[18/N] Fix clang-tidy warnings in jit (pytorch#132963)

Follows pytorch#132753

Pull Request resolved: pytorch#132963
Approved by: https://github.com/Skylion007

Author: cyyever

torch/csrc/jit/mobile/code.h

@@ -6,9 +6,7 @@
 #include <ATen/core/operator_name.h>
 #include <torch/csrc/jit/runtime/instruction.h>
 
-namespace torch {
-namespace jit {
-namespace mobile {
+namespace torch::jit::mobile {
 
 using Stack = std::vector<c10::IValue>;
 using DebugHandle = int64_t;
@@ -34,6 +32,4 @@ struct Code {
   bool initialized = false;
 };
 
-} // namespace mobile
-} // namespace jit
-} // namespace torch
+} // namespace torch::jit::mobile

torch/csrc/jit/mobile/debug_info.cpp

@@ -9,8 +9,7 @@
 
 #include <c10/util/string_view.h>
 
-namespace torch {
-namespace jit {
+namespace torch::jit {
 
 namespace {
 
@@ -140,7 +139,7 @@ MobileDebugTable::MobileDebugTable(
       }
 
       for (auto& val : lines.toTuple()->elements()) {
-        auto tup_elems = std::move(*std::move(val).toTuple()).elements();
+        auto tup_elems = std::move(*val.toTuple()).elements();
         // For BC we decode only tuples with 3 elements
         // assuming it contains
         // byte_offset, debug_handle (=source range tag), source range
@@ -159,7 +158,7 @@ MobileDebugTable::MobileDebugTable(
         reader->getRecord(callstack_debug_file);
     CallStackDebugInfoUnpickler unpickler;
     callstack_ptr_map_ = unpickler.unpickle(
-        std::move(callstack_data), callstack_data_size, source_range_map, cu);
+        callstack_data, callstack_data_size, source_range_map, cu);
   }
 }
 
@@ -229,5 +228,4 @@ std::pair<std::string, std::string> MobileDebugTable::
       debug_infos, "top", top_module_type_name));
 }
 
-} // namespace jit
-} // namespace torch
+} // namespace torch::jit

torch/csrc/jit/mobile/debug_info.h

@@ -5,8 +5,7 @@
 #include <torch/csrc/jit/ir/scope.h>
 #include <torch/csrc/jit/serialization/source_range_serialization.h>
 
-namespace torch {
-namespace jit {
+namespace torch::jit {
 /*
  * MobileDebugTable:
  * Deserializes debug_pkl and callstack_map records from PT model's zip archive
@@ -53,5 +52,4 @@ class MobileDebugTable {
   ska::flat_hash_map<int64_t, DebugInfoTuple> callstack_ptr_map_;
 };
 
-} // namespace jit
-} // namespace torch
+} // namespace torch::jit

torch/csrc/jit/mobile/file_format.h

@@ -29,8 +29,7 @@
  * only be called from one or two locations per binary.
  */
 
-namespace torch {
-namespace jit {
+namespace torch::jit {
 
 /**
  * The format of a file or data stream.
@@ -119,9 +118,9 @@ static void file_not_found_error() {
   std::stringstream message;
   message << "Error while opening file: ";
   if (errno == ENOENT) {
-    message << "no such file or directory" << std::endl;
+    message << "no such file or directory" << '\n';
   } else {
-    message << "error no is: " << errno << std::endl;
+    message << "error no is: " << errno << '\n';
   }
   TORCH_CHECK(false, message.str());
 }
@@ -192,5 +191,4 @@ static inline std::tuple<std::shared_ptr<char>, size_t> get_rai_content(
   return std::make_tuple(data, buffer_size);
 }
 
-} // namespace jit
-} // namespace torch
+} // namespace torch::jit

torch/csrc/jit/mobile/flatbuffer_loader.cpp

@@ -55,8 +55,7 @@ namespace flatbuffers = flatbuffers_fbsource;
 #include <torch/csrc/jit/serialization/mobile_bytecode_generated.h> // NOLINT
 #endif
 
-namespace torch {
-namespace jit {
+namespace torch::jit {
 
 // Our own alignment requirement does not need to be exactly the same as what
 // flatbuffers supports, but what flatbuffers supports needs to satisfy our
@@ -91,9 +90,9 @@ class FlatbufferLoader final {
       ExtraFilesMap* jit_sources,
       std::vector<IValue>* constants);
 
-  typedef TypePtr (*TypeResolver)(
+  using TypeResolver = TypePtr (*)(
       const std::string& type_str,
-      std::shared_ptr<CompilationUnit> cu);
+      const std::shared_ptr<CompilationUnit>& cu);
 
   void internal_registerTypeResolver(TypeResolver type_resolver);
 
@@ -187,7 +186,7 @@ IValue parseEnum(
 
 TypePtr resolveType(
     const std::string& type_string,
-    std::shared_ptr<CompilationUnit> cu) {
+    const std::shared_ptr<CompilationUnit>& cu) {
   TypePtr type;
   c10::string_view type_str(type_string);
   if (type_str.starts_with(kCustomClassPrefix)) {
@@ -531,7 +530,7 @@ IValue parseList(
     const mobile::serialization::IValue& ivalue) {
   const mobile::serialization::List* list = ivalue.val_as_List();
   auto res = c10::impl::GenericList(AnyType::get());
-  for (int i : *list->items()) {
+  for (auto i : *list->items()) {
     res.emplace_back(loader.getIValue(i));
   }
   auto type = loader.getOrCreateTypeAnnotations(list->annotation_str());
@@ -575,11 +574,13 @@ IValue parseTuple(
     FlatbufferLoader& loader,
     const mobile::serialization::IValue& ivalue) {
   const auto& tuple = ivalue.val_as_Tuple();
+  const auto items = tuple->items();
   std::vector<IValue> res;
-  for (int i : *tuple->items()) {
+  res.reserve(items->size());
+  for (auto i : *items) {
     res.emplace_back(loader.getIValue(i));
   }
-  return c10::ivalue::Tuple::create(res);
+  return c10::ivalue::Tuple::create(std::move(res));
 }
 
 IValue parseDict(
@@ -939,5 +940,4 @@ bool register_flatbuffer_loader() {
   return true;
 }
 
-} // namespace jit
-} // namespace torch
+} // namespace torch::jit

torch/csrc/jit/mobile/flatbuffer_loader.h

@@ -18,8 +18,7 @@
  * types, to avoid leaking those details to PyTorch clients.
  */
 
-namespace torch {
-namespace jit {
+namespace torch::jit {
 
 /// All non-copied data pointers provided to `parse_and_initialize_*` functions
 /// must be aligned to this boundary. Since the Module will point directly into
@@ -132,5 +131,4 @@ TORCH_API mobile::Module parse_and_initialize_mobile_module(
 // no op, TODO(qihan) delete
 TORCH_API bool register_flatbuffer_loader();
 
-} // namespace jit
-} // namespace torch
+} // namespace torch::jit

torch/csrc/jit/mobile/frame.h

@@ -5,9 +5,7 @@
 #include <torch/csrc/jit/mobile/code.h>
 #include <optional>
 
-namespace torch {
-namespace jit {
-namespace mobile {
+namespace torch::jit::mobile {
 
 class Frame {
  public:
@@ -48,6 +46,4 @@ class Frame {
   size_t pc_{0};
 };
 
-} // namespace mobile
-} // namespace jit
-} // namespace torch
+} // namespace torch::jit::mobile

torch/csrc/jit/mobile/function.cpp

@@ -8,8 +8,7 @@
 #include <torch/csrc/jit/runtime/instruction.h>
 #include <torch/csrc/jit/runtime/operator.h>
 
-namespace torch {
-namespace jit {
+namespace torch::jit {
 
 char const* toString(OpCode op);
 namespace mobile {
@@ -27,7 +26,11 @@ const c10::QualifiedName& Function::qualname() const {
   return name_;
 }
 
-void Function::append_instruction(OpCode op, int X, int N, int64_t dbg_handle) {
+void Function::append_instruction(
+    OpCode op,
+    int64_t X,
+    int64_t N,
+    int64_t dbg_handle) {
   TORCH_CHECK(
       isOpSupportedInMobile(op),
       toString(op),
@@ -36,7 +39,7 @@ void Function::append_instruction(OpCode op, int X, int N, int64_t dbg_handle) {
   code_.debug_handles_.emplace_back(dbg_handle);
 }
 
-void Function::append_instruction(OpCode op, int X, int N) {
+void Function::append_instruction(OpCode op, int64_t X, int64_t N) {
   TORCH_CHECK(
       isOpSupportedInMobile(op),
       toString(op),
@@ -166,7 +169,7 @@ const std::vector<int64_t>& Function::getExceptionDebugHandles() const {
 }
 
 std::optional<std::function<void(Stack&)>> makeOperatorFunction(
-    c10::OperatorName opname,
+    const c10::OperatorName& opname,
     std::optional<int> num_specified_args) {
   std::function<void(Stack&)> fn;
   const auto full_name = c10::toString(opname);
@@ -269,5 +272,4 @@ Function& Function::registerFunc(
 }
 
 } // namespace mobile
-} // namespace jit
-} // namespace torch
+} // namespace torch::jit

torch/csrc/jit/mobile/function.h

@@ -7,8 +7,7 @@
 #include <ATen/core/ivalue.h>
 #include <torch/csrc/jit/mobile/code.h>
 
-namespace torch {
-namespace jit {
+namespace torch::jit {
 enum OpCode : uint8_t;
 struct Instruction;
 struct OperatorString;
@@ -32,8 +31,8 @@ class TORCH_API Function : public torch::jit::Function {
   // NOTE: the APIs below is dangerous: if you call append_instruction with
   // dbg_handle and then call it without; then the dbg_handle will become
   // misaligned. Therefore only use ONE variant at time.
-  void append_instruction(OpCode op, int X, int N, int64_t dbg_handle);
-  void append_instruction(OpCode op, int X, int N);
+  void append_instruction(OpCode op, int64_t X, int64_t N, int64_t dbg_handle);
+  void append_instruction(OpCode op, int64_t X, int64_t N);
   void append_operator(
       const std::string& name,
       const std::string& overload_name,
@@ -76,11 +75,10 @@ class TORCH_API Function : public torch::jit::Function {
 };
 
 std::optional<std::function<void(Stack&)>> makeOperatorFunction(
-    c10::OperatorName opname,
+    const c10::OperatorName& opname,
     std::optional<int> num_specified_args);
 
 TORCH_API std::string operator_str(const c10::OperatorName& opname);
 
 } // namespace mobile
-} // namespace jit
-} // namespace torch
+} // namespace torch::jit

torch/csrc/jit/mobile/import.cpp

@@ -81,8 +81,7 @@
 //  - Argument::{known_length_,kwarg_only_}
 //  - FunctionSchema::{overload_name_, is_vararg_, is_varret_}
 
-namespace torch {
-namespace jit {
+namespace torch::jit {
 using caffe2::serialize::MemoryReadAdapter;
 using caffe2::serialize::PyTorchStreamReader;
 using caffe2::serialize::ReadAdapterInterface;
@@ -91,7 +90,7 @@ OpCode parseOpCode(const char* str);
 
 TypePtr resolveTypeNameMobile(
     const c10::QualifiedName& qn,
-    std::shared_ptr<CompilationUnit> compilation_unit) {
+    const std::shared_ptr<CompilationUnit>& compilation_unit) {
   // HACK: first we check whether the name starts with special prefix to
   // tell if it's a supported pytorch class type. There are two special
   // prefixes. "__torch__" for nn module, and "torch.jit" from to_backend.
@@ -146,7 +145,7 @@ c10::intrusive_ptr<c10::ivalue::Object> objLoaderMobile(
     custom_class_type->getMethod("__setstate__").run(stack);
     return obj;
   } else {
-    auto dict = std::move(input).toGenericDict();
+    auto dict = input.toGenericDict();
     size_t ndict = dict.size();
     auto obj = c10::ivalue::Object::create(type, ndict);
     auto it = dict.begin();
@@ -223,8 +222,8 @@ class BytecodeDeserializer final {
   // dynamically. It's used for finding the minimum required runtime to run all
   // operators from the given model. If it's less than the current runtime,
   // upgrader will be applied at loading stage.
-  uint64_t operator_version_;
-  uint64_t bytecode_version_;
+  uint64_t operator_version_{0};
+  uint64_t bytecode_version_{0};
 };
 
 BytecodeDeserializer::BytecodeDeserializer(
@@ -486,8 +485,7 @@ c10::IValue BytecodeDeserializer::readArchive(
   };
 
   bool bytecode_tensor_in_constants_archive =
-      (archive_name == "bytecode" &&
-       !isTensorInBytecodeArchive(*reader_.get()));
+      (archive_name == "bytecode" && !isTensorInBytecodeArchive(*reader_));
 
   auto ivalues = torch::jit::readArchiveAndTensors(
       archive_name,
@@ -497,7 +495,7 @@ c10::IValue BytecodeDeserializer::readArchive(
       type_resolver,
       obj_loader,
       device_,
-      *reader_.get(),
+      *reader_,
       nullptr);
   return ivalues;
 }
@@ -734,5 +732,4 @@ std::set<std::string> _export_operator_list(
 }
 
 } // namespace mobile
-} // namespace jit
-} // namespace torch
+} // namespace torch::jit