diff --git a/paddle/fluid/operators/conv_op_xpu.cc b/paddle/fluid/operators/conv_op_xpu.cc
index 21fe9ad9bab5ab66cf5c0ac2256cf8971f57ad54..65ed34e8a5e4cc9036ed59b6937d739cd450ba32 100644
--- a/paddle/fluid/operators/conv_op_xpu.cc
+++ b/paddle/fluid/operators/conv_op_xpu.cc
@@ -27,10 +27,10 @@ class GemmConvXPUKernel : public framework::OpKernel<T> {
     // that avoids modifying the variable in the Scope.
     Tensor filter = *context.Input<Tensor>("Filter");
     Tensor* output = context.Output<Tensor>("Output");
-    Tensor* max_input = context.Output<Tensor>("MaxInput");
-    Tensor* max_filter = context.Output<Tensor>("MaxFilter");
-    max_input->mutable_data<T>(context.GetPlace());
-    max_filter->mutable_data<T>(context.GetPlace());
+    // Tensor* max_input = context.Output<Tensor>("MaxInput");
+    // Tensor* max_filter = context.Output<Tensor>("MaxFilter");
+    // max_input->mutable_data<T>(context.GetPlace());
+    // max_filter->mutable_data<T>(context.GetPlace());
     output->mutable_data<T>(context.GetPlace());
     int groups = context.Attr<int>("groups");
     std::vector<int> strides = context.Attr<std::vector<int>>("strides");
@@ -47,28 +47,28 @@ class GemmConvXPUKernel : public framework::OpKernel<T> {
         dilations[0] == 1 && dilations[1] == 1, true,
         platform::errors::InvalidArgument("XPU only support dilation == 1."));
     auto& dev_ctx = context.template device_context<DeviceContext>();
-    PADDLE_ENFORCE_EQ(
-        xpu::findmax(dev_ctx.x_context(), input->data<T>(), input->numel(),
-                     max_input->data<T>()) == xpu::Error_t::SUCCESS,
-        true, platform::errors::InvalidArgument(
-                  "XPU conv kernel error,can not finde max_input,please "
-                  "check whether Baidu Kunlun "
-                  "Card is properly installed."));
-    PADDLE_ENFORCE_EQ(
-        xpu::findmax(dev_ctx.x_context(), filter.data<T>(), filter.numel(),
-                     max_filter->data<T>()) == xpu::Error_t::SUCCESS,
-        true, platform::errors::InvalidArgument(
-                  "XPU conv kernel error,can not find max_filter,please "
-                  "check whether Baidu Kunlun "
-                  "Card is properly installed."));
+    // PADDLE_ENFORCE_EQ(
+    //     xpu::findmax(dev_ctx.x_context(), input->data<T>(), input->numel(),
+    //                  max_input->data<T>()) == xpu::Error_t::SUCCESS,
+    //     true, platform::errors::InvalidArgument(
+    //               "XPU conv kernel error,can not finde max_input,please "
+    //               "check whether Baidu Kunlun "
+    //               "Card is properly installed."));
+    // PADDLE_ENFORCE_EQ(
+    //     xpu::findmax(dev_ctx.x_context(), filter.data<T>(), filter.numel(),
+    //                  max_filter->data<T>()) == xpu::Error_t::SUCCESS,
+    //     true, platform::errors::InvalidArgument(
+    //               "XPU conv kernel error,can not find max_filter,please "
+    //               "check whether Baidu Kunlun "
+    //               "Card is properly installed."));
     if (groups == 1) {
       int r = xpu::conv2d_forward_int16<float, float, float, float>(
           dev_ctx.x_context(), batch_size, img_c, img_h, img_w, f, win_h, win_w,
           strides[0], strides[1], paddings[0], paddings[1], dilations[0],
           dilations[1], groups, input->data<float>(), filter.data<float>(),
           output->data<float>(), nullptr, nullptr, xpu::Activation_t::LINEAR,
-          // nullptr, nullptr);
-          max_input->data<float>(), max_filter->data<float>());
+          nullptr, nullptr);
+      // max_input->data<float>(), max_filter->data<float>());
       PADDLE_ENFORCE_EQ(
           r, XPU_SUCCESS,
           platform::errors::External("XPU conv kernel return wrong value[%d], "
@@ -80,8 +80,8 @@ class GemmConvXPUKernel : public framework::OpKernel<T> {
           dev_ctx.x_context(), input->data<float>(), filter.data<float>(),
           output->data<float>(), batch_size, img_c, img_h, img_w, f, win_h,
           win_w, groups, strides[0], strides[1], paddings[0], paddings[1],
-          // nullptr, nullptr);
-          max_input->data<float>(), max_filter->data<float>());
+          nullptr, nullptr);
+      // max_input->data<float>(), max_filter->data<float>());
       PADDLE_ENFORCE_EQ(
           r, XPU_SUCCESS,
           platform::errors::External("XPU conv kernel return wrong value[%d], "
@@ -96,9 +96,9 @@ class GemmConvGradXPUKernel : public framework::OpKernel<T> {
  public:
   void Compute(const framework::ExecutionContext& context) const override {
     const Tensor* input = context.Input<Tensor>("Input");
-    const Tensor* max_input = context.Input<Tensor>("MaxInput");
-    const Tensor* max_filter = context.Input<Tensor>("MaxFilter");
-    Tensor* max_output_grad = context.Output<Tensor>("MaxOutputGrad");
+    // const Tensor* max_input = context.Input<Tensor>("MaxInput");
+    // const Tensor* max_filter = context.Input<Tensor>("MaxFilter");
+    // Tensor* max_output_grad = context.Output<Tensor>("MaxOutputGrad");
     const Tensor* output_grad =
         context.Input<Tensor>(framework::GradVarName("Output"));
     Tensor* input_grad =
@@ -133,25 +133,25 @@ class GemmConvGradXPUKernel : public framework::OpKernel<T> {
       filter_grad->mutable_data<T>(context.GetPlace());
     }
     auto& dev_ctx = context.template device_context<DeviceContext>();
-    max_output_grad->Resize({4});
-    max_output_grad->mutable_data<T>(context.GetPlace());
-    PADDLE_ENFORCE_EQ(
-        xpu::findmax(dev_ctx.x_context(), output_grad->data<T>(),
-                     output_grad->numel(),
-                     max_output_grad->data<T>()) == xpu::Error_t::SUCCESS,
-        true,
-        platform::errors::External(
-            "XPU conv kernel error, can not find max_output_grad, please check "
-            "whether Baidu Kunlun Card is "
-            "properly installed."));
+    // max_output_grad->Resize({4});
+    // max_output_grad->mutable_data<T>(context.GetPlace());
+    // PADDLE_ENFORCE_EQ(
+    //     xpu::findmax(dev_ctx.x_context(), output_grad->data<T>(),
+    //                  output_grad->numel(),
+    //                  max_output_grad->data<T>()) == xpu::Error_t::SUCCESS,
+    //     true,
+    //     platform::errors::External(
+    //         "XPU conv kernel error, can not find max_output_grad, please
+    //         check "
+    //         "whether Baidu Kunlun Card is "
+    //         "properly installed."));
     if (input_grad) {
       int r = xpu::conv2d_backward_int16(
           dev_ctx.x_context(), batch_size, img_c, img_h, img_w, f, win_h, win_w,
           strides[0], strides[1], paddings[0], paddings[1], dilations[0],
           dilations[1], groups, output_grad->data<float>(),
-          filter.data<float>(), input_grad->data<float>(),
-          // nullptr, nullptr,
-          max_output_grad->data<float>(), max_filter->data<float>());
+          filter.data<float>(), input_grad->data<float>(), nullptr, nullptr);
+      // max_output_grad->data<float>(), max_filter->data<float>());
       PADDLE_ENFORCE_EQ(
           r, XPU_SUCCESS,
           platform::errors::External("XPU conv kernel return wrong value[%d], "
@@ -164,9 +164,8 @@ class GemmConvGradXPUKernel : public framework::OpKernel<T> {
           dev_ctx.x_context(), batch_size, img_c, img_h, img_w, f, win_h, win_w,
           strides[0], strides[1], paddings[0], paddings[1], dilations[0],
           dilations[1], groups, output_grad->data<float>(),
-          input->data<float>(), filter_grad->data<float>(),
-          // nullptr, nullptr,
-          max_output_grad->data<float>(), max_input->data<float>());
+          input->data<float>(), filter_grad->data<float>(), nullptr, nullptr);
+      // max_output_grad->data<float>(), max_input->data<float>());
       PADDLE_ENFORCE_EQ(
           r, XPU_SUCCESS,
           platform::errors::External("XPU conv kernel return wrong value[%d], "