad_y->numel());
-      PADDLE_ENFORCE_XDNN_SUCCESS(r, "mul ");
+      PADDLE_ENFORCE_XDNN_SUCCESS(r, "mul");
       return;
     }
 
@@ -117,13 +115,13 @@ class DropoutGradXPUKernel : public framework::OpKernel<T> {
                          reinterpret_cast<const XPUType*>(mask->data<T>()),
                          reinterpret_cast<XPUType*>(mask_new), mask->numel(),
                          false, scale, 0.0f);
-      PADDLE_ENFORCE_XDNN_SUCCESS(r, "scale ");
+      PADDLE_ENFORCE_XDNN_SUCCESS(r, "scale");
       r = xpu::mul(dev_ctx.x_context(),
                    reinterpret_cast<const XPUType*>(grad_y->data<T>()),
                    reinterpret_cast<const XPUType*>(mask_new),
                    reinterpret_cast<XPUType*>(grad_x->data<T>()),
                    grad_y->numel());
-      PADDLE_ENFORCE_XDNN_SUCCESS(r, "mul ");
+      PADDLE_ENFORCE_XDNN_SUCCESS(r, "mul");
     } else {
       int r =
           xpu::dropout_grad(dev_ctx.x_context(),
@@ -131,7 +129,7 @@ class DropoutGradXPUKernel : public framework::OpKernel<T> {
                             reinterpret_cast<const XPUType*>(grad_y->data<T>()),
                             reinterpret_cast<XPUType*>(grad_x->data<T>()),
                             dropout_prob, grad_y->numel());
-      PADDLE_ENFORCE_XDNN_SUCCESS(r, "dropout_grad ");
+      PADDLE_ENFORCE_XDNN_SUCCESS(r, "dropout_grad");
     }
   }
 };
diff --git a/paddle/fluid/operators/elementwise/elementwise_add_op_xpu.cc b/paddle/fluid/operators/elementwise/elementwise_add_op_xpu.cc
index 6167452728a5931b0f4d9ab8b367bd4f23ed14c1..3df2c7d05d43de1984c9880142f1c9cac217f488 100644
--- a/paddle/fluid/operators/elementwise/elementwise_add_op_xpu.cc
+++ b/paddle/fluid/operators/elementwise/elementwise_add_op_xpu.cc
@@ -34,17 +34,6 @@ class ElementwiseAddXPUKernel : public framework::OpKernel<T> {
   }
 };
 
-static std::vector<int> get_rdims(const std::vector<int>& xdims,
-                                  const std::vector<int>& ydims) {
-  std::vector<int> rdims;
-  for (size_t i = 0; i < xdims.size(); i++) {
-    if (xdims[i] != ydims[i]) {
-      rdims.push_back(i);
-    }
-  }
-  return rdims;
-}
-
 template <typename T>
 class ElementwiseAddGradXPUKernel : public ElemwiseGradKernel<T> {
   using XPUType = typename XPUTypeTrait<T>::Type;
@@ -53,64 +42,19 @@ class ElementwiseAddGradXPUKernel : public ElemwiseGradKernel<T> {
   void Compute(const framework::ExecutionContext& ctx) const override {
     ElemwiseGradKernel<T>::Compute(ctx);
     auto* x = ctx.Input<framework::Tensor>("X");
-    auto* y = ctx.Input<framework::Tensor>("Y");
     auto* dz = ctx.Input<framework::Tensor>(framework::GradVarName("Out"));
     auto* dx = ctx.Output<framework::Tensor>(framework::GradVarName("X"));
     auto* dy = ctx.Output<framework::Tensor>(framework::GradVarName("Y"));
-    const framework::DDim& x_dims = x->dims();
-    const framework::DDim& y_dims = y->dims();
     const framework::DDim& dz_dims = dz->dims();
     int axis = ctx.Attr<int>("axis");
-    axis = (axis == -1 ? std::abs(x_dims.size() - y_dims.size()) : axis);
-    int max_dim = std::max(x_dims.size(), y_dims.size());
-    PADDLE_ENFORCE_GE(
-        axis, 0,
-        platform::errors::InvalidArgument(
-            "Axis should be great than or equal to 0, but received axis is %d.",
-            axis));
-    PADDLE_ENFORCE_LT(
-        axis, max_dim,
-        platform::errors::InvalidArgument(
-            "Axis should be less than %d, but received axis is %d.", max_dim,
-            axis));
-
-    std::vector<int> x_dims_vec(max_dim, 1);
-    std::vector<int> y_dims_vec(max_dim, 1);
-    std::vector<int> z_dims_vec(max_dim, 1);
-    if (x_dims.size() == max_dim) {
-      for (int i = 0; i < max_dim; i++) {
-        x_dims_vec[i] = x_dims[i];
-      }
-    } else {
-      for (int i = 0; i < x_dims.size(); i++) {
-        x_dims_vec[i + axis] = x_dims[i];
-      }
-    }
 
-    if (y_dims.size() == max_dim) {
-      for (int i = 0; i < max_dim; i++) {
-        y_dims_vec[i] = y_dims[i];
-      }
-    } else {
-      for (int i = 0; i < y_dims.size(); i++) {
-        y_dims_vec[i + axis] = y_dims[i];
-      }
-    }
-
-    for (int i = 0; i < max_dim; i++) {
-      z_dims_vec[i] = dz_dims[i];
-    }
-    std::vector<int> rdims_for_x;
-    std::vector<int> rdims_for_y;
-    rdims_for_x = get_rdims(x_dims_vec, z_dims_vec);
-    rdims_for_y = get_rdims(y_dims_vec, z_dims_vec);
     const T* dz_data = dz->data<T>();
     auto& dev_ctx =
         ctx.template device_context<paddle::platform::XPUDeviceContext>();
 
     if (dx != nullptr) {
       T* dx_data = dx->mutable_data<T>(ctx.GetPlace());
-      if (rdims_for_x.size() == 0) {
+      if (dx->dims() == dz_dims) {
         if (dx_data != dz_data) {
           framework::TensorCopy(
               *dz, ctx.GetPlace(),
@@ -123,27 +67,31 @@ class ElementwiseAddGradXPUKernel : public ElemwiseGradKernel<T> {
           dx->clear();
           dx->mutable_data<T>(x->dims(), ctx.GetPlace());
         }
+        std::vector<int> reduce_dims = GetReduceDim(dx->dims(), dz_dims, axis);
+        std::vector<int> dz_vector = framework::vectorize<int>(dz_dims);
 
         int ret = xpu::reduce_sum<XPUType>(
             dev_ctx.x_context(), reinterpret_cast<const XPUType*>(dz_data),
-            reinterpret_cast<XPUType*>(dx_data), z_dims_vec, rdims_for_x);
-        PADDLE_ENFORCE_XDNN_SUCCESS(ret, "reduce_sum ");
+            reinterpret_cast<XPUType*>(dx_data), dz_vector, reduce_dims);
+        PADDLE_ENFORCE_XDNN_SUCCESS(ret, "reduce_sum");
       }
     }
 
     if (dy != nullptr) {
       T* dy_data = dy->mutable_data<T>(ctx.GetPlace());
-      if (rdims_for_y.size() == 0) {
+      if (dy->dims() == dz_dims) {
         if (dy_data != dz_data) {
           framework::TensorCopy(
               *dz, ctx.GetPlace(),
               ctx.template device_context<platform::DeviceContext>(), dy);
         }
       } else {
+        std::vector<int> reduce_dims = GetReduceDim(dy->dims(), dz_dims, axis);
+        std::vector<int> dz_vector = framework::vectorize<int>(dz_dims);
         int ret = xpu::reduce_sum<XPUType>(
             dev_ctx.x_context(), reinterpret_cast<const XPUType*>(dz_data),
-            reinterpret_cast<XPUType*>(dy_data), z_dims_vec, rdims_for_y);
-        PADDLE_ENFORCE_XDNN_SUCCESS(ret, "reduce_sum ");
+            reinterpret_cast<XPUType*>(dy_data), dz_vector, reduce_dims);
+        PADDLE_ENFORCE_XDNN_SUCCESS(ret, "reduce_sum");
       }
     }
   }