Enable modernize-use-default-member-init (#149046)

``modernize-use-default-member-init`` prefers initialisation in class members, that make more ``= default`` constructors possible. Some violations or modernize rules have been fixed.

Pull Request resolved: https://github.com/pytorch/pytorch/pull/149046
Approved by: https://github.com/zou3519

.clang-tidy

@@ -52,7 +52,6 @@ modernize-*,
 -modernize-macro-to-enum,
 -modernize-return-braced-init-list,
 -modernize-use-auto,
--modernize-use-default-member-init,
 -modernize-use-using,
 -modernize-use-trailing-return-type,
 -modernize-use-nodiscard,

aten/src/ATen/core/Dict.h

@@ -116,10 +116,7 @@ public:
 
   DictIterator(const DictIterator& rhs): entryRef_(rhs.entryRef_) {}
   DictIterator(DictIterator&& rhs) noexcept: entryRef_(std::move(rhs.entryRef_)) {}
-  DictIterator& operator=(const DictIterator& rhs) {
-    entryRef_ = rhs.entryRef_;
-    return *this;
-  }
+  DictIterator& operator=(const DictIterator& rhs) = default;
   DictIterator& operator=(DictIterator&& rhs) noexcept {
     entryRef_ = std::move(rhs.entryRef_);
     return *this;

aten/src/ATen/core/dispatch/DispatchKeyExtractor.h

@@ -225,8 +225,7 @@ struct TORCH_API DispatchKeyExtractor final {
 
   explicit DispatchKeyExtractor(c10::utils::bitset dispatch_arg_indices_reverse)
       : dispatch_arg_indices_reverse_(dispatch_arg_indices_reverse),
-        nonFallthroughKeys_(DispatchKeySet::FULL),
-        requiresBitsetPerBackend_(false) {
+        nonFallthroughKeys_(DispatchKeySet::FULL) {
     for (const auto i : c10::irange(nonFallthroughKeysPerBackend_.size())) {
       nonFallthroughKeysPerBackend_[i] = DispatchKeySet::FULL;
     }
@@ -252,7 +251,7 @@ struct TORCH_API DispatchKeyExtractor final {
   // Flag to tell us if we can use the single set of nonFallthroughKeys_ (fast
   // path), or if we need to fall back to the slower path and check
   // nonFallthroughKeysPerBackend_
-  bool requiresBitsetPerBackend_;
+  bool requiresBitsetPerBackend_{false};
 };
 
 } // namespace c10

aten/src/ATen/cuda/tunable/Tunable.h

@@ -40,7 +40,7 @@ enum TORCH_CUDA_CPP_API TuningStatus {
 class TORCH_CUDA_CPP_API ResultEntry {
   public:
     explicit ResultEntry(std::string  key, double time) : key_(std::move(key)), time_(time) {}
-    explicit ResultEntry(std::string  key, double time, const std::string& blas_sig ) : key_(std::move(key)), time_(time), blas_sig_(blas_sig) {}
+    explicit ResultEntry(std::string  key, double time, std::string blas_sig ) : key_(std::move(key)), time_(time), blas_sig_(std::move(blas_sig)) {}
     bool operator==(const ResultEntry& other) const { return key_ == other.key_; }
     bool operator!=(const ResultEntry& other) const { return key_ != other.key_; }
     operator std::string () { return key_; }

aten/src/ATen/native/RangeUtils.h

@@ -2,9 +2,9 @@
 #include <c10/core/Scalar.h>
 #include <limits>
 
-namespace at {
 
-namespace native {
+
+namespace at::native {
 
 template <typename scalar_t>
 int64_t compute_arange_size(const Scalar& start, const Scalar& end, const Scalar& step) {
@@ -42,4 +42,4 @@ int64_t compute_arange_size(const Scalar& start, const Scalar& end, const Scalar
   return static_cast<int64_t>(size_d);
 }
 
-}}  // namespace at::native
+} // namespace at::native

aten/src/ATen/native/SpectralOps.cpp

@@ -756,7 +756,7 @@ static DimVector default_alldims(const Tensor& self, at::OptionalIntArrayRef dim
     IntArrayRef dim_unwrapped = *dim_opt;
     dim.resize(dim_unwrapped.size());
     for (const auto i : c10::irange(dim.size())) {
-      dim[i] = maybe_wrap_dim(dim_unwrapped[i], self.dim(), /*wrap_scalars=*/false);
+      dim[i] = maybe_wrap_dim(dim_unwrapped[i], self.dim(), /*wrap_scalar=*/false);
     }
   } else {
     dim.resize(self.dim());

aten/src/ATen/native/UnaryOps.cpp

@@ -887,7 +887,7 @@ static inline void mvlgamma_check(const Tensor& self, int64_t p) {
 Tensor mvlgamma(const Tensor& self, int64_t p) {
   mvlgamma_check(self, p);
   auto dtype = c10::scalarTypeToTypeMeta(self.scalar_type());
-  if (at::isIntegralType(self.scalar_type(), /*include_bool=*/true)) {
+  if (at::isIntegralType(self.scalar_type(), /*includeBool=*/true)) {
     // int -> float promotion
     dtype = c10::get_default_dtype();
   }

aten/src/ATen/native/cuda/CuFFTPlanCache.h

@@ -16,7 +16,7 @@
 #include <string>
 #include <unordered_map>
 
-namespace at { namespace native { namespace detail {
+namespace at::native::detail {
 
 // Enum representing the FFT type
 enum class CuFFTTransformType : int8_t {
@@ -58,7 +58,7 @@ struct CuFFTParams
   }
 };
 
-static_assert(std::is_trivial_v<CuFFTParams>, "");
+static_assert(std::is_trivial_v<CuFFTParams> );
 
 // Returns true if the transform type has complex input
 inline bool cufft_complex_input(CuFFTTransformType type) {
@@ -491,4 +491,4 @@ void cufft_set_plan_cache_max_size_impl(DeviceIndex device_index, int64_t max_si
 int64_t cufft_get_plan_cache_size_impl(DeviceIndex device_index);
 void cufft_clear_plan_cache_impl(DeviceIndex device_index);
 
-}}} // namespace at::native::detail
+} // namespace at::native::detail

aten/src/ATen/native/cuda/MiscUtils.h

@@ -4,8 +4,8 @@
 #include <ATen/cuda/CUDAConfig.h>
 #include <ATen/cuda/PinnedMemoryAllocator.h>
 
-namespace at {
-namespace native {
+
+namespace at::native {
 
 static inline int cuda_int_cast(int64_t value, const char* varname) {
   auto result = static_cast<int>(value);
@@ -28,5 +28,4 @@ static inline Storage pin_memory(int64_t size) {
       /*resizable=*/false);
 }
 
-} // namespace native
-} // namespace at
+} // namespace at::native

aten/src/ATen/native/cuda/Resize.h

@@ -5,7 +5,7 @@
 
 #include <c10/cuda/CUDAGuard.h>
 
-namespace at { namespace native {
+namespace at::native {
 
 TORCH_CUDA_CPP_API void resize_bytes_cuda(StorageImpl* storage, size_t size_bytes);
 
@@ -50,4 +50,4 @@ inline TensorImpl* resize_impl_cuda_(
   return self;
 }
 
-}}
+}

aten/src/ATen/native/cuda/linalg/BatchLinearAlgebraLib.h

@@ -36,8 +36,8 @@
 // The current pytorch implementation sets gesvdj tolerance to epsilon of a C++ data type to target the best possible precision.
 constexpr int cusolver_gesvdj_max_sweeps = 400;
 
-namespace at {
-namespace native {
+
+namespace at::native {
 
 void geqrf_batched_cublas(const Tensor& input, const Tensor& tau);
 void triangular_solve_cublas(const Tensor& A, const Tensor& B, bool left, bool upper, TransposeType transpose, bool unitriangular);
@@ -90,4 +90,4 @@ C10_EXPORT void registerLinalgDispatch(const LinalgDispatch&);
 }} // namespace cuda::detail
 #endif
 
-}}  // namespace at::native
+} // namespace at::native

aten/src/ATen/native/cudnn/RNNUtils.h

@@ -6,9 +6,8 @@
 #include <ATen/cudnn/cudnn-wrapper.h>
 
 // Declares utilities used by RNN.cpp and also needed by external consumers
-namespace at {
-namespace native {
-namespace cudnn_rnn {
+
+namespace at::native::cudnn_rnn {
 
 TORCH_CUDA_CPP_API std::tuple<Tensor, std::vector<Tensor>>
 copy_weights_to_flat_buf_views(
@@ -27,6 +26,4 @@ copy_weights_to_flat_buf_views(
     bool allow_type_change = false,
     bool include_bias = true);
 
-} // namespace cudnn_rnn
-} // namespace native
-} // namespace at
+} // namespace at::native::cudnn_rnn

aten/src/ATen/native/mkldnn/MKLDNNCommon.h

@@ -20,7 +20,7 @@
 #endif
 #endif
 
-namespace at { namespace native {
+namespace at::native {
 
 // Mapping ScalarType to ideep tensor data_type
 TORCH_API ideep::tensor::data_type get_mkldnn_dtype(ScalarType type);
@@ -62,6 +62,6 @@ TORCH_API ideep::tensor itensor_from_tensor(const Tensor& tensor, bool from_cons
 // Set MKLDNN verbose level
 TORCH_API int set_verbose(int level);
 
-}}
+}
 
 #endif // AT_MKLDNN_ENABLED

aten/src/ATen/native/mkldnn/xpu/detail/Attr.h

@@ -131,7 +131,7 @@ struct PostOpParam {
 
 class Attr {
  public:
-  Attr() : q_scale_(1.f), q_zero_point_(0) {}
+  Attr() : q_scale_(1.f) {}
   Attr(float q_scale, int64_t zp = 0) : q_scale_(q_scale), q_zero_point_(zp) {}
 
   /***** eltwise *****/

aten/src/ATen/quantized/QTensorImpl.h

@@ -51,8 +51,8 @@ struct TORCH_API QTensorImpl : public c10::TensorImpl {
     auto impl = c10::make_intrusive<QTensorImpl>(
         Storage(storage()), key_set(), data_type_, quantizer_);
     copy_tensor_metadata(
-      /*src_impl=*/this,
-      /*dest_impl=*/impl.get(),
+      /*src_q_impl=*/this,
+      /*dest_q_impl=*/impl.get(),
       /*version_counter=*/version_counter,
       /*allow_tensor_metadata_change=*/allow_tensor_metadata_change);
     impl->refresh_numel();
@@ -72,8 +72,8 @@ struct TORCH_API QTensorImpl : public c10::TensorImpl {
     auto impl = c10::make_intrusive<QTensorImpl>(
         Storage(storage()), key_set(), data_type_, quantizer_);
     copy_tensor_metadata(
-      /*src_impl=*/this,
-      /*dest_impl=*/impl.get(),
+      /*src_q_impl=*/this,
+      /*dest_q_impl=*/impl.get(),
       /*version_counter=*/std::move(version_counter),
       /*allow_tensor_metadata_change=*/allow_tensor_metadata_change);
     impl->refresh_numel();
@@ -91,8 +91,8 @@ struct TORCH_API QTensorImpl : public c10::TensorImpl {
     AT_ASSERT(has_compatible_shallow_copy_type(impl->key_set()));
     auto q_impl = static_cast<const QTensorImpl*>(impl.get());
     copy_tensor_metadata(
-      /*src_impl=*/q_impl,
-      /*dest_impl=*/this,
+      /*src_q_impl=*/q_impl,
+      /*dest_q_impl=*/this,
       /*version_counter=*/version_counter(),
       /*allow_tensor_metadata_change=*/allow_tensor_metadata_change());
     refresh_numel();

torch/_inductor/codegen/cpp_prefix.h

@@ -86,7 +86,7 @@ struct WelfordHelper {
   std::vector<Welford<T>> welford_stk;
   uint64_t depth; // depth of welford_stk.
   uint64_t num_chunks; // number of chunks stored in welford_stk.
-  WelfordHelper() {}
+  WelfordHelper() = default;
   WelfordHelper(uint64_t N) {
     uint64_t m = (N + kChunkSize - 1) / kChunkSize; //div up
     depth = m > 0 ? ceil(log2(m)) : 0;

torch/csrc/autograd/profiler_python.cpp

@@ -1152,16 +1152,13 @@ std::vector<std::shared_ptr<Result>> PythonTracer::getEvents(
 // Assuming python_tracer::PythonMemoryTracerBase is defined elsewhere
 class PythonMemoryTracer final : public python_tracer::PythonMemoryTracerBase {
  public:
-  explicit PythonMemoryTracer();
-  ~PythonMemoryTracer() override;
+  explicit PythonMemoryTracer() = default;
+  ~PythonMemoryTracer() override = default;
   void start() override;
   void stop() override;
   void export_memory_history(const std::string path) override;
 };
 
-PythonMemoryTracer::PythonMemoryTracer() {}
-PythonMemoryTracer::~PythonMemoryTracer() {}
-
 static void toggle_memory_tracing(bool enable) {
   PyGILState_STATE gil_state = PyGILState_Ensure();
   THPObjectPtr torch_cuda_memory_module(
@@ -1182,9 +1179,9 @@ static void toggle_memory_tracing(bool enable) {
   PyTuple_SetItem(args, 3, THPUtils_packInt64(100000)); // max_entries
   PyTuple_SetItem(args, 4, Py_None); // device (None)
   PyTuple_SetItem(args, 5, PyBool_FromLong(0)); // clear_history (False)
-  PyObject* result = PyObject_Call(snapshot_func.get(), args, NULL);
+  PyObject* result = PyObject_Call(snapshot_func.get(), args, nullptr);
   Py_DECREF(args);
-  if (result == NULL) {
+  if (result == nullptr) {
     return;
   }
   PyGILState_Release(gil_state);
@@ -1209,9 +1206,9 @@ void PythonMemoryTracer::export_memory_history(const std::string path) {
   PyObject* py_filename = PyUnicode_FromString(path.c_str());
   // Call the function with arguments (e.g., a file path)
   PyObject* args = PyTuple_Pack(1, py_filename);
-  PyObject* result = PyObject_Call(snapshot_func.get(), args, NULL);
+  PyObject* result = PyObject_Call(snapshot_func.get(), args, nullptr);
   Py_DECREF(args);
-  if (result == NULL) {
+  if (result == nullptr) {
     return;
   }
   PyGILState_Release(gil_state);

torch/csrc/jit/codegen/onednn/LlgaTensorImpl.cpp

@@ -31,7 +31,7 @@ dnnl::engine& Engine::getEngine() {
   static dnnl::graph::allocator alloc{
       pytorch_default_allocator, pytorch_default_deallocator};
   static dnnl::engine cpu_engine = dnnl::graph::make_engine_with_allocator(
-      dnnl::engine::kind::cpu, /* device_id = */ 0, alloc);
+      dnnl::engine::kind::cpu, /* index = */ 0, alloc);
   return cpu_engine;
 }
 

torch/csrc/jit/runtime/interpreter/code_impl.h

@@ -18,9 +18,7 @@
 TORCH_DECLARE_bool(torch_jit_enable_expanded_stacks);
 TORCH_DECLARE_bool(torch_jit_expanded_stacks_mangled);
 
-namespace torch::jit {
-
-namespace interpreter {
+namespace torch::jit::interpreter {
 
 template <class Ttarget, class Tsource>
 Ttarget safe_narrow_cast(Tsource v) {
@@ -64,7 +62,7 @@ struct NodeSourceInfo {
   const char* func_name_{nullptr};
   const char* file_name_{nullptr};
   size_t line_{0};
-  NodeSourceInfo() {}
+  NodeSourceInfo() = default;
 };
 
 struct CodeImpl {
@@ -1060,5 +1058,4 @@ struct MobileCodeImpl : CodeImpl {
   bool emit_promoted_ops_;
 };
 
-} // namespace interpreter
-} // namespace torch::jit
+} // namespace torch::jit::interpreter

torch/lib/libshm/socket.h

@@ -17,12 +17,12 @@
 class Socket {
  public:
   int socket_fd;
+  Socket(const Socket& other) = delete;
 
  protected:
   Socket() {
     SYSCHECK_ERR_RETURN_NEG1(socket_fd = socket(AF_UNIX, SOCK_STREAM, 0));
   }
-  Socket(const Socket& other) = delete;
   Socket(Socket&& other) noexcept : socket_fd(other.socket_fd) {
     other.socket_fd = -1;
   };
@@ -122,7 +122,7 @@ class ManagerServerSocket : public Socket {
       SYSCHECK_ERR_RETURN_NEG1(unlink(socket_path.c_str()));
   }
 
-  virtual ~ManagerServerSocket() {
+  ~ManagerServerSocket() override {
     unlink(socket_path.c_str());
   }