[clang-tidy] NO.15 enable performance-inefficient-vector-operation (#61715)

enkilee · web-flow · commit d84616c115a8 · 2024-02-20T11:44:44.000+08:00
diff --git a/paddle/fluid/framework/new_executor/interpreter/plan.cc b/paddle/fluid/framework/new_executor/interpreter/plan.cc
@@ -68,6 +68,7 @@ const std::vector<std::shared_ptr<Job>>& Plan::JobList() const {
 
 const std::vector<std::string> Plan::JobTypes() const {
   std::vector<std::string> res;
+  res.reserve(type_to_program_.size());
   for (auto kv : type_to_ir_program_) {
     res.emplace_back(kv.first);
   }
diff --git a/paddle/fluid/jit/function_schema.cc b/paddle/fluid/jit/function_schema.cc
@@ -28,6 +28,7 @@ const std::string& Argument::Name() const { return name_; }
 
 const std::vector<std::string> FunctionSchema::InputArgNames() const {
   std::vector<std::string> input_arg_names;
+  input_arg_names.reserve(input_args.size());
   for (auto& arg : input_args) {
     input_arg_names.emplace_back(arg.Name());
   }
@@ -36,6 +37,7 @@ const std::vector<std::string> FunctionSchema::InputArgNames() const {
 
 const std::vector<std::string> FunctionSchema::OutputArgNames() const {
   std::vector<std::string> output_arg_names;
+  output_arg_names.reserve(output_args.size());
   for (auto& arg : output_args) {
     output_arg_names.emplace_back(arg.Name());
   }
diff --git a/paddle/fluid/pir/dialect/operator/utils/utils.cc b/paddle/fluid/pir/dialect/operator/utils/utils.cc
@@ -188,41 +188,47 @@ static std::unordered_map<
 
            if (element_type == AttrType::BOOL) {
              std::vector<bool> vec_bools;
+             vec_bools.reserve(attr_vec.size());
              for (auto vec_element : attr_vec) {
                vec_bools.push_back(
                    vec_element.dyn_cast<pir::BoolAttribute>().data());
              }
              return VariantType{vec_bools};
            } else if (element_type == AttrType::INT32) {
              std::vector<int> vec_int32;
+             vec_int32.reserve(attr_vec.size());
              for (auto vec_element : attr_vec) {
                vec_int32.push_back(
                    vec_element.dyn_cast<pir::Int32Attribute>().data());
              }
              return VariantType{vec_int32};
            } else if (element_type == AttrType::INT64) {
              std::vector<int64_t> vec_int64;
+             vec_int64.reserve(attr_vec.size());
              for (auto vec_element : attr_vec) {
                vec_int64.push_back(
                    vec_element.dyn_cast<pir::Int64Attribute>().data());
              }
              return VariantType{vec_int64};
            } else if (element_type == AttrType::FLOAT) {
              std::vector<float> vec_float;
+             vec_float.reserve(attr_vec.size());
              for (auto vec_element : attr_vec) {
                vec_float.push_back(
                    vec_element.dyn_cast<pir::FloatAttribute>().data());
              }
              return VariantType{vec_float};
            } else if (element_type == AttrType::DOUBLE) {
              std::vector<double> vec_double;
+             vec_double.reserve(attr_vec.size());
              for (auto vec_element : attr_vec) {
                vec_double.push_back(
                    vec_element.dyn_cast<pir::DoubleAttribute>().data());
              }
              return VariantType{vec_double};
            } else if (element_type == AttrType::STRING) {
              std::vector<std::string> vec_string;
+             vec_string.reserve(attr_vec.size());
              for (auto vec_element : attr_vec) {
                vec_string.push_back(
                    vec_element.dyn_cast<pir::StrAttribute>().AsString());
diff --git a/paddle/fluid/pir/drr/src/rewrite_pattern.cc b/paddle/fluid/pir/drr/src/rewrite_pattern.cc
@@ -75,6 +75,7 @@ bool DrrRewritePattern::PatternGraphMatch(
     return false;
   }
   std::vector<const OpCall*> drr_output_sequence;
+  drr_output_sequence.reserve(bind_map.size());
   std::vector<pir::Operation*> ir_output_sequence;
   std::unordered_map<const OpCall*, pir::Operation*> output_op_map;
   for (const auto& pair : bind_map) {
diff --git a/paddle/phi/backends/device_manager.cc b/paddle/phi/backends/device_manager.cc
@@ -323,6 +323,7 @@ std::vector<std::string> DeviceManager::GetAllDeviceTypes() {
   phi::AutoRDLock lock(&_global_device_manager_rw_lock);
   auto& dev_impl_map = Instance().device_impl_map_;
   std::vector<std::string> devices;
+  devices.reserve(dev_impl_map.size());
   for (const auto& map_item : dev_impl_map) {
     devices.push_back(map_item.first);
   }
diff --git a/paddle/phi/core/distributed/auto_parallel/dist_mapper.cc b/paddle/phi/core/distributed/auto_parallel/dist_mapper.cc
@@ -26,6 +26,7 @@ void DistributedMapper::set_process_id_to_device_ids(
     const std::map<int64_t, std::pair<std::string, std::vector<int64_t>>>&
         process_id_to_device_ids) {
   std::vector<std::string> device_mesh_names;
+  device_mesh_names.reserve(device_meshes_.size());
   for (const auto& item : device_meshes_) {
     device_mesh_names.push_back(item.first);
   }
@@ -83,6 +84,7 @@ DistributedMapper DistributedMapper::from_proto(
         proto.process_id_to_device_ids(i).device_mesh_name();
     std::vector<int64_t> device_ids;
     int num_devices = proto.process_id_to_device_ids(i).device_ids_size();
+    device_ids.reserve(num_devices);
     for (int j = 0; j < num_devices; ++j) {
       device_ids.push_back(proto.process_id_to_device_ids(i).device_ids(j));
     }
diff --git a/paddle/phi/core/distributed/auto_parallel/reshard/s_to_r_reshard_function.cc b/paddle/phi/core/distributed/auto_parallel/reshard/s_to_r_reshard_function.cc
@@ -76,6 +76,7 @@ void ReshardSToRWithPadding(DeviceContext* dev_ctx,
 
     // Concat the result after split on correct axis.
     std::vector<const DenseTensor*> concat_input_vec;
+    concat_input_vec.reserve(split_out_vec.size());
     for (const auto& tensor : split_out_vec) {
       concat_input_vec.emplace_back(&tensor);
     }
diff --git a/paddle/phi/infermeta/spmd_rules/utils.cc b/paddle/phi/infermeta/spmd_rules/utils.cc
@@ -407,7 +407,7 @@ void AlignDimsSharding(std::vector<TensorDistAttr>* input_attrs_ptr,
     for (auto pair : partial_dim_to_type) {
       placements[pair.first] = std::make_shared<PartialStatus>(pair.second);
     }
-    new_input_attrs.emplace_back(FromPlacements(e, placements));
+    new_input_attrs.emplace_back(FromPlacements(e, placements));  // NOLINT
   }
   std::swap(input_attrs, new_input_attrs);
 }
diff --git a/paddle/phi/kernels/cpu/overlap_add_grad_kernel.cc b/paddle/phi/kernels/cpu/overlap_add_grad_kernel.cc
@@ -137,6 +137,7 @@ void OverlapAddGradKernel(const Context& dev_ctx,
   // Restore output dims when the number of dims is larger than 2.
   if (out_grad_rank > 2) {
     std::vector<int64_t> restored_x_grad_shape;
+    restored_x_grad_shape.reserve(preserved_dims.size());
     for (int i = 0; i < preserved_dims.size(); i++) {
       restored_x_grad_shape.push_back(preserved_dims[i]);
     }
diff --git a/paddle/phi/kernels/cpu/overlap_add_kernel.cc b/paddle/phi/kernels/cpu/overlap_add_kernel.cc
@@ -127,6 +127,7 @@ void OverlapAddKernel(const Context& dev_ctx,
   // Restore output dims when the number of dims is larger than 2.
   if (out_rank > 2) {
     std::vector<int64_t> restored_out_shape;
+    restored_out_shape.reserve(preserved_dims.size());
     for (int i = 0; i < preserved_dims.size(); i++) {
       restored_out_shape.push_back(preserved_dims[i]);
     }
diff --git a/paddle/phi/kernels/funcs/jit/benchmark.cc b/paddle/phi/kernels/funcs/jit/benchmark.cc
@@ -113,6 +113,7 @@ void BenchAllImpls(const typename KernelTuple::attr_type& attr, Args... args) {
   BenchFunc<KernelTuple, Args...> benchmark;
   std::vector<std::pair<std::string, double>> infos;
   auto funcs = jit::GetAllCandidateFuncsWithTypes<KernelTuple, PlaceType>(attr);
+  infos.reserve(funcs.size());
   for (auto const& f : funcs) {
     infos.push_back(std::make_pair(f.first, benchmark(f.second, args...)));
   }
diff --git a/paddle/phi/kernels/sparse/cpu/sum_grad_kernel.cc b/paddle/phi/kernels/sparse/cpu/sum_grad_kernel.cc
@@ -82,6 +82,7 @@ void SumCooGradCPUKernel(const Context& dev_ctx,
   std::map<std::vector<IntT>, int64_t> indices_map;
   for (auto j = 0; j < dout_indices.dims()[1]; ++j) {
     std::vector<IntT> pos;
+    pos.reserve(dout_indices.dims()[0]);
     for (int i = 0; i < dout_indices.dims()[0]; ++i) {
       pos.push_back(dout_indices_data[j + i * dout_indices.dims()[1]]);
     }
diff --git a/paddle/phi/kernels/stride/split_kernel.cc b/paddle/phi/kernels/stride/split_kernel.cc
@@ -56,6 +56,7 @@ void SplitWithNumStridedKernel(const Context& dev_ctx,
   int axis_value = axis_scalar.to<int>();
   auto input_axis_dim = x.dims().at(axis_value);
   std::vector<int64_t> sections_vec;
+  sections_vec.reserve(num);
   for (int i = 0; i < num; ++i) {
     sections_vec.push_back(input_axis_dim / num);
   }
diff --git a/paddle/testing/paddle_gtest_main.cc b/paddle/testing/paddle_gtest_main.cc
@@ -26,6 +26,7 @@ int main(int argc, char** argv) {  // NOLINT
   paddle::memory::allocation::UseAllocatorStrategyGFlag();
   testing::InitGoogleTest(&argc, argv);
   std::vector<char*> new_argv;
+  new_argv.reserve(argc);
   for (int i = 0; i < argc; ++i) {
     new_argv.push_back(argv[i]);
   }
diff --git a/test/cpp/fluid/framework/attribute_test.cc b/test/cpp/fluid/framework/attribute_test.cc
@@ -296,6 +296,7 @@ TEST(Attribute, ProtoAttrToAttribute_scalars) {
   proto_attr_scalars.set_type(paddle::framework::proto::SCALARS);
 
   std::vector<paddle::experimental::Scalar> scalars;
+  scalars.reserve(10);
   for (int i = 0; i < 10; i++) {
     scalars.emplace_back(i);
   }
diff --git a/test/cpp/fluid/framework/op_desc_test.cc b/test/cpp/fluid/framework/op_desc_test.cc
@@ -29,6 +29,7 @@ TEST(OpDesc, SetScalarsAttr) {
   paddle::experimental::Scalar scalar(std::complex<double>(42.1, 42.1));
 
   std::vector<paddle::experimental::Scalar> scalars;
+  scalars.reserve(4);
   for (int i = 0; i < 4; i++) {
     scalars.emplace_back(i);
   }
diff --git a/test/cpp/phi/kernels/test_fused_adam_kernel.cc b/test/cpp/phi/kernels/test_fused_adam_kernel.cc
@@ -71,6 +71,7 @@ auto GenerateConstantTensorVectors(
 
 static auto ToConstTensorPtrVector(const std::vector<DenseTensor> &tensors) {
   std::vector<const DenseTensor *> results;
+  results.reserve(tensors.size());
   for (const auto &t : tensors) {
     results.push_back(&t);
   }
@@ -80,6 +81,7 @@ static auto ToConstTensorPtrVector(const std::vector<DenseTensor> &tensors) {
 static auto ToMutableTensorPtrVector(
     std::vector<DenseTensor> &tensors) {  // NOLINT
   std::vector<DenseTensor *> results;
+  results.reserve(tensors.size());
   for (auto &t : tensors) {
     results.push_back(&t);
   }
@@ -88,6 +90,7 @@ static auto ToMutableTensorPtrVector(
 
 static auto ToMetaTensorVector(const std::vector<DenseTensor> &tensors) {
   std::vector<MetaTensor> results;
+  results.reserve(tensors.size());
   for (auto &t : tensors) {
     results.emplace_back(t);
   }
@@ -97,6 +100,7 @@ static auto ToMetaTensorVector(const std::vector<DenseTensor> &tensors) {
 static auto ToConstMetaTensorPtrVector(
     const std::vector<MetaTensor> &meta_tensors) {
   std::vector<const MetaTensor *> results;
+  results.reserve(meta_tensors.size());
   for (auto &t : meta_tensors) {
     results.push_back(&t);
   }
@@ -106,6 +110,7 @@ static auto ToConstMetaTensorPtrVector(
 static auto ToMutableMetaTensorPtrVector(
     std::vector<MetaTensor> &meta_tensors) {  // NOLINT
   std::vector<MetaTensor *> results;
+  results.reserve(meta_tensors.size());
   for (auto &t : meta_tensors) {
     results.push_back(&t);
   }

Original file line number	Diff line number	Diff line change
`@@ -68,6 +68,7 @@ const std::vector<std::shared_ptr<Job>>& Plan::JobList() const {`
`68`	`68`
`69`	`69`	`const std::vector<std::string> Plan::JobTypes() const {`
`70`	`70`	`std::vector<std::string> res;`
	`71`	`+ res.reserve(type_to_program_.size());`
`71`	`72`	`for (auto kv : type_to_ir_program_) {`
`72`	`73`	`res.emplace_back(kv.first);`
`73`	`74`	`}`
Original file line number	Diff line number	Diff line change
`@@ -28,6 +28,7 @@ const std::string& Argument::Name() const { return name_; }`
`28`	`28`
`29`	`29`	`const std::vector<std::string> FunctionSchema::InputArgNames() const {`
`30`	`30`	`std::vector<std::string> input_arg_names;`
	`31`	`+ input_arg_names.reserve(input_args.size());`
`31`	`32`	`for (auto& arg : input_args) {`
`32`	`33`	`input_arg_names.emplace_back(arg.Name());`
`33`	`34`	`}`
`@@ -36,6 +37,7 @@ const std::vector<std::string> FunctionSchema::InputArgNames() const {`
`36`	`37`
`37`	`38`	`const std::vector<std::string> FunctionSchema::OutputArgNames() const {`
`38`	`39`	`std::vector<std::string> output_arg_names;`
	`40`	`+ output_arg_names.reserve(output_args.size());`
`39`	`41`	`for (auto& arg : output_args) {`
`40`	`42`	`output_arg_names.emplace_back(arg.Name());`
`41`	`43`	`}`
Original file line number	Diff line number	Diff line change
`@@ -75,6 +75,7 @@ bool DrrRewritePattern::PatternGraphMatch(`
`75`	`75`	`return false;`
`76`	`76`	`}`
`77`	`77`	`std::vector<const OpCall*> drr_output_sequence;`
	`78`	`+ drr_output_sequence.reserve(bind_map.size());`
`78`	`79`	`std::vector<pir::Operation*> ir_output_sequence;`
`79`	`80`	`std::unordered_map<const OpCall, pir::Operation> output_op_map;`
`80`	`81`	`for (const auto& pair : bind_map) {`
Original file line number	Diff line number	Diff line change
`@@ -323,6 +323,7 @@ std::vector<std::string> DeviceManager::GetAllDeviceTypes() {`
`323`	`323`	`phi::AutoRDLock lock(&_global_device_manager_rw_lock);`
`324`	`324`	`auto& dev_impl_map = Instance().device_impl_map_;`
`325`	`325`	`std::vector<std::string> devices;`
	`326`	`+ devices.reserve(dev_impl_map.size());`
`326`	`327`	`for (const auto& map_item : dev_impl_map) {`
`327`	`328`	`devices.push_back(map_item.first);`
`328`	`329`	`}`
Original file line number	Diff line number	Diff line change
`@@ -76,6 +76,7 @@ void ReshardSToRWithPadding(DeviceContext* dev_ctx,`
`76`	`76`
`77`	`77`	`// Concat the result after split on correct axis.`
`78`	`78`	`std::vector<const DenseTensor*> concat_input_vec;`
	`79`	`+ concat_input_vec.reserve(split_out_vec.size());`
`79`	`80`	`for (const auto& tensor : split_out_vec) {`
`80`	`81`	`concat_input_vec.emplace_back(&tensor);`
`81`	`82`	`}`
Original file line number	Diff line number	Diff line change
`@@ -407,7 +407,7 @@ void AlignDimsSharding(std::vector<TensorDistAttr>* input_attrs_ptr,`
`407`	`407`	`for (auto pair : partial_dim_to_type) {`
`408`	`408`	`placements[pair.first] = std::make_shared<PartialStatus>(pair.second);`
`409`	`409`	`}`
`410`		`- new_input_attrs.emplace_back(FromPlacements(e, placements));`
	`410`	`+ new_input_attrs.emplace_back(FromPlacements(e, placements)); // NOLINT`
`411`	`411`	`}`
`412`	`412`	`std::swap(input_attrs, new_input_attrs);`
`413`	`413`	`}`
Original file line number	Diff line number	Diff line change
`@@ -137,6 +137,7 @@ void OverlapAddGradKernel(const Context& dev_ctx,`
`137`	`137`	`// Restore output dims when the number of dims is larger than 2.`
`138`	`138`	`if (out_grad_rank > 2) {`
`139`	`139`	`std::vector<int64_t> restored_x_grad_shape;`
	`140`	`+ restored_x_grad_shape.reserve(preserved_dims.size());`
`140`	`141`	`for (int i = 0; i < preserved_dims.size(); i++) {`
`141`	`142`	`restored_x_grad_shape.push_back(preserved_dims[i]);`
`142`	`143`	`}`
Original file line number	Diff line number	Diff line change
`@@ -127,6 +127,7 @@ void OverlapAddKernel(const Context& dev_ctx,`
`127`	`127`	`// Restore output dims when the number of dims is larger than 2.`
`128`	`128`	`if (out_rank > 2) {`
`129`	`129`	`std::vector<int64_t> restored_out_shape;`
	`130`	`+ restored_out_shape.reserve(preserved_dims.size());`
`130`	`131`	`for (int i = 0; i < preserved_dims.size(); i++) {`
`131`	`132`	`restored_out_shape.push_back(preserved_dims[i]);`
`132`	`133`	`}`