diff --git a/cmake/flags.cmake b/cmake/flags.cmake
index 765bfefa427b64a2457b2355cd450f7bed367e38..7dbad9621f9cbc0957c3e7f34eca05a50ad80a61 100644
--- a/cmake/flags.cmake
+++ b/cmake/flags.cmake
@@ -154,7 +154,6 @@ if(NOT WIN32)
       -Wno-error=terminate # Warning in PADDLE_ENFORCE
       -Wno-error=int-in-bool-context # Warning in Eigen gcc 7.2
       -Wimplicit-fallthrough=0 # Warning in tinyformat.h
-      -Wno-error=maybe-uninitialized # Warning in Paddle-Lite
       ${fsanitize})
 
   if(WITH_IPU)
diff --git a/paddle/fluid/framework/ir/delete_weight_dequant_linear_op_pass.cc b/paddle/fluid/framework/ir/delete_weight_dequant_linear_op_pass.cc
index 5a6796550619b912c7e6a6729b7688e9dd67de96..40453313d3c797f2580bcf358f544bfa8b57c86e 100644
--- a/paddle/fluid/framework/ir/delete_weight_dequant_linear_op_pass.cc
+++ b/paddle/fluid/framework/ir/delete_weight_dequant_linear_op_pass.cc
@@ -45,8 +45,8 @@ void DeleteWeightDequantLinearOpPass::ApplyImpl(ir::Graph* graph) const {
     if (n->IsOp()) {
       auto* op = n->Op();
       if (op->Type() == "dequantize_linear") {
-        Node *weight_var_node, *dequantized_weight_var_node, *scale_var_node,
-            *calcu_op_node, *while_op_node;
+        Node *weight_var_node, *calcu_op_node, *while_op_node;
+        Node *dequantized_weight_var_node = nullptr, *scale_var_node = nullptr;
         // 1. Judge whether for dequant weight and find
         // weight_var_node/scale_var_node
         for (auto* input_node : n->inputs) {
diff --git a/paddle/fluid/inference/tensorrt/convert/op_converter.h b/paddle/fluid/inference/tensorrt/convert/op_converter.h
index 7d6a2772a5679be8376935c3fc07993a3d041f20..db19e5c45d3dec71d13bb5e1bec7cb538b365176 100644
--- a/paddle/fluid/inference/tensorrt/convert/op_converter.h
+++ b/paddle/fluid/inference/tensorrt/convert/op_converter.h
@@ -602,6 +602,9 @@ class OpConverter {
       const std::string& layer_type,
       const std::vector<std::string>& output_tensor_names,
       bool test_mode = false) {
+    if (layer == nullptr) {
+      return;
+    }
     size_t num_out = output_tensor_names.size();
     std::string layer_name = layer_type + " (Output: ";
     for (size_t i = 0; i < num_out; i++) {
diff --git a/paddle/fluid/inference/tensorrt/convert/unary_op.cc b/paddle/fluid/inference/tensorrt/convert/unary_op.cc
index 3186bf5fd33d01f904f136badbe758e4d44293d0..ce9b7e5b2f653f71d24be05841862957de020503 100644
--- a/paddle/fluid/inference/tensorrt/convert/unary_op.cc
+++ b/paddle/fluid/inference/tensorrt/convert/unary_op.cc
@@ -52,7 +52,7 @@ class UnaryOpConverter : public OpConverter {
     nvinfer1::ITensor* input_tensor =
         engine_->GetITensor(op_desc.Input("X")[0]);
     auto op_pair = ops.find(op_type_);
-    nvinfer1::ILayer* layer;
+    nvinfer1::ILayer* layer = nullptr;
 #if !IS_TRT_VERSION_GE(8500)
     nvinfer1::DataType org_type = input_tensor->getType();
     bool cast = org_type == nvinfer1::DataType::kINT8 ||
diff --git a/paddle/fluid/operators/fused/fused_gemm_epilogue_op_xpu.cc b/paddle/fluid/operators/fused/fused_gemm_epilogue_op_xpu.cc
index 687ce97068a35b14955357be4c31d8fc10145a9f..82b437b943cb43f7ef682355561e425642bd9ec0 100644
--- a/paddle/fluid/operators/fused/fused_gemm_epilogue_op_xpu.cc
+++ b/paddle/fluid/operators/fused/fused_gemm_epilogue_op_xpu.cc
@@ -142,7 +142,7 @@ class FusedGemmEpilogueXPUGradKernel : public framework::OpKernel<T> {
         (reserve_space == NULL)
             ? (reinterpret_cast<const XPUType*>(NULL))
             : (reinterpret_cast<const XPUType*>(reserve_space->data<T>()));
-    XPUType* d_act_input_ptr;
+    XPUType* d_act_input_ptr = NULL;
     if (activation != "none") {
       d_act_input_ptr = RAII_GUARD.alloc_l3_or_gm<XPUType>(dout->numel());
       dout_fc_ptr = d_act_input_ptr;
diff --git a/paddle/phi/kernels/xpu/pad3d_grad_kernel.cc b/paddle/phi/kernels/xpu/pad3d_grad_kernel.cc
index 035ab6c4b1adcb47de372bf03ed02b6f05c56303..d5960b02cf91ac833ebfa022eb7d44a605acdcd8 100644
--- a/paddle/phi/kernels/xpu/pad3d_grad_kernel.cc
+++ b/paddle/phi/kernels/xpu/pad3d_grad_kernel.cc
@@ -34,7 +34,7 @@ void Pad3dGradKernel(const Context& dev_ctx,
 
   auto* d_out = &out_grad;
   auto* d_in = x_grad;
-  auto d_in_dims = d_in->dims();
+  auto d_in_dims = vectorize<int>(d_in->dims());
   const T* d_out_data = d_out->data<T>();
   T* d_in_data = dev_ctx.template Alloc<T>(d_in);