[clang-tidy][task 5] enable `modernize-make-shared` and fix existing linter errors (#55807)

.clang-tidy

@@ -170,7 +170,7 @@ modernize-avoid-c-arrays,
 -modernize-deprecated-headers,
 -modernize-deprecated-ios-base-aliases,
 modernize-loop-convert,
--modernize-make-shared,
+modernize-make-shared,
 modernize-make-unique,
 -modernize-pass-by-value,
 modernize-raw-string-literal,

paddle/fluid/eager/api/manual/eager_manual/forwards/add_n_fwd_func.cc

@@ -83,8 +83,8 @@ paddle::Tensor add_n_ad_func(const std::vector<paddle::Tensor>& x) {
     egr::EagerUtils::PassStopGradient(false, out_autograd_meta);
 
     // Node Construction
-    auto grad_node =
-        std::shared_ptr<AddNGradNodeFinal>(new AddNGradNodeFinal(1, 1));
+    auto grad_node = std::shared_ptr<AddNGradNodeFinal>(  // NOLINT
+        new AddNGradNodeFinal(1, 1));
 
     // Set forward's stack
     if (FLAGS_check_nan_inf) {

paddle/fluid/eager/api/manual/eager_manual/forwards/conv2d_fwd_function.cc

@@ -137,8 +137,8 @@ paddle::Tensor conv2d_ad_func(const paddle::Tensor& input,
     egr::EagerUtils::PassStopGradient(false, out_autograd_meta);
 
     // Node Construction
-    auto grad_node =
-        std::shared_ptr<Conv2dGradNodeFinal>(new Conv2dGradNodeFinal(1, 2));
+    auto grad_node = std::shared_ptr<Conv2dGradNodeFinal>(  // NOLINT
+        new Conv2dGradNodeFinal(1, 2));
 
     // Set forward's stack
     if (FLAGS_check_nan_inf) {

paddle/fluid/eager/api/manual/eager_manual/forwards/multiply_fwd_func.cc

@@ -132,8 +132,8 @@ paddle::Tensor multiply_ad_func(const paddle::Tensor& x,
     egr::EagerUtils::PassStopGradient(false, out_autograd_meta);
 
     // Node Construction
-    auto grad_node =
-        std::shared_ptr<MultiplyGradNode>(new MultiplyGradNode(1, 2));
+    auto grad_node = std::shared_ptr<MultiplyGradNode>(  // NOLINT
+        new MultiplyGradNode(1, 2));
     // Set for forward trace
     if (FLAGS_check_nan_inf) {
       grad_node->SetForwardTrace(egr::Controller::Instance().GetPythonStack());
@@ -275,7 +275,8 @@ paddle::Tensor& multiply__ad_func(paddle::Tensor& x,  // NOLINT
         paddle::platform::TracerEventType::OperatorInner,
         1);
 
-    grad_node = std::shared_ptr<MultiplyGradNode>(new MultiplyGradNode(1, 2));
+    grad_node = std::shared_ptr<MultiplyGradNode>(  // NOLINT
+        new MultiplyGradNode(1, 2));
     // Set for forward trace
     if (FLAGS_check_nan_inf) {
       grad_node->SetForwardTrace(egr::Controller::Instance().GetPythonStack());
@@ -462,8 +463,8 @@ paddle::Tensor multiply_ad_func(const paddle::Tensor& x,
     egr::EagerUtils::PassStopGradient(false, out_autograd_meta);
 
     // Node Construction
-    auto grad_node =
-        std::shared_ptr<MultiplyGradNode>(new MultiplyGradNode(1, 2));
+    auto grad_node = std::shared_ptr<MultiplyGradNode>(  // NOLINT
+        new MultiplyGradNode(1, 2));
     // Set for forward trace
     if (FLAGS_check_nan_inf) {
       grad_node->SetForwardTrace(egr::Controller::Instance().GetPythonStack());

paddle/fluid/eager/api/manual/eager_manual/forwards/sync_batch_norm_fwd_func.cc

@@ -225,8 +225,8 @@ sync_batch_norm__ad_func(const paddle::Tensor& x,
                                       reserve_space_autograd_meta);
 
     // Node Construction
-    auto grad_node =
-        std::shared_ptr<SyncBatchNormGradNode>(new SyncBatchNormGradNode(6, 5));
+    auto grad_node = std::shared_ptr<SyncBatchNormGradNode>(  // NOLINT
+        new SyncBatchNormGradNode(6, 5));
 
     // Set forward's stack
     if (FLAGS_check_nan_inf) {
@@ -567,8 +567,8 @@ sync_batch_norm__ad_func(const paddle::Tensor& x,
                                       reserve_space_autograd_meta);
 
     // Node Construction
-    auto grad_node =
-        std::shared_ptr<SyncBatchNormGradNode>(new SyncBatchNormGradNode(6, 5));
+    auto grad_node = std::shared_ptr<SyncBatchNormGradNode>(  // NOLINT
+        new SyncBatchNormGradNode(6, 5));
     egr::Controller::Instance().PushBackForceSequentialNodes(grad_node.get());
     // SetAttributes if needed
     grad_node->SetAttributemomentum(momentum);

paddle/fluid/eager/api/manual/eager_manual/nodes/conv2d_nodes.cc

@@ -123,7 +123,7 @@ Conv2dGradNodeFinal::operator()(
         1);
 
     // Node Construction
-    auto grad_node = std::shared_ptr<Conv2dDoubleGradNodeFinal>(
+    auto grad_node = std::shared_ptr<Conv2dDoubleGradNodeFinal>(  // NOLINT
         new Conv2dDoubleGradNodeFinal(2, 3));
     // SetAttributes if needed
     grad_node->SetAttributestrides(strides);

paddle/fluid/eager/api/manual/eager_manual/nodes/multiply_node.cc

@@ -158,7 +158,7 @@ MultiplyGradNode::operator()(
           1);
 
       // Node Construction
-      auto grad_node = std::shared_ptr<MultiplyDoubleGradNode>(
+      auto grad_node = std::shared_ptr<MultiplyDoubleGradNode>(  // NOLINT
           new MultiplyDoubleGradNode(2, 3));
       // SetAttributes if needed
       grad_node->SetAttributeaxis(axis);

paddle/fluid/eager/api/manual/fluid_manual/forwards/fused_attention_fwd_func.cc

@@ -390,8 +390,9 @@ fused_attention_dygraph_function(
                                         p_autograd_CacheKVOut,
                                         p_autograd_Y);
       // Create GradOpNode
-      auto grad_node = std::shared_ptr<fused_attentionGradNodeCompat>(
-          new fused_attentionGradNodeCompat(20, 23));
+      auto grad_node =
+          std::shared_ptr<fused_attentionGradNodeCompat>(  // NOLINT
+              new fused_attentionGradNodeCompat(20, 23));
 
       bool pre_layer_norm = false;
       if (attrs.count("pre_layer_norm")) {

paddle/fluid/eager/api/manual/fluid_manual/forwards/fused_bias_dropout_residual_layer_norm_fwd_func.cc

@@ -184,9 +184,9 @@ fused_bias_dropout_residual_layer_norm_dygraph_function(
                                         p_autograd_LnVariance,
                                         p_autograd_Y);
       // Create GradOpNode
-      auto grad_node =
-          std::shared_ptr<fused_bias_dropout_residual_layer_normGradNodeCompat>(
-              new fused_bias_dropout_residual_layer_normGradNodeCompat(5, 5));
+      auto grad_node = std::shared_ptr<  // NOLINT
+          fused_bias_dropout_residual_layer_normGradNodeCompat>(
+          new fused_bias_dropout_residual_layer_normGradNodeCompat(5, 5));
 
       // Set Attributes
       grad_node->SetAttrMap(std::move(attrs));

paddle/fluid/eager/api/manual/fluid_manual/forwards/fused_feedforward_fwd_func.cc

@@ -310,8 +310,9 @@ fused_feedforward_dygraph_function(
                                         p_autograd_Dropout1Out,
                                         p_autograd_Dropout2Out);
       // Create GradOpNode
-      auto grad_node = std::shared_ptr<fused_feedforwardGradNodeCompat>(
-          new fused_feedforwardGradNodeCompat(11, 11));
+      auto grad_node =
+          std::shared_ptr<fused_feedforwardGradNodeCompat>(  // NOLINT
+              new fused_feedforwardGradNodeCompat(11, 11));
 
       bool pre_layer_norm = false;
       if (attrs.count("pre_layer_norm")) {

paddle/fluid/eager/api/manual/fluid_manual/forwards/fused_gate_attention_fwd_func.cc

@@ -301,8 +301,9 @@ fused_gate_attention_dygraph_function(
                                         p_autograd_GateOut,
                                         p_autograd_Out);
       // Create GradOpNode
-      auto grad_node = std::shared_ptr<fused_gate_attentionGradNodeCompat>(
-          new fused_gate_attentionGradNodeCompat(9, 12));
+      auto grad_node =
+          std::shared_ptr<fused_gate_attentionGradNodeCompat>(  // NOLINT
+              new fused_gate_attentionGradNodeCompat(9, 12));
 
       bool merge_qkv = true;
       if (attrs.count("merge_qkv")) {

paddle/fluid/eager/api/manual/fluid_manual/forwards/fused_gemm_epilogue_fwd_func.cc

@@ -102,8 +102,9 @@ paddle::Tensor fused_gemm_epilogue_dygraph_function(
       VLOG(6) << " Construct Grad for fused_gemm_epilogue ";
       egr::EagerUtils::PassStopGradient(false, p_autograd_Out);
       // Create GradOpNode
-      auto grad_node = std::shared_ptr<fused_gemm_epilogueGradNodeCompat>(
-          new fused_gemm_epilogueGradNodeCompat(1, 3));
+      auto grad_node =
+          std::shared_ptr<fused_gemm_epilogueGradNodeCompat>(  // NOLINT
+              new fused_gemm_epilogueGradNodeCompat(1, 3));
 
       // Set Attributes
       grad_node->SetAttrMap(std::move(attrs));

paddle/fluid/eager/auto_code_generator/eager_generator.cc

@@ -827,9 +827,8 @@ static bool CollectGradInformationFromOpInfo(
     const std::string& in_name = op_proto.inputs()[0].name();
     ins[in_name] = {};
     for (size_t i = 0; i < NUM_CREATED_DUP_INPUTS; i++) {
-      ins[in_name].emplace_back(std::shared_ptr<paddle::imperative::VarBase>(
-          new paddle::imperative::VarBase("auto_" + in_name + "_" +
-                                          std::to_string(i))));
+      ins[in_name].emplace_back(std::make_shared<paddle::imperative::VarBase>(
+          "auto_" + in_name + "_" + std::to_string(i)));
       ins[in_name][i]->SetOverridedStopGradient(false);
       ins[in_name][i]->MutableVar()->GetMutable<phi::DenseTensor>();
     }
@@ -852,8 +851,8 @@ static bool CollectGradInformationFromOpInfo(
       // but we only need to identify the slot name order,
       // therefore fill in 1 single input VarBase is enough in this scenario
 
-      ins[in_name] = {std::shared_ptr<paddle::imperative::VarBase>(
-          new paddle::imperative::VarBase("auto_" + in_name))};
+      ins[in_name] = {
+          std::make_shared<paddle::imperative::VarBase>("auto_" + in_name)};
       ins[in_name][0]->SetOverridedStopGradient(false);
       ins[in_name][0]->MutableVar()->GetMutable<phi::DenseTensor>();
     }
@@ -870,8 +869,8 @@ static bool CollectGradInformationFromOpInfo(
     // We always create output VarBase regardless of its dispensability.
     // We dont know the exact number of outputs during code generation,
     // however, simply identifying the slot name order would be enough
-    outs[out_name] = {std::shared_ptr<paddle::imperative::VarBase>(
-        new paddle::imperative::VarBase("auto_" + out_name))};
+    outs[out_name] = {
+        std::make_shared<paddle::imperative::VarBase>("auto_" + out_name)};
     outs[out_name][0]->SetOverridedStopGradient(false);
     outs[out_name][0]->MutableVar()->GetMutable<phi::DenseTensor>();
   }
@@ -1179,7 +1178,7 @@ static std::string GenerateGradNodeCreationContent(
   const char* GRAD_OP_NODE_TEMPLATE =
       "      auto grad_node = std::shared_ptr<%sGradNodeCompat>(new "
       "%sGradNodeCompat(%d, "
-      "%d));\n";
+      "%d)); // NOLINT\n";
   grad_node_creation_str += "    // Create GradOpNode\n";
   grad_node_creation_str += paddle::string::Sprintf(GRAD_OP_NODE_TEMPLATE,
                                                     op_type,

paddle/fluid/eager/auto_code_generator/generator/eager_gen.py

@@ -953,13 +953,13 @@ class DygraphFunctionGeneratorBase(FunctionGeneratorBase):
 
         # Helper
         indent = GetIndent(2)
-        # NOTE(Aurelius74): DO NOT use make_shared here. Because some Node contains experimental::Scalar
+        # NOTE(Aurelius84): DO NOT use make_shared here. Because some Node contains experimental::Scalar
         # which contains "complex128" as data. "complex128" is memory-aligned manually. But make_shared
         # request MEMALIGN for allocation (Maybe).
         # See https://stackoverflow.com/questions/31228656/how-can-shared-ptr-disrupt-alignment
         # and https://github.com/MRtrix3/mrtrix3/issues/957
-        node_construction_str = f"{indent}auto grad_node = std::shared_ptr<{grad_node_name}>(new {grad_node_name}({num_backward_inputs}, {num_backward_outputs}));"
-        node_assignment_str = f"{indent}grad_node = std::shared_ptr<{grad_node_name}>(new {grad_node_name}({num_backward_inputs}, {num_backward_outputs}));"
+        node_construction_str = f"{indent}auto grad_node = std::shared_ptr<{grad_node_name}>(new {grad_node_name}({num_backward_inputs}, {num_backward_outputs})); // NOLINT"
+        node_assignment_str = f"{indent}grad_node = std::shared_ptr<{grad_node_name}>(new {grad_node_name}({num_backward_inputs}, {num_backward_outputs})); // NOLINT"
 
         # SetAttributes
         set_attributes_list = []

paddle/fluid/pybind/eager_functions.cc

@@ -1187,8 +1187,8 @@ static PyObject* eager_api_to_uva_tensor(PyObject* self,
                                          PyObject* kwargs) {
   EAGER_TRY
   VLOG(4) << "Running in eager_api_to_uva_tensor.";
-  auto new_tensor = std::shared_ptr<paddle::Tensor>(
-      new paddle::Tensor(egr::Controller::Instance().GenerateUniqueName()));
+  auto new_tensor = std::make_shared<paddle::Tensor>(
+      egr::Controller::Instance().GenerateUniqueName());
   PyObject* obj = PyTuple_GET_ITEM(args, 0);
   auto array = py::cast<py::array>(py::handle(obj));
 

paddle/fluid/pybind/imperative.cc

@@ -976,8 +976,8 @@ void BindImperative(py::module *m_ptr) {
       "to_uva_tensor",
       [](const py::object &obj, int device_id) {
         const auto &tracer = imperative::GetCurrentTracer();
-        auto new_tensor = std::shared_ptr<imperative::VarBase>(
-            new imperative::VarBase(tracer->GenerateUniqueName()));
+        auto new_tensor =
+            std::make_shared<imperative::VarBase>(tracer->GenerateUniqueName());
         auto array = obj.cast<py::array>();
         if (py::isinstance<py::array_t<int32_t>>(array)) {
           SetUVATensorFromPyArray<int32_t>(new_tensor, array, device_id);

test/cpp/eager/performance_tests/benchmark_utils.cc

@@ -241,9 +241,7 @@ void benchmark_fluid_scale(const std::shared_ptr<imperative::VarBase>& X,
   for (size_t i = 0; i < max_num_runs; i++) {
     imperative::NameVarBaseMap ins = {{"X", {tmp_out}}};
     imperative::NameVarBaseMap outs = {
-        {"Out",
-         {std::shared_ptr<imperative::VarBase>(
-             new imperative::VarBase(true, "Out"))}}};
+        {"Out", {std::make_shared<imperative::VarBase>(true, "Out")}}};
 
     tracer.TraceOp<VarBase>("scale", ins, outs, attrs, place, true);
 
@@ -277,9 +275,7 @@ void benchmark_fluid_matmul(const std::shared_ptr<imperative::VarBase>& X,
     framework::AttributeMap attrs;
     imperative::NameVarBaseMap ins = {{"X", {tmp_out}}, {"Y", {Y}}};
     imperative::NameVarBaseMap outs = {
-        {"Out",
-         {std::shared_ptr<imperative::VarBase>(
-             new imperative::VarBase(true, "Out"))}}};
+        {"Out", {std::make_shared<imperative::VarBase>(true, "Out")}}};
 
     tracer.TraceOp<VarBase>("matmul_v2", ins, outs, attrs, place, true);
 
@@ -316,17 +312,13 @@ void benchmark_fluid_mlp(
   for (size_t i = 0; i < MLP_NUM_LINEAR; i++) {
     // Matmul0
     ins = {{"X", {input0}}, {"Y", {Ws[0]}}};
-    outs = {{"Out",
-             {std::shared_ptr<imperative::VarBase>(
-                 new imperative::VarBase(true, "Out"))}}};
+    outs = {{"Out", {std::make_shared<imperative::VarBase>(true, "Out")}}};
 
     tracer.TraceOp<VarBase>("matmul_v2", ins, outs, attrs, place, true);
 
     // EW-Add0
     ins = {{"X", outs["Out"]}, {"Y", {Bs[i]}}};
-    outs = {{"Out",
-             {std::shared_ptr<imperative::VarBase>(
-                 new imperative::VarBase(true, "Out"))}}};
+    outs = {{"Out", {std::make_shared<imperative::VarBase>(true, "Out")}}};
 
     tracer.TraceOp<VarBase>("elementwise_add", ins, outs, attrs, place, true);
     input0 = outs["Out"][0];
@@ -334,9 +326,7 @@ void benchmark_fluid_mlp(
 
   // ReduceSum
   ins = {{"X", {input0}}};
-  outs = {{"Out",
-           {std::shared_ptr<imperative::VarBase>(
-               new imperative::VarBase(true, "Out"))}}};
+  outs = {{"Out", {std::make_shared<imperative::VarBase>(true, "Out")}}};
   attrs = {{"reduce_all", true}};
 
   tracer.TraceOp<VarBase>("reduce_sum", ins, outs, attrs, place, true);