【Hackathon 6th Fundable Projects 2 No.28】Fix modernize-avoid-c-arrays_2-part

paddle/fluid/distributed/ps/service/graph_brpc_client.cc

@@ -248,7 +248,7 @@ std::future<int32_t> GraphBrpcClient::add_graph_node(
             reinterpret_cast<char *>(request_bucket[request_idx].data()),
             sizeof(int64_t) * node_num);
     if (add_weight) {
-      bool weighted[is_weighted_bucket[request_idx].size() + 1];
+      bool weighted[is_weighted_bucket[request_idx].size() + 1];  // NOLINT
       for (size_t j = 0; j < is_weighted_bucket[request_idx].size(); j++)
         weighted[j] = is_weighted_bucket[request_idx][j];
       closure->request(request_idx)

paddle/fluid/framework/device_worker.cc

@@ -93,7 +93,8 @@ void PrintLodTensorType<float>(phi::DenseTensor* tensor,
                                char separator,
                                bool need_leading_separator,
                                int num_decimals) {
-  char buf[MAX_FLOAT_BUFF_SIZE];
+  std::string buf;
+  buf.resize(MAX_FLOAT_BUFF_SIZE);
   auto count = tensor->numel();
   if (start < 0 || end > count) {
     VLOG(3) << "access violation";
@@ -108,7 +109,7 @@ void PrintLodTensorType<float>(phi::DenseTensor* tensor,
       out_val += "0";
     } else {
       std::string format = "%." + std::to_string(num_decimals) + "f";
-      sprintf(buf, &format[0], tensor->data<float>()[i]);  // NOLINT
+      sprintf(&buf[0], &format[0], tensor->data<float>()[i]);  // NOLINT
       out_val += buf;
     }
   }

paddle/fluid/inference/tensorrt/convert/temporal_shift_op.cc

@@ -177,17 +177,18 @@ class TemporalShiftOpConverter : public OpConverter {
     // Concatenate slices along the third dimension (C)
     nvinfer1::IConcatenationLayer* concat_layer;
     if (!slice_c) {
-      nvinfer1::ITensor* concat_inputs[2] = {slice2_layer->getOutput(0),
-                                             slice3_layer->getOutput(0)};
+      std::vector<nvinfer1::ITensor*> concat_inputs = {
+          slice2_layer->getOutput(0), slice3_layer->getOutput(0)};
       concat_layer =
-          TRT_ENGINE_ADD_LAYER(engine_, Concatenation, concat_inputs, 2);
+          TRT_ENGINE_ADD_LAYER(engine_, Concatenation, concat_inputs.data(), 2);
       concat_layer->setAxis(2);
     } else {
-      nvinfer1::ITensor* concat_inputs[3] = {slice1_layer->getOutput(0),
-                                             slice2_layer->getOutput(0),
-                                             slice3_layer->getOutput(0)};
+      std::vector<nvinfer1::ITensor*> concat_inputs = {
+          slice1_layer->getOutput(0),
+          slice2_layer->getOutput(0),
+          slice3_layer->getOutput(0)};
       concat_layer =
-          TRT_ENGINE_ADD_LAYER(engine_, Concatenation, concat_inputs, 3);
+          TRT_ENGINE_ADD_LAYER(engine_, Concatenation, concat_inputs.data(), 3);
       concat_layer->setAxis(2);
     }
 

paddle/fluid/inference/tensorrt/test_tensorrt.cc

@@ -119,7 +119,7 @@ void Execute(nvinfer1::IExecutionContext* context,
   const int input_index = engine.getBindingIndex(kInputTensor);
   const int output_index = engine.getBindingIndex(kOutputTensor);
   // Create GPU buffers and a stream
-  void* buffers[2];
+  std::vector<void*> buffers(2);
   ASSERT_EQ(0, cudaMalloc(&buffers[input_index], sizeof(float)));
   ASSERT_EQ(0, cudaMalloc(&buffers[output_index], sizeof(float)));
   cudaStream_t stream;
@@ -131,7 +131,7 @@ void Execute(nvinfer1::IExecutionContext* context,
                             sizeof(float),
                             cudaMemcpyHostToDevice,
                             stream));
-  context->enqueue(1, buffers, stream, nullptr);
+  context->enqueue(1, buffers.data(), stream, nullptr);
   ASSERT_EQ(0,
             cudaMemcpyAsync(output,
                             buffers[output_index],

paddle/fluid/operators/controlflow/pylayer_op.cc

@@ -26,11 +26,11 @@ namespace {  // NOLINT
 enum class PyLayerBlockIndex { kFORWARD = 0, kBACKWARD = 1, kNONE = 2 };
 }  // namespace
 
-const char PyLayerOp::kInputs[] = "Input";  // NOLINT
-const char PyLayerOp::kOutputs[] = "Out";   // NOLINT
-const char PyLayerOp::kScope[] = "Scope";   // NOLINT
-const char PyLayerOp::kSkipEagerDeletionVars[] =
-    "skip_eager_deletion_vars";              // NOLINT
+const char PyLayerOp::kInputs[] = "Input";        // NOLINT
+const char PyLayerOp::kOutputs[] = "Out";         // NOLINT
+const char PyLayerOp::kScope[] = "Scope";         // NOLINT
+const char PyLayerOp::kSkipEagerDeletionVars[] =  // NOLINT
+    "skip_eager_deletion_vars";
 const char PyLayerOp::kBlocks[] = "blocks";  // NOLINT
 
 void PyLayerOp::CreateInterpreter(

paddle/fluid/pir/dialect/kernel/ir/kernel_op.cc

@@ -181,11 +181,8 @@ phi::KernelKey OneDNNPhiKernelOp::kernel_key() {
   return attributes().at("kernel_key").dyn_cast<KernelAttribute>().data();
 }
 
-const char* OneDNNMixedPhiKernelOp::attributes_name[attributes_num] =
-    {  // NOLINT
-        "op_name",
-        "kernel_name",
-        "kernel_key"};
+const char* OneDNNMixedPhiKernelOp::attributes_name[attributes_num] =  // NOLINT
+    {"op_name", "kernel_name", "kernel_key"};
 
 void OneDNNMixedPhiKernelOp::VerifySig() {
   VLOG(4) << "Verifying inputs, outputs and attributes for: "

paddle/fluid/pir/dialect/operator/ir/manual_onednn_op.cc

@@ -47,7 +47,7 @@ namespace paddle {
 namespace onednn {
 namespace dialect {
 
-const char* ExpandOp::attributes_name[1] = {"mkldnn_data_type"};
+const char* ExpandOp::attributes_name[1] = {"mkldnn_data_type"};  // NOLINT
 
 OpInfoTuple ExpandOp::GetOpInfo() {
   std::vector<paddle::dialect::OpInputInfo> inputs = {

paddle/fluid/pir/dialect/operator/ir/manual_op.cc

@@ -2050,7 +2050,8 @@ std::vector<pir::Type> ArrayWrite_Op::InferMeta(
   return argument_outputs;
 }
 
-const char *ArrayToTensorOp::attributes_name[2] = {"axis", "use_stack"};
+const char *ArrayToTensorOp::attributes_name[2] = {"axis",
+                                                   "use_stack"};  //  NOLINT
 
 OpInfoTuple ArrayToTensorOp::GetOpInfo() {
   std::vector<paddle::dialect::OpInputInfo> inputs = {
@@ -2242,7 +2243,8 @@ std::vector<pir::Type> ArrayToTensorOp::InferMeta(
   return argument_outputs;
 }
 
-const char *TensorToArrayOp::attributes_name[2] = {"axis", "use_stack"};
+const char *TensorToArrayOp::attributes_name[2] = {"axis",
+                                                   "use_stack"};  // NOLINT
 
 OpInfoTuple TensorToArrayOp::GetOpInfo() {
   std::vector<paddle::dialect::OpInputInfo> inputs = {

paddle/fluid/pir/dialect/operator/ir/manual_pylayer_op.cc

@@ -39,7 +39,8 @@ paddle::dialect::PyLayerOp
 namespace paddle {
 namespace dialect {
 
-const char *PyLayerOp::attributes_name[1] = {kBackwardFunctionIdAttrName};
+const char *PyLayerOp::attributes_name[1] = {
+    kBackwardFunctionIdAttrName};  // NOLINT
 
 void PyLayerOp::Build(pir::Builder &builder,             // NOLINT
                       pir::OperationArgument &argument,  // NOLINT

paddle/fluid/platform/profiler/cpu_utilization.cc

@@ -54,7 +54,7 @@ void CpuUtilization::RecordBeginTimeInfo() {
 #elif defined(__linux__)
   start_ = times(&process_tms_start_);
 #define proc_path_size 1024
-  static char proc_stat_path[proc_path_size] = "/proc/stat";  // NOLINTf
+  static char proc_stat_path[proc_path_size] = "/proc/stat";  // NOLINT
   FILE *stat_file = fopen(proc_stat_path, "r");
   if (stat_file != nullptr) {
     std::array<char, 200> temp_str;

paddle/phi/kernels/fusion/onednn/fc_kernel.cc

@@ -602,11 +602,12 @@ void FCKernel(const Context& dev_ctx,
       dev_ctx.HasDnnAttr("force_fp32_output")
           ? PADDLE_GET_CONST(bool, dev_ctx.GetDnnAttr("force_fp32_output"))
           : false;
-  std::string mkldnn_data_type_list[] = {"float32", "int8", "bfloat16"};
+  std::vector<std::string> mkldnn_data_type_list = {
+      "float32", "int8", "bfloat16"};
   PADDLE_ENFORCE_EQ(
-      std::find(std::begin(mkldnn_data_type_list),
-                std::end(mkldnn_data_type_list),
-                mkldnn_data_type) != std::end(mkldnn_data_type_list),
+      std::find(mkldnn_data_type_list.begin(),
+                mkldnn_data_type_list.end(),
+                mkldnn_data_type) != mkldnn_data_type_list.end(),
       true,
       phi::errors::InvalidArgument("The mkldnn_data_type should be [float32, "
                                    "int8, bfloat16], but found %s.",

paddle/phi/kernels/fusion/onednn/fusion_gru_kernel.cc

@@ -573,11 +573,12 @@ void FusionGRUKernel(const Context& dev_ctx,
           ? PADDLE_GET_CONST(std::string,
                              dev_ctx.GetDnnAttr("mkldnn_data_type"))
           : "float32";
-  std::string mkldnn_data_type_list[] = {"float32", "int8", "bfloat16"};
+  std::vector<std::string> mkldnn_data_type_list = {
+      "float32", "int8", "bfloat16"};
   PADDLE_ENFORCE_EQ(
-      std::find(std::begin(mkldnn_data_type_list),
-                std::end(mkldnn_data_type_list),
-                mkldnn_data_type) != std::end(mkldnn_data_type_list),
+      std::find(mkldnn_data_type_list.begin(),
+                mkldnn_data_type_list.end(),
+                mkldnn_data_type) != mkldnn_data_type_list.end(),
       true,
       phi::errors::InvalidArgument("The mkldnn_data_type shoule be [float32, "
                                    "int8, bfloat16], but found %s.",

paddle/pir/src/dialect/shape/ir/shape_attribute.cc

@@ -18,7 +18,7 @@
 
 namespace pir::shape {
 
-const char SymbolAttribute::attr_name[] = "sym_shape_str";
+const char SymbolAttribute::attr_name[] = "sym_shape_str";  // NOLINT
 
 symbol::ShapeOrDataDimExprs SymbolAttribute::data() const {
   return storage()->data();

test/cpp/pir/tools/test_op.cc

@@ -41,7 +41,7 @@ void BranchOp::VerifySig() const {
       phi::errors::InvalidArgument("successor[0] can't be nullptr"));
 }
 
-const char *Operation1::attributes_name[2] = {"op1_attr1",
+const char *Operation1::attributes_name[2] = {"op1_attr1",   // NOLINT
                                               "op1_attr2"};  // NOLINT
 
 void Operation1::Build(pir::Builder &builder,               // NOLINT